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

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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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. …”

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

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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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.…”

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

“…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …”

Statistical results of various algorithms. by ZeSheng Lin (20501356)

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Images of partial benchmark functions. by ZeSheng Lin (20501356)

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Fitness function parameters. by Ankita Sharma (3183054)

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

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. …”

Predictive Mixing for Density Functional Theory (and Other Fixed-Point Problems) by L. D. Marks (1949689)

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Algorithm operation steps. by Junyan Wang (4738518)

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

Step by step comparison of enrichment algorithms. by Nicholas Markarian (18251668)

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

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

“…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. …”

The main steps of the general algorithm. by Dror Mughaz (18017583)

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)