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Related Concept Videos

Aggregates Classification01:29

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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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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Updated: Aug 3, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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DropAGG: Robust Graph Neural Networks via Drop Aggregation.

Bo Jiang1, Yong Chen1, Beibei Wang1

  • 1School of Computer Science and Technology, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, NO.111 Jiu Long Road, Hefei, 230601, Anhui Province, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Drop Aggregation (DropAGG), a novel random message propagation method for Graph Neural Networks (GNNs). DropAGG enhances GNN robustness against noise and adversarial attacks while mitigating over-smoothing issues in graph data learning.

Keywords:
Drop aggregationGraph neural networksGraph random aggregation networkRobust data learning

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Area of Science:

  • Data Mining
  • Machine Learning
  • Graph Analytics

Background:

  • Graph Neural Networks (GNNs) are crucial for graph data representation and learning.
  • Deterministic message propagation in GNNs is vulnerable to structural noise, adversarial attacks, and over-smoothing.
  • Existing GNNs lack robustness and can suffer from performance degradation due to these issues.

Purpose of the Study:

  • To develop a robust learning mechanism for GNNs.
  • To address the limitations of deterministic message propagation in GNNs.
  • To propose a novel random message propagation technique to enhance GNN performance and stability.

Main Methods:

  • Introduced Drop Aggregation (DropAGG), a random message propagation mechanism for GNNs.
  • DropAGG randomly selects nodes for information aggregation, enhancing robustness.
  • Developed Graph Random Aggregation Network (GRANet) by integrating DropAGG with GNNs.

Main Results:

  • DropAGG significantly enhances the robustness of GNNs against structural noises and adversarial attacks.
  • The proposed DropAGG effectively mitigates the over-smoothing issue in GNNs.
  • GRANet demonstrated superior performance and robustness on benchmark datasets.

Conclusions:

  • Drop Aggregation (DropAGG) is a general and effective scheme for improving GNN robustness and mitigating over-smoothing.
  • GRANet, powered by DropAGG, offers a robust solution for graph data learning tasks.
  • The findings highlight the potential of random propagation mechanisms in advancing GNN capabilities.