Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.