Python file of the computational framework. by Benoit Haut (9753029)

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”

S1 Code - by Christina M. Homer (3561131)

ZILLNB_Model by Qinhuan Luo (21288548)

“…<p dir="ltr">Acquire latent variables using deep-learning based model implemented in python</p>…”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

ThermoPred: AI-Enhanced Quantum Chemistry Data Set and ML Toolkit for Thermochemical Properties of API-Like Compounds and Their Degradants by Diullio P. Santos (22770713)

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Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

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BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

“…However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. …”

Predicting coding regions on unassembled reads, how hard can it be? - Genome Informatics 2024 by Amanda Clare (98717)

“…The locations and directions of the predictions on the reads are then combined with the information about locations and directions of the reads on the genome using Python code to produce detailed results regarding the correct, incorrect and alternative starts and stops with respect to the genome-level annotation.…”

Finites differences python code to solve CH equation with a source term and Comsol routine to solve Brusselator equation in radial domains. by Giulio Facchini (9031490)

Configurations used in ALAAMs for undirected networks. by Alex Stivala (8356257)

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Type II error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

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Comparison of MPNet and ALAAMEE software for ALAAM estimation. by Alex Stivala (8356257)

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Configurations used in ALAAMs for directed networks. by Alex Stivala (8356257)

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Type I error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

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Example ALAAMEE output. by Alex Stivala (8356257)

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Type II error rate as the number of runs used for each sample is varied. by Alex Stivala (8356257)

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Parameter estimates and estimated standard errors from the EE algorithm. by Alex Stivala (8356257)

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