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Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
<p><strong>Associated publication</strong></p> <p>[1] Samuel Ponting*, <strong>Takuma Morimoto</strong>*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a></p> <p>[2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244</p> <p> </p> <p>Datasets contain 2 folders and 1 mat file.</p> <p> </p> <p><strong>(Folder 1) Stimuli</strong></p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p> </p> <p>Details are described below.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 1) Stimuli</strong></p> <p> </p> <p><strong>Overview of datasets</strong></p> <p>This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.</p> <p> </p> <p>The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees).</p> <p>Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).</p> <p> </p> <p><strong>Each image file</strong></p> <p>Filename denotes a condition name and the camera angle as formatted in a following way.</p> <p> </p> <p>stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat</p> <p>e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”</p> <p> </p> <p>Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).</p> <p> </p> <p>Each image file contains two valuables : MacLeodBoynton, XYZ</p> <p> </p> <p>Each variable contains an image of 128*128*3 pixels (height*width*channel).</p> <p> </p> <p>MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M)</p> <p>XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)</p> <p> </p> <p>Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p>Filename denotes the condition and observers formatted in a following way.</p> <p> </p> <p>data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat</p> <p>e.g. data_en2_matte_session4_JH.mat or .csv</p> <p> </p> <p>Each file includes following variables:</p> <p> </p> <p>(Variable 1) threshold</p> <p>Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).</p> <p> </p> <p>MacLeod-Boynton chromaticity coordinates were calculated in a following way. </p> <p>These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).</p> <p> </p> <p>First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org).</p> <p>Each sensitivity curve was normalised to have 1.0 at the peak.</p> <p> </p> <p>Then, MB coordinates were calculated using equation (1-3).</p> <p> </p> <p>L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1)</p> <p>S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2)</p> <p>L+M = Lw*L+Mw*M - (3)</p> <p> </p> <p>where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.</p> <p> </p> <p>L, M and S denote L-cone, M-cone, S-cone excitations, respectively.</p> <p> </p> <p>Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.</p> <p> </p> <p>(Variable 2) staircase</p> <p> </p> <p>Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable.</p> <p>(staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)</p> <p> </p> <p>There are 5 fields:</p> <p>(i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle</p> <p> </p> <p>For each trial, the location of objects was defined in a following way.</p> <p>| 1 3 |</p> <p>| 2 4 |</p> <p> </p> <p>And each field stores following information for all trials in the staircase.</p> <p> </p> <p>(i) groundtruth</p> <p>Location of the target object</p> <p> </p> <p>(ii) response</p> <p>Location that the participant chose</p> <p> </p> <p>(iii) correct</p> <p>If the response was correct (1) or incorrect (0)</p> <p> </p> <p>(iv) magnitude</p> <p>Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).</p> <p> </p> <p>(v) Camera angle</p> <p>Camera angles assigned for four objects in each trial.</p> <p>This data and (i) groundtruth allow reconstruct of the exact image for each trial.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p>This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.</p> <p> </p> <p>1st channel: hue direction from 0 degree to 315 degree, 45 degree step</p> <p>2nd channel: magnitude from 1 to 100</p> <p>3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)</p> <p> </p>
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
<p>Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.</p>
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </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>
Using neural networks to model Main Belt Asteroid albedos as a function of their proper orbital elements
<p>This repository contains a copy of the following repository https://github.com/r-zachary-murray/Asteroid-Albedos. It contains weights for an ensemble of neural nets trained on the Asteroid Family Portal proper elements and NEOWISE albedos. These weights can be used to predict albedos of asteroids based of their proper elements. Example.ipynb contains an ipython notebook that shows how these predictions can be made.</p>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
<p>We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example.</p> <p>We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at <a href="https://github.com/cecileane/PhyloCoalSimulations.jl">https://github.com/cecileane/PhyloCoalSimulations.jl</a>.</p>
