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134 results for “Gaussian”
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
<p>This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (<a href="https://doi.org/10.5194/gmd-13-1945-2020">https://doi.org/10.5194/gmd-13-1945-2020</a>). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train500.h5 contains 500 samples). The <em>reference </em>dataset contains 10000 samples and was used as reference for evaluating Gaussian Processes emulators.</p> <table> <tbody><tr> <th>Input Variables</th> <th>Units</th> <th>Min</th> <th>Max</th> </tr> </tbody><tbody> <tr> <td>O3 column concentration</td> <td>atm-cm</td> <td>0.25</td> <td>0.45</td> </tr> <tr> <td>Columnar Water Vapor</td> <td>g/cm2</td> <td>0.2</td> <td>4</td> </tr> <tr> <td>Aerosol Optical Thickness</td> <td>-</td> <td>0.04</td> <td>0.6</td> </tr> <tr> <td>Asymmetry parameter</td> <td>-</td> <td>0.5</td> <td>0.85</td> </tr> <tr> <td>Angstrom exponent</td> <td>-</td> <td>0.1</td> <td>2</td> </tr> <tr> <td>Single Scattering Albedo</td> <td>-</td> <td>0.8</td> <td>1</td> </tr> <tr> <td>Surface elevation</td> <td>km</td> <td>0</td> <td>2.5</td> </tr> <tr> <td>Solar Zenith Angle</td> <td>deg</td> <td>0</td> <td>70</td> </tr> <tr> <td>Relative Zenith Angle</td> <td>deg</td> <td>0</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Pixel-wise Gaussian AD visualization (ResNet18, MVTecAD)
<p>A multivariate Gaussian model is fitted with the pixel-wise feature vectors [1] (all pixel positions confounded) extracted from a pre-trained convolutional neural network using the maximum likelihood estimator (empirical mean and empirical covariance matrix). From the eigendecomposition of the empirical covariance matrix, a whitening transformation is applied to the feature vectors, which yields a new vector of the same number of "components" (cf. [2]). </p> <p>This dataset shows visualizations of the component maps (cf. [2]) of all the categories in the MVTec-AD dataset. All the layer blocks [3] from a ResNet18 [4] are used as feature map extractors for the model described above. Each combination of category and layer is trained separately.</p> <p>The visualizations are composed of heatmaps of the square of the component maps; their sum (the Mahalanobis distance of each pixel, yielding an anomaly score map), and (for images containing defects, a.k.a. "anomalies") the ground truth annotation provided in the MVTec-AD dataset. </p> <p>For more details, refer to our paper.</p> <p>---</p> <p>[1] Pixel-wise feature vectors: given a stacked feature map of a 2D image (i.e. multiple 2D images, or "channels," containing information about the 2D input image), extract the values of all channels for a single pixel position.</p> <p>[2] The axes of the whitened [feature] vectors are referred to as "components" instead of "features" as a reminder that they come from a projection of the feature vectors along the eigenvectors, or eigen-<em>components</em>, of the covariance matrix. As the whitened vectors are in the same 2D grid structure as their original feature maps, we refer to them as "component maps." </p> <p>[3] "layer1", "layer2", "layer3", "layer4".</p> <p>[4] From torchvision version "0.15.2", model weights "ResNet18_Weights.IMAGENET1K_V1".</p> <p> </p> <p>TODO ref to paper</p>
STGMVA: clustering, imputation, and integration for spatial resolved transcriptomics using spatiotemporal gaussian mixture variational autoencoder
<p> In this study, we present STGMVA, a comprehensive analysis toolkit employs a spatiotemporal gaussian mixture variational autoencoder to tackle these tasks effectively. STGMVA consists of two stages: pretraining the gene expression and spatial location using a gaussian mixture model, and learning the embedding vectors through a variational graph autoencoder. Results demonstrate STGMVA surpasses state-of-the-art approaches on various spatial transcriptomics datasets, exhibiting superior performance across different scales and resolutions. Notably, STGMVA achieves the highest clustering accuracy in human brain, mouse hippocampus, and mouse olfactory bulb tissues. Furthermore, STGMVA enhances and denoises gene expression patterns for gene imputation task. Additionally, STGMVA has the capability to correct batch effects and achieve joint analysis when integrating multiple tissue slices.</p>
Experimental Results for "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models"
<p>This package contains the raw data / logs (fetched from WandB) for the experiments of the following publication:</p> <p>O. Arenz, P. Dahlinger, Z. Ye, M. Volpp, and G. Neumann. A unified perspective on natural gradient variational inference with gaussian mixture models. Transactions on Machine Learning Research, 2023. URL: <a href="https://openreview.net/forum?id=tLBjsX4tjs">https://openreview.net/forum?id=tLBjsX4tjs</a>.</p> <p> </p>
