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310 results for “State Model”
Novel disease state model finds most juvenile green turtles develop and recover from fibropapillomatosis
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Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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The Current State and 125 Kyr History of Permafrost in the Kara Sea Shelf: Modeling Constraints
<p>The database for modeling the evolution of permafrost in the Kara shelf for the past 125 kyr presented in the manuscript <a href="https://www.the-cryosphere-discuss.net/tc-2019-112/">https://www.the-cryosphere-discuss.net/tc-2019-112/</a> </p>
TESTAR State Model extracted while executing MyThaiStar as web system under test
<p>TESTAR extracted State Model datasets with TESTAR tool using MyThaiStar web application as System Under Test (SUT). This State Models has been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>MyThaiStar (<a href="https://github.com/devonfw/my-thai-star">github.com/devonfw/my-thai-star</a>) is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will use the Document Object Model (DOM) information extracted from MyThaiStar SUT, to generate and save a TESTAR State Model in the OrientDB graph database. This model contains information about the Widgets, States and Actions, that were found in the SUT.<br> <br> - MyThaiStar.json.gz: JSON file exported from OrientDB that contains a database with the TESTAR State Model. It can be imported into OrientDB using the TESTAR tool, to analyze and interact with the State Model.<br> <br> - ArtefactStateModel_MyThaiStar_2020.1_zpnffj5c3407972370_2020-06-15_12h14m24s: for DECODER project purposes, the knowledge extracted with TESTAR in the generation of the State Model has been summarized and referenced in an artifact JSON file to be adapted to PKM input requirements.</p>
A non‐steady state model based on dual nitrogen and oxygen isotopes to constrain moss nitrate uptake and reduction
<p><span><span>Epilithic mosses are early colonizers of the terrestrial biosphere, constitute a special ecosystem regulating rock-atmosphere interactions, and may be more restricted in their nitrogen (N) supply than other mosses.<sup> </sup>Terrestrial mosses can take up nitrate (NO<sub>3</sub><sup>-</sup>), a major form of bioavailable N, from soil substrates. However, the importance of substrate NO<sub>3</sub><sup>-</sup> relative to atmospheric NO<sub>3</sub><sup>-</sup> remains unclear in moss NO<sub>3</sub><sup>-</sup> utilization. This has prevented the understanding of moss NO<sub>3</sub><sup>-</sup> dynamics and its responses to environmental N loadings. Here we investigated the monthly concentrations, δ<sup>15</sup>N, and δ<sup>18</sup>O of NO<sub>3</sub><sup>-</sup> in four epilithic moss species from August, 2006 to August, 2007 in Guiyang, southwestern China. We developed a non-steady state isotope mass-balance model based on dual N and O isotopes to evaluate fractional contributions of atmospheric NO<sub>3</sub><sup>-</sup> (<i>Ф</i><sub>atm</sub>) and soil NO<sub>3</sub><sup>-</sup> (<i>Ф</i><sub>soil</sub>), moss NO<sub>3</sub><sup>-</sup> uptake flux (<i>F</i><sub>influx</sub>), moss NO<sub>3</sub><sup>-</sup> reduction flux (<i>F</i><sub>reduction</sub>), and the percentage of NO<sub>3</sub><sup>-</sup> reduction in total NO<sub>3</sub><sup>-</sup> uptake of mosses (expressed as <i>f</i><sub>reduced</sub>). Monthly <i>Ф</i><sub>soil</sub> values averaged 53 ± 13% and monthly <i>f</i><sub>reduced</sub> values averaged 50 ± 35%. Both monthly<i> F</i><sub>reduction</sub> and <i>f</i><sub>reduced</sub> values increased with monthly <i>F</i><sub>influx</sub> values, particularly when <i>Ф</i><sub>soil</sub> values were higher than <i>Ф</i><sub>atm</sub> values. However, the amount of annual NO<sub>3</sub><sup>-</sup> reduction (219.7 ± 30.5 μg-N/g, dw) accounted for only 1.0 ± 0.2% in bulk N of mosses. We conclude that half of NO<sub>3</sub><sup>-</sup> in epilithic mosses is derived from soil NO<sub>3</sub><sup>-</sup> and that NO<sub>3</sub><sup>-</sup> uptake from soil induces moss NO<sub>3</sub><sup>-</sup> reduction, but the total NO<sub>3</sub><sup>-</sup> assimilation contributed a low fraction to total N of study mosses. These findings are important for understanding N sources and dynamics in terrestrial mosses.</span></span></p>
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Exact Spin-Boson-Model Tunneling Dynamics with Time Dependent Variation Matrix Product States (TDVMPS). Barrier height and temperature parameter space
<p>Spin-Boson tunnelling data acquired using the T-TEDOA method for Time-Dependent-Variational-Matrix-Product-States (TDVMPS) accompanying the paper <a href="https://doi.org/10.3389/fchem.2020.600731">https://doi.org/10.3389/fchem.2020.600731</a>.</p> <p> </p>
State-space model for Svalbard ptarmigan
<p>To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point-transect distance-sampling during 2005-2019 and developed two types of predictions: 1) <i>explanatory predictions</i> to quantify the effect of potential drivers of ptarmigan population dynamics, and 2) <i>anticipatory predictions</i> to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved non-linearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.</p>
Multi-state modeling of the PhoQ two-component system
<p>This directory contains the input data, protocols and output model for the modeling of the PhoQ homodimer, using cysteine crosslinking and multi-state Bayesian modeling in IMP.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/phoq or the README file.</p>
Proper modelling of ligand binding requires an ensemble of bound and unbound states
<p>Crystallographic data for structures described in the manuscript "Proper modelling of ligand binding requires an ensemble of bound and unbound states".</p>
Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet
This dataset repository contains the dataset that supports the study of Minder et al. (2024): Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet. The datasets in this archive include output from distributed simulations of the WRF Hydro model, output from point simulations with the Noah-MP land surface model, and manual and automated snow water equivalent (SWE) observations at New York State Mesonet (NYSM) station locations. The formatting and file naming conventions for each dataset are described in detail in the minder_etal_NWM_snow_dataset_ReadMe.pdf file.
