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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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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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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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Propagation of Uncertainty from Systematic Error01:10

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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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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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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Data uncertainty in face recognition.

Yong Xu, Xiaozhao Fang, Xuelong Li

    IEEE Transactions on Cybernetics
    |September 16, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances face recognition accuracy by reducing uncertainty with synthesized virtual training samples. A new method selects optimal samples, improving performance and reducing computational load in face recognition systems.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Face recognition accuracy is challenged by high uncertainty due to variations in illumination, pose, and expression.
    • Limited availability of training images in real-world systems exacerbates this uncertainty.
    • Accurate face representation is crucial for reliable identification.

    Purpose of the Study:

    • To improve face recognition accuracy by reducing representational uncertainty.
    • To develop a novel approach for selecting effective training samples.
    • To enhance the efficiency of face recognition systems.

    Main Methods:

    • Synthesizing virtual training samples to augment limited datasets.
    • Developing a theorem to establish the upper bound for useful training samples.
    • Implementing a representation approach utilizing selected training samples for recognition.

    Main Results:

    • Achieved high face recognition accuracy across five benchmark databases.
    • Demonstrated significantly lower computational complexity compared to existing methods.
    • Validated the effectiveness of synthesized samples and selective training sample strategies.

    Conclusions:

    • The proposed method effectively reduces uncertainty in face representation.
    • Synthesizing virtual samples and selective training sample selection are key to improved accuracy.
    • This approach offers a computationally efficient and accurate solution for face recognition.