Data source for "A universal programmable Gaussian Boson Sampler for drug discovery"
<p>Source Data for the figures in paper.</p>
Hinode/EIS spectra and Gaussian fit parameters for cool loop footpoint datasets
<p>Contains derived data for the article "Updated reference wavelengths for Si VII and Mg VII lines in the 272-281 Angstrom range" submitted to The Astrophysical Journal and authored by Dr. Peter Young (NASA Goddard Space Flight Center).</p> <p>The zip file contains 13 sub-directories corresponding to the 13 datasets studied in the article. In each directory there are two files: an IDL save file, and an ASCII text file.</p> <p>Upon restoring the IDL save file into IDL, you will see four quantities (the names may vary slightly between datasets):</p> <p>MAP: an IDL map containing the Si VII 275.37 image from which the pixel mask was selected.<br> MASK: a pixel mask generated with the routine pixel_mask_gui.pro using the input MAP. The pixels in the map are used to create the spectrum.<br> SWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS short-wavelength channel spectrum.<br> LWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS long-wavelength channel spectrum.</p> <p>LWSPEC was input to the Gaussian-fitting routine spec_gauss_eis.pro and fits were generated for the emission lines listed in the article. The routine writes the fit parameters to the ASCII text file.</p> <p>The Gaussian fit parameters can be read into IDL using the routine read_line_fits.pro. You can also use the routine line_fits_summary.pro to generate a printout of the fit parameters.</p> <p>The IDL software described here is available with the Solarsoft repository (https://sohoftp.nascom.nasa.gov/solarsoft/).</p>
Data from: Evaluation of the Laguerre-Gaussian mode purity produced by 3D-printed microwave spiral phase plates
Computer-aided design software and additive manufacturing provide flexibility in the direct fabrication of multi-material devices. This design and fabrication versatility has been investigated for the manufacture of dielectric spiral phase plates (SPP) to generate electromagnetic waves with helical wave-fronts. Three types of SPPs designed to produce an orbital angular momentum (OAM) mode number l=|1| were additively manufactured using multi-material extrusion and multijet fabrication methods. The phase mode and mode characteristics of transformed helical microwaves as a function of the SPP geometrical features was investigated experimentally in the 12 to 18 GHz frequency range, providing high purity at characteristic frequencies. The SPPs were further combined with an additively manufactured dielectric lens that provided a marked improvement in OAM mode purity. Finally, multiplexing and de-multiplexing of two OAM modes were demonstrated successfully using the optimum SPP geometry and design.
Data for Gaussian-Process-Based Emulators for Building Performance Simulation
<p>The ZIP folder contains the MAT files you need to rerun the experiment described in</p> <p>Rastogi, Parag, Mohammad Emtiyaz Khan, and Marilyne Andersen. 2017. “<strong>Gaussian-Process-Based Emulators for Building Performance Simulation</strong>.” In <em>Proceedings of BS 2017</em>. San Francisco, CA, USA: IBPSA.</p> <p>-------------------------------------------------------------</p> <p>The two m-scripts (MATLAB) help you to load the results reported in the paper. Make sure to CHECK the file paths inside the scripts, especially to the MAT files. Usually, the paths should be fine if you update the variable <em>pathMATfolder</em> inside the script <em>RunThis.m</em> .</p> <p>There are two types of MAT files inside the folder called "Data" :</p> <p>1. Original data (building simulations) --> gpdata_BaseSimulation.mat</p> <p>2. Errors and predictions - errs_BaseSimulation_N_M.mat and ystore_BaseSimulation_N_M.mat --> The first contains all the error quantities and the second the 'y' predictions. The number N represents the run number (subset of master training data set sampled for the given run). The number M can take only two values - 1 or 2. The models for heating load are represented by 1 and for cooling by 2.</p> <p>3. Metadata - trainN* --> These files contain metadata for setting up the plots.</p> <p>See github repository <strong>https://github.com/paragrastogi/GPregressionInBS.git</strong> for more scripts. See <strong>www.paragrastogi.com</strong> or <strong>www.ibpsa.org</strong> for the conference paper.</p>
Quasi-periodic Gaussian process rotation period posterior samples of Kepler light curves.
