Paper List
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Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
This paper addresses the core pain point of efficiently extracting standardized, comparable features from massive (terabyte to petabyte-scale) biomedi...
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Topological Enhancement of Protein Kinetic Stability
This work addresses the long-standing puzzle of why knotted proteins exist by demonstrating that deep knots provide a functional advantage through enh...
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A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
This paper addresses the critical limitation of existing TF binding prediction methods that treat transcription factors as independent entities, faili...
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Social Distancing Equilibria in Games under Conventional SI Dynamics
This paper solves the core problem of proving the existence and uniqueness of Nash equilibria in finite-duration SI epidemic games, showing they are a...
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Binding Free Energies without Alchemy
This paper addresses the core bottleneck of computational expense in Absolute Binding Free Energy calculations by eliminating the need for numerous al...
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SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
This paper addresses the core bottleneck in cryo-EM helical reconstruction: eliminating the dependency on accurate initial symmetry parameter estimati...
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Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
This paper addresses the critical gap in evaluating AI-guided scientific selection strategies under realistic budget constraints, where existing metri...
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Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
This paper addresses the core challenge of accurately decomposing shared (joint) and dataset-specific (individual) sources of variation in multi-modal...
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
Department of Computer Science and Technology, Capital University of Economics and Business, Beijing 100070, China.
30秒速读
IN SHORT: This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by proposing a mechanism of rapid subgroup merging driven by topological network dynamics.
核心创新
- Theory Introduces a topological coordination theory based on time-varying directed interaction networks, identifying a single dominant Strongly Connected Component (SCC) as the driver of group velocity.
- Methodology Proposes the 'velocity inheritance' mechanism, where a trailing subgroup aligns with and inherits the velocity of the leading subgroup's dominant SCC during merging events.
- Biology Provides a unified, mechanistic explanation for multiple empirical features of animal groups, including broad neighbor-distance distributions, directional asymmetry, and narrow-front/wide-rear geometry.
主要结论
- Large moving groups form not by slow accumulation but through rapid merging of pre-existing subgroups under high-density conditions, driven by topological network structure.
- The long-term interaction network of any coordinated group contains a single dominant SCC that dictates the collective velocity (speed and direction) for the entire group.
- Repeated subgroup merging, governed by velocity inheritance, predicts that larger groups move more slowly than the mean speed of the original constituent subgroups—a testable hypothesis for existing 3D tracking datasets.
摘要: Large animal groups—bird flocks, fish schools, insect swarms—are often assumed to form by gradual aggregation of sparsely distributed individuals. Using a mathematically precise framework based on time-varying directed interaction networks, we show that this widely held view is incomplete. The theory demonstrates that large moving groups do not arise by slow accumulation; instead, they emerge through the rapid merging of multiple pre-existing subgroups that are simultaneously activated under high-density conditions. The key mechanism is topological: the long-term interaction structure of any moving group contains a single dominant strongly connected component (SCC). This dominant SCC determines the collective velocity—both speed and direction—of the entire group. When two subgroups encounter one another, the trailing subgroup aligns with—and ultimately inherits—the velocity of the dominant SCC of the leading subgroup. Repeated merging events naturally generate large groups whose speed is predicted to be lower than the mean speed of the original subgroups. The same dynamics explain several universal empirical features: broad neighbour-distance distributions, directional asymmetry in neighbour selection, and the characteristic narrow-front, wide-rear geometry of real flocks. The framework yields testable predictions for STARFLAG-style 3D datasets, offering a unified explanation for the formation, maintenance, and geometry of coordinated animal groups.