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相关概念视频

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...
561
Reducing Line Loss01:18

Reducing Line Loss

141
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...
141
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

145
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.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
145
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.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
103
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.
On...
327

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相关实验视频

猎:特征标签受限制的图形网崩,用于存储效率高的GNN.

Christopher Adnel, Islem Rekik

    IEEE transactions on neural networks and learning systems
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    图形神经网络 (GNN) 是强大的,但内存密集型. 一种新的图形缩小技术FALCON显著缩小了图形大小,同时保留了特征标签分布,从而实现了可扩展的GNN训练.

    相关实验视频

    科学领域:

    • 机器学习 机器学习
    • 图形神经网络的神经网络
    • 数据挖掘 数据挖掘

    背景情况:

    • 图形神经网络 (GNN) 能够在相互连接的数据上进行机器学习,但由于对大图形的高内存要求,它们面临可扩展性挑战.
    • 现有的GNN记忆减少方法往往侧重于推断或忽视特征标签分布,限制其在训练期间的有效性.
    • 当前的图形缩小技术很少,并且经常无法解决训练内存的足迹或保存关键数据分布.

    研究的目的:

    • 介绍FALCON,一种新的拓意识图形缩小技术,旨在克服GNN的内存限制.
    • 开发一种方法,在图形缩小过程中保持特征标签分布,确保准确的GNN训练.
    • 为了提高可扩展性,将FALCON与现有的内存减少策略集成在一起.

    主要方法:

    • 猎采用k-means集群与一个新的维度规范化的欧几里德距离来执行拓意识的图形缩小.
    • 该方法通过将其纳入图形缩小过程来保持特征标签分布.
    • 猎与微分批GNN和量子化技术一起实施和评估.

    主要成果:

    • 猎显著降低了图形大小,像PPI和Flickr这样的数据集崩至其原始节点的34%.
    • 拟议的方法在各种GNN模型中保持了预测质量,证明了其有效性.
    • 与最先进的方法相比,基准和废除研究证实了FALCON优越的记忆减少能力.

    结论:

    • 猎提供了一种有效的解决方案,可以在训练期间减少GNN的内存足迹.
    • 该技术成功地保留了基本的特征标签分布,确保了模型在减少后的准确性.
    • 猎增强了GNN的可扩展性,用于现实世界的应用程序,使用大规模的图形数据集.