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1,782 results for “algorithms”
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Progress Measures and Tangle Learning algorithm implementation and benchmarks
<p>The <code>pmtl.cpp</code> and <code>pmtl.hpp</code> files implement the PMTL solver in the <a href="https://github.com/trolando/oink">Oink framework</a>. The <code>benchmarks</code> folder contains the <code>run_benchmarks.sh</code> shell script which performs all the benchmarks automatically using <a href="https://github.com/sharkdp/hyperfine">hyperfine</a>. The games in the <code>benchmarks/real_world_games</code> folder are a selection from the <a href="https://github.com/SYNTCOMP/benchmarks/releases/tag/v2023.4">SYNTCOMP 2023 HOA files</a> which were converted to parity games using <a href="https://github.com/trolando/knor">Knor</a>. The games in the folders <code>benchmarks/rngames{25,50,100,200,2000}</code> are sparse games generated using Oink with 25, 50, 100, 200, and 2000 vertices respectively. These games can also be regenerated with a random seed using the <code>benchmarks/prepare_games.sh</code>.</p>
Benchmark dataset for the Iterative Rotations and Assignments (IRA) algorithm
<p>In the publication of the Iterative Rotations and Assignments (<a href="https://doi.org/10.1021/acs.jcim.1c00567">IRA</a>) algorithm, a dataset of atomic structures was used for the benchamrking of the algorithm. Two other algorithms were included in the benchmark, <a href="https://doi.org/10.1021/acs.jcim.6b00546">ArbAlign</a> and <a href="https://doi.org/10.1021/acs.jctc.7b00543">fastoverlap</a>. The atomic structures were obtained from several other publications, for details please refer to <a href="https://doi.org/10.1021/acs.jcim.1c00567">IRA</a> publication. </p> <p>The data shared here contains the atomic structures, copies of algorithms, and all scripts used in the benchmark of the reference paper.<br>The source code of IRA algorithm is accessible on <a href="https://github.com/mammasmias/IterativeRotationsAssignments/tree/master">github</a>.</p> <p>Each algorithm and dataset contained in this archive may be subject to its own license, please refer to the README files inside.</p>
Artifact for "Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays"
<p>This artifact accompanies the paper "Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays", accepted to the International Conference on Performance Engineering (ICPE) 2024.</p> <p>Please see <code>README.md</code> for more information.</p>
A supervised Graph-based deep learning algorithm to detect and quantify clustered particles
<p>In this data repository, we provide the necessary data for replicating results, including both simulated and biological datasets. Additionally, the repository includes trained models to infer from these datasets.</p>
Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms
<p>Single-cell RNA (scRNA) and single-cell TCR (scTCR)-sequencing data data for patients number 11, 12 and 14 from the article " Identification of clinically relevant T-cell receptors for personlized T-cell therapy". </p> <p>Each compressed file contains two subfolders. One is for the scRNA-seq (GEX-sequencing) and the other for the scTCR-seq (VDJ-sequencing). </p> <p>In the original article, scRNA-seq and scTCR-seq were aligned to the GRCh38 reference genome using <em>cellranger count </em>(10X Genomics, version 3.0.1) and <em>vdj </em>(10X Genomics, version 3.1.0) respectively. The subsequent data processing was performed using Seurat library (V.4.3.0) on R Statistical Software (V.4.0.3).</p>
NASTRA: Accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm
<p><span>Forensic short-tandem repeats (STR) genetic markers are multi-allelic and widely utilized for individual identification, kinship testing, and cell-line authentication. Nanopore sequencing, known for its portability, is emerging as a promising approach for STR typing, facilitating real-time and in-field testing. However, its efficacy is often hampered by sequencing noise. Previous methods rely on alignment-based genotyping, necessitating known alleles, which limits their applicability to unknown alleles. Here, we introduced NASTRA, an innovative allele reference-free tool for precise germline analysis of STR genetic markers. NASTRA incorporates a recursive algorithm to infer repeat structures of allele sequences using only known repeat motifs. Our tests, conducted on 80 individual samples and 8 DNA standards, have demonstrated NASTRA's exceptional 100% accuracy in genotyping nearly all diploid STRs across various multiplex kits and flow cells. It surpasses alignment-based methods in accuracy and speed. In a paternity testing case study, NASTRA accurately identified three relationships among six individuals within an 18-minute sequencing duration. These results underscore NASTRA's ability to perform STR analysis on both NGS and nanopore sequencing platforms, significantly enhancing the utility of nanopore sequencing in relevant applications.</span></p>
Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components - All Figures and Datasets
<p>Figures and Datasets for "Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components"</p> <ul> <li>dataset.zip: All datasets evaluated</li> <li>figures.zip: All plots (1080) for all results for our paper</li> </ul>
Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings
<p>This page is provided as supplementary material for the article: Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings. More specifically, we provide the results, the refactorings detected by each tool, as a downloadable JSON file. </p> <p>Results.json: You can access the results comparing ACA with RefDetect, RefactoringMiner, NSGA-II, and the Greedy algorithm.</p> <ul> <li>Overview of JSON Template: Each entry in this file provides details about commits, including their SHA-1 hash, GitHub URL, and an exhaustive list of refactorings identified in that specific commit. Refactorings are defined by their type (e.g., 'RenameClass') and are accompanied by detailed descriptions of the refactoring. Additionally, the entry specifies the tools that detected the refactoring, such as 'ACA' and 'RefactoringMiner.' The validation field indicates whether the refactoring was confirmed, using 'TP' for true positive and 'FP' for false positive. Optional comment information may be also included.</li> </ul> <p>Times.xlsx: The Excel file containing the time taken by each tool to identify refactoring in each commit.</p> <p>Results.xlsx: The Excel file contains precision, recall, and F-score metrics for all identified refactoring types.</p>
Model checking of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p>This video shows model checking of a proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in this model. It shows the functions written in ML language and the result of calling them for customized analysis of the proposed model's state space graph. The last ML code at the end of the page, "state space," summarizes the model checking and operates by calling previously displayed functions. This video aimed to demonstrate the proposed verification process of the parametric model.</p>
Automatic message sequence chart creation from simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p><span>These videos show the creation of two message sequence charts from simulation runs of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with three constituting processes. </span></p> <p><strong><span>Message Sequence Chart of <span> </span>Parametric Model With 3 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in the model and was set to three. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 3 Processes via Step-By-Step Simulation Run_SuppInfo.mp4:</span></strong><span> This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.0. The number of constituting processes is parametric in the model and was set to three. We manually fired our selected enabled transition of the model to create this video. </span></p>
The fully executable procedure of the U-Net model combined with the Multi-textRG algorithm to achieve fine ice-water classification ---- another 332 scenes of data-fused SIC labels.
<p>This data source is related to the manuscript titled "Combining the U-Net model and a Multi-textRG algorithm for fine SAR ice-water classification", which will be submitted to the journal---The Cryosphere. </p> <ul> <li>The"ready-to-train-fused_01.zip" to "ready-to-train-fused_10.zip" includes 200 scenes of data-fused SIC labels accessible with doi: 10.5281/zenodo.10973107, https://zenodo.org/records/10973107. </li> <li>The "ready-to-train-fused_11.zip" to "ready-to-train-fused_21.zip" includes another 332 scenes of data-fused SIC labels. </li> </ul>
Data for "First simulations of feedback algorithm-regulated marine cloud brightening"
<p>Data for the manuscript "First simulations of feedback algorithm-regulated marine cloud brightening," submitted for publication in the journal Geophysical Research Letters.</p>
Data for manuscript: An extrapolation algorithm for estimating river bed grain size distributions across basins
<p>Pebble counts collected and used for the analysis presented in the manuscript: An extrapolation agorithm for estimating river bed grain size distributions across drainage basins.</p>
Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"
