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125 results for “model transfer”
Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs [DATASET]
<p>This is the CRP dataset for the IEEE TIFS paper titled "Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs". It contains 2 packages with details as below:</p> <p>Package 1: "CRPs_14k_2048TPs_bit5_bit6.zip"<br> Description: It consists of 2048*3 files, which contains all the CRPs extracted from 2048 TPs with DOA disable. Using these data, we can train each ANN model for each TP(i,j), and the corresponding prediction results are shown in Fig. 14 and Fig. 15 in the paper.</p> <p>- "tdc_stim_i_j.csv" is the 64-bit challenges of TP(i,j).<br> - "tdc_resp_F1_i_j_bit5.csv" is the responses of TP(i,j) with bit(5) of TDC used as PUF response<br> - "tdc_resp_F1_i_j_bit6.csv" is the responses of TP(i,j) with bit(6) of TDC used as PUF response</p> <p>Package 2: "CRPs_DOA_330818x1024_bit5_bit6.zip"<br> Description: It consists of 3 files. Using the dataset, we trained each ANN model for each bit in RSobf, and the prediction results of all the ANN models are shown in Fig.17. Notice that the length of RS is dependent on the specific challenge. Here, only those RS consisting of ≥1024 responses are selected and only the first 1024 bits in the selected RS are used for authentication accordingly.</p> <p>- "chal_330818.csv" is the 330818 64-bit challenges.<br> - "resp_330818x1024_bit5.csv" is the 330818 1024-bit obfuscated response streams (RSobf) extracted from 2048 TPs using bit(5) of TDC.<br> - "resp_330818x1024_bit6.csv" is the 330818 1024-bit RSobf extracted from 2048 TPs using bit(6) of TDC.<br> </p>
Improved dual-permeability model for characterizing the mass transfer process inside matrix blocks
<p>The dual-permeability model (DPM) is highly efficient for describing bimodal transport in heterogeneous porous media. However, it uses only one domain to describe the matrix blocks, and it therefore ignores the impact of the mass transfer process inside the matrix blocks at the microscale. Therefore, in this study, to investigate the effect of the mass transfer process in dual-permeability media and the computational accuracy when considering it, the dual-permeability model with a transition domain (DPMTD) is proposed based on the DPM. Comparison of the DPMTD with the DPM by applying them to a sand column experiment with the same concept as the model reveals that the DPMTD captures the bimodal transport (especially the first peak) more effectively because it calculates the rapid exchange of solute in the early stage more accurately. Subsequently, the same conclusion is reached when both models are applied to a reported solute displacement experiment for an Andisol. In short, we suggest that the mass transfer process inside matrix blocks needs to be characterized in the model to achieve higher accuracy and provides a new approach for modeling the solute transport of preferential flow.</p>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Datasets and code for "Multi-site transfer function approach for real-time modeling of the ground electric field induced by laterally-nonuniform ionospheric source" by Kruglyakov et al. (2023)
<ol> <li>Archive calculate_weights_for_rt.tgz contains the code for calculation of weights used for computation of electric fields based on multi-site transfer function approach following Kruglyakov et al. (2023). The code is written in Fortran 2003 and the only external dependency is LAPACK/BLAS -compatible library, for example OpenBLAS from https://www.openblas.net. See READ.ME for details.</li> <li>Files GICs*.dat contain observed and modelled geomagnetically induced currents (GICs) at Mäntsälä compressor station in southern Finland (60.6 N, 25.2 E) (https://space.fmi.fi/gic/) for three events (in 2000, 2001, and 2003).</li> <li>Files E_x*. E_y* contain corresponding components of measured (detrended and downsampled from 1s to 10s) and modeled electric fields at sites M02 and M05 from 05:15 to 06:15 UT, 11 Sep 2005.</li> <li>Files MS_TF*.dat contain multi-site transfer functions for different sets of IMAGE magnetometers (based on the data availability during the simulated events) in the frequency domain and the corresponding weights for calculation of electric field in the time domain.</li> <li>File E_to_GICs_W.dat contains coefficients for computation of GICs at Mäntsälä station from electric fields at 18 sites used in the simulation. See Equation (15) of Kruglyakov et al. (2023) for details.</li> </ol> <p> </p> <p> </p>
Proficiency Based Robotics Training Curriculum: Skill Acquisition & Transferability of Skills to Live Porcine Models
ClinicalTrials.gov study NCT02895347. IPD Sharing: YES. Countries: 1. Publications: 9.
Improved dual-permeability model for characterizing the mass transfer process inside matrix blocks
Open the record for dataset details and reuse information.
Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species
Open the record for dataset details and reuse information.
Choosing predictors and complexity for ecosystem distribution models: effects on performance and transferability
Open the record for dataset details and reuse information.
Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
GAP model parameter files for "Combining phonon accuracy with high transferability in Gaussian approximation potential models"
<p>GAP model parameter files to accompany the publication</p> <p>"Combining phonon accuracy with high transferability in Gaussian approximation potential models"</p> <p>by Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer</p> <p>All models are defined by the main parameter file "gp_iter6C.xml" and the associated file "gp_iter6C.xml.sparseX.GAP_00001", where "GAP_0000" is a placeholder for the unique identifier of the potential (also given in the XML header), and the trailing "1" indicates that only one set of descriptor (here, SOAP) parameters is given.</p> <p>The directories in this dataset follow the figures in the publication for which the respective potentials have been first used.</p> <p>Fig_2/only_random/M_1000<br> Fig_2/only_random/M_3000<br> Fig_2/only_random/M_5000<br> Fig_2/only_random/M_7000<br> Fig_2/only_random/M_9000</p> <p>Fig_2/only_individual/M_1000<br> Fig_2/only_individual/M_3000<br> Fig_2/only_individual/M_5000<br> Fig_2/only_individual/M_7000<br> Fig_2/only_individual/M_9000</p> <p>Fig_2/combined/M_1000<br> Fig_2/combined/M_3000<br> Fig_2/combined/M_5000<br> Fig_2/combined/M_7000<br> Fig_2/combined/M_9000</p> <p>Fig_4/only_individual/f_0.1000<br> Fig_4/only_individual/f_0.0100<br> Fig_4/only_individual/f_0.0010<br> Fig_4/only_individual/f_0.0001</p> <p>Fig_4/combined/f_0.1000<br> Fig_4/combined/f_0.0100<br> Fig_4/combined/f_0.0010<br> Fig_4/combined/f_0.0001</p> <p>Fig_5/GAP-18_plus_SCs_f0.01 <br> Fig_5/GAP-18_plus_SCs_f0.001</p> <p> </p>
Supplementary material 1 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299
Variable selection using cluster analsys based on Spearman's rank corellation and UPGMA method for agglomeration
Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification
<p>The file <em>data.tar.gz</em> compromises three HDF5 datasets. This file has been split into 100 files. The files can be merged, decompressed and unpacked with the following commands:</p> <pre><code class="language-bash">cat data* > data.tar.gz tar -xzf data.tar.gz</code></pre> <p>Afterwards, the three files: <em>train_test_data_ss8_supervised_new.hdf5</em>, <em>train_test_data_ss16_supervised_new.hdf5</em>, <em>train_test_data_ss32_supervised_new.hdf5</em> are ready to get processed. </p> <p>Internal structure of the datasets:<br> <strong>Search Window (SW) 8 m^2:</strong><br> HDF5 "train_test_data_ss8_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 76288, 40, 40, 8 ) / ( 76382, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 76382, 1 ) / ( 76382, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 380928, 40, 40, 8 ) / ( 380998, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 380998, 1 ) / ( 380998, 1 ) }<br> }<br> }<br> }}</p> <p><br> <strong>SW 16 m^2:</strong><br> HDF5 "train_test_data_ss16_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 17024, 80, 80, 8 ) / ( 17054, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 17054, 1 ) / ( 17054, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 84992, 80, 80, 8 ) / ( 85068, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 85068, 1 ) / ( 85068, 1 ) }<br> }<br> }<br> }}</p> <p><br> <strong>SW 32 m^2:</strong><br> HDF5 "train_test_data_ss32_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 3328, 160, 160, 8 ) / ( 3359, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 3359, 1 ) / ( 3359, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 16768, 160, 160, 8 ) / ( 16793, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 16793, 1 ) / ( 16793, 1 ) }<br> }<br> }<br> }}</p> <p>The sample count of the various generated dataset is as follows: <br> <strong>SW 8 m^2:</strong> {train: 380,928 with {0: 190,464, 1: 190,464}, test: 76,288 with {0: 38,152, 1: 38,136}}<br> <strong>SW 16 m^2: </strong>{train: 84,992 with {0: 42,498, 1: 42,494}, test: 17,024 with {0: 8,516, 1: 8,508}}<br> <strong>SW 32 m^2: </strong>{train: 16,768 with {0: 8,424, 1: 8,344}, test: 3,328 with {0: 1,672, 1: 1,656}}</p> <p>Each sample is composed as follows:<br> RGB = sample[:,:,<strong>:3</strong>]; Theoretically: [0, 1] per color channel<br> NIR = sample[:,:,<strong>3</strong>]; Theoretically: [0, 1]<br> Slope = sample[:,:,<strong>4</strong>]; Theoretically: [0, 90]<br> Roughness = sample[:,:,<strong>5</strong>]; Theoretically: [0, 78.78]<br> NDVI = sample[:,:,<strong>6</strong>]; Theoretically: [-1, 1]<br> DOM = sample[:,:,<strong>7</strong>]; Theoretically: [0, 429.90]</p> <p>====================================================================================================</p> <p>The following three files compromise the model and optimizer state variable of our PyTorch models trained on the aforementioned datasets: <em>best_alexnet_final.pth</em>, <em>best_resnet18_final.pth</em>, <em>best_wide_resnet50_2_final.pth</em></p> <p>Below find a more precise description of each model:<br> <strong>best_resnet18_final.pth</strong></p> <ul> <li>Model: ResNet-18</li> <li>Dataset: SW 8</li> <li>Input: RGB-NIR-Slope -> R: [0,224,224], G: [1,224,224], B: [2,224,224], NIR: [3,224,224], Slope: [4,224,224]</li> </ul> <p><strong>best_wide_resnet50_2_final.pth</strong></p> <ul> <li>Model: Wide-ResNet-50-2</li> <li>Dataset: SW 16</li> <li>Input: NDVI-Slope -> NDVI: [0,224,224], Slope: [1,224,224]</li> </ul> <p><strong>best_alexnet_final.pth</strong></p> <ul> <li>Model: AlexNet</li> <li>Dataset: SW 32</li> <li>Input: RGB-Slope -> R: [0,224,224], G: [1,224,224], B: [2,224,224], Slope: [3,224,224]</li> </ul> <p>Each model is capable of performing a binary classification, distinguishing between landable and unlandable samples</p>
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
<p>These are the data files for use with the codes in https://github.com/envfluids/Burgers_DDP_and_TL.</p>
