Add hstrat efflux entry

Author: mmore500

blog_efflux/_posts/2022-11-07-moreno2022hstrat.md

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+---
+layout: efflux
+title: "hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations"
+date: 2022-11-07
+permalink: "/pubs/:title"
+category: journal
+download: https://github.com/mmore500/hstrat/releases/download/as-submitted-joss/paper.pdf
+# doi: "10.1145/3520304.3533937"
+authors:
+  - Matthew Andres Moreno
+  - Emily Dolson
+  - Charles Ofria
+venue: Journal of Open Source Science (Under Revision)
+projects:
+  - hstrat
+abstract: |
+  Digital evolution systems instantiate evolutionary processes over populations of virtual agents *in silico*.
+  These programs can serve as rich experimental model systems.
+  Insights from digital evolution experiments expand evolutionary theory, and can often directly improve heuristic optimization techniques .
+  Perfect observability, in particular, enables *in silico* experiments that would be otherwise impossible *in vitro* or *in vivo*.
+  Notably, availability of the full evolutionary history (phylogeny) of a given population enables very powerful analyses.
+
+  As a slow but highly parallelizable process, digital evolution will benefit greatly by continuing to capitalize on profound advances in parallel and distributed computing [@moreno2020practical;@ackley2014indefinitely], particularly emerging unconventional computing architectures [@ackley2011homeostatic;@lauterbach2021path;@furber2014spinnaker].
+  However, scaling up digital evolution presents many challenges.
+  Among these is the existing centralized perfect-tracking phylogenetic data collection model, which is inefficient and difficult to realize in parallel and distributed contexts.
+  Here, we implement an alternative approach to tracking phylogenies across vast and potentially unreliable hardware networks.
+
+  The `hstrat` Python library exists to facilitate application of hereditary stratigraphy, a cutting-edge technique to enable phylogenetic inference over distributed digital evolution populations.
+  This technique departs from the traditional perfect-tracking approach to phylogenetic record-keeping.
+  Instead, hereditary stratigraphy enables phylogenetic history to be inferred from heritable annotations attached to evolving digital agents.
+  This approach aligns with phylogenetic reconstruction methodologies in evolutionary biology.
+  Hereditary stratigraphy attaches a set of immutable historical "checkpoints" --- referred to as _strata_ --- as an annotation on evolving genomes.
+  Checkpoints can be strategically discarded to reduce annotation size at the cost of increasing inference uncertainty.
+  A particular strategy for which checkpoints to discard when is referred to as a _stratum retention policy_.
+  We refer to the set of retained strata as a _hereditary stratigraphic column_.
+
+  Appropriate stratum retention policy choice varies by application.
+  For example, if annotation size is not a concern it may be best to preserve all strata.
+  In other situations, it may be necessary to constrain annotation size to remain within a fixed memory budget.
+
+  Key features of the library include:
+
+  - object-oriented hereditary stratigraphic column implementation to annotate arbitrary genomes,
+  - modular interchangeability and user extensibility of stratum retention policies,
+  - programmatic interface to query guarantees and behavior of stratum retention policy,
+  - modular interchangeability and user extensibility of back-end data structure used to store annotation data,
+  - a suite of visualization tools to elucidate stratum retention policies,
+  - support for automatic parameterization of stratum retention policies to meet user size complexity or inference precision specifications,
+  - tools to compare two columns and extract information about the phylogenetic relationship between them,
+  - [extensive documentation](https://hstrat.readthedocs.io) hosted on [ReadTheDocs](https://readthedocs.io),
+  - a comprehensive test suite to ensure stability and reliability,
+  - convenient availability as a Python package via the [PyPI repository](https://pypi.org/), and
+  - pure Python implementation to ensure universal portability.
+bibtex: |-
+  @article{moreno2022hstrat,
+    author = {Moreno, Matthew Andres and Dolson, Emily and Ofria, Charles},
+    title = "{hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations}",
+    journal = {Journal of Open Source Software},
+    year = {Under Revision},
+  }
+citation: "Matthew Andres Moreno, Emily Dolson, and Charles Ofria. hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations. Journal of Open Source Software. Under Revision."
+---