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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Propagation of Uncertainty from Random Error00:59

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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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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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不确定性意识无源域 适应性语义细分 不确定性意识无源域 适应性语义细分

Zhihe Lu, Da Li, Yi-Zhe Song

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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    概括
    此摘要是机器生成的。

    本研究介绍了贝叶斯神经网络用于语义细分中的无源域适应. 通过利用伪标签的不确定性,新方法显著提高了对目标域的模型性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 无源域调整 (SFDA) 解决了培训和部署数据之间的分配转移,而不需要源数据.
    • 语义细分通常依赖于伪标签来实现目标域的自我训练,但源模型生成的伪标签可能由于域转移而不可靠.

    研究的目的:

    • 通过提高伪标签的可靠性来增强源代码免费域名适应的语义细分.
    • 引入一种使用贝叶斯神经网络 (BNN) 来估计和利用伪标签不确定性的新方法.

    主要方法:

    • 利用贝叶斯神经网络 (BNN) 来量化为目标域生成的伪标签中的不确定性.
    • 开发了两个新的自我训练组件:不确定性意识的在线教师学生学习 (UOTSL) 和不确定性意识的FeatureMix (UFM).
    • 实施并评估了GTA 5 → Cityscapes和SYNTHIA → Cityscapes基准的建议方法.

    主要成果:

    • 提出的基于BNN的方法通过有效估计其不确定性,显著提高了伪标签的质量.
    • 在GTA 5 → Cityscapes上获得了3.6%的微量增长,在SYNTHIA → Cityscapes上增长了5.7%.
    • 在源代码免费域适应用于语义细分方面,在现有最先进的方法中表现出优越性.

    结论:

    • 贝叶斯神经网络为SFDA中的不确定性估计提供了一个强大的机制,从而产生更可靠的伪标签.
    • 拟议的UOTSL和UFM组件有效地利用不确定性信息来提高语义细分性能.
    • 这项工作在语义细分方面在SFDA中取得了重大进展,特别是在来源数据有限或不存在的场景中.