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

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<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.…”

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

Skeletonisation algorithm. by Jordan Hembrow (16908051)

Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

“…<p>(A) Nodule depth estimation by the algorithm with the likelihood functions obtained by FEM simulation. …”

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

“…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”

Algorithmic image feature identification. by Alex T. Keeley (16612727)

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

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

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

Demographic, murmur, and clinical outcome distributions across the Challenge training, validation, and test data. by Matthew A. Reyna (16965038)

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Reconstruction performance across algorithms, dynamics, cell types, and recording length. by Christian Donner (3663007)

“…<b>D</b> The mean average precision score (APS, upper panel) and Matthews correlation coefficient (MCC, lower panel), estimated across all connections, obtained from all inference algorithms and the eANN across three different dynamical regimes. …”

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

A) Ranking of models trained on different feature sets across all tested algorithms, sorted by their F1-score. by Jose Cleydson F. Silva (8685594)

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

Train and validation accuracies of the TabNet model across 187 epochs. by Aastha Vatsyayan (9981916)

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

The model learning rate and loss of the TabNet model across187 epochs. by Aastha Vatsyayan (9981916)

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Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)