Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sign Test for Matched Pairs

Sign Test for Matched Pairs

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...

Wilcoxon Signed-Ranks Test for Matched Pairs

Wilcoxon Signed-Ranks Test for Matched Pairs

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...

Cross Product

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

Geometric Mean

Geometric Mean

The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Myocardia-Injected Synergistically Anti-Apoptotic and Anti-Inflammatory Poly(amino acid) Hydrogel Relieves Ischemia-Reperfusion Injury.

Advanced materials (Deerfield Beach, Fla.)·2025

Same author

Phosphorylation of PA at serine 225 enhances viral fitness of the highly pathogenic H5N1 avian influenza virus in mice.

Veterinary microbiology·2025

Same author

Comprehensive Analysis of Immune Characteristics of Fluorosis and Cuprotosis-Related Genes in Fluorosis Targeted Drugs.

Biological trace element research·2025

Same author

FOXM1 promotes malignant biological behavior and metabolic reprogramming by targeting SPINK1 in hepatocellular carcinoma and affecting the p53 pathway.

Biochimica et biophysica acta. Molecular basis of disease·2025

Same author

MXene/Ag-Based Zwitterionic Double-Network Hydrogels with Enhanced Mechanical Strength and Antifouling Performances.

ACS applied materials & interfaces·2025

Same author

Identification and validation of Atp5f1c in CD4<sup>+</sup> T cell as a hub protein in Parkinson's disease.

International journal of biological macromolecules·2025

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Geometric Matching for Cross-Modal Retrieval.

Zheng Wang, Zhenwei Gao, Yang Yang

IEEE Transactions on Neural Networks and Learning Systems

|April 23, 2024

Summary

This study introduces geometric representation learning to improve cross-modal retrieval by addressing one-to-many matching. Geometric methods capture semantic uncertainty, enhancing retrieval accuracy for complex queries.

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 28, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval faces challenges with one-to-many matching, where a single query corresponds to multiple instances in another modality.
Current methods using deterministic point embeddings inadequately represent the rich semantics and uncertainty in one-to-many correspondences.

Purpose of the Study:

To develop novel geometric representation learning methods for cross-modal retrieval.
To effectively address the one-to-many matching problem by capturing semantic uncertainty.

Main Methods:

Extended deterministic point embeddings to closed geometries to represent semantic uncertainty.
Introduced point-to-rectangle matching (P2RM) and rectangle-to-rectangle matching (R2RM) for one-to-many correspondence.
Utilized Euclidean distance and intersection volume for evaluating semantic similarity between heterogeneous data.

Main Results:

Geometric matching strategies (P2RM and R2RM) effectively handle one-to-many semantic correspondences.
Proposed methods significantly improve retrieval performance on standard image-text and video-text datasets.
The geometric approach enhances existing cross-modal retrieval baselines.

Conclusions:

Geometric representation learning offers a superior approach to cross-modal retrieval, particularly for complex one-to-many matching scenarios.
The proposed methods provide a robust framework for capturing semantic uncertainty and improving retrieval accuracy.