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171 results for “Model inference”
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference
<p>The current knowledge about how protein structures influence sequence evolution is rarely incorporated into substitution models adopted for phylogenetic inference, which are commonly based on independent with the same substitution process and ignore the known variation of the evolutionary rates across sites with different structural properties. In previous works, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a strong selective force. Here we present novel structurally constrained substitution models (Str-CPE) based on Julián Echave's model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) of the wild-type protein, and better predict the observed substitution rates. Next, we combine Str-CPE and Stab-CPE models to obtain structure and stability constrained substitution models (SSCPE) that fit the empirical MSAs even better. Importantly, these SSCPE models present a relevant improvement of the phylogenetic likelihood for all ten protein families that we analyzed with the program RAxML-NG. We implemented the SSCPE models in the program Prot evol, freely available at <a href="https://github.com/ugobas/Prot_evol">https://github.com/ugobas/Prot_evol</a>.</p>
Fig. 1 in Echinoderm model systems, homology, and phylogenetic inference: comment and reply to Paul (2021)
Fig. 1. Tree comparison between two phylogenetic inference methods with bootstrap support at the nodes. A. Phylogenetic hypothesis from Paul (2021) inferred via maximum parsimony. B. Phylogenetic hypothesis inferred via maximum likelihood.
Fig. 5 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 5 BEAST phylogenetic tree based on the COI sequences. Node values indicate divergence estimated in MYA
Fig. 7 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 7 Areas of climatic stability over time periods from the LGM through the present, based on summed climatic suitability models for the LGM, mid-Holocene, and present day for three differed GCMs. Stability increase from red to yellow color. White-filled areas show the
Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes - model outputs
<p>This dataset contains the model output corresponding to the paper entitled "Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes" submitted to Paleoceanography. For the description of the model and simulations we refer to this article.</p> <p>Model outputs:</p> <p>The outputs of two series of simulations can be found in the files.</p> <p>1) MIS experiments:</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) for each interglacial are in: iloveclim_CO2_MIS.txt</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_PI_CC.nc for the pre-industrial</p> <p>iloveclim_MISXX.nc for MISXX (XX being 1,5,7,9,11,13,17 or 19)</p> <p> </p> <p>2) Sensitivity experiments</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) are in: iloveclim_CO2_sensitivity_expe.txt</p> <p>- Streamfunction values (Sv) are in:</p> <p>iloveclim_PI-CC_stream.nc for the pre-industrial</p> <p>iloveclim_MIS17_CC_stream.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv_stream.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv_stream.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4_stream.nc for the simulation with the sinking of brines</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_MIS17-CC.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4.nc for the simulation with the sinking of brines</p>
OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference
<h3><strong>About</strong></h3> <p>OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference. The work follows the "LMs-as-KBs" literature but focuses on conceptualised knowledge extracted from formalised KBs such as the OWL ontologies. Specifically, the subsumption inference (SI) task is introduced and formulated in the Natural Language Inference (NLI) style, where the sub-concept and the super-concept involved in a subsumption axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively. The sampled axioms are verified through ontology reasoning. The SI task is further divided into Atomic SI and Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts. Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic SI datasets and two Complex SI datasets.</p> <p> </p> <table> <tbody> <tr> <th>Dataset Source</th> <th>#Concepts</th> <th>#EquivAxioms</th> <th>#Datasets(Train/Dev/Test)</th> </tr> </tbody> <tbody> <tr> <td>Schema.org</td> <td>894</td> <td>N/A</td> <td> <p>Atomic SI: 808/404/2, 830</p> </td> </tr> <tr> <td>DOID</td> <td>11,157</td> <td>N/A</td> <td> <p>Atomic SI: 90,500/11,312/11,314</p> </td> </tr> <tr> <td>FoodOn</td> <td>30,995</td> <td>2,383</td> <td> <p>Atomic SI: 768,486/96,060/96,062</p> <p>Complex SI: 3,754/1,850/13,080</p> </td> </tr> <tr> <td>GO</td> <td>43,303</td> <td>11,456</td> <td> <p>Atomic SI: 772,870/96,608/96,610</p> <p>Complex SI: 72,318/9,040/9,040</p> </td> </tr> <tr> <td>MNLI</td> <td>N/A</td> <td>N/A</td> <td> <p>biMNLI: 235,622/26,180/12,906</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Citation</strong></h3> <p>The relevant paper has been accepted at Findings of ACL 2023: <a href="https://aclanthology.org/2023.findings-acl.213/">https://aclanthology.org/2023.findings-acl.213/</a>.</p> <pre>```<br>@inproceedings{he2023language, title={Language Model Analysis for Ontology Subsumption Inference}, author={He, Yuan and Chen, Jiaoyan and Jimenez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={3439--3453}, year={2023} }<br>```</pre> <h3><strong>Links</strong></h3> <ul> <li>See instructions at: <a href="https://krr-oxford.github.io/DeepOnto/ontolama/">https://krr-oxford.github.io/DeepOnto/ontolama/</a></li> <li>We have made available a convenient access of these datasets through Huggingface: <a href="https://huggingface.co/datasets/krr-oxford/OntoLAMA">https://huggingface.co/datasets/krr-oxford/OntoLAMA</a></li> <li>The arxiv version is available at: <a href="https://arxiv.org/abs/2302.06761">https://arxiv.org/abs/2302.06761</a></li> </ul> <h3><strong>Contact</strong></h3> <p>Yuan He (<code>yuan.he(at)cs.ox.ac.uk</code>)</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>
Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936
<p>Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p> <p>------------------------------------------------------------------------------------------------</p> <p>Responsible Researchers:<br> - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain<br> fuensant@ucm.esResponsible Researchers: </p> <p>- José Arnoso Sampedro, Instituto de Geociencias (CSIC-UCM), Spain<br> jose_arnoso@csic.es</p> <p> </p> <p><br> >> The use of this data set is limited to academic or research purposes and it have to be referenced</p> <p><br> Zone:Cumbre Vieja (La Palma Island, Spain)<br> Geodetic Coordinates Datum WGS84<br> Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3)</p> <p>The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.</p>
The equation of state for neutron star matter has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.
