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Masking and Demasking Agents01:19

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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.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
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    此摘要是机器生成的。

    本研究介绍了DynamicPAE,这是一种用于实时物理对抗攻击的新框架. 它通过启用现场意识攻击来增强深度学习安全性,显著优于静态方法.

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    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:

    • 物理对抗实例 (PAE) 突出了深度学习中的真实风险.
    • 目前的PAE世代缺乏适应多样化,动态场景的能力.
    • 需要实时,观察条件下的动态PAE.

    研究的目的:

    • 为现场意识,实时物理对抗性攻击 (DynamicPAE) 开发第一个生成框架.
    • 在攻击训练期间在杂的反下解决学习稀疏关系的挑战.
    • 为了使生成的PAE与现实世界的场景保持一致,以进行有效的物理攻击.

    主要方法:

    • 引入了剩余引导的对抗模式探索,以克服噪音反.
    • 模拟的训练退化与有限的反信息限制.
    • 拟议的分布匹配攻击场景调整,包括条件不确定性调整数据和偏差调整目标重权.

    主要成果:

    • 动态PAE在数字和物理评估中展示了优越的攻击性能.
    • 与物体探测器相比,实现了2.07倍的提升和58.8%的平均AP下降.
    • 超越了最先进的静态PAE生成方法.

    结论:

    • 动态PAE是第一个允许对动态PAE进行端到端建模的框架.
    • 提出的方法有效地解决了噪音反和场景调整挑战.
    • 动态PAE显著提升了实时物理对抗攻击的能力.