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163 results for “tensor”
Datasets and Codes for "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc"
<p>This Zenodo record contains the supplementary datasets and code for the paper titled "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc."<br><br></p> <p><strong>Datasets</strong></p> <ul> <li>phases.txt<br> Contains phases used for the event location and moment tensor inversion.<br> Format: ID, station, phase, year, month, day, secday<br> ID: Event identifier (same for all files)<br> station: Station name<br> phase: 1 for P-wave or 2 for S-wave<br> secday: Seconds in the day</li> <li>polarity.txt<br> Contains first motion P polarity used in the study.<br> Format: ID, station, polarity, trust, type<br> polarity: 1 for up or -1 for down<br> trust: Value between 0 and 1, indicating confidence level.<br> type: E for emergent or I for impulsive<br> Note that the arrival type has been automatically assigned and not double-checked.</li> <li>relocation.txt<br> HypoDD relocation file (see hypoDD manual for full description).<br> Format: ID, LAT, LON, DEPTH, X, Y, Z, EX, EY, EZ, YR, MO, DY, HR, MI, SC, MAG, NCCP, NCCS, NCTP, NCTS, RCC, RCT, CID</li> <li>MT_soluton.txt<br> Contains all double-couple moment tensor solutions.<br> Format: ID, strike, dip, rake, mag, kagan_std<br> mag: Moment magnitude (Mw); "None" if the event is not considered stable<br> kagan_std: Quality interpretation of the moment tensors using Kagan angle standard deviation, as described in the main paper.</li> </ul> <p> </p> <p><strong>Codes</strong></p> <p>Future development of the relative moment tensor algorithm will be conducted on GitHub as part of the Marie-Sklodowska-Curie Action relMT funded by the European Union (https://github.com/wasjabloch/relMT)</p> <p>Here are the files in Codes.zip:</p> <ul> <li>synthetics.zip<br> Contains codes for performing and testing synthetic moment tensor inversion.</li> <li>relMT.zip<br> Contains the code for performing moment tensor inversion on real data.</li> <li>intrustion.txt<br> Contains instructions for setting up and running the codes.</li> <li>environment_MAC.yml<br> File to create the python environment on a MAC or LINUX machine.</li> <li>environment_WINDOWS.yml<br> File to create the python environment on a WINDOWS machine.</li> </ul>
Moment tensors of 2017 seismic swarm in Reykjanes Peninsula, Iceland
<p>Data for the paper: Pavla Hrubcová and Václav Vavryčuk, 2023. Tectonic stress changes related to plate spreading prior to the 2021 Fagradalsfjall eruption in SW Iceland. Tectonophysics, https://doi.org/10.1016/j.tecto.2023.229761</p>
Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow -- data release
<p>This repository contains the lattice two-point function measurements required to reproduce the results of the paper "Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow" (<a href="https://arxiv.org/abs/2009.14767">https://arxiv.org/abs/2009.14767</a>).</p> <p>The code required to perform the data analysis can be found in <a href="https://github.com/josephleekl/scalar_emt_analysis">https://github.com/josephleekl/scalar_emt_analysis</a>.</p> <p>For any questions please get in touch: joseph.lee@ed.ac.uk </p>
Dataset for "Verifying Monte Carlo simulations of diffusion tensor cardiovascular magnetic resonance using a finite volume method"
<p>This dataset contains the results of random walk and finite volume simulations of diffusion in cardiac tissue. The data was used for the work presented at the 8th World Congress of Biomechanics in 2018.</p>
Data associated to the paper "Reduced basis surrogates for quantum spin systems based on tensor networks"
<p>Within the reduced basis methods approach, an effective low-dimensional subspace of a quantum many-body Hilbert space is constructed in order to investigate, e.g., the ground-state phase diagram. The basis of this subspace is built from solutions of snapshots, i.e., ground states corresponding to particular and well-chosen parameter values. Here, we show how a greedy strategy to assemble the reduced basis and thus to select the parameter points can be implemented based on matrix-product-state calculations. Once the reduced basis has been obtained, observables required for the computation of phase diagrams can be computed with a computational complexity independent of the underlying Hilbert space for any parameter value. We illustrate the efficiency and accuracy of this approach for different one-dimensional quantum spin-1 models, including anisotropic as well as biquadratic exchange interactions, leading to rich quantum phase diagrams.</p>
The Tensor Brain - Entity Level Visual Relationship Detection Challenge
<p>This dataset contains images and annotations from real-world scenarios. It is aimed at testing a machine's perception performance in terms of object recognition, relationship detection, and instance and semantic memory retrieval.</p>
tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format
