Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

441
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
441
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

660
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,...
660
Deconvolution01:20

Deconvolution

645
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
645
Multimachine Stability01:25

Multimachine Stability

592
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
592

You might also read

Related Articles

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

Sort by
Same author

MMRN1 suppresses thyroid cancer progression via activation of the Hippo signaling pathway.

Endocrine research·2026
Same author

Personalized Adaptive Gabor Filtering with Three-Stage Semi-Supervised Domain-Adversarial Learning for Cross-Subject SSVEP Decoding.

Sensors (Basel, Switzerland)·2026
Same author

Identification and analysis of the AP2/ERF gene family in <i>Dendrobium officinale</i> based on pan-genome and functional characterization of <i>DofERF109_2</i>.

Frontiers in plant science·2026
Same author

SomaScan proteomics reveals novel biomarkers in the progression of liver cirrhosis to hepatocellular carcinoma.

BMC medical genomics·2026
Same author

LINC00184 promotes esophageal squamous cell carcinoma progression via DNMT1-mediated methylation of the NDRG2 promoter and PI3K/AKT pathway activation.

Oncology letters·2026
Same author

SERS Mixture Recognition from Pure-Substance Spectra via Component Evidence Learning and Two-Stage Inference.

Molecules (Basel, Switzerland)·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Virtual Domain-Guided Cross-Modal Distillation With Multiview Correlation Awareness for Domain-Specific Multimodal

Zhenyu Hou, Junjun Guo, Zhengtao Yu

    IEEE Transactions on Neural Networks and Learning Systems
    |February 26, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for domain-specific multimodal neural machine translation (DMNMT) that addresses visual imbalance issues. The approach enhances translation accuracy by integrating virtual domain visual scenes, achieving state-of-the-art results.

    More Related Videos

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

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

    Published on: October 27, 2023

    1.7K

    Related Experiment Videos

    Last Updated: Feb 28, 2026

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

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

    Published on: October 27, 2023

    1.7K

    Area of Science:

    • Natural Language Processing
    • Computer Vision
    • Machine Translation

    Background:

    • Domain-specific multimodal neural machine translation (DMNMT) leverages images for context but faces challenges with visual imbalance (e.g., multiple or missing images).
    • Effectively integrating visual data under these conditions is crucial for improving translation, particularly for domain-specific terminology.

    Purpose of the Study:

    • To enhance the robustness and performance of DMNMT in scenarios with visual imbalance.
    • To improve the translation of domain-specific terms by effectively integrating visual information.

    Main Methods:

    • Introduced a virtual domain distillation-enhanced multimodal fusion approach.
    • Employed a multiview correlation-aware cross-modal distillation strategy to generate virtual domain visual scenes using multikernel representations.
    • Integrated these pseudo-domain visual scenes with text to boost domain-specific translation.

    Main Results:

    • The proposed approach demonstrated state-of-the-art (SOTA) machine translation scores on benchmark datasets.
    • Experimental results confirmed the effectiveness and robustness of the method across various domain-specific and general-domain scenarios.
    • The approach successfully captures domain visual representations, leading to more effective domain-specific translation.

    Conclusions:

    • The developed method effectively tackles visual imbalance issues in DMNMT.
    • The approach enhances translation quality and robustness in diverse multimodal domain settings.
    • This work contributes a significant advancement to the field of domain-specific machine translation.