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

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.

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...

You might also read

Related Articles

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

Sort by

Same author

Outcomes of liver transplantation using extended criteria brain-dead donors: a retrospective study with initial experiences from a Vietnamese center.

Clinical transplantation and research·2026

Same author

3D Face Reconstruction with Deep Learning: Architectures, Datasets, and Benchmark Analysis.

Sensors (Basel, Switzerland)·2026

Same author

Ready or not, here they come: a practical guide to adoptive T-cell therapies for solid tumors for medical oncologists.

The oncologist·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

MyVivarium: A cloud-based lab animal colony management application with realtime ambient sensing.

Computational and structural biotechnology journal·2025

Same author

A retrospective analysis exploring the association of pretreatment neutrophil-to-lymphocyte ratio and immune checkpoint inhibitor outcomes in patients with advanced NSCLC and liver metastases.

Therapeutic advances in medical oncology·2025

Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 12, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Unsupervised Deep Cross-modality Spectral Hashing.

Tuan Hoang, Thanh-Toan Do, Tam V Nguyen

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 14, 2020

Summary

This summary is machine-generated.

This study introduces Deep Cross-modality Spectral Hashing (DCSH), a new unsupervised learning framework for efficient cross-modal retrieval using binary hash codes. DCSH effectively learns representations and mapping functions, outperforming existing methods.

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Related Experiment Videos

Last Updated: Dec 12, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval is challenging due to data heterogeneity.
Unsupervised learning of binary hash codes is crucial for efficient information retrieval.
Existing methods struggle with preserving modality-specific structures while uncovering cross-modal patterns.

Purpose of the Study:

To propose a novel unsupervised framework for efficient cross-modal retrieval.
To develop a two-step hashing approach for learning binary hash codes.
To enhance the performance of cross-modal retrieval systems.

Main Methods:

Introduced Deep Cross-modality Spectral Hashing (DCSH), a two-step hashing framework.
Employed a spectral embedding-based algorithm for single-modality and cross-modality representation learning.
Utilized Convolutional Neural Networks (CNNs) for image data and a CNN-based deep architecture for text data.

Main Results:

DCSH effectively preserves local structure within modalities.
The framework successfully reveals hidden patterns across multiple modalities.
Achieved superior performance compared to state-of-the-art methods on benchmark datasets.

Conclusions:

DCSH provides an effective solution for unsupervised cross-modal retrieval.
The proposed two-step approach optimizes binary representation and hashing functions.
The method demonstrates strong generalization capabilities across different datasets.