<p>Data sets required to re-produce numerical examples in</p> <p>Lücke, M. and Nüske, F. <em>tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format</em>, arxiv 2111.09606 (2021)</p> <p><strong>Lemon Slice Example:</strong></p> <p>- Simulation_LS_Full.npy: Complete set of ten independent simulations, at time spacing 10^{-3}, each comprising 300,000 steps.</p> <p>- Simulation_LS_delta_100.npy: Downsampled set of ten independent simulations, at time spacing 10^{-1}, each comprising 3,000 steps.</p> <p><strong>Deca Alanine Example:</strong></p> <p>- Dih_Traj_*.npy: Trajectories of sixteen backbone dihedral angles for 50,000 steps each, at 10ps time spacing.</p> <p>- Dih_Jac_Traj_*.npy: Trajectories of Jacobian matrices for sixteen backbone dihedral angles. Derivatives are taken with respect to the Euclidean coordinates of 26 atoms required for the computation of the dihedrals, and evaluated for 50,000 steps each, at 10ps time spacing.</p> <p>- Timescales_MSM.npy: Implied timescales computed by MSM analysis of the same data set. Contains the first 499 timescales computed using seven different MSM lag times.</p>
Data and example reconstruction code for "Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space"
<p>Data sets and example code for the paper "Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space".</p> <p> </p> <p>Requires the software Mumott, see: https://doi.org/10.5281/zenodo.7798530</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Inverted full deviatoric stress tensors in Zhang et al. 2024
<p>The inverted full deviatoric stress tensors from both 12-block inversion and pixel inversion are uploaded here. The coordinate of the tensors is N-E-down. The result from 12-block inversion assumes a uniform stress before the 2019 Ridgecrest doublet and uses the slip model from Xu et al. (2020). The result from pixel inversion uses Xu et al. (2020)'s slip model as well. Locations of the pixels are uploaded together.</p>
Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines
<p>Modern artificial intelligence and machine learning workflows rely on efficient tensor libraries. However, tuning tensor libraries without considering the actual problems they are meant to execute can lead to a mismatch between expected performance and the actual performance. Einsum libraries are tuned to efficiently execute tensor expressions with only a few, relatively large, dense, floating-point tensors. But, practical applications of einsum cover a much broader range of tensor expressions than those that can currently be executed efficiently. For this reason, we have created a benchmark dataset that encompasses this broad range of tensor expressions, allowing future implementations of einsum to build upon and be evaluated against. In addition, we also provide generators for einsum expression and converters to einsum expressions in our repository, so that additional data can be generated as needed. The benchmark dataset, the generators and converters are released openly and are publicly available at <a href="https://benchmark.einsum.org" target="_blank" rel="noopener">https://benchmark.einsum.org</a>.</p> <p>The broader data collection process included contributions from individuals whose data was transformed. We duly acknowledge the following for making their data publicly available:</p> <ul> <li><strong>Fichte, Johannes; Hecher, Markus; Florim Hamiti</strong>: <a href="../records/10031810" rel="nofollow">Model Counting Competition 2020</a></li> <li><strong>Fichte, Johannes; Hecher, Markus</strong>: Model Counting Competition <a href="../records/10006441" rel="nofollow">2021</a> <a href="../records/10014715" rel="nofollow">2022</a> <a href="../records/10012822" rel="nofollow">2023</a></li> <li><strong>Fichte, Johannes; Hecher, Markus; Woltran, Stefan; Zisser, Markus</strong>: <a href="../records/1299752" rel="nofollow">A Benchmark Collection of #SAT Instances and Tree Decompositions</a></li> <li><strong>Meel, Kuldeep S.</strong>: <a href="../records/3793090" rel="nofollow">Model Counting and Uniform Sampling Instances</a></li> <li><strong>Automated Reasoning Group at the University of California, Irvine</strong>: <a href="https://github.com/dechterlab/uai-competitions">UAI Competitions</a></li> <li><strong>Martinis, John M. et al.</strong>: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.k6t1rj8" rel="nofollow">Quantum supremacy using a programmable superconducting processor Dataset. Dryad.</a></li> </ul> <p>Moreover, we thank the following authors of open source software used to generated instances:</p> <ul> <li><strong>Gray, Johnnie</strong>: <a href="https://quimb.readthedocs.io/en/latest/index.html" rel="nofollow">quimb</a>, <a href="https://cotengra.readthedocs.io/en/latest/" rel="nofollow">cotengra</a></li> <li><strong>Soos, Mate, Meel, Kuldeep S</strong>: <a href="https://github.com/meelgroup/arjun">Arjun</a></li> <li><strong>Stoian, Mihail</strong>: <a href="https://github.com/stoianmihail/Netzwerk">Netzwerk</a></li> <li><strong>Liu, Jinguo; Lua, Xiuzhe; Wang, Lei</strong>: <a href="https://github.com/QuantumBFS/Yao.jl">Yao.jl</a></li> <li><strong>Liu, Jinguo</strong>: <a href="https://github.com/QuantumBFS/YaoToEinsum.jl">YaoToEinsum.jl</a></li> </ul> <p> </p>
