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When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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相关实验视频

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Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
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在深度排名中的对抗性攻击和防御.

Mo Zhou, Le Wang, Zhenxing Niu

    IEEE transactions on pattern analysis and machine intelligence
    |February 13, 2024
    PubMed
    概括

    图像排名中的深度神经网络 (DNN) 容易受到对抗性攻击. 这项研究引入了新的攻击和防御机制,显著提高了排名系统对干扰的稳定性.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 深度神经网络 (DNN) 容易受到对抗性攻击,导致错误分类.
    • 基于DNN的图像排名系统对此类攻击的脆弱性尚未得到充分了解.

    研究的目的:

    • 提出针对深层排名系统的新型对抗性攻击 (候选人攻击,查询攻击).
    • 开发一个有效的防御机制 (抗崩三重防御),以提高模型的稳定性.

    主要方法:

    • 将排名顺序表示为不等式,并设计一个三重函数的客观函数,以获得最佳的扰动.
    • 开发一种抗崩三重防御,以防止正负样本崩.
    • 提出一个实证的强度评分来评估对各种攻击的防御有效性.

    主要成果:

    • 证明拟议的攻击可以有效地破坏深层排名系统.
    • 展示了拟议的防御方法显著提高了排名系统的稳定性.
    • 在多个数据集中验证了有效性 (MNIST,时尚-MNIST,CUB200-2011,CARS196,斯坦福在线产品).

    结论:

    • 深度排名系统容易受到特定的对抗性攻击.

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  • 拟议的防御战略增强了强度,并减轻了广泛的攻击.
  • 这项工作有助于保护基于DNN的图像排名应用程序.