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Ranks01:02

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

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The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
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Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Re-ranking High-Dimensional Deep Local Representation for NIR-VIS Face Recognition.

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    This study introduces a novel re-ranking method to improve heterogeneous face recognition, specifically for near-infrared and visual images. The technique refines initial rankings, enhancing accuracy in public security applications.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Biometrics

    Background:

    • Heterogeneous face recognition (HFR) matches images from different sensors, crucial for security.
    • Significant feature gaps exist between heterogeneous images due to varied sensing.
    • Current HFR methods struggle with appearance variations, impacting first-rank accuracy.

    Purpose of the Study:

    • To develop an unsupervised re-ranking technique for heterogeneous face recognition.
    • To enhance the matching accuracy of near-infrared (NIR) and visual (VIS) facial images.
    • To leverage initial ranking results for improved identification.

    Main Methods:

    • Constructing high-dimensional deep local representations using convolutional neural networks (CNNs) on facial patches.
    • Generating initial NIR-VIS face recognition rankings by comparing compressed deep features.
    • Applying a novel, efficient locally linear re-ranking (LLRe-Rank) technique as an unsupervised post-processing step.

    Main Results:

    • The LLRe-Rank method effectively refines initial ranking results in NIR-VIS face recognition.
    • The approach demonstrates significant improvements on challenging NIR-VIS databases (Oulu-CASIA and CASIA NIR-VIS 2.0).
    • The unsupervised nature of the re-ranking requires no human interaction or data annotation.

    Conclusions:

    • The proposed LLRe-Rank technique offers an effective unsupervised post-processing solution for heterogeneous face recognition.
    • This method addresses the feature gap challenge in NIR-VIS face matching.
    • The approach shows strong potential for real-world applications in public security and law enforcement.