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Related Concept Videos

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Reducing Line Loss01:18

Reducing Line Loss

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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.
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Compacting Factor test01:22

Compacting Factor test

103
The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Videos

FALCON: Feature-Label Constrained Graph Net Collapse for Memory-Efficient GNNs.

Christopher Adnel, Islem Rekik

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Graph neural networks (GNNs) are powerful but memory-intensive. FALCON, a novel graph reduction technique, significantly shrinks graph size while preserving feature-label distribution, enabling scalable GNN training.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Graph Neural Networks
    • Data Mining

    Background:

    • Graph neural networks (GNNs) enable machine learning on interconnected data but face scalability challenges due to high memory requirements for large graphs.
    • Existing GNN memory reduction methods often focus on inference or neglect feature-label distribution, limiting their effectiveness during training.
    • Current graph reduction techniques are scarce and often fail to address the training memory footprint or preserve crucial data distributions.

    Purpose of the Study:

    • To introduce FALCON, a novel topology-aware graph reduction technique designed to overcome the memory limitations of GNNs.
    • To develop a method that preserves the feature-label distribution during graph reduction, ensuring accurate GNN training.
    • To integrate FALCON with existing memory reduction strategies for enhanced scalability.

    Main Methods:

    • FALCON employs k-means clustering with a novel dimension-normalized Euclidean distance to perform topology-aware graph reduction.
    • The method preserves the feature-label distribution by incorporating it into the graph reduction process.
    • FALCON is implemented and evaluated in conjunction with mini-batched GNNs and quantization techniques.

    Main Results:

    • FALCON significantly reduces graph size, collapsing datasets like PPI and Flickr to as low as 34% of their original nodes.
    • The proposed method maintains prediction quality across various GNN models, demonstrating its effectiveness.
    • Benchmarking and ablation studies confirm FALCON's superior memory reduction capabilities compared to state-of-the-art methods.

    Conclusions:

    • FALCON offers an effective solution for reducing the memory footprint of GNNs during training.
    • The technique successfully preserves essential feature-label distributions, ensuring model accuracy post-reduction.
    • FALCON enhances the scalability of GNNs for real-world applications with massive graph datasets.