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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Detection of Black Holes01:10

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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对抗数据增强用于基于HMM的异常检测.

Alberto Castellini, Francesco Masillo, Davide Azzalini

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    此摘要是机器生成的。

    这项研究引入了对抗性学习,以改善物理系统中的异常检测. 这种新的方法提高了系统对未知的威胁的稳定性,通过增加生成的对抗性示例来增强数据.

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    科学领域:

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

    背景情况:

    • 由于所有可能的异常的不可预测性,在物理系统中检测异常是具有挑战性的.
    • 现有的方法经常与多变量时间序列数据和一类分类场景作斗争.
    • 敌对数据增强技术主要用于图像识别,而不是时间序列分析.

    研究的目的:

    • 开发和评估一种新的数据增强和再培训方法,用于使用对抗式学习检测异常.
    • 提高异常检测系统的性能和稳定性,特别是对于多变量时间序列.
    • 解决当前处理物理系统中未知异常的方法的局限性.

    主要方法:

    • 定义了一种针对基于隐藏马尔科夫模型 (HMM) 的异常探测器量身打造对抗示例的方法.
    • 开发了一种数据增强和再培训技术,利用这些生成的对抗性示例.
    • 该方法在四个不同的数据集上进行了评估,重点是多变量时间序列和一类分类.

    主要成果:

    • 提出的对抗性数据增强和再培训方法在异常检测性能上显示了统计学上显著的改进.
    • 该方法显著提高了异常检测系统对抗敌对攻击的稳定性.
    • 在多个数据集中观察到性能增长,验证了该技术的有效性.

    结论:

    • 与HMM相结合的对抗性学习提供了一种强大的策略,用于增强物理系统中的异常检测.
    • 开发的技术有效地提高了检测准确度和对新型,对抗性威胁的弹性.
    • 这项工作在一类分类框架内对多变量时间序列的异常检测中推进了最先进的技术.