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

“…</p><p dir="ltr">GPU：NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.…”

Probabilistic-QSR-GeoQA by Mohammad Kazemi (19442467)

“…Also we have written Python API for Probcog (ProbCog-API.py) and SparQ reasoners (SparQ-API.py).…”

Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) by Winston Yap (13771969)

“…</p><p dir="ltr">Each zipped folder consists the following files:</p><ul><li>Graph data - City object nodes (.parquet) and COO format edges (.txt)</li><li>predictions.txt (model predictions from GraphSAGE model)</li><li>final_energy.parquet (Compiled training and validation building energy data)</li></ul><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. …”

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. …”

Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data by Jeanette Leue (22470409)

“…This Zenodo package includes: the full research paper (PDF), a complete Python implementation generating the LMC field and conductivity model, a numerical plot comparing discrete LMC values with the smoothed continuum field, a cover letter and supporting documentation. …”

Error reduction over time by the HOFA-SMC. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

Comparison of SMC techniques. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

Proposed HOFA-SMC with experimental validation. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation by Hong Zhu (109912)

“…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. This algorithm is implemented in ReaxANA, a parallel Python package designed to extract reaction mechanisms from both heterogeneous and homogeneous reactive MD trajectories. …”

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation by Hong Zhu (109912)

“…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. This algorithm is implemented in ReaxANA, a parallel Python package designed to extract reaction mechanisms from both heterogeneous and homogeneous reactive MD trajectories. …”

Overview of generalized weighted averages. by Nobuhito Manome (8882084)

“…<div><p>The multi-armed bandit (MAB) problem is a classical problem that models sequential decision-making under uncertainty in reinforcement learning. …”

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

“…</li><li>Easily modifiable to fit other scenarios or cognitive models.</li></ul><p dir="ltr"><b>Usage:</b> Run the script in a Python environment after installing the required dependencies (<code>numpy</code> and <code>scipy</code>). …”

Table 3_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…”

Table 2_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…”

Table 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…”

Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…”

Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games" by Krishnendu Chatterjee (15367413)

“…<br></pre></pre><h2>Structure and How to run</h2><p dir="ltr">There are four Python files in the repository.</p><pre><pre>(i) `StrategyIteration.py` is the backend code, containing the implementation of the RPPI algorithm described in the paper.…”

<b>Anthropogenic nutrient inputs cause excessive algal growth for nearly half the world’s population</b> by Richard McDowell (7311011)

“…</p><p dir="ltr">Models: R code to explore different models for implementation via Python in ArcGIS</p><p dir="ltr">!…”

Deep Learning-Based Visual Enhancement and Real-Time Underground-Mine Water Inflow Detection by Huichao Yin (14589020)

“…<p dir="ltr">Python image preprocessing and model implementation for research of "Deep Learning-Based Visual Enhancement and Real-Time Underground-Mine Water Inflow Detection".…”