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1,782 results for “Algorithm”
Dataset from publication Modeling of small molecule's affinity to phospholipids using IAM-HPLC andQSRR approach enhanced by similarity-based machine algorithms
Open the record for dataset details and reuse information.
Supplementary material - Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms
<p>This repository contains the data presented in the publication: "Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms".</p> <p> </p>
Immune Imbalance Transcriptomics (IIT) Algorithm Supplementary Materials
Open the record for dataset details and reuse information.
Integrating Novel Stellarator Single-Stage Optimization Algorithms to Design the Columbia Stellarator Experiment - Dataset
<p>Data related to the publication by A. Baillod <em>et.al., </em>Integrating Novel Stellarator Single-Stage Optimization Algorithms to Design the Columbia Stellarator Experiment, https://arxiv.org/abs/2409.05261</p>
Simulation (static & dynamic) and experimental (dynamic) raw data for evaluating the performance of the developed adaptive MPPT algorithm
<p>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union) along with the research on the developed adaptive maximum power point tracking (MPPT) algorithm to achieve boosting efficiency in heterogeneous power scenarios. As part of the project’s work packages (specifically WP4), the published work presented here introduces the development of a chip and corresponding algorithm designed to efficiently convert energy harvested by the regenerative shock absorber into stable DC electricity.</p> <p>Underlying data for the publication Zhao, W. et al. Adaptive MPPT: Boosting efficiency in heterogeneous power scenarios. Sustainable Energy Technologies and Assessments, 2024, 67, 103843. https://doi.org/10.1016/j.seta.2024.103843. Data related to Figures 1b, 4, 5, and 7 in the article.</p> <p>Contact Person: Dr. Weiwei Zhao (Birmingham Centre for Energy Storage, University of Birmingham).</p> <p>ORCID: https://orcid.org/0000-0001-9708-4252</p>
Filming the sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms
<p>This repository makes available the dataset related to the paper:</p> <p>Zafer Çınar, Alessandro Russo, Matteo Spanio, Niccolò Pretto, and Sergio Canazza, <em>Filming the Sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms</em>, IAI4CH, Bozen, 2024.</p> <p>The dataset and the experiment are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li><strong>video samples</strong>: the actual videos used in the paper's experiment. This folder contains four subdirectories - 3.75 ips, 7.5 ips, 15 ips, and 30 ips - each representing a different playback speed (in inches per second). Within each subdirectory are several MP4 files, recorded on an A810 Studer open reel recorder, documenting the playback of magnetic audio tapes. The files follow the naming convention “Xips (Y).mp4,” where <em>X</em> represents the tape playback speed and <em>Y</em> is a serial number identifier for each video.</li> <li><strong>irregularities</strong>: the metadata for each video with timestamp and type of irregularity. The folder includes four CSV files - 3.75.csv, 7.5.csv, 15.csv, and 30.csv - corresponding to the playback speeds of the video samples. Each CSV file provides handmade annotations for its respective videos, with three columns: <ul> <li><em>video_id</em>: name of the video file in the format “Xips (Y).mp4,” where <em>X</em> is the tape speed and <em>Y</em> is the ID number.</li> <li><em>time_label</em>: timestamp indicating the irregularity, formatted as HH:MM:SS.mls.</li> <li><em>irregularity_type</em>: category of the detected anomaly, which may be one of the following: “splice,” “shadow,” “end-of-tape,” or “annotation.”</li> </ul> </li> </ul>
road network inference algorithms
<p>The document includes the Chicago trajectory dataset and several map inference algorithms.</p>
ARBic: an all-round biclustering algorithm for analyzing gene expression data
<p>datasets of ARBic: an all-round biclustering algorithm for analyzing gene expression data</p>
Plots Scripts and Associated Data Sets for: "A Divide-and-Conquer Algorithm for Disordered and Interacting Few-Particle Systems in One Dimension"
<p>The dataset includes data and Python scripts for all figures in the corresponding preprint/article.</p>
yukimayuli-gmz/data: Two parts data used in our paper about Lpnet algorithm
<p>The tree files, sequence alignments and distance matrices for the simulated data sets, as well as the distance matrices for the random distances are available at Two parts data used in our paper about Lpnet algorithm.</p>
What is the Best Algorithm for MDP Model Checking? Replication Package
<p>This artefact allows to review and replicate the experiments from the paper <strong>What is the Best Algorithm for MDP Model Checking?</strong>.<br> The package contains all original logfiles and the scripts that extract the relevant data from those logs to generate the plots as in the paper.<br> Furthermore, the artefact contains the exercised version of the model checking tool `Storm` with its dependencies and convenient installation scripts as well as all benchmark instances.<br> The user can thus replicate all experiments from the paper.<br> An appropriate subset of the experiments is given to allow a review in a timely manner. In addition, single experiments can be handpicked for replication.</p> <p><em>This is a mild adaptation of the <a href="https://doi.org/10.5281/zenodo.7509473">artefact</a> for the paper "A Practitioner's Guide to MDP Model Checking Algorithms by Hartmanns et al. (TACAS'23)".</em></p>
VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION
<p><strong>dataset of this paper VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION</strong></p>
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
<p>This repository provides additional files for in the publication "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)" by Stefan Dittmar, Aki Sebastian Ruhl and Martin Jekel (DOI: <a href="https://www.doi.org/10.1021/acsestwater.3c00457">10.1021/acsestwater.3c00457</a>) </p><p>It contains:</p><p>- image processing routine for particle tracking written in Python (<strong>1_particle_tracking_algorithm.zip</strong>)<br>- single particle raw data from settling experiments (<strong>2_settling_data.zip</strong>)<br>- single particle data from appyling empirical model for interactions between settling particles (<strong>3_model_results_data.zip</strong>)<br>- additional video & animated graph referenced in publication or SI (<strong>4_videos.zip</strong>)</p>
Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Rating Confusion Matrix
<p>Resulting rating confusion matrix for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".</p>
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
<p><strong>Abstract</strong></p> <p>Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understood<br> functionality. To address this gap, we developed INFLAMeR, an advanced machine learning<br> model trained on CRISPRi screening data, to predict functional lncRNAs using comprehensive<br> genetic features. We experimentally validated the predictions by assessing their impact on cell<br> proliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showed<br> significant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.<br> Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.<br> INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insights<br> into their regulatory landscape. By integrating INFLAMeR with experimental validation, we can<br> identify and characterize functional lncRNAs in a cell-type-specific manner, contributing to a<br> deeper understanding of their involvement in cellular processes. Our findings revealed insights<br> into lncRNA biology and a framework for improving the identification of functional lncRNAs.</p>
Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"
<p>The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the build.sh script in the solver's sub-directory.</p> <p>The archive christen-et-al-ecai2023-data contains all the scripts necessary to run the experiments<br> presented in the paper, as well as all the raw and processed data.</p> <p>The archive christen-et-al-ecai2023-benchmarks contains all the benchmarks from the competition as well as our compiled PDDL and SAS+ versions, and the scripts used for the compilation.</p> <p>For a detailed explanation, see the README file within the archives. For licensing information about the solvers, consult the LICENSE file within the solvers archive.</p>
Reversible image enhancement processing via hybrid quantum algorithms
<p> In order to solve the problems of pixel distortion, image clarity, and detail reduction during reversible image enhancement processing, a reversible image enhancement method with a hybrid quantum algorithm is proposed. Based on classical image processing technology, the algorithm first extracts the feature information of the original image and converts the classical image into a quantum image form by qubit encoding. Then the image enhancement is initially realized by Quantum Fourier transform. Finally, in order to avoid the loss and destruction of information, the Quantum Fourier inverse transform is used to reverse the processing of the image. Compared with the traditional image enhancement method, this method has better reversibility and fidelity and can take advantage of quantum parallel computing to improve image processing efficiency.</p>
A Genetic Algorithm Optimization for Fracture Length to Prevent Subsurface Interference during drilled cuttings reinjection Files
<p>The files associated with the publication.</p>
Data for "No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models"
<p>Datasets to reproduce the experiments associated with the paper: https://doi.org/10.48550/arXiv.2307.06440</p> <p>The readme contains instructions for how to use them: https://github.com/JeanKaddour/NoTrainNoGain/blob/main/bert/README.md</p> <p>c4-subset-random.tar.bz2 is a subset of the C4 dataset (https://arxiv.org/abs/1910.10683), licensed under ODC-BY 1.0.</p>
Towards Efficient Program Repair with APR Tools Based on Genetic Algorithms
<p>This dataset contains experimental results and related artifacts.</p>
ScienceDex guides
Understand access before you commit
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.