Functional enrichment analysis. by Junjie Su (368807)

Subjects:

Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Skin transfer function ankle render diagram. by Yuliang Yuan (4478698)

Bone transfer function pelvis render diagram. by Yuliang Yuan (4478698)

<b>Fig. 3 | Performance analysis of microrobot navigation in various environments using reinforcement learning algorithms.</b> by Mahmoud Medany (20766911)

“…<b>e.</b> Impact of different reward functions on the rate of target achievement. …”

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)

<b>Optimization of the whole life capacity configuration of the hydrogen production system based on improved whale optimization algorithm</b> by Fan Jiang (21178691)

MSX-60 module: (a) <i>I</i>-<i>U</i> and (b) <i>P-U</i> characteristics. by Ziad M. Ali (12516532)

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Boxplots showcasing variability of additional key topological metrics obtained after 50 algorithm runs for the generation of a) chorionic vessels and b) villous vessels. by Diana C. de Oliveira (9220729)

Functions in nhppp. by Thomas A. Trikalinos (5785877)

“…We developed it to facilitate the sampling of event times in discrete event and statistical simulations. The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …”

Structural diagram of the overall SCF-YOLO algorithm. by Yazhou Li (277980)

RTC-F solar cell: (a) <i>I</i>-<i>U</i> and (b) <i>P</i>-<i>U</i> characteristics. by Ziad M. Ali (12516532)

PWP201 solar module: (a) <i>I</i>-<i>U</i> and (b) <i>P</i>-<i>U</i> characteristics. by Ziad M. Ali (12516532)

Risk and density functions for standard relapse from remission condition to disease <i>b</i> condition (similar shapes for standard relapse to disease <i>a</i>). by Benoîte de Saporta (20454662)

MAE versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³. by Limin Ma (556873)