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相关概念视频

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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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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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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基于多层一致性的弱监督的微观和宏观表达的发现.

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    此摘要是机器生成的。

    本研究介绍了MC-WES,这是一种用于弱监督表达物发现 (WES) 的新框架,它使用多一致性机制从视频级标签中实现准确的级发现,克服现有方法的局限性.

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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:

    • 由于广泛的数据收集和级注释要求,在视频中发现微观和宏观表达是具有挑战性的.
    • 现有的弱监督的表达发现 (WES) 方法,通常基于多个实例学习 (MIL),面临显著的跨模式,跨样本和跨任务差距.
    • 样本间的差距,特别是关于样本分布和持续时间的差距,阻碍了当前WES方法的性能.

    研究的目的:

    • 提出一种新且简化的WES框架,MC-WES,旨在仅使用视频级标签实现细粒度的级发现.
    • 通过减轻各种间隙问题和整合先前的知识来解决现有的WES方法的局限性.
    • 开发一个系统,减轻框架智能注释的负担,同时保持高定位精度.

    主要方法:

    • MC-WES采用多一致性协作机制,包括模式级突出性,视频级分发,标签级持续时间和细分级特征一致性策略.
    • 模态级突出一致性捕捉原始图像和光学流之间的相关性.
    • 视频级分发一致性利用时间稀疏差异;标签级持续时间一致性利用面部肌肉持续时间变化;分段级特征一致性确保相同标签下的特征相似性.

    主要成果:

    • MC-WES仅使用视频级标签来展示有效的细级探测能力.
    • 提出的多重一致性策略成功地弥补了现有的WES方法中发现的差距.
    • 在CAS(ME) $^{2}$,CAS(ME) $^{3}$和SAMM-LV数据集上的实验结果显示,MC-WES的性能与最先进的完全监督的方法相美.

    结论:

    • MC-WES提供了一种简化但强大的方法来对弱监督的表达式发现,减少注释的复杂性.
    • 多重一致性框架有效地合并了先前的知识,并解决了基于MIL的WES中固有的差距.
    • MC-WES实现了竞争性性能,表明其可行性是作为对表达式发现任务完全监督方法的替代方案.