Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Second Uniqueness Theorem01:16

Second Uniqueness Theorem

973
Consider a region consisting of several individual conductors with a definite charge density in the region between these conductors. The second uniqueness theorem states that if the total charge on each conductor and the charge density in the in-between region are known, then the electric field can be uniquely determined.
In contrast, consider that the electric field is non-unique and apply Gauss's law in divergence form in the region between the conductors and the integral form to the...
973
Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

203
Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
203
Castigliano's Theorem01:18

Castigliano's Theorem

361
Castigliano's theorem analyzes displacements and rotations in elastic structures. It relates the derivative of elastic strain energy to the applied forces or moments, allowing for the calculation of deformations. The theorem states that the partial derivative of the total strain energy of a system with respect to a specific load results in the displacement at the point where the load is applied. This principle applies to both forces and moments.
361
Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

1.5K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
1.5K
Theorems of Pappus and Guldinus01:10

Theorems of Pappus and Guldinus

1.8K
The two theorems developed by Pappus and Guldinus are widely used in mathematics, engineering, and physics to find the surface area and volume of any body of revolution. This is done by revolving a plane curve around an axis that does not intersect the curve to find its surface area or revolving a plane area around a non-intersecting axis to calculate its volume.
For finding the surface area, consider a differential line element that generates a ring with surface area dA when revolved.
1.8K
Thevinin's Theorem01:15

Thevinin's Theorem

432
Thévenin's theorem plays a pivotal role in electrical circuit analysis, offering a solution to the challenges posed by variable loads within a circuit. In practical applications, it is common to encounter circuits where certain elements remain fixed while others fluctuate, often referred to as the "load." A typical household electrical outlet serves as a prime example of a variable load, as it can be connected to a variety of appliances, each with its own unique electrical...
432

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

HIGHER ORDER GAUGE EQUIVARIANT CONVOLUTIONS FOR NEURODEGENERATIVE DISORDER CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2024
Same author

Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space.

Advances in neural information processing systems·2024
Same author

An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices.

Journal of the American Statistical Association·2024
Same author

Geometric Deep Learning for Unsupervised Registration of Diffusion Magnetic Resonance Images.

Information processing in medical imaging : proceedings of the ... conference·2024
Same author

A Protocol for a Single-Centered, Pragmatic, Randomized, Controlled, Parallel Trial Comparing Comprehensive Nonsurgical Therapy Options for Individuals with Lumbar Spinal Stenosis.

Journal of pain research·2023
Same author

Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2023
Same journal

Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction.

IEEE transactions on information theory·2026
Same journal

Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net.

IEEE transactions on information theory·2025
Same journal

Uniform Convergence of Deep Neural Networks With Lipschitz Continuous Activation Functions and Variable Widths.

IEEE transactions on information theory·2025
Same journal

Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms.

IEEE transactions on information theory·2024
Same journal

Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO.

IEEE transactions on information theory·2024
Same journal

On Support Recovery with Sparse CCA: Information Theoretic and Computational Limits.

IEEE transactions on information theory·2023
查看所有相关文章

相关实验视频

Updated: Jun 4, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K

关于 Lie 组的 Kernel Stein 不一致:理论和应用

Xiaoda Qu1, Xiran Fan2, Baba C Vemuri3

  • 1Department of Statistics, University of Florida, Gainesville, FL 32611 USA.

IEEE transactions on information theory
|December 20, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的正常化无损失函数,用于Lie组的分布式近似,这对于科学和工程中的机器学习至关重要. 这种新方法,即最小内核斯坦恩差异估计器 (MKSDE),比传统技术具有优势.

更多相关视频

Theoretical Calculation and Experimental Verification for Dislocation Reduction in Germanium Epitaxial Layers with Semicylindrical Voids on Silicon
06:57

Theoretical Calculation and Experimental Verification for Dislocation Reduction in Germanium Epitaxial Layers with Semicylindrical Voids on Silicon

Published on: July 17, 2020

2.2K
An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

8.5K

相关实验视频

Last Updated: Jun 4, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K
Theoretical Calculation and Experimental Verification for Dislocation Reduction in Germanium Epitaxial Layers with Semicylindrical Voids on Silicon
06:57

Theoretical Calculation and Experimental Verification for Dislocation Reduction in Germanium Epitaxial Layers with Semicylindrical Voids on Silicon

Published on: July 17, 2020

2.2K
An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

8.5K

科学领域:

  • 机器学习 机器学习
  • 计算数学 计算数学 计算数学
  • 数据科学数据科学数据科学

背景情况:

  • 分布近似在机器学习和科学应用中至关重要.
  • 可提取的规范化常量带来了挑战,特别是对于像旋转矩阵这样的多重值数据.
  • 谎言组经常用于计算机视觉,机器人和医学成像.

研究的目的:

  • 为了解决李群上的分布式近似问题.
  • 为这些复杂分布开发一种新的,无正常化损失函数.
  • 根据这个损失函数引入和分析一个新的估计器.

主要方法:

  • 开发了一种针对 Lie 组的新型 Stein 运算符.
  • 引入了一个内核斯坦因差异 (KSD) 作为一个无正常化损失函数.
  • 导出并分析了最小KSD估计器 (MKSDE).

主要成果:

  • 建立了关于Lie组的新KSD的理论性质.
  • 证明了MKSDE的强一致性和中央极限定理 (CLT).
  • 导出了MKSDE的封闭式解决方案,用于Lie组上的特定分布.

结论:

  • 新的KSD和MKSDE提供了一种有效的方法来对Lie组进行分布式近似.
  • 在实验结果中,MKSDE在最大概率估计上表现出优势.
  • 这项工作为涉及多重值数据的机器学习应用提供了强大的工具.