Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts. by Quynh-Anh Nguyen (847240)

“…(Bottom) Trial-by-trial applications of the signal detection algorithm from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>] with A: <i>C</i>_<i>th</i> = 4.01 and B: <i>C</i>_<i>th</i> = 4.21 yield exponentially distributed subsequent percept durations for <i>I</i> (blue) and <i>S</i> (red). …”

Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

The graphical bar outputs of the <i>ceRNAFunction</i> function based on over-representation analysis (ORA). by Yi-Wen Hsiao (13767916)

The graphical bubble outputs of the <i>ceRNAFunction</i> function are based on over-representation analysis (ORA). by Yi-Wen Hsiao (13767916)

An example of pairwise distance tables between three sites where each site has three nodes: Site₁={<i>A</i>, <i>B</i>, <i>C</i>}, Site₂={<i>a</i>, <i>b</i>, <... by Shuwen Wang (457921)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

Functional enrichment analysis. by Junjie Su (368807)

Subjects:

Functional phenomic descriptors that describe the phenotypic diversity of <i>Phaseolus</i> spp. accessions. by Diego Felipe Conejo-Rodríguez (18495740)

Functional phenomic descriptors that describe the phenotypic diversity of <i>Arachis</i> spp. accessions. by Diego Felipe Conejo-Rodríguez (18495740)

Sparsity-promoting cost functions <i>c</i> considered and their corresponding thresholding operators. by Ilias Rentzeperis (10215602)

Iteration versus fitness function. by B. Venkatesh (17202747)

a) DPR based Single Round MISTY1 / KASUMI, b) FI function. by Huang Jiexian (17060975)

Analysis of full-length Ste5 (1–917) coding sequence with bioinformatics algorithms. by Francis W. Farley (17199744)

Sensitivity (<i>d</i>′) and criterion values as a function of prior identity decision (threshold = 0.5). by John J. Howard (9267903)

(a) ROAL Accuracies and (b) ROAL label requests per episode for the 1^st, 2^nd, 5^th, and 10^th instances of all classes. by Honglan Huang (341951)

Samples from IPPs with a mixture of squared exponential intensity function (<i>p</i> = 2). by M. Hassan Tanveer (9596437)

Boxplot of IWOAIKFS versus other algorithms over all datasets (<i>k</i> = 5). by Wei Liu (20030)

Boxplot of PLNMFG and other eight state-of-the-art algorithms measured by (a) ACC and (b) AMI on six real datasets. by Hui Yuan (402180)

Logarithmic mean convergence curves of different algorithms. by Wei Liu (20030)

<b>The Application of the Optimized Genetic Algorithm in SerDes Circuit Design</b> by Xuan Wei (18564826)

“…<p dir="ltr">This paper aims to solve the complex design problem of replacing inefficient, traditional parallel structures with high-speed serial interface SerDes. The Genetic Algorithm (GA) is optimized by adding a selection and substitution procedure. …”