بدائل البحث:
code implementation » model implementation (توسيع البحث), time implementation (توسيع البحث), world implementation (توسيع البحث)
pre implementation » time implementation (توسيع البحث), _ implementation (توسيع البحث), new implementation (توسيع البحث)
code implementation » model implementation (توسيع البحث), time implementation (توسيع البحث), world implementation (توسيع البحث)
pre implementation » time implementation (توسيع البحث), _ implementation (توسيع البحث), new implementation (توسيع البحث)
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81
High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch
منشور في 2025"…Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. …"
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82
Comparison data 7 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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83
Sample data for <i>Neolamprologus multifasciatus</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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84
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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85
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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86
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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87
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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88
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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89
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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90
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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91
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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92
Explained variance ration of the PCA algorithm.
منشور في 2025"…All our simulation is performed in computation softwares, Matlab and Python. The image pre processing and spectral moments generation is performed in Matlab and the implementation of the classifiers is performed with python. …"
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93
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. …"
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94
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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95
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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96
kececilayout
منشور في 2025"…<p dir="ltr"><b>Kececi Layout (Keçeci Yerleşimi)</b>: A deterministic graph layout algorithm designed for visualizing linear or sequential structures with a characteristic "zig-zag" or "serpentine" pattern.</p><p dir="ltr"><i>Python implementation of the Keçeci layout algorithm for graph visualization.…"
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97
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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98
Concurrent spin squeezing and field tracking with machine learning
منشور في 2025"…<p dir="ltr">The dataset contains:</p><ol><li>Steady_squeezing.zip <b>a)</b> data for steady squeezing data and characteraztion <b>b)</b> data for pulse RF magnetormeter</li><li>Tracking1.zip <b>a)</b> data of OU process for Deep learning <b>b)</b> data of OU-jump process for Deep learning</li><li>Tracking2.zip <b>a)</b> data of white noise process in backaction experiment <b>b) </b>data of white noise process in rearrange experiment</li><li>Code <b>a)</b> Randomly signal generating code <b>b)</b> Deep learning codec.data pre-processing code</li></ol><p dir="ltr">The network is implemented using the torch 1.13.1 framework and CUDA 11.6 on Python 3.8.8. …"
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99
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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100
IGD-cyberbullying-detection-AI
منشور في 2024"…</p><h2>Requirements</h2><p dir="ltr">To run this code, you'll need the following dependencies:</p><ul><li>Python 3.x</li><li>TensorFlow</li><li>scikit-learn</li><li>pandas</li><li>numpy</li><li>matplotlib</li><li>imbalanced-learn</li></ul><p dir="ltr">You can install the required dependencies using the provided <code>requirements.txt</code> file.…"