Overview of deep learning terminology. by Aaron E. Maxwell (8840882)

“…Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. …”

Data from: Circadian activity predicts breeding phenology in the Asian burying beetle <i>Nicrophorus nepalensis</i> by Hao Chen (20313552)

Summary of Tourism Dataset. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Segment-wise Spending Analysis. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Hyperparameter Parameter Setting. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Marketing Campaign Analysis. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Visitor Segmentation Validation Accuracy. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Integration of VAE and RNN Architecture. by Jing Zhang (23775)

“…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”

Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory by Zhuo Li (165589)

“…To use our model easily, a software package written in Python is provided in the Supporting Information.…”

Soulware-Lite by Abhiram Gnyanijaya (21572942)

“…</p><p><br></p><p dir="ltr">Soulware-Lite is the first live implementation of a cognitive conscience layer, born from architectural failures in AI output hallucination and anchored by integrity principles like MAP/ARP and RDIP. …”

A Fully Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture by Nagarajan Kandasamy (8400168)

“…QUANTISENC’s software-defined hardware design methodology allows the user to train an SNN model using Python and evaluate performance of its hardware implementation, such as area, power, latency, and throughput. …”

PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units by Vishikh Athavale (12623809)

“…We report the development and implementation of electronic excited-state capabilities for semiempirical quantum chemical methods at both the Configuration Interaction Singles and Time-Dependent Hartree–Fock levels of theory, integrated within the PYSEQM 2.0 software package (https://github.com/lanl/PYSEQM). …”

Dataset for: Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis, Nature Communications，DOI：10.1038/s41467-024-53943-6 by Xiao-Xiao Wang (2447920)

“…Extract homologs for all the 501 samples</p><p dir="ltr"><i>python2 blast_to_mcl.py all.rawblast 0.25 >mcl_all_rawblast_out_nohup_out_0.25</i></p><p dir="ltr"><i>mcl mcl_all_rawblast_out_nohup_out_0.25 --abc -te 80 -tf 'gq(5)' -I 1.5 -o hit-frac0.25_I1.5_e5</i></p><p dir="ltr"><i>python2 write_fasta_files_from_mcl.py all.fa hit-frac0.25_I1.5_e5 minimal_taxa outDIR</i></p><p><br></p><p dir="ltr"><b>Step 5: Build Maximum likelihood tree for each homolog group</b></p><p dir="ltr"><i>python2 fasta_to_tree_pxclsq.py fasta_dir number_cores dna bootstrap(y)</i></p><p><br></p><p dir="ltr"><b>Step 6: Extract ortholog groups</b></p><p dir="ltr">Instead of 1to1, MI, RT, or MO methods in Ya et al. (2014), we used a revised version of DISCO (https://github.com/JSdoubleL/DISCO) to infer homologs. …”

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

“…Our implementation shows processing time reductions of up to 210× compared to traditional workflows, even on standard hardware. …”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

“…</p><p dir="ltr">The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:</p><p dir="ltr">https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589</p><p dir="ltr">Peer-Review Reference and Proper Citation:</p><p dir="ltr">Zomer, R.J.; Xu, J.; Trabuco, A. 2022. …”

Elements: Streaming Molecular Dynamics Simulation Trajectories for Direct Analysis – Applications to Sub-Picosecond Dynamics in Microsecond Simulations by Matthias Heyden (17087794)

“…<p dir="ltr">We present a software development effort aimed at enabling real-time analysis of data from molecular dynamics (MD) simulations of biomolecules such as proteins. …”

The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025) by zixuan yuan (17602152)

“…</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. …”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<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. …”

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.…”