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

Diffusion01:21

Diffusion

6.2K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.2K
Diffusion01:12

Diffusion

216.4K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
216.4K
Facilitated Diffusion01:16

Facilitated Diffusion

1.2K
The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
In this process, substrates such as organic compounds and ions interact with a transporter on one side, triggering conformational changes in proteins that enable...
1.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

243
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
243
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K

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

Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.

European biophysics journal : EBJ·2026
Same author

Single-molecule approaches to study G-quadruplex, R-loop, and protein interactions.

Current opinion in structural biology·2026
Same author

Chiral phosphoric acid-catalyzed atroposelective iodination of N-arylindoles.

Communications chemistry·2025
Same author

Evaluation of imputation performance based on the single nucleotide polymorphism panel density and the reference population size in Korean native chicken.

Animal bioscience·2025
Same author

Enantioselective Desymmetrization of Biaryls via Cooperative Photoredox/Brønsted Acid Catalysis and Its Application to the Total Synthesis of Ancistrobrevolines.

Journal of the American Chemical Society·2025
Same author

Selection signature analysis using whole genome resequencing data reveals candidate genes for white plumage color in Korean native ducks.

Animal bioscience·2025

相关实验视频

Updated: Jan 18, 2026

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

1.0K

在非IID数据上进行沟通效率高的联合学习,以拍卖为导向的模型传播.

Seyoung Ahn1, Soohyeong Kim1, Yongseok Kwon1

  • 1Department of Computer Science and Engineering, Hanyang University, 15588, South Korea.

Neural networks : the official journal of the International Neural Network Society
|September 10, 2025
PubMed
概括
此摘要是机器生成的。

6G中的联合学习 (FL) 面临着非IID数据的挑战,导致模型分歧. 我们的新FedDif策略通过在聚合之前实现设备对设备的学习来提高全球模型性能并降低通信成本.

关键词:
合作学习学习合作学习.联合学习是联合学习.移动通讯是移动通讯的一种方式.非IID数据的数据

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

相关实验视频

Last Updated: Jan 18, 2026

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

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

科学领域:

  • 人工智能的人工智能
  • 移动通信系统 移动通信系统
  • 机器学习 机器学习

背景情况:

  • 6G系统利用人工智能驱动的网络功能,采用联合学习 (FL) 来保护用户数据隐私.
  • 在FL中,非独立且相同分布的 (非IID) 数据集可能会由于梯度分歧而降低全球模型性能,导致权重分歧问题.

研究的目的:

  • 提出一个新的传播策略,FedDif,以提高机器学习 (ML) 模型在6G系统中使用非IID数据的性能.
  • 从理论上证明FedDif能够克服FL固有的重量分歧问题,使用非IID数据.

主要方法:

  • 在参数聚合之前,FedDif通过设备对设备通信促进了本地模型学习各种数据分布.
  • 拍卖理论被用来开发一种有效的传播策略,平衡学习绩效和沟通成本.
  • 理论分析表明,FedDif能够规避重量分歧问题.

主要成果:

  • FedDif 显著提高了顶级-1 测试准确度,高达 20.07 个百分点.
  • 与标准的FedAvg算法相比,拟议的策略将通信成本降低多达45.27%.
  • 实验验证证了FedDif在处理非IID数据和优化资源分配方面的有效性.

结论:

  • 在6G环境中,FedDif提供了一个强大的解决方案,用于提高非IID数据的FL性能.
  • 扩散策略有效地减轻了重量差异,从而导致了卓越的全球模型准确性.
  • FedDif提出了一种有效的沟通方法,优化了学习收益和传输开销之间的权衡.