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

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

87
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
87
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

76
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
76
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

79
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
79
Associative Learning01:27

Associative Learning

276
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...
276
Aggregates Classification01:29

Aggregates Classification

298
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...
298

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

Predictive and prognostic significance of M descriptors of the 8th TNM classification for advanced NSCLC patients treated with immune checkpoint inhibitors.

Translational lung cancer research·2020
Same author

The PPR-SMR Protein ATP4 Is Required for Editing the Chloroplast <i>rps8</i> mRNA in Rice and Maize.

Plant physiology·2020
Same author

Nanoparticle-enhanced chemo-immunotherapy to trigger robust antitumor immunity.

Science advances·2020
Same author

Disease burden and prognostic factors for clinical failure in elderly community acquired pneumonia patients.

BMC infectious diseases·2020
Same author

Stachydrine promotes angiogenesis by regulating the VEGFR2/MEK/ERK and mitochondrial-mediated apoptosis signaling pathways in human umbilical vein endothelial cells.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2020
Same author

Antibacterial Spiropyrimidinetriones with N-Linked Azole Substituents on a Benzisoxazole Scaffold Targeting DNA Gyrase.

Journal of medicinal chemistry·2020
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

原型匹配学习用于不完整的多视图集群.

Honglin Yuan, Yuan Sun, Fei Zhou

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了不完整多视图集群的原型匹配学习 (PMIMC),以解决多视图集群中的数据丢失和原型错位问题. PMIMC增强了集群性能和稳定性,优于现有方法.

    更多相关视频

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.4K
    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

    相关实验视频

    Last Updated: May 24, 2025

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.4K
    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

    科学领域:

    • 数据科学数据科学数据科学
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 不完整的多视图集群 (IMVC) 面临着由于传感器或设备故障导致部分数据丢失的挑战.
    • 现有的基于原型的IMVC方法通常假定交叉视图原型对齐,这可能不成立,导致原型不对齐问题 (PUP) 和潜在的过拟合.
    • 在IMVC中数据归算可能会受到性能不稳定 (PIP) 的影响,因为在不同的缺失率下,数据质量会变化.

    研究的目的:

    • 提出一种新的方法,即为不完整多视图集群的原型匹配学习 (PMIMC),以有效处理不完整的多视图数据.
    • 在IMVC中解决原型失调 (PUP) 和性能不稳定 (PIP) 的挑战.
    • 为了提高不完整的多视图集群算法的稳定性和集群精度.

    主要方法:

    • PMIMC使用关系一致性学习来管理多视图数据异质性.
    • 一个强大的原型对比学习损失被用来减轻PUP的影响.
    • 开发了一个基于原型的归算策略,以减少归算不稳定性,特别是在高缺失率的情况下.

    主要成果:

    • 与13种最先进的方法相比,PMIMC表现出优越的集群性能.
    • 拟议的方法在处理不完整的多视图数据方面显示了增强的稳定性.
    • 实验结果验证了PMIMC在治疗PUP和PIP方面的有效性.

    结论:

    • 通过有效处理数据异质性,原型错位和归算不稳定性,PMIMC为不完整的多视图集群提供了强大的解决方案.
    • 该方法实现了最先进的性能和更强大的稳定性.
    • 开发的技术在多视图集群领域取得了重大进展.