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

Survival Tree01:19

Survival Tree

84
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
84
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.3K
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
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
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

318
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...
318
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K

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

Conductivity optimization of La<sub>0.3</sub>Sr<sub>0.7</sub>TiO<sub>3</sub>/La<sub>0.8</sub>Sr<sub>0.2</sub>MnO<sub>3</sub> bilayer interconnects <i>via</i> interfacial oxygen partial pressure regulation and its application in FT-SIS-SOFCs.

Chemical science·2026
Same author

Beyond numbers: The governance role of data assets in real earnings management.

PloS one·2026
Same author

Intra-nanoparticle Drug-protein Interactions Mediate Sequential Therapeutic Release.

bioRxiv : the preprint server for biology·2026
Same author

Coal Interface Modified by the Nanofluid: Insights from Dynamic Adsorption Wetting to Structural Weakening.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

A Novel Approach to Zero-Shot Drug-Drug Interaction Prediction Enabled by EHR-Augmented Knowledge Graphs.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

The evolving landscape of gene editing therapies for human genetic diseases: a twenty-year bibliometric analysis.

Frontiers in medicine·2026

相关实验视频

Updated: Jun 28, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

550

TO-UGDA:以目标为导向的无监督图域调整.

Zhuo Zeng1,2, Jianyu Xie1,2, Zhijie Yang1,2

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Scientific reports
|April 21, 2024
PubMed
概括

本研究介绍了TO-UGDA,这是一种用于图域适应 (GDA) 的新框架,通过增强特征表示和下游适应来克服现有方法的局限性. 这种新方法提高了与未标记目标数据的节点级和图表级任务的性能.

关键词:
有条件的班次转移.一般化 一般化 一般化图形域适应 图形域适应不变的特征表示表示不变的特征表示.这是一个meta伪标签.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

529

相关实验视频

Last Updated: Jun 28, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

550
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

529

科学领域:

  • 机器学习 机器学习
  • 图形神经网络的神经网络
  • 人工智能的人工智能

背景情况:

  • 图域适应 (GDA) 面临的挑战是,目标图域中的标记数据有限.
  • 现有的GDA方法通常仅依赖于表示对齐,它可能会受到不相关信息的影响,并忽略条件转移.

研究的目的:

  • 提出一个面向目标的无监督图域自适应框架 (TO-UGDA),以有效地解决GDA的局限性.
  • 提高标签信息从标记源域到未标记目标域的可转移性.

主要方法:

  • 使用图形信息瓶提取域不变特征表示.
  • 通过对抗对齐来最大限度地减少域差异,以实现统一的特征分布.
  • 使用元伪标签来提高下游适应性和模型通用性.

主要成果:

  • 拟议的TO-UGDA框架在各种节点级和图级适应任务中表现出色.
  • 在现实世界的图形数据集上的实验验验证了框架的有效性.

结论:

  • TO-UGDA为无监督图域适应提供了一个强大的解决方案.
  • 该框架有效处理条件转移和无关信息,提高模型通用性.