Paper List
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Mapping of Lesion Images to Somatic Mutations
This paper addresses the critical bottleneck of delayed genetic analysis in cancer diagnosis by predicting a patient's full somatic mutation profile d...
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Reinventing Clinical Dialogue: Agentic Paradigms for LLM‑Enabled Healthcare Communication
This paper addresses the core challenge of transforming reactive, stateless LLMs into autonomous, reliable clinical dialogue agents capable of longitu...
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Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
通过将序列映射到二元潜在空间进行基于QUBO的适应度优化,桥接蛋白质表示学习和组合优化。
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Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
证明了无模型强化学习可以利用虚拟视觉刺激有效引导鱼群,克服了缺乏精确行为模型的问题。
Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition
Department of Mathematics, Florida State University | Department of Mathematics and Statistics, Cleveland State University | Institute of Molecular Biophysics, Florida State University
30秒速读
IN SHORT: This paper addresses the fundamental question of how microscopic stochastic advantages in spatial exploration translate into macroscopic resource dominance, revealing that initial discovery and final monopolization are governed by distinct physical mechanisms.
核心创新
- Methodology Introduces a dimensionless scaling parameter χ = (N₂/N₁)8^(d₁-d₂) that completely determines competitive symmetry, showing that a linear spatial disadvantage requires an exponential population advantage to overcome.
- Theory Demonstrates that extreme first-passage statistics govern initial discovery, while non-reciprocal interaction bias (β) controls the sharpness of the competitive phase transition and stability of the absorbing state.
- Biology Reveals a strict hierarchy of symmetry-breaking factors: proximity to resource > population size > interaction bias, with β being necessary but not sufficient for dominance.
主要结论
- Proximity imparts the strongest competitive advantage: a colony with distance d₁ < d₂ requires N₂/N₁ ~ 8^(d₂-d₁) ants to compensate (Equation 3).
- The interaction bias β acts as a phase transition tuner: for β → 0, outcomes remain probabilistic; for large β, the symmetry-breaking boundary sharpens into a step function (Figure 3).
- Discovery and monopolization are decoupled: extreme first-passage statistics govern initial finding (⟨T_i⟩ = d_i + (1-p_i)^(N_i)), while β strictly controls stability of the absorbing state.
摘要: How do competing populations convert a spatial advantage into macroscopic dominance? We introduce a stochastic model for resource competition that decouples the transient discovery phase from monopolization. Initial symmetry breaking is governed by extreme value statistics of first-passage times: a linear spatial disadvantage requires an exponentially larger population to overcome. However, transient superiority cannot stabilize dominance. A non-reciprocal interaction bias is strictly necessary to arrest local fluctuations and drive the system into a robust absorbing state.