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相关概念视频

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Directing Effect of Substituents: meta-Directing Groups01:09

Directing Effect of Substituents: meta-Directing Groups

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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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Mixing Concrete01:30

Mixing Concrete

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Concrete mixing ensures a homogenous blend where aggregates are well-coated with cement paste. Concrete mixing is typically done using two main types of mixers: batch and continuous. Batch mixers handle one batch at a time, thoroughly combining materials before discharging and receiving the next batch. In contrast, continuous mixers receive a steady flow of ingredients, mixing them consistently and discharging without interruption. Within batch mixers, tilting drum mixers mix with internal...
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Mixing Time01:19

Mixing Time

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The concept of mixing time is significant in producing a uniform concrete mix with the required strength. The mixing period starts once all components are in the mixer. Initially, the mixer is charged with 10% of the water, followed by the consistent addition of solids and then 80% of the water. The remaining water is added later, within the first quarter of the mixing period. The minimum mixing time varies according to the mixer's capacity; for example, mixers with up to 1 cubic yard...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Ready Mixed Concrete01:26

Ready Mixed Concrete

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Ready-mixed concrete, also known as pre-mixed concrete, is prepared in a centralized plant and then transported in trucks to construction sites where it is ready for placement. This type of concrete is categorized into central-mixed, truck-mixed (or transit-mixed), and shrink-mixed. Central-mixed concrete is entirely prepared at a plant and moved to the site in agitator trucks that rotate at a speed of 2 to 6 rpm. Truck-mixed concrete, on the other hand, has the ingredients batched at the plant...
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Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
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Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

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通过特征混合和超级学习探索频率,以提高对手的可转移性.

Juanjuan Weng, Zhiming Luo, Shaozi Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 28, 2026
    PubMed
    概括

    本研究介绍了一种基于频率的特征混合方法,以改善对深度神经网络 (DNN) 的对抗性攻击. 该方法通过战略性地结合来自清洁和对抗样本的特征,提高了对正常和防御模型的攻击可转移性.

    科学领域:

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

    背景情况:

    • 深度神经网络 (DNN) 容易受到敌对攻击.
    • 频域分析揭示了高频组件的影响预测,而低频组件有助于黑子攻击的可转移性.

    研究的目的:

    • 开发基于频率分解的特征混合方法,以利用频率特征来改进对抗性攻击.
    • 解决因同时应用不同特征混合策略而产生的冲突.

    主要方法:

    • 引入了一种基于频率分解的特征混合方法.
    • 提出了一种跨频度元优化方法,包括元训练,元测试和最终更新步骤.
    • 杆低频组件用于防御模型攻击和对抗样本用于正常训练模型攻击.

    主要成果:

    • 在正常训练模型中,将清洁样本特征纳入对抗特征是有效的.
    • 将清洁特征与低频对抗特征相结合,可以改善对防御模型的攻击.
    • 交叉频率元优化方法有效地提高了针对这两种模型类型的攻击可转移性.

    结论:

    • 提出的基于频率的特征混合和元优化方法显著提高了对抗性攻击的可转移性.

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  • 该研究展示了一种利用频率特征进行更有效的DNN攻击的新方法.
  • 该方法在正常训练和防御模型的 ImageNet-Compatible 数据集上显示出有效性.