A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(c)</i> Pseudocode for self-consistently computing <b>R</b>* and , which is identical to standard expectation-maximization algorithms employed for problems with latent variables in machine learning.…”

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…<div><p>We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. …”

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

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<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

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Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Ridge wave transformation flowchart. by Bingbing Li (461702)

“…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”

Excited-State DMRG Made Simple with FEAST by Alberto Baiardi (1914601)

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Excited-State DMRG Made Simple with FEAST by Alberto Baiardi (1914601)

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Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)