Search alternatives:
tool implementation » world implementation (Expand Search), model implementation (Expand Search), proof implementation (Expand Search)
model implementing » model implemented (Expand Search), model implementation (Expand Search), model representing (Expand Search)
python model » python code (Expand Search), action model (Expand Search), motion model (Expand Search)
tool implementation » world implementation (Expand Search), model implementation (Expand Search), proof implementation (Expand Search)
model implementing » model implemented (Expand Search), model implementation (Expand Search), model representing (Expand Search)
python model » python code (Expand Search), action model (Expand Search), motion model (Expand Search)
-
201
Reinforcement Learning based traffic steering inOpen Radio Access Network (ORAN)- oran-ts GitHub Repository
Published 2025“…It features a modular Python framework implementing various RL agents (Q-Learning, SARSA, N-Step SARSA, DQN) and a traditional baseline evaluated in a realistic cellular network environment. …”
-
202
Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789
Published 2025“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”
-
203
Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning
Published 2025“…<p dir="ltr">This dataset and code package presents a modular framework for supervised classification of burned and unburned land surfaces using satellite-derived spectral reflectance. Six Python scripts are provided, each implementing a distinct machine learning algorithm—Random Forest, k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision Tree, Naïve Bayes, and Logistic Regression. …”
-
204
OHID-FF dataset for forest fire detection and classification
Published 2025“…Prepare the YOLODataset structure (if you need to rebuild it):</p><p dir="ltr">```bash</p><p dir="ltr">python "train val scripts/prepare_data.py"</p><p>```</p><p dir="ltr"><br></p><p dir="ltr">3. …”
-
205
The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025)
Published 2025“…</p><pre><pre>sudo apt-get install -y wget git build-essential python3 python python-pip python3-pip tmux cmake libtool libtool-bin automake autoconf autotools-dev m4 autopoint libboost-dev help2man gnulib bison flex texinfo zlib1g-dev libexpat1-dev libfreetype6 libfreetype6-dev libbz2-dev liblzo2-dev libtinfo-dev libssl-dev pkg-config libswscale-dev libarchive-dev liblzma-dev liblz4-dev doxygen libncurses5 vim intltool gcc-multilib sudo --fix-missing<br></pre></pre><pre><pre>pip install numpy && pip3 install numpy && pip3 install sysv_ipc<br></pre></pre><h4><b>Download the Code</b></h4><p dir="ltr">Download <b>DD4AV</b> from the Figshare website to your local machine and navigate to the project directory:</p><pre><pre>cd DD4AV<br></pre></pre><h4><b>Configure Environment and Install the Tool</b></h4><p dir="ltr">For convenience, we provide shell scripts to automate the installation process. …”
-
206
Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation
Published 2024“…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …”
-
207
Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games"
Published 2024“…<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.…”
-
208
Research Database
Published 2025“…</p><p dir="ltr">Statistical analysis was conducted through <b>multiple regression models</b> implemented in <b>Jamovi</b>, supported by Geographic Information System (GIS) tools to visualize spatial patterns. …”
-
209
<b>Anthropogenic nutrient inputs cause excessive algal growth for nearly half the world’s population</b>
Published 2025“…</p><p dir="ltr">Models: R code to explore different models for implementation via Python in ArcGIS</p><p dir="ltr">!…”
-
210
<b>Data Availability</b>
Published 2025“…</p><p dir="ltr">Reproducibility Resources:</p><p dir="ltr">Python scripts for reproducing figures, preprocessing data, and training machine learning models (SVM, MLP, XGB, BRR, KRR).…”
-
211
<b>Data Availability</b>
Published 2025“…</p><p dir="ltr">Reproducibility Resources:</p><p dir="ltr">Python scripts for reproducing figures, preprocessing data, and training machine learning models (SVM, MLP, XGB, BRR, KRR).…”
-
212
<b>Algorithm Pseudocode</b>
Published 2025“…The pseudo-code follows standard Python syntax specifications for functions and loops and is easy to understand and implement. …”
-
213
Deep Learning-Based Visual Enhancement and Real-Time Underground-Mine Water Inflow Detection
Published 2025“…<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".…”
-
214
Reproducible Code and Data for figures
Published 2025“…<br>✅ <b>Generated Figures</b> – High-resolution images illustrating model outputs and analytical results.<br>✅ <b>Machine Learning Models</b> – Implementation of <b>K-Nearest Neighbors (KNN) regression</b> with different distance metrics (<b>Mahalanobis, Fuzzy Mahalanobis, Euclidean</b>).…”
-
215
Curvature-Adaptive Embedding of Geographic Knowledge Graphs in Hyperbolic Space
Published 2025“…/CAH-GKGE/model/supplementary instruction.md </p>…”
-
216
Supplementary Material
Published 2025“…The supplementary material includes the full Python-based implementation of the AI-driven optimization framework described in the manuscript. …”
-
217
Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats
Published 2024“…<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). …”
-
218
NanoDB: Research Activity Data Management System
Published 2024“…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”
-
219
A Hybrid Ensemble-Based Parallel Learning Framework for Multi-Omics Data Integration and Cancer Subtype Classification
Published 2025“…<p dir="ltr">The code supports replication of results on TCGA Pan-cancer and BRCA datasets and includes data preprocessing, model training, and evaluation scripts:<br>Python scripts for data preprocessing and integration</p><ul><li>Autoencoder implementation for multimodal feature learning</li><li>Hybrid ensemble training code (DL/ML models and meta-learner)</li><li>PSO and backpropagation hybrid optimization code</li><li>Parallel execution scripts</li><li>Instructions for replicating results on TCGA Pan-cancer and BRCA datasets</li></ul><p></p>…”
-
220
RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices
Published 2025“…<br>│ │ └── uml_dag.py # UML dependency graph analysis.<br>│ ├── model_gen/ # Code generation using various LLMs.<br>│ │ ├── generate/ # LLM inference implementations.…”