Paper List
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
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Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
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Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
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.
The 30-Second View
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.
Innovation (TL;DR)
- 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.
Key conclusions
- 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.
Abstract: 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.