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Graphical Representation of Inequalities01:28

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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

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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

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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

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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.
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Sequence Networks of Rotating Machines01:24

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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

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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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Video Experimental Relacionado

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: Ataques Adversarios Generativos en Redes Neuronales Heterogéneas

Shaoxin Li, Xiaofeng Liao, Huanzhang Zhu

    IEEE transactions on neural networks and learning systems
    |January 13, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta GHAttack (Generative Heterogeneous Attack), un nuevo método para atacar eficientemente Redes Neuronales Heterogéneas (HGNN). GHAttack genera perturbaciones rápidamente, haciendo más prácticos los ataques adversarios en HGNN.

    Palabras clave:
    ataque adversario generativoredes neuronales heterogéneasredes neuronales gráficasseguridad de grafosaprendizaje automático

    Videos de Experimentos Relacionados

    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

    Área de la Ciencia:

    • Inteligencia Artificial
    • Aprendizaje Automático
    • Redes Neuronales Gráficas

    Sus antecedentes:

    • Las Redes Neuronales Heterogéneas (HGNN) se utilizan cada vez más, pero son vulnerables a ataques adversarios.
    • Los métodos actuales de ataque de HGNN son computacionalmente costosos, lo que limita su uso durante la inferencia.

    Objetivo del estudio:

    • Desarrollar un método de ataque adversario eficiente y eficaz para HGNN.
    • Abordar la ineficiencia computacional de las estrategias de ataque de HGNN existentes.

    Principales métodos:

    • Introducción de GHAttack (Generative Heterogeneous Attack), un nuevo enfoque de ataque generativo.
    • Desarrollo de un generador de perturbaciones entrenado mediante un problema de optimización, utilizando una columna vertebral de HGNN y una capa de salida sensible a las relaciones.
    • Permite que las perturbaciones modifiquen los bordes dentro de las relaciones de grafos heterogéneos para mejorar la efectividad del ataque.

    Principales resultados:

    • GHAttack demostró alta eficiencia y excelente efectividad en experimentos.
    • Validado en diez HGNN representativas y seis conjuntos de datos.
    • El enfoque generativo permite la generación rápida de perturbaciones a través de un simple pase hacia adelante.

    Conclusiones:

    • GHAttack ofrece una solución computacionalmente eficiente para ataques adversarios en HGNN.
    • El método es eficaz para degradar el rendimiento de HGNN al perturbar las estructuras de grafos.
    • Este trabajo avanza el campo de la robustez adversaria para modelos de aprendizaje automático basados en grafos.