Paper List
-
Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
This work addresses the core challenge of extracting reusable, interpretable, and high-performance biological algorithms from the opaque internal repr...
-
MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structura...
-
Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
This review addresses the core challenge of fragmented and heterogeneous evidence that hinders the clinical translation of intracranial language BCIs,...
-
Less Is More in Chemotherapy of Breast Cancer
通过纳入细胞周期时滞和竞争项,解决了现有肿瘤-免疫模型的过度简化问题,以定量比较化疗方案。
-
Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
This paper addresses the critical bottleneck of GPU memory limitations that restrict AlphaFold 3-like models to processing only a few thousand residue...
-
Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable sea...
-
Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
This paper addresses the critical need for continuous, real-time monitoring of diabetic foot health by developing an unsupervised anomaly detection fr...
-
Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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.