AI2_ARC experimental results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

TQA experimental results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

E-EVAL experimental results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

TQA Accuracy Comparison Chart on different LLM. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

ScienceQA experimental results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

Code interpreter with LLM. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

Evaluation and Statistical Analysis Code for "Multi-Task Learning for Joint Fisheye Compression and Perception for Autonomous Driving" by Basem Ahmed (18127861)

“…</li></ul><p dir="ltr">These scripts are implemented in Python using the PyTorch framework and are provided to ensure the reproducibility of the experimental results presented in the manuscript.…”

Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats by Raul MATSUSHITA (10276562)

“…<p dir="ltr">This Python code implements a Monte Carlo simulation to evaluate the impact of cognitive biases on penalty shootouts under two formats: ABAB (alternating shots) and ABBA (similar to tennis tiebreak format). …”

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

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

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

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

Extending the pyAFQ software. by John Kruper (18809386)

<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

“…</li><li>Python/Kaggle notebooks (<code>.ipynb</code>): reproducibility pipeline for accuracy metrics and statistical analysis.…”

<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

“…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

Data Sheet 1_COCαDA - a fast and scalable algorithm for interatomic contact detection in proteins using Cα distance matrices.pdf by Rafael Pereira Lemos (9104911)

“…Here, we introduce COCαDA (COntact search pruning by Cα Distance Analysis), a Python-based command-line tool for improving search pruning in large-scale interatomic protein contact analysis using alpha-carbon (Cα) distance matrices. …”

HCC Evaluation Dataset and Results by Jens-Rene Giesen (18461928)

“…The only requirement for running this script is a Python 3.6+ interpreter as well as an installation of the <code>numpy</code> package. …”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</p><h2><b>Included Files</b></h2><h3><b>1. </b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.…”