EuroSAT Model Zoo: A Dataset of Diverse Populations of Neural Network Models - EuroSAT
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on EuroSAT. All zoos with extensive information and code can be found at <a href="http://www.modelzoos.cc">www.modelzoos.cc</a>.</p> <p>This repository contains two types of model populations: the base model zoo ("eurosat_cnn_kaiming_uniform.zip"), as well as a collection of sparsified model zoos (filenames ending in "magn_XX.zip" or "ard.zip"). Zoos are trained with CNN models in configurations varying the seed only (seed), and sparsification is done through magnitude-based weight pruning ("magn_XX.zip") or varational dropout ("ard.zip").</p> <p>For more information on the zoos and code to access and use the zoos, please see <a href="http://www.modelzoos.cc">www.modelzoos.cc</a>.</p>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
CePNEM model analysis data and ANTSUN and microscopy neural network weights
<p><strong>Citation and publication</strong></p> <p>To cite this work or access the publication, please use the citation information listed here: <a href="https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation">https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation</a></p> <p> </p> <p>Initially published as preprint in:</p> <p>Brain-wide representations of behavior spanning multiple timescales and states in C. elegans</p> <p><strong>Adam A. Atanas*</strong>, <strong>Jungsoo Kim*</strong>, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell<br> bioRxiv 2022.11.11.516186; doi: <a href="https://doi.org/10.1101/2022.11.11.516186">https://doi.org/10.1101/2022.11.11.516186</a></p> <p>* Equal Contribution</p> <p> </p> <p><strong>Contents</strong></p> <p>1. deepnet-weights.tar.bz2</p> <p>contains the trained weights of the neural networks used in this project.</p> <p>3dunet_540nm_voxels: 3D U-Net for segmenting neurons</p> <p>head_detector_unet: finding worm head landmark used in ANTSUN registration</p> <p>head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets</p> <p>microscope_tracker: detecting keypoints for online tracking on the microscope</p> <p>behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification</p> <p>2. data files</p> <p>ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (<a href="https://github.com/flavell-lab/AtanasKim-Cell2023/">https://github.com/flavell-lab/AtanasKim-Cell2023/</a>) on using these datasets.</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
Photometric Completeness Modelled With Neural Networks
<p>Neural networks associated with the paper "Photometric Completeness Modelled With Neural Networks" (Harris & Speagle 2023).</p> <p>Neural networks (`nn_clf_[...].joblib`) are included for all possible parameter combinations and trained over various numbers of artificial star tests (`ngc[...].dat`). See the example notebook (`nn_example.ipynb`) for detailed explanations of the files, their contents, and some usage examples.</p>
RIDDLER: An updated astrochemical network for modeling TMC-1
<p>The input files in RIDDLER.zip are those used in Byrne et al. 2023 (in revision) for modeling the propargyl radical and related species in TMC-1. This network is based on the <a href="https://arxiv.org/abs/1503.01594">kida.uva.2014</a> network and intended to be used with the three-phase gas-grain code NAUTILUS. A number of updates have been made by the GOTHAM collaboration (<a href="https://arxiv.org/abs/1801.04228">McGuire et al. 2018</a>, <a href="https://arxiv.org/abs/2008.12345">Xue et al. 2020</a>, <a href="https://arxiv.org/abs/2008.12349">McGuire et al. 2020</a>, <a href="https://arxiv.org/abs/2009.13546">McCarthy et al. 2021</a>, <a href="https://arxiv.org/abs/2103.09984">McGuire et al. 2021</a>, <a href="https://www.nature.com/articles/s41550-020-01261-4">Loomis et al. 2021</a>) as well as the most recent update targeting propargyl radical chemistry. The files in GOTHAM-DR1.zip correspond to the previous GOTHAM astrochemical network, with which comparisons have been made.</p>
Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks
<p>Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.</p>
Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"
<p>Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: <a href="https://doi.org/10.5281/zenodo.8387558">10.5281/zenodo.8387558</a></p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
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A mathematical model to predict network growth in physarum polycephalum as a function of extracellular matrix viscosity, measured by a novel viscometer
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An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks
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Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
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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.