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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Conservative Site-specific Recombination and Phase Variation02:53

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Variation of Atmospheric Pressure01:18

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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Coefficient of Variation01:10

Coefficient of Variation

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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Semisupervised Text Classification by Variational Autoencoder.

Weidi Xu, Ying Tan

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    |March 26, 2019
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    This study introduces a new semisupervised sequential variational autoencoder (SSVAE) model for text classification. The SSVAE effectively derives label distributions for unlabeled data, significantly improving classification accuracy.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Semisupervised text classification is a key area in machine learning.
    • Existing models face challenges with unlabeled data and credit assignment.

    Purpose of the Study:

    • To propose a novel semisupervised sequential variational autoencoder (SSVAE) model.
    • To address limitations in current semisupervised text classification methods.

    Main Methods:

    • Developed the SSVAE model treating categorical labels as discrete latent variables.
    • Investigated two types of decoders to overcome autoregressive model ineffectiveness.
    • Implemented a reweighting approach to solve credit assignment problems in sparse data.

    Main Results:

    • The SSVAE model effectively derives underlying label distributions for unlabeled data.
    • The proposed decoders and reweighting approach enhance model performance.
    • Experimental results demonstrate significant improvements in classification accuracy.

    Conclusions:

    • The SSVAE model offers a powerful new approach to semisupervised text classification.
    • The model shows superior performance compared to existing modern methods.