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3,264 results for “FAST”
Artifact for Paper ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving
<p>The artifact for the paper "ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving" submitted to the <a href="https://tacas.info/artifacts-23.php">TACAS2023 artifact evaluation</a>.</p> <p>We thank the reviewers for their comments and added the missing dependencies to this revised version. No other parts have been changed.</p>
Fast Initiating Furan-Containing Hoveyda-Type Complexes: Synthesis and Applications in Metathesis Reactions
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Chemistry</em> <strong>2022</strong>, <em>4</em>(3), 786-795; <a href="https://doi.org/10.3390/chemistry4030056">https://doi.org/10.3390/chemistry4030056</a></p> <p>The research was performed within SONATA BIS project and was funded by National Science Centre, Poland, grant number DEC-2021/42/E/ST4/00187.</p> <p> </p> <p> </p>
Design and optimization of a Chloride Molten Salt Fast Reactor - dataset
<p>Dataset used for the figures presented in the paper "Design and optimization of a Chloride Molten Salt Fast Reactor" for the ICAPP2023 conference</p>
Calculation data for "Scattering of Radiation Belt Electrons by Fast Magnetosonic Waves: Considering the Kinetic Effects"
<p>Here are the calculation data for "Scattering of Radiation Belt Electrons by Fast Magnetosonic Waves: Considering the Kinetic Effects", using the format of ".mat". </p> <p>Term of "cold_plasma_dispersion_relation.mat" is the numerical results of cold plasma dispersion relation for MS waves;</p> <p>Term of "kinetic_dispersion_relation.mat" is the numerical results of kinetic dispersion relation for MS waves, using the WHAMP code by K. Rönnmark;</p> <p>Term of "Diffusion_coefficients_cold.mat" is the diffusion rates calculated based on the cold plasma dispersion relation;</p> <p>Term of "Diffusion_coefficients_kinetic.mat" is the diffusion rates calculated based on the kinetic dispersion relation;</p> <p>To be noted, the "D_{\alpha\alpha}_Bw2" in term "Diffusion_coefficients_kinetic.mat" is derived from the inferred wave magnetic field power spectral density.</p>
Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"
<p>This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023).</p> <p>* arXiv:<a href="https://arxiv.org/abs/2205.12362">arXiv:2205.12362</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202244107">10.1051/0004-6361/202244107</a></p> <p> </p>
Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video
<p>Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey.</p> <p>1. Video Files</p> <p>MarmoReach 1 is an illustrative example.</p> <p>MarmoReach 2 illustrates the reaching trial shown in Figure 3D.</p> <p>MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2. </p> <p>2. Data</p> <p>marmo_reach_model.mat is a Matlab struct.</p> <p>2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4. </p> <p>x.hand,y.hand = position data of the central hand marker for each trial.</p> <p>x.cricket,y.cricket = position data of the cricket marker for each trial.</p> <p>x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses. </p> <p>To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].</p> <p> </p>
Dazzled by shine: gloss as an antipredator strategy in fast moving prey
<p>Previous studies on stationary prey have found mixed results for the role of gloss in predator avoidance – some have found that gloss can act as warning colouration or improve camouflage, whereas others detected no survival benefit. An alternative untested hypothesis is that gloss could provide protection in the form of dynamic dazzle. Fast-moving animals that are glossy produce flashes of light that increase in frequency at higher speeds, which could make it harder for predators to track and accurately locate prey. We tested this hypothesis by presenting praying mantids with glossy or matte targets moving at slow and fast speeds. Mantids were less likely to strike glossy targets, independently of speed. Additionally, we found that compared to matte targets, mantids were less likely to track glossy targets and more likely to hit the target with one rather than both raptorial arms, but only when targets were moving fast. These results support the hypothesis that gloss may have a function as an antipredator strategy by reducing the ability of predators to track and accurately target fast-moving prey. </p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 1
<p>This is dataset 1 of the “Fast Calorimeter Simulation Challenge 2022”. It is based on the ATLAS GEANT4 open datasets that were published <a href="http://opendata-qa.cern.ch/record/15012">here</a>. There are four files, two for photons and two for charged pions. Each dataset contains the voxelised shower information obtained from single particles produced at the calorimeter surface in the η range (0.2-0.25) and simulated in the ATLAS detector. Each file contains "incident_energies" of shape (num_showers, 1) and "showers" of shape (num_showers, num_voxels). There are 15 incident energies from 256 MeV up to 4 TeV produced in powers of two. 10k events are available in each sample with the exception of those at higher energies that have a lower statistics. These samples were used to train the corresponding two GANs presented in the AtlFast3 paper <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SIMU-2018-04/">SIMU-2018-04</a> and in the FastCaloGAN note <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-SOFT-PUB-2020-006/">ATL-SOFT-PUB-2020-006</a>. The number of radial and angular bins varies from layer to layer and is also different for photons and pions, resulting in 368 voxels for photons and 533 for pions.</p> <p>dataset_1_photons_1.hdf5 and dataset_1_pions_1.hdf5 should be used for training, dataset_1_photons_2.hdf5 and dataset_1_pions_2.hdf5 for evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Dataset: Evaluating approximate asymptotic distributions for fast neutrino flavor conversions in a periodic 1D box
