Jove
Visualize
Contact Us

Related Concept Videos

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
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Associative Learning01:27

Associative Learning

1.7K
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...
1.7K
Observational Learning01:12

Observational Learning

1.1K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.1K
Randomized Experiments01:13

Randomized Experiments

9.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.2K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

483
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
483

You might also read

Related Articles

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

Sort by
Same author

After Skin Wounding, Noncoding dsRNA Coordinates Prostaglandins and Wnts to Promote Regeneration.

The Journal of investigative dermatology·2017
Same author

Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer.

Oncotarget·2017
Same author

Identification of a six-lncRNA signature associated with recurrence of ovarian cancer.

Scientific reports·2017
Same author

Ropivacaine versus levobupivacaine in peripheral nerve block: A PRISMA-compliant meta-analysis of randomized controlled trials.

Medicine·2017
Same author

Fabrication of fluorescent composite hydrogel using in situ synthesis of upconversion nanoparticles.

Nanotechnology·2017
Same author

Ammonium assimilation: An important accessory during aerobic denitrification of Pseudomonas stutzeri T13.

Bioresource technology·2017
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 Experiment Video

Updated: Mar 14, 2026

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

1.3K

Co-Boosting++: Coupled Optimization of Data and Ensemble for One-Shot Federated Learning.

Xun Yang, Rong Dai, Yonggang Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 12, 2026
    PubMed
    Summary

    Co-Boosting++ enhances One-shot Federated Learning (OFL) by iteratively improving synthetic data and model ensembles. This novel approach tackles data and model heterogeneity for better global model training with minimal communication.

    Related Experiment Videos

    Last Updated: Mar 14, 2026

    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

    1.3K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • One-shot Federated Learning (OFL) trains global models with low communication overhead by distilling server models from client ensembles.
    • Ensembles aid synthetic data generation for knowledge distillation, but data and ensemble quality are often optimized separately.
    • Data and model heterogeneity pose significant challenges in OFL.

    Purpose of the Study:

    • To introduce Co-Boosting++, a novel OFL framework that jointly optimizes synthetic data generation and ensemble construction.
    • To address the coupled optimization problem and mitigate data/model heterogeneity in OFL.
    • To enable efficient adaptation to diverse device constraints via multi-model generation.

    Main Methods:

    • Co-Boosting++ iteratively enhances synthetic data generation and ensemble construction.
    • Adversarial generation of hard samples improves synthetic data quality and knowledge transfer robustness.
    • A Mixture of Experts (MoE) mechanism dynamically adjusts ensemble weights using hard samples.

    Main Results:

    • Co-Boosting++ consistently outperforms state-of-the-art OFL methods on benchmark datasets.
    • The framework demonstrates superior performance due to coupled optimization of data and ensemble quality.
    • Co-Boosting++ is practical for real-world scenarios without requiring local training modifications or additional transmissions.

    Conclusions:

    • Co-Boosting++ offers a unified solution to data and model heterogeneity in OFL.
    • The iterative, coupled optimization significantly boosts global model performance.
    • The framework's practicality and adaptability make it suitable for diverse applications.