adjust order of stflow

Author: Boltzmachine

config/publications.ts

@@ -30,6 +30,17 @@ export const publications: Publication[] = [
     impact: "MindLLM achieves state-of-the-art performance on a wide range of fMRI-to-text decoding tasks, and demonstrates strong generalization ability to unseen subjects and tasks. This work paves the way for future research on high-quality fMRI-to-text decoding.",
     tags: [Tag.MultiModalFoundationModel],
   },
+  {
+    title: "Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching",
+    authors: "Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, Rex Ying",
+    venue: "ICML, 2025 (Spotlight)",
+    page: "stflow",
+    code: "https://github.com/Graph-and-Geometric-Learning/STFlow",
+    paper: "https://graph-and-geometric-learning.github.io/publications",
+    abstract: "Propose STFlow, a flow matching generative model to directly infer the spatial gene expression from whole slide images.",
+    impact: "We reformulate the original regression task as a generative modeling problem, allowing gene regulation to be integrated across cells. Additionally, the proposed geometry-aware denoiser achieves significantly higher efficiency compared to previous methods.",
+    tags: [Tag.Applications, Tag.GeometricAndGraphLearning],
+  },
   {
     title: "MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering",
     authors: "Jialin Chen, Aosong Feng, Ziyu Zhao, Juan Garza, Gaukhar Nurbek, Ali Maatouk, Leandros Tassiulas, Yifeng Gao, Rex Ying",
@@ -77,17 +88,6 @@ export const publications: Publication[] = [
     impact: "STPath is the first model to generalize spatial gene expression prediction across organs and gene panels from WSIs. We believe that our work will contribute to this emerging field and provide a better understanding of pathology practice with the help of spatial transcriptomics.",
     tags: [Tag.Applications, Tag.GeometricAndGraphLearning],
   },
-  {
-    title: "Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching",
-    authors: "Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, Rex Ying",
-    venue: "ICML, 2025 (Spotlight)",
-    page: "stflow",
-    code: "https://github.com/Graph-and-Geometric-Learning/STFlow",
-    paper: "https://graph-and-geometric-learning.github.io/publications",
-    abstract: "Propose STFlow, a flow matching generative model to directly infer the spatial gene expression from whole slide images.",
-    impact: "We reformulate the original regression task as a generative modeling problem, allowing gene regulation to be integrated across cells. Additionally, the proposed geometry-aware denoiser achieves significantly higher efficiency compared to previous methods.",
-    tags: [Tag.Applications, Tag.GeometricAndGraphLearning],
-  },
   {
     title: "Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer",
     authors: "Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin",