Paper List
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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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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
演化稳定斯塔克尔伯格均衡
Ganzfried Research
30秒速读
IN SHORT: 通过要求追随者策略对突变入侵具有鲁棒性，弥合了斯塔克尔伯格领导力模型与演化稳定性之间的鸿沟。
核心创新
- Methodology Introduces the first formal integration of Stackelberg equilibrium with evolutionary stability, creating the SESS concept.
- Methodology Develops computational algorithms for both discrete normal-form games and continuous-trait games, enabling practical application.
- Biology Provides a natural framework for modeling asymmetric interactions in biological systems, such as physician-cancer cell dynamics in treatment optimization.
主要结论
- SESS框架通过将追随者响应限制为演化稳定策略（ESS），成功精炼了斯塔克尔伯格均衡，确保了对突变入侵的鲁棒性。
- 计算复杂性分析表明，确定ESS存在性是Σ₂ᴾ完全的，比计算纳什均衡要困难得多，凸显了稳定性保证的附加价值。
- 该模型为癌症治疗优化提供了直接且自然的应用，其中医生（主导者）针对稳定演化的癌细胞表型群体（追随者）优化治疗方案。
摘要: 我们提出了一种新的解概念，称为演化稳定斯塔克尔伯格均衡（SESS）。我们研究了斯塔克尔伯格演化博弈场景，其中存在一个单一的主导玩家和一个对称的追随者群体。主导玩家选择最优混合策略，预期追随者群体在诱导的子博弈中采用演化稳定策略（ESS），并可能满足额外的生态条件。我们考虑了ESS中的主导者最优和追随者最优选择，它们作为我们框架的特殊情况出现。先前处理斯塔克尔伯格演化博弈的方法要么通过演化动力学定义追随者响应，要么假设理性最佳响应行为，而没有明确强制执行对突变入侵的稳定性。我们提出了在离散和连续博弈中计算SESS的算法，并对后者进行了实证验证。我们的模型自然适用于生物学场景；例如，在癌症治疗中，主导者代表医生，追随者对应于竞争的癌细胞表型。