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相关概念视频

End Point Prediction: Gran Plot01:07

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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.
For potentiometric titration, the Gran plot is created by plotting...
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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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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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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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相关实验视频

Updated: Sep 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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有效的联合图形聚合用于维护隐私的基于GNN的会话建议.

Jing Lou1, Cheng Rong2, Hanshen Chen2

  • 1College of Intelligent Transportation, Zhejiang Institute of Communications, Hangzhou, China. loujing@zjvtit.edu.cn.

Scientific reports
|July 3, 2025
PubMed
概括

联合图形聚合 (FedGA) 通过在联合学习 (FL) 中有效地合并本地模型来增强保护隐私的建议. 这种方法克服了基于会话的建议与非IID数据的挑战,实现了最先进的性能.

科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 图形神经网络 (GNN) 在推系统中表现出色,但在隐私的联邦学习 (FL) 中面临挑战.
  • FL约束阻止了全局图形构建,非IID会话数据降低了模型性能.
  • 从稀疏的局部图表中合并本地模型在保护隐私的场景中是低效的.

研究的目的:

  • 引入一种新的自适应联合学习方法,即联合图形聚合 (FedGA),用于保护隐私的基于会话的建议.
  • 在基于FL的GNN建议中解决分散图形构建和非IID数据的挑战.
  • 开发一个高效的聚合器,用于合并在局部图形嵌入上训练的本地模型.

主要方法:

  • 介绍了联邦图汇总 (FedGA),这是一个自适应的FL方法,结合了阻差聚合 (DRA) 和条件第二时刻估计 (C-SME).
  • 开发了一种高效的聚合器,用于合并在未见的本地图嵌入上训练的本地模型.
  • 在极端非IIDness下,结合了优化模型的策略,而不会受到激进学习率的干扰.

主要成果:

  • 即使在极端的非IID数据条件下,FedGA也有效地优化模型.
  • 理论分析表明,FedGA实现了与其他自适应FL方法相比的收率.

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  • 在开放和现实数据集上的经验验证证明了与现有的自适应FL方法相比,最先进的性能.
  • 结论:

    • FedGA成功地弥合了基于FL和GNN的会议建议之间的差距.
    • 拟议的方法实现了最先进的性能,同时保持了与集中式方法可比的结果.
    • 在联邦设置中,FedGA为保护隐私的GNN建议提供了高效和有效的解决方案.