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

Introduction to Membrane Traffic01:44

Introduction to Membrane Traffic

9.3K
The ER, Golgi apparatus, endosomes, and lysosomes work in tandem to modify, sort, and package proteins and lipids. An integrated membrane trafficking network facilitates the back and forth shuttling of molecules within different organelles in the same cell or across the cell membrane.
The transport of soluble and membrane proteins is mediated by transport vesicles that collect cargo from one cellular compartment and deliver it to another by fusing with the target organelle membrane. The Rab...
9.3K
Ogive Graph01:07

Ogive Graph

6.6K
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...
6.6K
Graphing Antiderivatives01:30

Graphing Antiderivatives

43
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
43
Bar Graph01:07

Bar Graph

21.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

8.9K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.9K

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

MEDiT: A mask-enhanced diffusion transformer model for fetal heart rate signal generation.

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

Evidence of competing ground states between fractional Chern insulator and antiferromagnetism in moiré MoTe<sub>2</sub>.

Nature communications·2026
Same author

Mining user features with hyperbolic representations for diffusion prediction.

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

BiGCN Learns B Cell Functional States by Integrating Single-Cell Transcriptomes and BCR Repertoires.

Small methods·2026
Same author

Bronchial Arterial Chemoembolization Combined with Tislelizumab for Non-Small Cell Lung Cancer: An Exploratory, Prospective, Single-Arm, Phase II Trial.

Journal of vascular and interventional radiology : JVIR·2026
Same author

Characterization of m<sup>6</sup>A-regulated targets and immune cells in dental caries: insights from multi-omics analysis.

European journal of medical research·2025
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
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

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: Jan 22, 2026

Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.5K

MLGO:用于交通预测的多层图形神经ODE.

Mengzhou Gao1, Huangqian Yu1, Pengfei Jiao1

  • 1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.

Neural networks : the official journal of the International Neural Network Society
|January 20, 2026
PubMed
概括
此摘要是机器生成的。

常规微分方程 (MLGO) 通过整合不同的空间结构来增强交通预测. 这种方法比单一结构方法更好地捕捉复杂的空间相关性.

关键词:
多层图形多层图形神经的ODE是神经的ODE.时间空间图.时间序列时间序列预测交通情况.

更多相关视频

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
05:52

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

38.2K
Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits
12:13

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

Published on: January 25, 2013

27.8K

相关实验视频

Last Updated: Jan 22, 2026

Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.5K
Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
05:52

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

38.2K
Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits
12:13

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

Published on: January 25, 2013

27.8K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 交通工程是交通工程.

背景情况:

  • 由于网络结构,时空图神经网络是交通预测的关键.
  • 现有的模型未充分利用空间相关性,经常使用单个,固定或自适应的图形结构.
  • 这种局限性阻碍了交通网络中各种空间依赖性的全面建模.

研究的目的:

  • 提出一个新的框架,多层图神经普通微分方程 (MLGO),用于增强交通预测中的空间表示.
  • 整合多个互补的图形结构,以实现更强大的空间建模方法.
  • 提高交通预测模型的准确性和可解释性.

主要方法:

  • 开发了一个多层图形架构,集成了时间变化,预定义的道路网络和自适应图形.
  • 使用神经常规微分方程来进行层间和层内空间聚合.
  • 在空间建模框架内确保时间连续性.

主要成果:

  • 与最先进的基线相比,MLGO在五个现实世界交通数据集中表现出更高的性能.
  • 综合多层图形方法有效地捕捉了复杂的空间相关性.
  • 该框架通过明确和互补的图形结构提供了更好的解释性.

结论:

  • MLGO提供了一种通用和可扩展的解决方案,用于在交通预测中利用多样化的空间信息.
  • 多个图形结构的集成显著增强了时空建模能力.
  • 拟议的方法代表了准确和可解释的交通预测的重大进步.