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Bootstrapping01:24

Bootstrapping

571
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
571
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

416
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
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Probability Histograms01:17

Probability Histograms

11.0K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Poisson Probability Distribution01:09

Poisson Probability Distribution

7.7K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.7K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Probability Distributions01:32

Probability Distributions

6.6K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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相关实验视频

Updated: May 15, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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在超图形上的bootstrap泄露.

Hao Peng1,2, Chenyi Wang1, Dandan Zhao1

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, China.

Chaos (Woodbury, N.Y.)
|April 10, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了对超图的通用化引导透模型,以了解网络在级联失效时的稳定性. 高阶相互作用显著影响网络行为,影响巨大的活跃组件.

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Last Updated: May 15, 2025

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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科学领域:

  • 网络科学 网络科学
  • 统计物理 统计物理
  • 复杂的系统复杂的系统.

背景情况:

  • 引导透模型分析网络的稳定性,以防止级联失败.
  • 现实世界的数据揭示了超越对对关系的更高阶交互,通常是通过超图建模的.
  • 现有的模型往往忽略了这些高阶相互作用.

研究的目的:

  • 提出和分析一个概括的引导透模型在超图上.
  • 调查高阶相互作用对网络稳定性和相位转换的影响.
  • 了解感染值和更高阶边缘的比例如何影响网络行为.

主要方法:

  • 开发一个通用的引导式传递模型,通过超图集结合更高阶的交互.
  • 数字模拟用于观察不同条件下的网络行为.
  • 理论分析来推导透值和表征相位过渡.

主要成果:

  • 引导透值和相位过渡类型取决于感染值和更高阶边缘的比例.
  • 显著的感染值导致巨型活性成分 (GAC) 的持续增长,占用概率增加.
  • 一个小的感染值导致GAC大小从连续增长转变为不连续增长,随着初始激活概率的增加.
  • 增加更高阶边缘降低了透值,提高了网络的稳定性.
  • 高阶边缘增加了激活机会,将 GAC 的增长从连续转变为不连续.

结论:

  • 概括的超图启动透模型有效地捕捉了高阶交互对网络稳定性的影响.
  • 通过增加更高阶边缘的比例来增强网络的稳定性.
  • 感染值和更高阶边缘比例之间的相互作用决定了这些网络中相位过渡的性质.