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

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
State Space Representation01:27

State Space Representation

208
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
208
Associative Learning01:27

Associative Learning

370
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
370
Long-term Potentiation01:35

Long-term Potentiation

55.2K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.2K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.3K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.3K
Ogive Graph01:07

Ogive Graph

5.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.6K

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

Recommendations for the management of septic arthritis after ACL reconstruction.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2013
Same author

Poly(ADP-ribose) polymerase 1 promotes oxidative-stress-induced liver cell death via suppressing farnesoid X receptor α.

Molecular and cellular biology·2013
Same author

[Clinical significance of changes in T wave and ST segment amplitudes on electrocardiogram from supine to standing position among children with unexplained chest tightness or pain in resting stage].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2013
Same author

Biomimetic synthesis of equisetin and (+)-fusarisetin A.

Chemistry (Weinheim an der Bergstrasse, Germany)·2013
Same author

ERG Protein Expression Is of Limited Prognostic Value in Men with Localized Prostate Cancer.

ISRN urology·2013
Same author

Syphilis screening among 27,150 pregnant women in South Chinese rural areas using point-of-care tests.

PloS one·2013
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

ConTIG:在时间交互图表上进行持续的表示学习.

Zihui Wang1, Peizhen Yang1, Xiaoliang Fan1

  • 1Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, 361000, Fujian, China; Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, PR China.

Neural networks : the official journal of the International Neural Network Society
|February 1, 2024
PubMed
概括
此摘要是机器生成的。

ConTIG模型连续节点嵌入轨迹在时间交互图 (TIG). 这种新的方法通过捕捉不断变化的节点状态和时间模式来改善动态网络分析,以便更好地预测.

关键词:
图形嵌入式嵌入式图形神经网络是一个神经网络.图形表示图形表示.在图表中挖矿和学习.时间相互作用图表.

更多相关视频

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.5K
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

152

相关实验视频

Last Updated: Jul 4, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.5K
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

152

科学领域:

  • 图形表示学习学习学习图形表示学习
  • 网络科学 网络科学
  • 机器学习 机器学习

背景情况:

  • 时间交互图 (TIG) 对于各种应用中的动态网络建模至关重要.
  • 现有的TIG方法往往无法捕捉连续嵌入演变或忽视时间模式,导致性能不佳.

研究的目的:

  • 提出ConTIG,一个新的两模块框架,用于TIG的代表性学习.
  • 在TIG中捕捉节点嵌入轨迹的连续动态演变.
  • 为了提高动态网络分析任务的准确性.

主要方法:

  • ConTIG采用一个由两个模块组成的框架,包括连续推断和自我注意机制.
  • 第一个模块使用普通微分方程来从时间相邻的相互作用中学习节点状态轨迹.
  • 第二个模块使用自我注意力来预测未来的嵌入,通过聚合历史的时间交互数据.

主要成果:

  • 在时间链接预测,节点推和动态节点分类任务上,ConTIG表现出卓越的性能.
  • 在四个数据集上进行了实验,将ConTIG与最先进的基线进行比较.
  • 该模型在长时间间隔预测相互作用方面表现出特别高的有效性.

结论:

  • 在动态网络中,ConTIG有效地建模了连续节点嵌入轨迹.
  • 拟议的框架增强了时间交互图的理解和预测能力.
  • 与现有的动态网络分析方法相比,ConTIG提供了显著的进步.