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相关概念视频

Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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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Graphing Antiderivatives01:30

Graphing Antiderivatives

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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相关实验视频

Updated: Jan 15, 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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GHAttack:对异质图神经网络的生成对抗性攻击

Shaoxin Li, Xiaofeng Liao, Huanzhang Zhu

    IEEE transactions on neural networks and learning systems
    |January 13, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了生成异质攻击 (GHAttack),这是有效攻击异质图形神经网络 (HGNN) 的新方法. GHAttack很快就会产生干扰,这使得对HGNN的对抗性攻击变得更加实用.

    相关实验视频

    Last Updated: Jan 15, 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

    1.3K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形神经网络的神经网络

    背景情况:

    • 不同质的图形神经网络 (HGNNs) 越来越多地使用,但容易受到对抗性攻击.
    • 目前的HGNN攻击方法在计算上昂贵,限制了它们在推理过程中的使用.

    研究的目的:

    • 为HGNNs开发一个高效和有效的对抗性攻击方法.
    • 解决现有的HGNN攻击策略的计算效率低下问题.

    主要方法:

    • 引入了生成异质攻击 (GHAttack),一种新的生成攻击方法.
    • 开发了一个通过优化问题进行训练的扰动发生器,利用HGNN骨干和关系意识输出层.
    • 启用扰动来修改异质图关系中的边缘,以提高攻击效率.

    主要成果:

    • 在实验中,GHAttack表现出高效率和卓越的有效性.
    • 通过十个代表性的HGNN和六个数据集进行验证.
    • 生成式方法允许通过简单的前进传递快速产生扰动.

    结论:

    • 对于对HGNN的对抗性攻击,GHAttack提供了一个计算效率高的解决方案.
    • 该方法有效地通过扰乱图形结构来降低HGNN性能.
    • 这项工作推进了基于图形的机器学习模型的对抗性强度领域.