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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
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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Leveling Effect01:29

Leveling Effect

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In acid-base chemistry, the leveling effect refers to the limitation imposed by the solvent on the strength of acids and bases in solution. When a base stronger than the solvent's conjugate base is used, it deprotonates the solvent until the base is entirely consumed, making it ineffective against weaker acids. Conversely, an acid stronger than the solvent's conjugate acid protonates the solvent until the acid is depleted, rendering it ineffective against weaker bases. Essentially, the...
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Aggregates Classification01:29

Aggregates Classification

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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Ogive Graph01:07

Ogive Graph

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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: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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跨层次图形对比学习用于社区价值预测.

Wenjie Yang1, Shengzhong Zhang2, Zengfeng Huang1

  • 1Fudan University, 220 Handan Road, Shanghai, 200433, China.

Neural networks : the official journal of the International Neural Network Society
|September 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了社会商业中社区价值预测 (CVP) 的跨层次社区对比学习 (CCCL). 通过学习多层次图表表示,CCCL有效地预测社区价值,优于现有方法.

关键词:
社区价值预测预测图表对比学习学习的图表.图表神经网络的神经网络

相关实验视频

Last Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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科学领域:

  • 社会 商业 社会 商业
  • 图表 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 社区价值预测 (CVP) 在社会商业中至关重要,但由于社区和个人结构的复杂性而具有挑战性.
  • 现有的图形机器学习方法难以充分解决CVP任务.
  • 需要一种新的方法来有效地为CVP建模多层次的社会联系.

研究的目的:

  • 引入一种新的跨层次图形对比学习方法,跨层次社区对比学习 (CCCL),用于像CVP这样的子图层次任务.
  • 通过利用节点级和社区级的图形信息来增强社区值的预测.
  • 建立一个新的基准在图形对比学习的社会商务应用程序.

主要方法:

  • 开发了CCCL,一个跨层次的图形对比学习框架.
  • 生成了两个不同的图形视图:一个增强的节点级图形和一个通过图形粗化生成的社区级图形.
  • 使用交叉视图对比损失来捕捉节点和社区视图之间的相互信息.

主要成果:

  • CCCL有效地学习使用多层社区和节点信息的嵌入式.
  • 拟议的方法在CVP任务上显著优于端到端和自我监督的基线.
  • CCCL模型表现出对边缘扰动攻击的强大抵抗力,表明其稳定性.

结论:

  • CCCL是第一个专门为CVP问题设计的图形对比学习方法.
  • 理论分析表明,CCCL最大化了不同图形表示之间相互信息的下界.
  • CCCL为社会商业中的社区价值预测提供了一个高效和强大的解决方案.