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414 results for “generative model”
Multi-fidelity modelling of shark skin denticle flows: Insights into drag generation mechanisms
<p>We investigate the flow over smooth (non-ribletted) shark skin denticles in an open-channel flow using Direct Numerical Simulation (DNS) and two Reynolds Averaged Navier-Stokes (RANS) closures. Large peaks in pressure and viscous drag are observed at the denticle crown edges, where they are exposed to high-speed fluid which penetrates between individual denticles, increasing shear and turbulence. Strong lift forces lead to a positive spanwise torque acting on individual denticles, potentially encouraging bristling if the denticles were not fixed. However, DNS predicts that denticles ultimately increase drag by 58 % compared to a flat plate.</p> <p>Good predictions of drag distributions are obtained by RANS models, although an underestimation of turbulent kinetic energy production leads to an underprediction of drag. Nevertheless, RANS methods correctly predict trends in the drag data and the regions contributing most to viscous and pressure drag. Subsequently, RANS models are used to investigate the dependence of drag on the flow blockage ratio (boundary layer to roughness height ratio), finding that the drag increase due to denticles is halved when the blockage ratio δ /h is increased from 14 to 45. Our results provide an integrated understanding of the drag over non-ribletted denticles, enabling existing diverse drag data to be explained.</p>
Grounded Copilot: How Programmers Interact with Code-Generating Models
<p>Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants---with a range of prior experience using the assistant---as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.</p> <p> </p> <p>This artifact contains:<br> - The scripts to generate our plots</p> <p>- Detailed study information to re-run our user study</p> <p>- Livestreams that we observed and included in our dataset</p> <p>- Our codebook</p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Datasets for manuscript - Dirichlet diffusion score model for biological sequence generation.
<p>This repository holds the trained Dirichlet Diffusion Score models for various datasets.</p> <p><strong>best_models.tar.gz</strong></p> <p>It also contains all input data required to train your own models with scripts provided via <a href="https://github.com/jzhoulab/ddsm">github repository</a>.</p> <p><strong>data.tar.gz</strong></p> <p>This archive contains the following folders: </p> <ul> <li><strong>satnet_sudoku </strong>contains dataset with sudoku examples which we used for evaluation of sudoku model.</li> <li><strong>promoter_design</strong> contains dataset used for training promoter design model as well as Sei model weights. Please, read provided readme file before using it for training scripts. </li> </ul>
Timeline of generative models by type
<p>Timeline of generative models by type. Part of the study "What do we mean by GenAI?"</p>
Multi-fidelity modelling of shark skin denticle flows: Insights into drag generation mechanisms
Open the record for dataset details and reuse information.
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
Open the record for dataset details and reuse information.
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
Open the record for dataset details and reuse information.
Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans
Open the record for dataset details and reuse information.
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
Open the record for dataset details and reuse information.
RosettaAntibody generated models for a dataset of 49 antibody-Fv structures
<p><strong>Structures of antibody-Fv domains computationally generated by RosettaAntibody based on the protocol of </strong><a href="https://www.nature.com/articles/nprot.2016.180?proof=true&draft=marketing">Weitzner, Jeliazkov, Lyskov et al.</a><strong> (Nature Protocols 12, 401–416, 2017). There are 49 antibody targets, with about 2800 decoy structures provided per antibody. A table is also provided with the H3-loop rmsd of each structure from the experimental crystal structure.</strong></p> <p><strong>These structures can be used to evaluate whether a score function can identify the near-native structures from the pool of decoys. These structures can also be used for comparison with other sets of structures generated by other antibody structure prediction programs.</strong></p> <p><strong>The research study on this set is unpublished and a manuscript is under preparation. Please cite Jeliazkov, Frick, Zhou & Gray, “Robustification of RosettaAntibody and Rosetta SnugDock,” in preparation, 2020.</strong></p> <p><strong>The homology modeling stage of RosettaAntibody was run with stringent homolog exclusion settings, excluding CDR templates of over 95% identity and FR templates of over 90% identity. The H3 modeling stage was run as described by <a href="https://www.nature.com/articles/nprot.2016.180">Weitzner, Jeliazkov, Lyskov et al.</a> (Nature Protocols 12, 401–416, 2017). </strong></p> <p><strong>The set of 49 antibody-Fv domains was originally compiled by <a href="https://academic.oup.com/peds/article/29/10/409/2462315">Marze et al. </a>(Protein Eng. Des. Sel. 29(10), 409-418, 2016). The dataset was first used to evaluate CDR-H3 loop rmsds in <a href="https://www.jimmunol.org/content/early/2016/11/18/jimmunol.1601137">Weitzner and Gray </a>(J. Immunology 198(1):505-515, 2017).