Adding a new function module. by Yukako Yamane (9034133)

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

“…<p dir="ltr"><b>Summary</b></p><p dir="ltr">The Progressive Sample Scaling (PSS) algorithm is a deterministic iterative method with the purpose of solving Many-Affine BBOB type problems provided by the IOHprofiler environment<a href="https://markdownlivepreview.com/#cite_note-1" target="_blank">^[1]</a>. …”

Brief sketch of the quasi-attraction/alignment algorithm. by Takayuki Niizato (162226)

“…(B) A sketch of the cover function, which returns the minimum cap on the interaction sphere , which covers all points <b><i>p</i></b>_<i>i</i> (for the mathematical definition, see the Section 2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010869#pcbi.1010869.s008" target="_blank">S1 Appendix</a>). …”

Compare topologies when using the WOA algorithm to solve the RNP problem by different methods. by Le Huu Binh (18340233)

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Compare topologies when using the MVO algorithm to solve the RNP problem by different methods. by Le Huu Binh (18340233)

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Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…</p><p dir="ltr"><br></p><p dir="ltr">For more information about the data, one may refer to the article below:</p><p dir="ltr">Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. …”

Posteriors for parameters <i>θ</i> of the dimerization system for different weights in the ABC distance function. by Jonathan U. Harrison (5447603)

A two-particle GPSA solving a one-dimensional UMO function. by Yoshikazu Yamanaka (10325863)

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)

(Movie for Fig 2B) Same as S1 Movie but using a small value for the <i>r</i> parameter. by Toviah Moldwin (10866708)

Similarity between the hierarchical community structure of structural and functional brain graphs. by Arian Ashourvan (6685232)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…Our ISA algorithm correlates with the much more sophisticated ROSETTA algorithm with a Pearson correlation coefficient of 0.88 (B). …”

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. …”

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)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

The cost function Φ(<i>x</i>, <i>w</i>) 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)

3D profiles of <i>β</i> and λ with respect to the MAE for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³. by Limin Ma (556873)

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

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The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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