Paper List
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Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
This paper addresses the fundamental limitation of current AI-assisted scientific research by enabling truly autonomous, decentralized investigation w...
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D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing
This paper addresses the fundamental scalability bottleneck in LLM agentic memory systems: the O(N²) computational complexity and unbounded API token ...
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Countershading coloration in blue shark skin emerges from hierarchically organized and spatially tuned photonic architectures inside skin denticles
This paper solves the core problem of how blue sharks achieve their striking dorsoventral countershading camouflage, revealing that coloration origina...
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Human-like Object Grouping in Self-supervised Vision Transformers
This paper addresses the core challenge of quantifying how well self-supervised vision models capture human-like object grouping in natural scenes, br...
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Hierarchical pp-Adic Framework for Gene Regulatory Networks: Theory and Stability Analysis
This paper addresses the core challenge of mathematically capturing the inherent hierarchical organization and multi-scale stability of gene regulator...
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Towards unified brain-to-text decoding across speech production and perception
This paper addresses the core challenge of developing a unified brain-to-text decoding framework that works across both speech production and percepti...
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Dual-Laws Model for a theory of artificial consciousness
This paper addresses the core challenge of developing a comprehensive, testable theory of consciousness that bridges biological and artificial systems...
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Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
This work addresses the core challenge of determining optimal pulse timing and intensity for desynchronizing pathological neural oscillations when the...
癌症-细菌疗法的数学建模：基于物理信息神经网络的数值模拟与分析
International University of Rabat | Université de Lorraine, CNRS, CRAN | Université de Lorraine, CNRS, IECL | Univ Rennes, INSA, CNRS, IRMAR-UMR 6625
30秒速读
IN SHORT: 提供了一个严格的、无网格的PINN框架，用于模拟和分析细菌癌症疗法中复杂的、空间异质的相互作用。
核心创新
- Methodology First coupled PDE model integrating tumor, bacteria, oxygen, immunosuppressive cytokines, and quorum-sensing signals for bacterial cancer therapy.
- Theory Proved global well-posedness and stability of the five-species reaction-diffusion system, establishing a rigorous mathematical foundation.
- Methodology Established convergence guarantees for PINNs on this nonlinear system, with an overall error bound of 𝒪(n^{-2}ln^{4}(n)+N^{-1/2}).
主要结论
- PINN框架实现了𝒪(n^{-2}ln^{4}(n)+N^{-1/2})的可量化误差率，能够对耦合系统进行准确的无网格模拟。
- 敏感性分析表明，治疗效果高度依赖于缺氧维持（通过KH/(KH+O)建模）和细菌的氧耐受性。
- 该模型识别了三个生物学相关的稳态，并确认扩散不会引发图灵不稳定性，表明时空动力学的稳定性。
摘要: 细菌癌症疗法利用厌氧细菌靶向缺氧肿瘤区域的能力，然而肿瘤生长、细菌定植、氧气水平、免疫抑制细胞因子和细菌通讯之间的相互作用仍然缺乏量化。我们提出了一个二维组织域中五个耦合非线性反应-扩散方程的数学模型。我们证明了模型的全局适定性，并确定了其稳态以分析稳定性。此外，物理信息神经网络（PINN）无需网格和大量数据即可求解该系统。它通过结合残差稳定性和Sobolev近似误差界提供了收敛保证。这导致整体误差率为𝒪(n^{-2}ln^{4}(n)+N^{-1/2})，该误差率取决于网络宽度n和配置点数量N。我们进行了多项数值实验，包括预测肿瘤对治疗的反应。我们还对某些参数进行了敏感性分析。结果表明，长期治疗效果可能需要维持肿瘤中的缺氧区域，或者使用更能耐受氧气的细菌，这对于持久的肿瘤控制可能是必要的。