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

Aggregates Classification01:29

Aggregates Classification

305
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...
305
Classification of Systems-I01:26

Classification of Systems-I

169
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:
169
Classification of Systems-II01:31

Classification of Systems-II

134
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
134
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
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...
3.2K
Functional Classification of Joints01:09

Functional Classification of Joints

3.8K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.8K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

31.7K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
31.7K

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

Quantitative Lipidomics Reveals Dynamic Lipid Profiles in <i>Cinnamomum camphora</i> Seed Kernels at Different Developmental Stages.

Plants (Basel, Switzerland)·2026
Same author

Knowledge Graph Augmented Large Language Models for Disease Prediction.

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

Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.

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

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

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

Minimum Inoculum of Resistance Assay for Evaluating Antitoxoplasmosis Compounds That Target Phenylalanine tRNA Synthetase.

ACS infectious diseases·2026
Same author

Targeting CDK4/6 potentiates the efficacy of anti-CD47 therapy via modulating the suppressive function of tumor-associated macrophages.

Apoptosis : an international journal on programmed cell death·2026
Same journal

MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2026
Same journal

Bridging the Scientific Knowledge Gap and Reproducibility: A Survey of Provenance, Assertion and Evidence Ontologies.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2025
Same journal

Uncertainty-Aware Pre-Trained Foundation Models for Patient Risk Prediction via Gaussian Process.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2025
Same journal

Preserving Missing Data Distribution in Synthetic Data.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2025
Same journal

DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2024
Same journal

Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2024
查看所有相关文章

相关实验视频

Updated: Jun 6, 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

6.9K

在带有隐性链接类型异质性的图形上进行联合节点分类.

Han Xie1, Li Xiong1, Carl Yang1

  • 1Emory University, Atlanta, GA, United States.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

联合学习 (FL) 通过在图表中发现隐藏的链接类型来解决数据异质性. FedLit框架有效地模拟了这些多样化的链接类型之间的消息传递,以提高性能.

关键词:
集群集成是指集群集成.联合学习的联合学习图表采矿是指采矿的采矿方式.图形神经网络的神经网络链接类型的异质性

更多相关视频

Glycan Node Analysis: A Bottom-up Approach to Glycomics
11:36

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Published on: May 22, 2016

10.3K
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

相关实验视频

Last Updated: Jun 6, 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

6.9K
Glycan Node Analysis: A Bottom-up Approach to Glycomics
11:36

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Published on: May 22, 2016

10.3K
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

科学领域:

  • 机器学习 机器学习
  • 图形神经网络 图形神经网络
  • 联邦学习学习 (Federated Learning) 是一种学习方式.

背景情况:

  • 联合学习 (FL) 从去中心化数据中训练全球模型,但非IID (非独立和相同分布) 数据带来了挑战.
  • 图形数据经常表现出链接类型的异质性,其中链接具有不同的语义和同类性,在客户端上有所不同.

研究的目的:

  • 提出一个新的图形FL框架,同时发现潜在的链接类型和模型链接特定的消息传递.
  • 为了应对图形联合学习中链接类型异质性的挑战.

主要方法:

  • 开发了FedLit,这是一个用于图形的联合学习框架.
  • 采用基于EM的集群算法来进行动态隐藏链路类型检测.
  • 利用多个卷积通道来区分基于发现的链接类型的消息传递.

主要成果:

  • 合成了现实的图形数据集,具有潜在的异质链接类型.
  • 分区数据集以模拟不同级别的链接类型异质性.
  • 通过全面的实验证明了FedLit框架的卓越性能和合理行为.

结论:

  • 在图形联合学习中,FedLit有效地处理链接类型异质性.
  • 该框架显示了改善复杂,现实世界的图形数据的FL性能的前景.