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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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...
106
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.5K
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...
10.5K
Associative Learning01:27

Associative Learning

370
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...
370
Independent and Dependent Sources01:18

Independent and Dependent Sources

1.1K
In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
1.1K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

447
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
447

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

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

From our hospitals to their kitchens: a dual-perspective plea for continuous nutrition in chronic obstructive pulmonary disease care.

Journal of thoracic disease·2026
Same author

De novo design of miniproteins targeting GPCRs.

Nature·2026
Same author

ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision.

Medical image analysis·2026
Same author

Few-shot video object segmentation in X-ray angiography using local matching and spatio-temporal consistency loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Confidence-Aware Pseudo-Label Self-Correction for Weakly Supervised Visual Grounding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

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

572

Evidential Multi-Source-Free Unsupervised Domain Adaptation.

Jiangbo Pei, Aidong Men, Yang Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 5, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an evidential learning approach for Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) to improve knowledge aggregation from multiple models. The proposed Evidential Aggregation and Adaptation Framework (EAAF) achieves state-of-the-art results by addressing domain-level and local structure challenges.

    More Related Videos

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.0K

    Related Experiment Videos

    Last Updated: Jul 4, 2025

    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

    572
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.0K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) aims to leverage knowledge from multiple source models for target domain adaptation.
    • Existing MSFUDA methods face challenges in effectively aggregating knowledge from diverse sources and preventing unreliable semantic propagation.
    • Suboptimal domain-level aggregation and risky local structure-based semantic propagation hinder performance.

    Purpose of the Study:

    • To propose a novel evidential learning method to address the limitations in MSFUDA.
    • To develop fine-grained, instance-level knowledge aggregation and trustworthy semantic propagation.
    • To enhance the performance of MSFUDA through a robust framework.

    Main Methods:

    • Formulated Evidential Prediction Uncertainty (EPU) to capture sample-model fitting uncertainty for instance-level aggregation.
    • Developed an EPU-Based Multi-Source Aggregation module for fine-grained knowledge integration.
    • Introduced Evidential Adjacency-Consistent Uncertainty (EAU) for robust consistency measurement among target domain samples.
    • Designed an EAU-Guided Local Structure Mining module for reliable semantic propagation.
    • Integrated these components into the Evidential Aggregation and Adaptation Framework (EAAF).

    Main Results:

    • The proposed EPU metric effectively guides instance-level aggregation, allowing target samples to express preferences for different source models.
    • The EAU metric ensures robust consistency among adjacent target samples, facilitating trustworthy semantic propagation.
    • The EAAF framework demonstrated superior performance compared to existing methods.
    • State-of-the-art results were achieved on three MSFUDA benchmarks.

    Conclusions:

    • The proposed evidential learning method effectively addresses key challenges in MSFUDA.
    • EAAF provides a robust framework for fine-grained knowledge aggregation and reliable semantic propagation.
    • The approach significantly advances the state-of-the-art in Multi-Source-Free Unsupervised Domain Adaptation.