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Related Concept Videos

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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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.
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Related Experiment Video

Updated: Aug 3, 2025

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Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query.

Wei Chen, Yating Wei, Zhiyong Wang

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    This study introduces a new privacy-preserving method for visualizing decentralized data using a federated learning approach. It enables secure aggregated visual queries without compromising data confidentiality.

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    Area of Science:

    • Computer Science
    • Data Privacy
    • Information Visualization

    Background:

    • Decentralized data presents challenges for aggregated visual querying due to privacy concerns.
    • Existing methods often struggle to balance data utility with robust privacy preservation.

    Purpose of the Study:

    • To develop a novel privacy preservation strategy for aggregated visual queries on decentralized data.
    • To adapt the federated learning framework for visualization tasks, ensuring data confidentiality.

    Main Methods:

    • A federated visualization framework is proposed, mimicking federated learning.
    • A shared global module aggregates encrypted visual features from local data modules.
    • Two schemes, prediction-based and query-based, are implemented for federated visualization.

    Main Results:

    • The effectiveness of the federated visualization approach is demonstrated using various visual forms.
    • Evaluations confirm the robustness of the proposed privacy-preserving strategy.
    • Expert review validates the practical value of federated visualization in real-world scenarios.

    Conclusions:

    • The federated visualization strategy offers a secure and effective method for analyzing decentralized data.
    • This approach enhances privacy preservation in aggregated visual querying.
    • The study highlights the potential of federated learning principles in information visualization.