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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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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: 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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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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MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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An Uncertainty-Aware Approach for Exploratory Microblog Retrieval.

Mengchen Liu, Shixia Liu, Xizhou Zhu

    IEEE Transactions on Visualization and Computer Graphics
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    This study introduces an uncertainty-aware visual analytics approach for effective microblog data retrieval. It helps analysts discover salient posts, users, and hashtags by analyzing rank uncertainty and propagation.

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    Area of Science:

    • Data Science
    • Information Retrieval
    • Human-Computer Interaction

    Background:

    • Analyzing microblog data for insights like customer opinions and breaking news is challenging due to retrieval difficulties.
    • Existing methods lack effective mechanisms for discovering and retrieving relevant information from microblogs.

    Purpose of the Study:

    • To develop an uncertainty-aware visual analytics approach for retrieving salient posts, users, and hashtags from microblogs.
    • To enhance the process of data discovery and refinement in microblogging platforms.

    Main Methods:

    • Extended an existing ranking technique to compute mutual reinforcement rank, rank uncertainty, and uncertainty propagation.
    • Designed a composite visualization including graph visualization with glyphs, an uncertainty glyph, and a flow map.

    Main Results:

    • The approach effectively retrieves salient microblog data by analyzing multifaceted retrieval results.
    • The composite visualization aids analysts in identifying uncertain results and interactively refining them.

    Conclusions:

    • The uncertainty-aware visual analytics approach shows promise for retrieving high-quality microblog data.
    • The method was successfully applied to Twitter datasets, with evaluations confirming its effectiveness.