摘要翻译:
我们提出了一个严格的数学框架来分析一类广泛的布尔网络模型的动力学。我们利用这个框架给出了布尔网络分析中许多标准临界转移结果的第一个形式证明,并给出了新的随机布尔网络类的类似刻画。我们精确地将布尔网络的短期动态行为与传递函数的平均影响联系起来。我们表明,在布尔网络的更常见的平均场分析中,传统上所做的一些假设并不普遍成立。例如,我们提供了一些证据,证明传递函数的不平衡或预期的内部不均匀性是一个重要的特征,它倾向于比以前观察到的更强烈地驱动静止行为。
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英文标题:
《Influence and Dynamic Behavior in Random Boolean Networks》
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作者:
C. Seshadhri and Yevgeniy Vorobeychik and Jackson R. Mayo and Robert
C. Armstrong and Joseph R. Ruthruff
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最新提交年份:
2011
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分类信息:
一级分类:Physics 物理学
二级分类:Disordered Systems and Neural Networks 无序系统与神经网络
分类描述:Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜;随机、非周期和准周期系统的性质;无序介质中的传输;本地化;由缺陷和无序介导的现象;神经网络
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Discrete Mathematics 离散数学
分类描述:Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
涵盖组合学,图论,概率论的应用。大致包括ACM学科课程G.2和G.3中的材料。
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一级分类:Physics 物理学
二级分类:Adaptation and Self-Organizing Systems 自适应和自组织系统
分类描述:Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应,自组织系统,统计物理,波动系统,随机过程,相互作用粒子系统,机器学习
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英文摘要:
We present a rigorous mathematical framework for analyzing dynamics of a broad class of Boolean network models. We use this framework to provide the first formal proof of many of the standard critical transition results in Boolean network analysis, and offer analogous characterizations for novel classes of random Boolean networks. We precisely connect the short-run dynamic behavior of a Boolean network to the average influence of the transfer functions. We show that some of the assumptions traditionally made in the more common mean-field analysis of Boolean networks do not hold in general. For example, we offer some evidence that imbalance, or expected internal inhomogeneity, of transfer functions is a crucial feature that tends to drive quiescent behavior far more strongly than previously observed.
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PDF链接:
https://arxiv.org/pdf/1107.3792