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Dataset results
16 results for “Neural Operator”
A Facial Motion Capture System Based on Neural Network Classifier Using RGB- Figure 4. An example of the uniform LBP operator (Huang et al., 2011)
<p>The original LBP operator labels the pixels of an image by means of decimal numbers called Local Binary Patterns or LBP codes, which encode the local structure around each pixel. It proceeds thus as illustrated in figure 4: Each pixel is compared with its eight neighbors in a 3x3 neighborhood by subtracting the center pixel value. The resulting strictly negative values are encoded with 0 and the others with 1. A binary number is obtained by concatenating all these binary codes in a clockwise direction starting from the top-left one and its corresponding decimal value is used for labeling. The derived binary numbers are referred to as Local Binary Patterns or LBP codes.</p>
Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators
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Data for performing wavepacket propagation using Fourier Neural Operators
<p>Datasets for training Fourier neural operator (FNO) [Li, Z., et al, 2021] and the trained models in <strong>Accelerating wavepacket propagation with Machine Learning </strong>by Kanishka Singh, Ka Hei Lee, Daniel Peláez and Annika Bande.</p> <ul> <li>data-gaussian-pulse-10000.pickle: Training dataset for 2D FNO model. The dataset contains 10000 wavefunction propagations under 1D double well which is generated by the split-operator method. Each propagation is influenced by a different laser pulse.</li> <li>data-gaussian-pulse-10000.pt: The trained 2D FNO model for wavefunction propagation under 1D double well influenced by laser pulse.</li> <li>anharmonic_FNO_dens.pt: Training dataset for 3D FNO model. The dataset contains 5100 wavefunction propagations under 2D anharmonic potential which is generated by the multi-configurational time-dependent Hartree (MCTDH) method [Beck, M., 2000]. The dataset is structured and a dictionary with input 'x_conv' and output 'y'.</li> <li>alllambda-anharmonic.pt: The trained 3D FNO model for wavefunction propagation under 2D anharmonic potential.</li> <li>anharmonic_FNO_dens-potential.pt: The 2D anharmonic potential of the 2D wavefunction propagations.</li> <li>anharmonic_FNO_dens-true.pt: The ground truth of the 2D wavefunction propagations by the MCTDH method.</li> <li>anharmonic_FNO_dens-pred.pt: The prediction of the 2D wavefunction propagations by the 3D FNO model.</li> </ul>
Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator
<p>This is the dataset for the research paper "<a href="https://arxiv.org/abs/2207.03239 ">Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator</a>"</p>
Emulator of PR-DNS: Accelerating Dynamical Fields with Neural Operators in Particle-Resolved Direct Numerical Simulation
<p>The codes directory includes the various machine learning models, such as FNO, UNet and ResNet. R128_init1 and R128_init2 are the PR-DNS time step simulations at different initial conditions. R64_init2, R128_init2 and R256_init2 are the PR-DNS time step simulations at different resolutions. </p> <p> </p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Data from: Social complexity influences brain investment and neural operation costs in ants
The metabolic expense of producing and operating neural tissue required for adaptive behaviour is considered a significant selective force in brain evolution. In primates, brain size correlates positively with group size, presumably owing to the greater cognitive demands of complex social relationships in large societies. Social complexity in eusocial insects is also associated with large groups, as well as collective intelligence and division of labour among sterile workers. However, superorganism phenotypes may lower cognitive demands on behaviourally specialized workers resulting in selection for decreased brain size and/or energetic costs of brain metabolism. To test this hypothesis, we compared brain investment patterns and cytochrome oxidase (COX) activity, a proxy for ATP usage, in two ant species contrasting in social organization. Socially complex Oecophylla smaragdina workers had larger brain size and relative investment in the mushroom bodies (MBs)—higher order sensory processing compartments—than the more socially basic Formica subsericea workers. Oecophylla smaragdina workers, however, had reduced COX activity in the MBs. Our results suggest that as in primates, ant group size is associated with large brain size. The elevated costs of investment in metabolically expensive brain tissue in the socially complex O. smaragdina, however, appear to be offset by decreased energetic costs.
Research data of variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
<ul> <li>This is the repository for the paper "<a href="https://www.sciencedirect.com/science/article/pii/S0022509624001807">Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations</a>" (accepted by <a href="https://www.sciencedirect.com/journal/journal-of-the-mechanics-and-physics-of-solids">Journal of the Mechanics and Physics of Solids</a>). Please refer <code>README.md</code> for detailed instructions to reproduce our experiments.</li> <li> <div>This work was supported by National Key Research and Development Program of China (2023YFB3306700 and 2021YFF0306404), and National Natural Science Foundation of China (U2341233 and U21A20429).</div> </li> <li>If you have any technical problems to report or interesting ideas to share, feel free to drop a issue at <a href="https://github.com/BraveDrXuTF/VOL/issues">this issue board.</a> <a href="https://bravedrxutf.github.io/">The first author of this work</a> is now actively seeking full-time internship opportunities and a doctoral position in AI4science, and if you are interested in collaborating with him, kindly consider <a href="mailto:3285935860@qq.com">drop him an email.</a></li> <li>Note: all data and codes in this repository are distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">CC BY-NC-SA 4.0,</a> and related algorithms are patent-protected. If you want to use materials in this repository for commercial purposes, please contact <a href="mailto:haopeng@dlut.edu.cn">haopeng@dlut.edu.cn</a>.</li> </ul>
Vandermonde Neural Operators: Burgers and shear layer data
<p>The burgers.tar.gz file contains the data used in the numerical experiments on Burgers' equation. Uniform corresponds to the uniform distribition, conexp to the contracting-expanding distribution, and rand to the random distribution.</p> <p> </p> <p>The ddsl.tar.gz file contains all the data used in the numerical experiments on the shear layer problem.</p>
Neural Operant Conditioning
ClinicalTrials.gov study NCT06113965. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Data from: Social complexity influences brain investment and neural operation costs in ants
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Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
Raw datasets for paper "River Surface Velocimetry Reconstruction via Graph-enhanced Neural Operator"
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Physics-informed Partitioned Coupled Neural Operator for Complex Networks Datasets
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Dataset - Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator
<p>The directory contains velocity models from OpenFWI dataset collection. These velocity models are the inputs of the FNO-based sovlers in the paper <strong><em>Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator</em></strong>.</p>
Is Neutrophil to Lymphocyte Ratio a Prognostic Factor of Sepsis in Newborns With Operated Neural Tube Defects?
ClinicalTrials.gov study NCT04135274. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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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.