<p>In this data package we include two txt files and two zip files.</p> <p>The file Gaussian.txt contains 4 columns. The first column shows the indices of the file numbers contained in the file Gaussian_100.zip. The second, third, and fourth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>The file Maxent.txt contains 5 columns. The first column shows the indices of the file numbers contained in the file Maxent_100.zip while the second column lists the corresponding indices in the file Gaussian.txt. The third, fourth, and fifth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>Files Gaussian_100 and Maxent_100 give the asymptotic simulated outcomes of fast neutrino flavor conversions with the initial Gaussian and maximum-entropy distributions, respectively. Each data file xxxx.dat lists four columns for <span class="math-tex">\(v_z\)</span>, <span class="math-tex">\(P_{ee}(v_z)\)</span>, <span class="math-tex">\(g_\nu(v_z)\)</span>, and <span class="math-tex">\(g_{\bar\nu}(v_z)\)</span>, respectively. We take 100 <span class="math-tex">\(v_z\)</span> grids for all data.</p>
Dataset for manuscript "Prefrontal stimulation as a tool to disrupt hippocampal and striatal reactivations underlying fast motor memory consolidation"
<p>Dataset containing the source data corresponding to figures and tables in the manuscript "Prefrontal stimulation as a tool to disrupt hippocampal and striatal reactivations underlying fast motor memory consolidation", as well as raw behavioral and MEP data and the corresponding analysis scripts.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Monthly averaged fast-ice thickness in McMurdo Sound from a sea-ice monitoring station
<p>Updated monthly averaged fast-ice thickness on level first year fast ice in McMurdo Sound, Antarctica from a sea-ice monitoring station. Fast-ice thickness is derived from temperature measurements and manual drill hole measurements, and linearly interpolated to produce mid-month values. Do not use the original version (SIMS_thickness.xlsx) as an error has been discovered. SIMS_thickness_V2.xlsx is the corrected dataset. A description of the stations and the methods used can be found in Gough et al., 2012 and Richter et al. 2022.</p> <p>Gough, A. J., Mahoney, A. R., Langhorne, P. J., Williams, M. J. M., Robinson, N. J., & Haskell, T. G. (2012). Signatures of supercooling: McMurdo Sound platelet ice. Journal of Glaciology, 58 (207), 38–50. doi:10.3189/2012jog10j218</p> <p>Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., Mahoney, A. R., & Parry, M. (2022). Accuracy and precision when deriving sea-ice thickness from thermistor strings: a comparison of methods. Journal of Glaciology, 1–20. doi:10.1017/jog.2022.108</p> <p> </p>
Fast-food consumption by adolescent girls may sow the seeds of breast cancer decades later
<p>An hypothesis of breast cancer development:</p> <p>•Breast development occurs largely during the years of puberty in adolescent girls</p> <p>•Environmental assaults during that vulnerable time window can seed changes that take decades to complete</p> <p>•Advanced glycation end-products (AGE) are found in high concentrations in processed foods, especially fast-food</p> <p>•In mice, AGE produce pubertal breast changes in both epithelium and stroma, including atypical hyperplasia</p> <p>•The stromal changes in humans may be manifest as increased breast density as measured by mammography</p> <p>•These data provide a link between processed, fast-food, high breast density, and future breast cancer</p> <p>•Breast cancer prevention may include the avoidance of fast-food, especially in adolescent girls undergoing pubertal breast development</p>
FABind: Fast and Accurate Protein-Ligand Binding
<p>The preprocessed PDBbind2020 dataset for paper "FABind: Fast and Accurate Protein-Ligand Binding" with associated code at <a href="https://github.com/QizhiPei/FABind">https://github.com/QizhiPei/FABind</a>.</p><p>The dataset files are saved as .pt and lmdb file for the convenience of use.</p><p>We follow the same preprocessing as TankBind.<br><br><strong>Paper Abstract:</strong></p><p>Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at <a href="https://github.com/QizhiPei/FABind">https://github.com/QizhiPei/FABind</a>.</p>
Daily Caloric Restriction and Intermittent Fasting in Overweight and Obese Adults With Autosomal Dominant Polycystic Kidney Disease
ClinicalTrials.gov study NCT03342742. IPD Sharing: YES. Countries: 1. Publications: 1.
Clofazimine- and Rifapentine-Containing Treatment Shortening Regimens in Drug-Susceptible Tuberculosis: The CLO-FAST Study
ClinicalTrials.gov study NCT04311502. IPD Sharing: YES. Countries: 5. Publications: 2.
Dazzled by shine: gloss as an antipredator strategy in fast moving prey
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Fat storage influences fasting endurance more than body size in an ungulate
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Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
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Do flower-colonizing microbes influence floral evolution? A test with fast-cycling Brassica
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ScienceDex guides
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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.