<p>The equation of state for matter in neutron stars has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.</p> <p>----------------------------<br> Dr. Tuhin Malik<br> Department of Physics, University of Coimbra<br> tm@uc.pt<br> Date: 22 Apr 2023<br> -----------------------------<br> The high density behavior of nuclear matter is analyzed within a relativistic mean field description with non-linear meson interactions. To assess the model parameters and their output, a Bayesian inference technique is used. The Bayesian setup is limited only by a few nuclear saturation properties, the neutron star maximum mass larger than 2 M$_\odot$, and the low-density pure neutron matter equation of state (EOS) produced by an accurate N$^3$LO calculation in chiral effective field theory. Depending on the strength of the non-linear scalar vector field contribution, we have found three distinct classes of EOSs, each one correlated to different star properties distributions. If the non-linear vector field contribution is absent, the gravitational maximum mass and the sound velocity at high densities are the greatest. However, it also gives the smallest speed of sound at densities below three times saturation density. On the other hand, models with the strongest non-linear vector field contribution, predict the largest radii and tidal deformabilities for 1.4 M$_\odot$ stars, together with the smallest mass for the onset of the nucleonic direct Urca processes and the smallest central baryonic densities for the maximum mass configuration. {These models have the largest speed of sound below three times saturation density, but the smallest at high densities, in particular, above four times saturation density the speed of sound decreases approaching approximately $\sqrt{0.4}c$ at the center of the maximum mass star. On the contrary, a weak non-linear vector contribution gives a monotonically increasing speed of sound.} {A 2.75 M$_\odot$ NS maximum mass was obtained in the tail of the posterior with a weak non-linear vector field interaction. This indicates that the secondary object in GW190814 could also be an NS. {The possible onset of hyperons and the compatibility of the different sets of models with pQCD are discussed. It is shown that pQCD favors models with a large contribution from the non-linear vector field term or which include hyperons.}}</p> <p>The article e-Print: <a href="https://arxiv.org/abs/2301.08169">2301.08169</a></p> <p>We release model parameters, its nuclear saturation properties, equation of state, and TOV solutions derived from Bayesian Inference with Prior Set 0, 1, 2, and 3. We also share Set 0 with Hyperon. <br> <br> For every Set, our data release packet contains four CSV files, namely "set{X}_prop.csv", "set{X}_eos.csv", "set{X}_tov.csv", and "set{X}_cs2.csv", where X in [0,1,2,3 and 0_hyp].<br> <br> set{X}_prop.csv:<br> The file contains the parameters for the RMF model, as well as a few NS properties and nuclear saturation properties. It has the following columns:<br> model name,gs,gv,gr,B,C,xi,lam,rho0,e0,k0,q0,z0,jsym0,lsym0,<br> ksym0,qsym0,zsym0,m_max,r_max,r14,lam14,cs2_max, ec,rhoc,rho_durca.<br> It is to be noted that the parameter B and C are the 10^3*b and 10^3 c (see article for details). <br> <br> set{X}_eos.csv:<br> For those models in set{X}_prop.csv, it is the NS matter EOS file. It has the following columns: model name, baryon number density, energy density and pressure. The units for baryon number density is fm-3 and MeV/fm3 is for both energy density and pressure. The EOS is for the core only. The crust is not added. <br> <br> set{X}_tov.csv:<br> For those models in set{X}_prop.csv, it is the TOV solution. It has the following columns: model name, ns radius (km), ns mass (msun), and dimensionless tidal deformability lambda. </p> <p>set{X}_cs2.csv:<br> For those models in set{X}_prop.csv, it is the square of the speed of sound over density. It has the following columns: model name, number density fm-3, and square of the speed of sound c2. <br> -------------------------------------------------------------------------</p>
Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
<p>When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package <em>kitchen</em> to perform CKS for generic application of the technique.</p>
Experimental Results for "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models"
<p>This package contains the raw data / logs (fetched from WandB) for the experiments of the following publication:</p> <p>O. Arenz, P. Dahlinger, Z. Ye, M. Volpp, and G. Neumann. A unified perspective on natural gradient variational inference with gaussian mixture models. Transactions on Machine Learning Research, 2023. URL: <a href="https://openreview.net/forum?id=tLBjsX4tjs">https://openreview.net/forum?id=tLBjsX4tjs</a>.</p> <p> </p>
Site-specific structure and stability constrained substitution models improve phylogenetic inference
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Data from: Phylogenetic biogeography inference using dynamic paleogeography models and explicit geographic ranges
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Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
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Inferring core processes using stochastic models of the geodynamo
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