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相关概念视频

Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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选择性修剪平均值:一个有弹性的联合学习算法,具有对最佳性近似的确定性保证.

Mojtaba Kaheni, Martina Lippi, Andrea Gasparri

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    此摘要是机器生成的。

    联合学习 (FL) 通过选择性修剪平均 (SETA) 算法得到增强,该算法提供了对抗攻击的弹性. 在不需要受信任服务器的情况下,SETA 过参数来保护全球模型.

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    科学领域:

    • 机器学习 机器学习
    • 分布式系统 分布式系统
    • 网络安全 网络安全

    背景情况:

    • 联合学习 (FL) 在不共享原始数据的情况下,在分散的数据源中训练模型.
    • 传统的FL依赖于服务器-工人架构,易受对抗代理的攻击,导致数据或模型中毒.
    • 现有的弹性方法通常假定一个可信的中央服务器,限制了它们的适用性.

    研究的目的:

    • 引入一种新的弹性算法,选择性修剪平均值 (SETA),用于联合学习.
    • 增强联合学习对恶意参与者和受损数据的稳定性.
    • 开发一种在服务器工作者和共享内存架构中有效的方法.

    主要方法:

    • 提出SETA,一种选和结合从代理商交换的参数的算法.
    • 通过数学证明SETA对数据和本地模型中毒攻击的弹性.
    • 评估SETA在MNIST和多类天气数据集 (MWD) 上的表现.

    主要成果:

    • 在联合学习中,SETA有效地减轻了行为不良代理人的影响.
    • 该算法证明了对数据和本地模型中毒的弹性.
    • 数字结果验证了对基准数据集的理论发现.

    结论:

    • 对于安全的联合学习,SETA提供了一个强大的解决方案,特别是在不可信的环境中.
    • 该算法的适用性扩展到共享内存架构,消除了对可信服务器的需求.
    • 在保护联合学习系统免受敌对威胁方面,SETA代表了一项重大进展.