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

11.9K
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...
11.9K
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
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

278
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
278
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
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...
5.1K
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K

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

A THz graphene-on-hBN stack patch antenna for future 6G communications.

Scientific reports·2025
Same author

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Metasurface-Programmable Wireless Network-On-Chip.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2022
Same author

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach.

Sensors (Basel, Switzerland)·2021
Same author

Reprogrammable Graphene-based Metasurface Mirror with Adaptive Focal Point for THz Imaging.

Scientific reports·2019
Same author

Reconfigurable THz Plasmonic Antenna Based on Few-Layer Graphene with High Radiation Efficiency.

Nanomaterials (Basel, Switzerland)·2018
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
Same journal

Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.

Frontiers in artificial intelligence·2026
Same journal

Resource-efficient retrieval-augmented question answering for the Indian Lok Sabha dataset.

Frontiers in artificial intelligence·2026
Same journal

Violation detection in power operation sites based on multi-scale detection and few-shot learning.

Frontiers in artificial intelligence·2026
Same journal

Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.

Frontiers in artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 6, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

天空地图:GNN基准测试的生成图形模型.

Axel Wassington1, Raúl Higueras1, Sergi Abadal1

  • 1Department of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, Spain.

Frontiers in artificial intelligence
|November 29, 2024
PubMed
概括
此摘要是机器生成的。

SkyMap生成了合成标记的属性图形,改善了图形神经网络 (GNN) 性能复制. 这种新模型提供了对图形拓和特征的精细控制,优于GNN基准测试的现有生成方法.

关键词:
图形神经网络 (GNN) 是一个神经网络.一个基准的基准指标.度分布分布的分度分布.图形生成模型模型的图形生成模型机器学习数据集是机器学习数据集.混合矩阵是一个混合矩阵.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641
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

995

相关实验视频

Last Updated: Jun 6, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641
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

995

科学领域:

  • 机器学习 机器学习
  • 图形理论 图形理论
  • 数据科学数据科学数据科学

背景情况:

  • 图形神经网络 (GNN) 越来越受欢迎,但研究受到一小部分基准数据集的限制.
  • 现有的合成图形生成模型 (例如,ALBTER,GenCAT) 通常无法准确地反映GNN在原始数据上的性能.

研究的目的:

  • 介绍SkyMap,一个用于标记属性图的新型生成模型.
  • 为合成数据生成提供对图形拓和特征分布的细粒度控制.
  • 通过使用更多样化和更具代表性的合成数据集,加强GNN的评估和基准分析.

主要方法:

  • 开发了SkyMap,这是一个具有可控制拓和特征分布的标记属性图的生成模型.
  • 评估了SkyMap在各种GNN架构 (图形卷积,注意力,等态网络) 中复制图形可学习性的能力.
  • 使用瓦瑟斯坦距离进行量化性能复制,并通过参数抽样证明了通过参数抽样生成数据集星座.

主要成果:

  • 与ALBTER和GenCAT相比,SkyMap在复制GNN可学习性方面表现出卓越的表现,实现了64%更低的瓦斯斯坦距离.
  • 该模型可以通过采样输入参数来创建各种合成图数据集.
  • 在GNN和多层感知子之间的性能比较中,SkyMap生成的数据集的实用性得到了说明.

结论:

  • 天空地图为GNN评估生成高保真度合成图形数据集提供了重大进步.
  • 该模型的细粒度控制和生成多种数据集的能力解决了当前GNN基准分析实践的局限性.
  • 通过提供量身定制的合成数据,SkyMap促进了更强大,更可靠的GNN研究.