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関連する概念動画

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.
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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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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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関連する実験動画

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: 異種グラフニューラルネットワークに対する敵対的生成攻撃

Shaoxin Li, Xiaofeng Liao, Huanzhang Zhu

    IEEE transactions on neural networks and learning systems
    |January 13, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    本研究では、異種グラフニューラルネットワーク（HGNN）を効率的に攻撃するための新しい手法であるGenerative Heterogeneous Attack（GHAttack）を紹介します。GHAttackは摂動を迅速に生成するため、HGNNに対する敵対的攻撃がより実用的になります。

    キーワード:
    異種グラフニューラルネットワーク敵対的攻撃生成モデルグラフニューラルネットワーク機械学習

    関連する実験動画

    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

    科学分野:

    • 人工知能
    • 機械学習
    • グラフニューラルネットワーク

    背景:

    • 異種グラフニューラルネットワーク（HGNN）はますます使用されていますが、敵対的攻撃に対して脆弱です。
    • 現在のHGNN攻撃手法は計算コストが高く、推論中の使用を制限しています。

    研究 の 目的:

    • HGNN向けの効率的かつ効果的な敵対的攻撃手法を開発すること。
    • 既存のHGNN攻撃戦略の計算上の非効率性に対処すること。

    主な方法:

    • 新しい生成攻撃アプローチであるGenerative Heterogeneous Attack（GHAttack）を導入しました。
    • HGNNバックボーンと関係認識出力層を利用して最適化問題を通じて訓練された摂動ジェネレータを開発しました。
    • 攻撃効果を高めるために、異種グラフ関係内のエッジを変更するように摂動を可能にしました。

    主要な成果:

    • GHAttackは実験において高い効率と優れた有効性を示しました。
    • 10の代表的なHGNNと6つのデータセットで検証されました。
    • 生成アプローチにより、単純なフォワードパスを通じて迅速な摂動生成が可能になります。

    結論:

    • GHAttackは、HGNNに対する敵対的攻撃のための計算効率の高いソリューションを提供します。
    • この手法は、グラフ構造を摂動させることによりHGNNのパフォーマンスを低下させるのに効果的です。
    • この研究は、グラフベースの機械学習モデルの敵対的堅牢性の分野を進歩させます。