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

Control Systems01:10

Control Systems

1.0K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.0K
Cognitive Learning01:21

Cognitive Learning

144
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
144

You might also read

Related Articles

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

Sort by
Same author

Identification of ubiquitination-related biomarkers in osteoarthritis: Combining transcriptome and Mendelian randomization analysis.

SLAS technology·2026
Same author

Oxidative Stress in Dry Eye Disease: Molecular Mechanisms and Emerging Therapeutic Strategies.

Biomolecules·2026
Same author

Bifunctional PVDF/MXene membranes with high piezoelectric sensitivity and near infrared photothermal antibacterial efficacy toward wearable human activity monitoring.

Nanoscale·2026
Same author

Direct Neodymium:YAG Laser Over the Occluded Phakic Implantable Collamer Lens Port to Manage Pupillary Block.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2026
Same author

Enhanced Gas Classification in Electronic Nose Systems Using an SMOTE-Augmented Machine Learning Framework.

Sensors (Basel, Switzerland)·2026
Same author

Identification of risk factors and construction of a predictive model for postoperative new-onset stress urinary incontinence in patients with pelvic organ prolapse: A single-center retrospective study.

African journal of reproductive health·2026

Related Experiment Video

Updated: May 24, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K

CUDA-X: Unsupervised Domain-Adaptive Vehicle-to-Everything Collaboration via Knowledge Transfer and Alignment.

Hongbo Yin, Daxin Tian, Chunmian Lin

    IEEE Transactions on Neural Networks and Learning Systems
    |March 4, 2025
    PubMed
    Summary

    The new CUDA-X framework enhances vehicle-to-everything (V2X) perception by enabling unsupervised domain adaptation for improved collaboration between vehicles and infrastructure, achieving state-of-the-art results across diverse datasets.

    More Related Videos

    Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
    07:15

    Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

    Published on: December 18, 2020

    4.4K
    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

    475

    Related Experiment Videos

    Last Updated: May 24, 2025

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
    06:28

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

    Published on: August 26, 2018

    5.9K
    Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
    07:15

    Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

    Published on: December 18, 2020

    4.4K
    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

    475

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Vehicle-to-Everything (V2X) perception offers potential to overcome single-vehicle limitations through agent interaction.
    • Existing V2X approaches often lack transferability across different simulation and real-world scenarios.

    Purpose of the Study:

    • To introduce CUDA-X, an unsupervised domain-adaptive V2X collaboration framework.
    • To enhance cross-scenario transferability in cooperative perception.

    Main Methods:

    • Utilizes a collective model with key-point information exchange and instance adaptation.
    • Employs Collaborative Knowledge Transfer (CKT) for domain-agnostic feature reconstruction.
    • Introduces Bin-based Location Correction (BLC) and Category-aware Pooling Alignment (CPA) for cross-dataset alignment.

    Main Results:

    • CUDA-X establishes new state-of-the-art performance in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) perception.
    • Achieves superior results across four benchmark datasets (OPV2V, V2X-Sim, V2V4Real, DAIR-V2X).
    • Demonstrates effectiveness in both simulated and real-world settings.

    Conclusions:

    • CUDA-X provides significant advancements in unsupervised domain generalization for multi-agent perception.
    • The framework offers insights into improving V2X perception robustness and adaptability.
    • Public code release is anticipated to foster further research.