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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Aggregates Classification01:29

Aggregates Classification

329
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
329
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Classification of Systems-I01:26

Classification of Systems-I

192
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
192
Block Diagram Reduction01:22

Block Diagram Reduction

221
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
221

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation Without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

LoRASculpt: Harmonious Low-Rank Adaptation for Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.

Nature biomedical engineering·2026
Same author

Systemic immune-inflammation index predicts post-thrombectomy outcomes and reveals a mediating role in the association between neurocardiac stress and prognosis: a multicenter study.

Frontiers in neurology·2026
Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Differentiable Clustering Graph Convolutional Network for Hyperspectral Unmixing: Methodology and Benchmark.

IEEE transactions on neural networks and learning systems·2026

相关实验视频

Updated: Jul 13, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

通过通勤时间距离增强的图形结构改革框架,用于图形分类.

Wenhang Yu1, Xueqi Ma2, James Bailey2

  • 1School of Computer Science, Wuhan University, China; Changjiang Schinta Software Technology Co., LTD. Wuhan, China.

Neural networks : the official journal of the International Neural Network Society
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的通勤时间距离信息传递神经网络 (CTD-MPNN) 框架,以解决图形神经网络的局限性. CTD-MPNN通过改善信息传播和捕获更高阶结构来增强图形分类.

关键词:
通勤时间距离距离.图形分类的图形分类.图形神经网络是一个神经网络.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K

相关实验视频

Last Updated: Jul 13, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K

科学领域:

  • 图形数据挖掘 图形数据挖掘
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 图形分类对于数据挖掘至关重要,并且具有广泛的应用.
  • 图形神经网络 (GNN),特别是消息传递神经网络 (MPNN),是主流的,但遭受过度压缩和有限的表达力.
  • 现有的解决方案单独解决这些问题,缺乏全面的方法.

研究的目的:

  • 解决局部聚合导致的信息丢失和无法在GNN中捕获更高阶结构的问题.
  • 提出一个全新的,插即用的框架,全面解决这些GNN限制.
  • 增强GNN的表达力,以提高图形分类性能.

主要方法:

  • 开发了一个基于通勤时间距离 (CTD) 的框架,用于在CTD社区内传播信息.
  • 通过考虑本地和全球图形连接,路径长度和路径数量来评估CTD.
  • 引入了基于通勤时间距离的传递信息的神经网络 (CTD-MPNNs),通过通勤路径捕获更高级的结构信息.

主要成果:

  • CTD-MPNN框架有效地传播和汇总来自重要邻国的信息.
  • 该框架增强了GNN的表达力,从而产生了更强大的模型.
  • 对现实世界的图形分类基准进行了广泛的实验,证明了该框架的有效性.

结论:

  • 拟议的CTD-MPNN框架为现有的GNN限制提供了一个全面的解决方案.
  • 这种方法通过捕获更丰富的结构信息来显著提高图形分类性能.
  • 该框架的plug-and-play性质允许简单的集成和增强的GNN建模.