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相关概念视频

Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
Consider a scalar function. The curl of its...
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Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

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The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
87
Definition of Laplace Transform01:22

Definition of Laplace Transform

2.0K
The Laplace transform is an indispensable mathematical technique for simplifying the resolution of differential equations by converting them into more manageable algebraic expressions. The Laplace transform of a function is denoted by L[x(t)], where x(t) is the time-domain function. The laplace transform is mathematically expressed as
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Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

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The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
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Image-based Lagrangian Particle Tracking in Bed-load Experiments
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一个贝叶斯联合学习框架与在线拉普拉斯近似.

Liangxi Liu, Xi Jiang, Feng Zheng

    IEEE transactions on pattern analysis and machine intelligence
    |October 9, 2023
    PubMed
    概括

    本研究引入了一个新的联合学习 (FL) 框架,使用拉普拉斯近似来减少异质数据设置中的错误和遗忘. 这种新的方法在多个基准指标上取得了最先进的结果.

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 分布式系统 分布式系统

    背景情况:

    • 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
    • 当前的FL方法经常平均参数,导致聚合错误和局部遗忘,特别是异质数据.

    研究的目的:

    • 提出一个新的联合学习框架,解决聚合错误和局部遗忘.
    • 在异质数据环境中改进模型性能.

    主要方法:

    • 使用在线拉普拉斯近似用于客户端和服务器端的后方近似.
    • 在服务器上采用多变量高斯积分机制来构建和最大化全局后置.
    • 在客户端引入先前损失,以全球后置参数为指导,以减轻本地遗忘.

    主要成果:

    • 显著减少局部模型之间的差异引起的聚合错误.
    • 通过对其他客户的学习约束来有效地减轻本地遗忘.
    • 在多个基准数据集中实现最先进的性能.

    结论:

    • 拟议的联合学习框架与现有方法相比,表现优越.
    • 在线拉普拉斯近似和一个新的客户端先前损失有效地解决异质FL设置中的挑战.

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