<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.…”

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

“…<p><i>(a)</i> Noninvadable states by definition can only exist in the region Ω of resource space where the growth rate <i>dN</i>_<i>i</i>/<i>dt</i> of each species <i>i</i> is zero or negative. …”

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. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

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

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.…”

Table_2_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Table_1_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

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). …”

PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

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Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

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

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

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ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …”

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

“…We note that the middle row is a special case: here, <i>f</i>_target only corresponds to the proportions of the embedded cohort, while “success” for these two panels is defined as recovering maximally diverse cohorts, as this particular algorithm is designed to do. The bottom row shows the same simulations as the middle row, but presents successes and failures when the targets that generated the embedded cohort are applied instead of the balanced targets that are part of the baseline’s objective function. …”

Algorithm. by Daniel Sanchez-Taltavull (11654630)

Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)