Dataset for "Drift instabilities in thin current sheets using a two fluid model with pressure tensor effects"
<p>Data for publication of the same title submitted to JGR space physics. Quantities and plotting scripts for the eigenmode figures are in the zip file. Simulation data for the time slice used in the figure are in the lhdi.tar and lhdi-fluid-2x2v.h5 files. "lhdi-fluid-2x2v.h5" contains electric field data for the five- and local ten-moment fluid simulations in 2x2v. The tar file contains kinetic simulation data, nonlocal ten-moment data, and the five- and ten-moment simulations using 2x3v. </p> <p>This is an update to the old dataset with the additional 2x2v simulation data.</p>
Moment tensor inversion and uncertainty analysis for 40 Uttarakhand Earthquakes (2010-2022)
<p>This repository provides detailed descriptions of the files that were used for the Moment tensor and uncertainty analysis study of earthquakes in the Uttarakhand Himalayas. These files contain the Moment Tensor (MT) estimation results and uncertainty quantification of 40 earthquakes using different networks.</p> <p> </p> <p><strong>Contents:</strong></p> <p>1. waveform_fits.docx - Waveform fits for MT estimation</p> <p>2. confidence_plots.docx - The confidence parameters associated with each MT</p> <p>3. depth_vs_misfit_plot.docx - The confidence in the MT solution for each depth against the misfit values</p> <p>4. weight_files.zip - Weight files for 40 events read by the MTUQ package</p> <p>5. CMT_solution_files.zip - Centroid Moment Tensor (CMT) solutions for 40 events</p>
Rapid portabilization of elasto-chemical evolution data for dental Ti-Cr alloy microstructure through sparsification and tensor computation
<ol> <li><strong>c_theta_interpolation_function_data.csv</strong>: Interpolation function is utilized in phase field model to interpolate the material properties along the interface region. In this work, the corresponding material property that has been interpolated is the elasticity tensor. The csv file (c_theta_interpolation_function_data.csv) consists of the data of the interpolation function <strong>h(theta,c)</strong> given by the expression <em><strong>h = 1/(1+ exp(-theta(2c-1)))</strong></em>. The input feature "c" in the weighted input feature for the sigmoidal function is the mole fraction of Chromium in the two metastable phase regions in the binary Ti-Cr alloy undergoing phase decomposition. The "c" column in the table represents the data of Chromium composition. For the present study, the value of weighted parameter in the input feature is taken as equal to 10 i.e. theta = 10 . Hence, the column h(10,c) represents the data of the interpolation function used by the phase field simulation (Eq. 8 in the paper). To illustrate on how the choice of theta alters the values of h, this csv file also presents four additional columns of h corresponding to different constant values of theta (theta = 5, 15, 20 and 50). The steepness of the sigmoidal interpolation function increases as theta increases, and the graphical representation of the table can be accessed at <a title="c-theta-interpolation-function" href="https://interpolationfunction.streamlit.app/" target="_blank" rel="noopener">https://interpolationfunction.streamlit.app/</a>. </li> <li><strong>spatial-coordinates_composition_data_Figure4c.csv</strong>: This file contains the data of Fig. 4(c) which is the result of the microstructure reconstruction after Tucker decomposition with 10 % sparsification for t = 1 h 23 min 20s. The spatial distribution of Cr composition is sufficient to represent the microstructural information for the binary Ti-Cr alloy. That is the data of spatial coordinates and mole fraction of Cu at each coordinate is sufficient to represent this microstructure. Thus, the csv file consists of the following three columns : X-Coordinate (nm), Y-Coordinate (nm) and Cr_MoleFraction. The unit for the values of X and Y coordinates is nm. The mole fraction is unitless. </li> <li><strong>bulk_free_energy.csv</strong>: The file contains the data of the coefficients alpha, beta, gamma, delta, epsilon and ceq in the equation for bulk thermodynamic free energy ( F_{chem}) at T = 700.15 K. Mathematically, F_{chem} = f_{chem}* Vmol where Vmol is the molar volume of a phase. The expression for molar bulk chemical free energy is: <em><strong>F_{chem} = alpha*(epsilon*c - ceq)^2 + beta*(epsilon*c - ceq) + beta*(epsilon*c - ceq)^6 + delta</strong></em>. </li> <li><strong>statistical_metrics_comparison.zip</strong>:This folder consists of three files that compares the outcome of tensor impainting results of Canonical Polyadic (CP) and Tucker methods for three sparsity values ( 10 %, 15 % and 25%). Each of the files corresponds to the sparsity value, and so the names of of the files are statistical_metrics_10percent.csv, statistical_metrics_15percent.csv and statistical_metrics_20percent.csv. Four types of statistical techniques are considered: <strong>root mean square error (RMSE)</strong>, microstructural similarity (micro sim), blob detection via deviation quantified from <strong>Determinant of Hessian (DoH)</strong>, and Shape Index. The first three methods: RMSE, microstructural similarity and blob detection via DoH are used quantitatively to compare the tensor inpainted images with the benchmark image from phase field method. Shape index is used to perform qualitative analysis, and it has been inferred that both CP and Tucker methods based decomposition and subsequent reconstruction/inpainting are in the acceptable from the viewpoint of tracking the curvature of interfaces. For 10% sparsity, the Tucker method is found to produce better results even if both CP and Tucker produce acceptable ones.</li> </ol>