<p>.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017). </p> <p>koi_results_02_15.tgz contains posterior samples for 1132 Kepler objects of interest.</p> <p>results_acfprior_02_16.tgz contains posterior samples for 998 simulated light curves (previously published in Aigrain et al., 2015: https://arxiv.org/abs/1504.04029) with an ACF-based prior.</p> <p>results_noprior_02_16.tgz contains posterior samples for 997 simulated light curves (previously published in Aigrain et al., 2015) with an uninformative prior.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("<filename>", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an uninformative prior) for 998 simulated Kepler light curves.
<p>997.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>997 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an ACF prior) for 998 simulated Kepler light curves.
<p>998.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>998 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p>
Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In this product, mapping is done in latitude and longitude with monthly subsets of data. Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. </p>
Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"
<h1>Data publication for the paper "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"</h1> <div>Contains scripts for performing fabrication uncertainty guided design optimization, example scripts, research data (raw data), cleanup), additional information on models, convergence plots, and field exports.</div> <h2>Funding</h2> <div> <div> <div>We acknowledge funding by the German Federal Ministry of Education and Research (BMBF project siMLopt number 05M20ZAA and BMBF Forschungscampus MODAL number 05M20ZBM) as well as funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689). This project (20FUN05 SEQUME) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. This project is co-financed by the European Regional Development Fund (EFRD, application no. 10184206, QD-Sense).</div> </div> </div> <p> </p> <p> </p> <p> </p> <p> </p>
Gaussian Splatting on the Move - Smartphone Dataset
<p>Casual smartphone 3D scans using three devices: iPhone 15 Pro, Samsung Galaxy S20 FE and Google Pixel 5. The raw data (prefixed "spectacular-rec-") has been captured with the Spectacular Rec applications for Android and iOS, and contains time-synchronized video and IMU data. The <strong>extras</strong> file also contains AprilGrid calibration sequences, as well as pre-computed calibration results, for the Android devices.</p> <p>The data is captured in non-ideal lighting conditions and has a moderate amount of motion blur and rolling shutter artefacts. The included metadata also contains the exposure times and the (Android) rolling shutter readout times, as well as the built-in calibration data, as reported by the devices.</p> <p>The dataset also contains three different processed variants (prefixed with "colmap-"), which are directly trainable with Nerfstudio. In the processed variants, suitable minimally blurry video frames have been selected as key frames and their poses have been registered with COLMAP. In addition, the local linear and angular velocities of each key frame has been estimated using VIO with Spectacular AI Mapping Tools. The "calib-intrinsics" and "orig-intrinsics" variants include manually calibrated and built-in intrinsics, respectively. They depend on the third variant with symbolic links. The third variant has COLMAP-estimated intrinsics, which are relatively inaccurate for the Android data with high levels rolling shutter deformation.</p>
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Effect of density gradients on the generation of a highly energetic and strongly collimated proton beam from a laser irradiated Gaussian-shaped Hydrogen microsphere
<p>Simulation input files and data sets of "Effect of density gradients on the generation of a highly energetic and strongly collimated proton beam from a laser irradiated Gaussian-shaped Hydrogen microsphere" by A. Bhagawati et al.</p> <p>Simulation code: </p> <p>Picpsi3D, Version 0.1 beta 11<br> (c) Copyright Kartik Patel, L & PTD, BARC<br> </p>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.