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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.0K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.0K
Reducing Line Loss01:18

Reducing Line Loss

302
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
302
Improving Translational Accuracy02:07

Improving Translational Accuracy

13.9K
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...
13.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.4K
3.4K
Aggregates Classification01:29

Aggregates Classification

900
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
900
Distributed Loads01:19

Distributed Loads

875
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
875

You might also read

Related Articles

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

Sort by
Same author

Perinatal outcomes following cell-free DNA screening in >32 000 women: Clinical follow-up data from a single tertiary center.

Prenatal diagnosis·2018
Same author

Research on Thermal Conductivity of Electrospun Polyacrilonitrile-Multi-Walled Carbon Nanotubes Composite Carbon Nanofiber Papers.

Journal of nanoscience and nanotechnology·2018
Same author

Investigation of potential toxic components based on the identification of Genkwa Flos chemical constituents and their metabolites by high-performance liquid chromatography coupled with a Q Exactive high-resolution benchtop quadrupole Orbitrap mass spectrometer.

Journal of separation science·2018
Same author

The caudal dorsal artery generates hematopoietic stem and progenitor cells via the endothelial-to-hematopoietic transition in zebrafish.

Journal of genetics and genomics = Yi chuan xue bao·2018
Same author

Response to Single Low-dose <sup>177</sup>Lu-DOTA-EB-TATE Treatment in Patients with Advanced Neuroendocrine Neoplasm: A Prospective Pilot Study.

Theranostics·2018
Same author

Berberine Hydrochloride-Loaded Chitosan Nanoparticles Effectively Targets and Suppresses Human Nasopharyngeal Carcinoma.

Journal of biomedical nanotechnology·2018
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Dec 24, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning.

Jingjing Zhang, Osvaldo Simeone

    IEEE Transactions on Neural Networks and Learning Systems
    |April 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study unifies gradient coding (GC) and worker grouping for distributed learning, introducing new strategies like lazily aggregated GC (LAGC) and grouped-LAG (G-LAG) to overcome delays and bottlenecks in parameter server (PS) architectures.

    Related Experiment Videos

    Last Updated: Dec 24, 2025

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.2K

    Area of Science:

    • Distributed computing
    • Machine learning algorithms
    • Parallel processing

    Background:

    • Parameter server (PS) architectures in distributed learning face challenges from random delays caused by straggling workers and communication bottlenecks.
    • Existing solutions like gradient coding (GC), worker grouping, and adaptive worker selection address these issues independently.

    Purpose of the Study:

    • To provide a unified analysis of existing techniques for mitigating delays and bottlenecks in PS architectures.
    • To introduce novel strategies, lazily aggregated GC (LAGC) and grouped-LAG (G-LAG), combining the strengths of GC, grouping, and adaptive selection.
    • To evaluate the performance of these novel strategies in terms of wall-clock time, communication, and computation complexity.

    Main Methods:

    • Unified analysis of gradient coding (GC), worker grouping, and adaptive worker selection.
    • Introduction and analysis of novel strategies: lazily aggregated GC (LAGC) and grouped-LAG (G-LAG).
    • Evaluation of methods based on wall-clock time, communication complexity, and computation complexity for varying worker node computing times.

    Main Results:

    • The grouped-LAG (G-LAG) strategy demonstrates superior performance in wall-clock time and communication efficiency.
    • G-LAG effectively balances robustness to stragglers with communication and computation load gains.
    • The proposed strategies show effectiveness across two representative distributions of worker node computing times.

    Conclusions:

    • G-LAG offers the optimal solution for enhancing distributed learning performance in PS architectures, particularly concerning speed and communication.
    • The unified analysis provides a framework for understanding and developing more efficient distributed learning systems.
    • Novel strategies like G-LAG are crucial for overcoming inherent challenges in large-scale gradient-based distributed learning.