</strong></p> <p><strong>The dataset can be extracted on a linux interface using: </strong></p> <p><strong> tar -xvzf <a href="https://www.zenodo.org/api/files/c2ba9da2-0d46-4aba-adbf-8ddd39b52b29/decoys_rosettaantibody_20200323.tar.gz?versionId=df264fc5-c79c-47b4-82bf-d1cda1e025e2">decoys_rosettaantibody_20200323.tar.gz</a> </strong></p> <p><strong>When extracted the output comprises RosettaAntibody generated models for 49 antibodies in the format:</strong></p> <p><strong><Antibody pdb id> / model-<id 1>.relaxed_<id 2>.pdb.gz</strong></p> <p><strong>The Rosetta "ref2015" scores (ref2015_score) and rmsd of the H3 loop (h3_rmsd) for every model (model) is provided in model_scores_and_rmsds.txt . RMSDs are calculated with respect to the corresponding crystal structure (pdb id) over all heavy atoms in the h3 loop (93–102 in Chothia numbering) after superposition of the framework residues. ID 1 comes from the homology model source (lower is better). ID 2 indicates the loop model.</strong></p> <p><strong>The decoy PDB files contain additional metrics following the ATOM records such as VH–VL relative orientation metrics (from Marze et al.) and per-residue Rosetta scores. Caveat: the “RMS” values reported within the decoy files were calculated against the input homology model and not the crystal structures (whereas the model_rmsds.txt file contains the H3 rmsds w.r.t. crystal).</strong></p>
Dataset for: Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates
<p>Published in:<br> Nature Biomedical Engineering. doi: 10.1038/s41551-020-0566-1.</p> <p>Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates</p> <p>Ian S. Kinstlinger (1), Sarah H. Saxton (2), Gisele A. Calderon (1), Karen Vasquez Ruiz (1), David R. Yalacki (1), Palvasha R. Deme (1), Jessica E. Rosenkrantz (3), Jesse D. Louis-Rosenberg (3), Fredrik Johansson (2), Kevin D. Janson (1), Daniel W. Sazer (1), Saarang S. Panchavati (1), Karl-Dimiter Bissig (4), Kelly R. Stevens (2,5), and Jordan S. Miller (1)</p> <p>1 Department of Bioengineering, Rice University, Houston, TX, USA.<br> 2 Department of Bioengineering, University of Washington, Seattle, WA, USA.<br> 3 Nervous System, Palenville, NY, USA.<br> 4 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.<br> 5 Department of Pathology, University of Washington, Seattle, WA, USA</p> <p>Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D-printing techniques. Here we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging as a result of fluid convection through these networks and of diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function.</p>
Deep Generative Model Samples
<p>This dataset contains the main results of <em>Deep Generative Models for Galaxy Image Simulations</em> (Lanusse et al. 2020).</p> <p>It consists of tuples of postage stamps of HST/ACS COSMOS galaxies, corresponding parametric light profiles, and samples from a Deep Generative Model conditioned on size, magnitude, and redshift of the corresponding real galaxies. For each tuple, the dataset also contains morphological statitics computed on each stamp.</p> <p>The details of the creation of this dataset and how to use it to reproduce the plots of the paper can be found at https://github.com/McWilliamsCenter/deep_galaxy_models .</p>
Dataset accompanying paper submission for "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"
<p>This data set accompanies code archived at DOI: <a href="https://doi.org/10.5281/zenodo.3833186">10.5281/zenodo.3833186</a>, which was used in the experiments for the paper submission "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"</p>
Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models
<p>The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015).<em> J Chem Theory Comput</em> <strong>11</strong>, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly <em>et al.</em>, (2016).<em> Acta Cryst. B, accepted</em>.), which is challenging due its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions. </p>
Sentence representations generated by Inner Attention model (arxiv: 1707.03103)
<p>600-dimensional sentence vector representations created by the model described in the paper "Refining Raw Sentence Representations for Textual Entailment Recognition via Attention".</p> <p>The dataset is in tab-delimited format: ID\tSENTENCE_TYPE\tVECTOR, where ID is the id corresponding to the sentence pair as specified in the Repeval 2017 test dataset for both matched and mismatched evaluations, available in https://inclass.kaggle.com/c/multinli-matched-evaluation/download/multinli_0.9_test_matched_unlabeled.jsonl and https://inclass.kaggle.com/c/multinli-mismatched-evaluation/download/multinli_0.9_test_mismatched_unlabeled.jsonl (you will probably have to create an account to download them).</p> <p>SENTENCE_TYPE can either be p, meaning the sentence is the premise or h, meaning it is the hypothesis.</p> <p>VECTOR is a space-delimited 600-dim vector.</p>
Expanding the space of self-reproducing ribozymes using probabilistic generative models
<p>This repository contains the code and data produced in "Expanding the space of self-reproducing ribozymes using probabilistic generative models".</p>
Dataset and figure generator for Variational Monte Carlo approach applied to the model describing WSe2 homo-bilayer
<p>This data set contains post-processed data obtained from variational Monte-Carlo approach for Hubbard model with complex, spin and direction dependent phase. This model is believed to properly describe the eseential features of WSe2 twisted homo-bilayer. The python notebook included, allows to generate figures regsarding formation of Mott insulating phase and spin ordering.</p>
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</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.