Data from: Microhabitat selection in the common lizard: implications of biotic interactions, age, sex, local processes, and model transferability among populations
Modeling species' habitat requirements are crucial to assess impacts of global change, for conservation efforts and to test mechanisms driving species presence. While the influence of abiotic factors has been widely examined, the importance of biotic factors and biotic interactions, and the potential implications of local processes are not well understood. Testing their importance requires additional knowledge and analyses at local habitat scale. Here, we recorded the locations of species presence at the microhabitat scale and measured abiotic and biotic parameters in three different common lizard (Zootoca vivipara) populations using a standardized sampling protocol. Thereafter, space use models and cross-evaluations among populations were run to infer local processes and estimate the importance of biotic parameters, biotic interactions, sex, and age. Biotic parameters explained more variation than abiotic parameters, and intraspecific interactions significantly predicted the spatial distribution. Significant differences among populations in the relationship between abiotic parameters and lizard distribution, and the greater model transferability within populations than between populations are in line with effects predicted by local adaptation and/or phenotypic plasticity. These results underline the importance of including biotic parameters and biotic interactions in space use models at the population level. There were significant differences in space use between sexes, and between adults and yearlings, the latter showing no association with the measured parameters. Consequently, predictive habitat models at the population level taking into account different sexes and age classes are required to understand a specie's ecological requirements and to allow for precise conservation strategies. Our study therefore stresses that future predictive habitat models at the population level and their transferability should take these parameters into account.
Data from: Seasonal difference in temporal transferability of an ecological model: near-term predictions of lemming outbreak abundances
Ecological models have been criticized for a lack of validation of their temporal transferability. Here we answer this call by investigating the temporal transferability of a dynamic state-space model developed to estimate season-dependent biotic and climatic predictors of spatial variability in outbreak abundance of the Norwegian lemming. Modelled summer and winter dynamics parametrized by spatial trapping data from one cyclic outbreak were validated with data from a subsequent outbreak. There was a distinct difference in model transferability between seasons. Summer dynamics had good temporal transferability, displaying ecological models' potential to be temporally transferable. However, the winter dynamics transferred poorly. This discrepancy is likely due to a temporal inconsistency in the ability of the climate predictor (i.e. elevation) to reflect the winter conditions affecting lemmings both directly and indirectly. We conclude that there is an urgent need for data and models that yield better predictions of winter processes, in particular in face of the expected rapid climate change in the Arctic.
CSAPSO-BPNN based modeling of end airbag stiffness of nursing transfer robot
<p>The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human-robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then the performance of the Back Propagation (BP) neural network is improved by using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load-displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its 3 distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags.</p>
Software file and numerical results of Modelling heat transfer for assessing the convection length in ventilated caves
<p>The Comsol file corresponding to the reference case as shown in Figures 4-6 as well as all the numerical results for the rest of the figures are available here.</p>
Supplemental material for "The Role of Momentum Transfer in Tropical Cyclogenesis: Insights from a Single-Column Model"
<p>This dataset includes:</p> <ul> <li>The single-column model for studying the mechanical development of a TC precursor vortex (MATLAB)</li> <li>The supplemental document that introduces the numerical discretization of the numerical model and the derivation of the low-order equation.</li> </ul> <p>For any questions, please contact Dr. Hao Fu (haofu@uchicago.edu or haofu736@gmail.com).</p>
Modeling Data from "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. Insights from Radiative Transfer Modeling"
<p>This dataset includes the results from the radiative transfer modeling done in the paper "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars: Insights from Radiative Transfer Modeling" by Sheehan et al. Included are the full posteriors from the model fitting for each source as a Python pickle file that contains a dictionary with keys given by the source names, e.g. "HOPS-2", that point to numpy arrays containing the posterior distributions. The information can be loaded like so:</p> <pre><code class="language-python">import pickle data, keys = pickle.load(open("posteriors.p","rb"))</code></pre> <p>Here "keys" is a list containing the names of the parameters from the model fit that are a part of the posterior distribution for each source.</p> <p>Also included are the configuration files and datasets used in the modeling for each source, as well as the results from the fit so that anyone can work with these models for their own purposes. An example script that shows how to use these files is included, and further information can be found at <a href="http://pdspy.readthedocs.io">http://pdspy.readthedocs.io</a>.</p>
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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.