Supplementary material for "Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion"
<p>This Zenodo archive contains supplementary data and code for the paper <strong>Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion</strong> (submitted to <em>Geophysical Journal International</em>). The archive provides scripts and data to reproduce some of the figures and results presented in the paper / supplementary material.</p>
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
<ul> <li><strong>D-Tensor Data</strong></li> </ul> <p>The reference data for 3500 configurations of [Co(N<sub>2</sub>S<sub>2</sub>O<sub>4</sub>C<sub>8</sub>H<sub>10</sub>)<sub>2</sub>]<sup>2−</sup> (CoSar), [Fe(tpa)<sup>Ph</sup>]<sup>−</sup> (FeTPAPh), and [Ni(HIM<sub>2</sub>−py)<sub>2</sub>NO<sub>3</sub>]<sup>+</sup> (NiComplex) is generated employing Molpro package [1]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the D-Tensor in cm<sup>-1</sup>. The data set contains four <span class="math-tex">\(np.ndarray\)</span></p> <pre><code class="language-python">import numpy as np data = np.load('CoSar.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. D = data['MAT'] # D-Tensor values in cm-1, D = (D11, D12, D13, D22, D23, D33) N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <ul> <li><strong>AIMD Data</strong></li> </ul> <p>To propagate the periodic cell containing four CoSar molecules, for which D-Tensor was computed above, a data set containing total energies as well as atomic forces of 3500 structures was generated employing VASP package [3-6]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the total energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains six <span class="math-tex">\(np.ndarray\)</span></p> <pre><code class="language-python">import numpy as np data = np.load('CoSar_bulk.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. C = data['C'] # Cell vectors in Ang. E = data['E'] # Total energy in kcal/mol F = data['F'] # Atomic forces in kcal/mol/Ang. N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <ul> <li><strong>References</strong></li> </ul> <p>[1] H.-J. Werner, P. J. Knowles, G. Knizia, F. R. Manby, M. Schütz,et al.,“Molpro, version 2020.0, a package of ab initio programs,” (2020), see https://www.molpro.net.</p> <p>[2] V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,”<strong> submitted</strong> (2021).</p> <p>[3] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994).</p> <p>[4] G. Kresse and J. Hafner, “Ab initio molecular dynamics for liquid metals,” Phys. Rev. B 47, 558 (1993).</p> <p>[5] G. Kresse and J. Furthmüller, “Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,” Comput. Mater. Sci. 6, 15 – 50 (1996).</p> <p>[6] G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 11169 (1996).</p> <p> </p>
Model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th
<p>The model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th</p>
Simulated data set for SAXS tensor tomography, sample "M"
<p>This is a simulated data set intended for testing SAXS tensor tomographic reconstructions, including the original field from which the data was generated.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
Supplemental Information in Support of "Moment tensor event identification for collapses"
<p>Supplemental Information in support of manuscript "Moment tensor event identification for collapses".</p> <p>File 00_Collapse_MT_Table.csv is a summary of moment tensor solutions for all of the collapses in this manuscript.</p> <p>A Jupyter notebook (file 00_Collapse_MT_Table.ipynb) is provided which loads the information from the provided table and creates an example plot of one of the full moment tensor solutions and plots all the solutions on a global map. The notebook creates two PNG images mt.png and map.png which are also provided.</p> <p>Directories (e.g., 1980-06-12_Southern_Nevada) contain figures showing waveform fits for the collapses. For each event, the waveforms to the left show displacement data (black) and synthetics (red - used in inversion, blue - predicted only) for the vertical, radial, and transverse components. Detailed information about the event and the moment tensor solution are provided on the right-hand side of the last figure.</p>
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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.