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相关概念视频

Lateralization01:28

Lateralization

364
Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
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State Space Representation01:27

State Space Representation

237
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Source Transformation01:15

Source Transformation

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Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
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相关实验视频

Updated: Jul 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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在潜空间中通过特征操纵生成语义对立示例.

Shuo Wang, Shangyu Chen, Tianle Chen

    IEEE transactions on neural networks and learning systems
    |August 10, 2023
    PubMed
    概括

    这项研究引入了一种新的对抗性攻击,通过操纵图像语义来针对深度神经网络 (DNN). 这种新方法创造了对黑子分类器有效的通用语义对抗示例.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习安全 机器学习安全

    背景情况:

    • 深度神经网络 (DNN) 容易受到对抗性攻击,其中微妙的干扰会误导分类器.
    • 传统攻击使用非结构化的像素变化,经常改变图像外观不自然,缺乏可解释性.

    研究的目的:

    • 开发一种新的对抗性攻击策略,专注于语义,结构图像扰乱.
    • 提高对DNN的对抗性示例的解释性和有效性.

    主要方法:

    • 提出了一种方法,通过解散的隐藏代码来操纵语义属性.
    • 通过改变单个或组合的潜在代码来设计对抗性干扰.
    • 引入了两个无监督的语义操纵策略:向量解和特征地图解的表示.

    主要成果:

    • 证明了攻击对现实世界图像数据的强大作用,特别是对黑子分类器.
    • 经验评估证实了面向语义的结构性扰动的有效性.
    • 确立了一个普遍的语义对立示例的存在,独立于特定的训练数据.

    结论:

    • 拟议的语义操纵攻击对DNN构成强大的威胁,特别是在黑子场景中.

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  • 语义对立的例子提供了一个更易于解释和潜在的更强大的攻击向量.
  • 发现普遍的语义对立例子对人工智能安全研究有重大影响.