بدائل البحث:
pre implementation » time implementation (توسيع البحث), _ implementation (توسيع البحث), new implementation (توسيع البحث)
tool implementing » model implementing (توسيع البحث), trial implementing (توسيع البحث), from implementing (توسيع البحث)
pre implementation » time implementation (توسيع البحث), _ implementation (توسيع البحث), new implementation (توسيع البحث)
tool implementing » model implementing (توسيع البحث), trial implementing (توسيع البحث), from implementing (توسيع البحث)
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41
Sample data for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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42
Comparison data 3 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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43
Sample data for <i>Telmatochromis temporalis</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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44
Comparison data 4 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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45
Comparison data 1 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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46
Comparison data 2 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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47
Comparison data 5 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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48
Comparison data 6 for <i>Lamprologus ocellatus</i>.
منشور في 2024"…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …"
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49
The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025)
منشور في 2025"…</p><h3><b>Installation</b></h3><h4><b>install Dependencies</b></h4><p dir="ltr">Our artifact depends on several packages, please run the following command to install all necessary dependencies.</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. …"
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50
Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games"
منشور في 2024"…</p><h2>Dependencies</h2><p dir="ltr">In order to run the code the following dependencies must be met:</p><pre><pre>- Python 3 should be installed. We used Python 3.9 to obtain the results in the paper. …"
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51
The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network"
منشور في 2024"…</li></ul><h2>Running the Code</h2><h3><b>Data processing and feature extraction</b></h3><pre>python run_process.py</pre><p dir="ltr">This step processes trajectory data, extracts graph node features and edge features, and saves them as CSV files in the `processed_data` folder.…"
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52
adnus
منشور في 2025"…<p dir="ltr">adnus (AdNuS): Advanced Number Systems</p><p dir="ltr">adnus is a Python library that provides an implementation of various advanced number systems. …"
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53
<b>Data Availability</b>
منشور في 2025"…</p><p dir="ltr">python scripts documenting the implementation of the Mixture Density Network (MDN) algorithm, including hyperparameter tuning and uncertainty quantification.…"
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54
<b>Data Availability</b>
منشور في 2025"…</p><p dir="ltr">python scripts documenting the implementation of the Mixture Density Network (MDN) algorithm, including hyperparameter tuning and uncertainty quantification.…"
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55
Gene Editing using Transformer Architecture
منشور في 2025"…</p><p dir="ltr">Once TASAG detects a deviation from a reference sequence (e.g., the H-Bot sequence), it facilitates on-screen gene editing, enabling targeted mutations or the insertion of desired genes. Implementation requires Python and deep learning frameworks like TensorFlow or PyTorch, with optional use of Biopython for genetic sequence handling. …"
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56
Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series
منشور في 2025"…The method also successfully recovers the location and nature of changes in more traditional changepoint tasks. An implementation of our method is available in the Python package bclr.…"
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57
RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices
منشور في 2025"…<br>The supplemented materials for Review #2570C is in the ''Response to Review #2570C.md''.<br><br><br></pre><p dir="ltr">This repository contains the implementation of **RealBench**, a comprehensive benchmark and evaluation framework for repository-level code aligned with real-world software development practices.…"
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58
Concurrent spin squeezing and field tracking with machine learning
منشور في 2025"…Randomly signal generating codeb.Deep learning codec.data pre-processing code The network is implemented using the torch 1.13.1 framework and CUDA 11.6 on Python 3.8.8. …"
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59
Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b>
منشور في 2025"…</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.…"
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60
Global Graph Dataset
منشور في 2025"…These functionalities have been implemented with the Urbanity python package which provides functionalities for saving, loading, visualising, processing, and converting urban graphs to machine learning friendly formats (PyG and DGL). …"