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相关概念视频

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
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Types of Skewness01:09

Types of Skewness

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If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Energy Diagrams - II01:10

Energy Diagrams - II

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Energy diagrams are important to understand the dynamics of a system. The topology of an energy diagram helps illustrate the equilibrium points of the system.
The point in the energy diagram at which the system’s potential energy is the lowest is known as the local minima. The system tends to stay in this position indefinitely unless acted upon by a net force. The slope of the potential energy diagram at the local minima is zero, indicating that zero net force is acting on the system. The...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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相关实验视频

Updated: Jun 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

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不平衡的图形通过混合最小化来学习.

Liwen Xu1, Huaguang Zhu1, Jiali Chen2

  • 1College of Science, North China University of Technology, Beijing, 100144, China.

Scientific reports
|October 22, 2024
PubMed
概括
此摘要是机器生成的。

GraphME引入了混合最小化,用于不平衡的图节点分类. 这种新的方法提高了准确性和稳定性,而不需要复杂的过量采样,提供了免费的失衡防御.

关键词:
图表学习学习图表学习不平衡节点的分类 不平衡节点的分类混合最小化 (ME) 方法

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相关实验视频

Last Updated: Jun 9, 2025

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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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科学领域:

  • 图表机器学习 图表机器学习
  • 不平衡的学习学习.

背景情况:

  • 在不平衡图形上对节点进行分类是具有挑战性的.
  • 传统的过量采样方法使训练复杂化.
  • 现有的方法可能会损害效率或稳定性.

研究的目的:

  • 介绍一种新的训练范式,用于在不平衡图上对节点进行分类.
  • 开发一种在没有额外步骤的情况下提供"自由失衡防御"的方法.
  • 高效地提高分类准确性和稳定性.

主要方法:

  • 提出GraphME,一种基于混合最小化 (ME) 的方法.
  • ME通过指导术语最大化正确的类概率,最小化不正确的概率.
  • 整合ME与对抗训练技术.

主要成果:

  • 在多个数据集上,GraphME的表现始终优于传统的交叉的目标.
  • 证明了增强的稳定性和分类准确性.
  • 在不影响效率的情况下实现了显著的改进.

结论:

  • 在不平衡的图形上,GraphME为节点分类提供了有效和高效的解决方案.
  • 该方法提供了强大的"自由失衡防御".
  • 可以将GraphME与对抗训练无集成,以进一步提高稳定性.