<p>this dataset contain: </p> <ol> <li>2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA</li> <li>2m temperature observaton data, obs_t2m</li> <li>24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip</li> <li>24H precipitation data, in raw_data_r24.zip</li> <li>source code (in python)</li> </ol> <p> </p> <p>how to use it: </p> <p>1. prepare data and python environment<br> 1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data<br> 1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva<br> 1.3 need cartopy to draw picture FigS01. </p> <p>2. try the 2m temperature blending methods <optional><br> 2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/<br> 2.2 run the Station_FCST_MMWB.py in python environment </p> <p>3. try the 24h QPF multi blending methods <optional><br> 3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/<br> 3.2 run the Station_FCST_MMWB_r24.py in python environment</p> <p>4. draw figures<br> 4.1 run Fig01.py in python environment <br> 4.2 run Fig02.py in python environment <br> 4.3 run Fig03.py in python environment <br> 4.4 run FigA01.py in python environment <br> 4.5 run FigS01.py in python environment </p>
Code and Data for the Study "Exact Algorithms in Bar Nesting: How to Cut General Items from Linear Stocks so that Wastage is Minimised"
<p>This resource contains the code and results used in the paper:</p> <p>Lewis, R. and L. Bonnet (2025) '<a href="https://www.sciencedirect.com/science/article/pii/S0360835224009604" target="_blank" rel="noopener">Exact Algorithms in Bar Nesting: How to Cut General Items from Linear Stocks so that Wastage is Minimised</a>'. Computers & Industrial Engineering, vol. 200, 110838.</p> <p>The paper can be found <a href="https://www.sciencedirect.com/science/article/pii/S0360835224009604" target="_blank" rel="noopener">here</a>.</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Modeling surface pCO2 variability in two contrasting basins of North Indian Ocean using advanced machine learning algorithms
<p>The dataset contains surface ocean <em>p</em>CO2, uncertainty and air-sea CO2 flux for the North Indian Ocean region. The data is available from 1993 to 2020 on a monthly time scale. Each of these data has a spatial resolution of 1/12º. Air-sea CO2 flux is calculated using a bulk parameterization, which is a function of wind speed. A positive CO2 flux value signifies CO2 outgassing, while a negative value indicates atmospheric CO2 uptake. </p> <p><strong>**It is recommended to use the latest version V3. Previous versions are depricated.</strong></p>
AMaze: fully discrete training with three regimes (direct, scaffolding, interactive) and two algorithms (A2C, PPO)
<p>Dataset containing all training artifacts (final models, training curves, intermediate visualizations, ...) as well as the raw data used to assert generalization capabilities.</p> <p>The associated archive final_behavior.tar.gz provides a visualization of every replicate's final behavior for easier navigation.</p> <p>Distribution files contain a sampling across 1000 seeds, 5 probabilities for traps and lures, 4 sizes and 5 set sizes resulting in 486356 mazes. Descriptive graphs provide an overview of the accessible "maze space".</p> <p> </p> <p>v2: Added script to aggregate run dynamics (mean reward, errors, maze lengths...) and resulting generated dataset (csv) and plots (pdf)</p>
PoreScript: Semi-automated Pore Size Analysis Algorithm Data Set
<p>This data set contains files related to the PoreScript semi-automatic pore size image analysis algorithm. The three MATLAB files needed for the PoreScript algorithm are named the following: </p> <p>(<a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_RelativeIntensityFinder_no_crop.m">Jenkins_RelativeIntensityFinder_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_UserInterface_no_crop.m">Jenkins_UserInterface_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_PoreSizeCalculator_no_crop.m">Jenkins_PoreSizeCalculator_no_crop.m</a>).</p> <p>Access the latest version of the program here:<a href="https://github.com/djenkins95/PoreScript_Update_9_26_23"> <strong>https://github.com/djenkins95/PoreScript_Update_9_26_23</strong></a></p> <p>Updated MATLAB files are more accessible to a wider range of SEM software. The updated version asks for the known length of your scale bar in pixels. There are many ways to measure the length of your scale bar. I recommend using the free software FIJI. Use the *Straight* (drawing tool to trace your scale bar), then click Analyze > Measure to determine the length in pixels. It should be noted that the length in pixels will be the same for any image taken on the same instrument, at the same magnification, and saved as the same file type (e.g., .tiff), so you can reference the length in future data sets without needed to remeasure the scale bar.</p> <p>The Zenodo repository includes the unanalyzed SEM images, analyzed images, raw pore size data, analyzed pore size data, and older .m versions.</p>
Data for: An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation
<p>Data for publication: J. Roulet, J. Vanicek, An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation, J. Chem. Phys. <strong>155</strong>, 204109 (2021).</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.