On the computation of stable coupled state-space models for dynamic substructuring applications
<p>This paper aims at introducing a methodology to compute stable coupled state-space models for dynamic substructuring applications by introducing two novel approaches targeted to accomplish this task: (a) a procedure to impose Newtons's second law without relying on the use of undamped RCMs (residual compensation modes) and (b) a novel approach to impose stability on unstable coupled state-space models. The enforcement of stability is performed by dividing the unstable model into two different models, one composed by the stable poles (stable model) and the other composed by the unstable ones (unstable model). Then, the poles of the unstable state-space model are forced to be stable, leading to the computation of a stabilized state-space model. If this model is composed by real poles, it should be divided into two different ones, one composed by the pairs of complex conjugate poles and the other composed by the real poles. Afterwards, to make sure that the Frequency Response Functions (FRFs) of the stabilized model well match the FRFs of the unstable model, the Least-Squares Frequency Domain (LSFD) method is exploited to update the modal parameters of the stabilized model composed by the pairs of complex conjugate poles. The validity of the proposed methodologies is presented and discussed by exploiting experimental data. Indeed, by exploiting the FRFs of a real system, accurate state-space models respecting Newton's second law are computed. Then, decoupling and coupling operations are performed with the identified state-space models, no matter the models resultant from the decoupling/coupling operations are unstable. Stability is then imposed on the computed unstable coupled model by following the approach proposed in this paper. The methodology proved to work well on these data. Moreover, the paper also shows that the coupled state-space models obtained using this methodology are suitable to be exploited in time-domain analyses and simulations.</p>
Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
<p>Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.</p>
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation
<h2>Overview</h2> <p>This dataset supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). This dataset includes the data used in training the model and inferring the sea ice parameters, as well as the outputs from that training process. The software used to perform the training is in Python and uses the Keras and Tensorflow software. See the draft manuscript for full details of this data.</p> <p>The code used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10013542">https://doi.org/10.5281/zenodo.10013542</a></p> <p>The data used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10033377">https://doi.org/10.5281/zenodo.10033377</a></p> <h2>Training data </h2> <h3>Observation space training and ancillary data</h3> <p>Training is done at the location of AMSR2 superobservations (superobs) over ocean with less than 1% land contamination and polewards of 45 degrees latitude, between 1st July 2020 and 30th June 2021. There are 64,184,021 superobs used. A superob is the average of all raw JAXA level 1B observations from one orbit falling into a grid box on an approximately constant area (reduced Gaussian) grid at approximately 40 km by 40 km resolution (noting that polar regions can thus have up to around 7 superobs per day). The superobs have been computed using the field of view central locations for each channel as derived from the JAXA level 1B data. A subset of 10 of the AMSR2 channels is used, from 10 GHz, V polarised, to 89 GHz, H polarised.</p> <p>At each superob location, the relevant fields from the ECMWF 12 hour 'background' forecast are interpolated to the observation time and location. The atmosphere is represented indirectly by the relevant radiative transfer terms from a scattering radiative transfer model. The sea ice concentration from the ECMWF OCEAN5 analysis is included as a validation reference but is not used in the training itself, except to provide a monthly mean first guess to speed up the training. Each field is provided in a separate netCDF file:</p> <ul> <li>field_v2_JULIAN_DAY.nc - superob time in days since 12 UTC on Nov 24th 4714 BC on the proleptic Gregorian calendar</li> <li>field_v2_LAT.nc - superob central latitude in degrees</li> <li>field_v2_LON.nc - superob central longitude in degrees</li> <li>field_v2_IGRID.nc - corresponding grid number on the map grid used in this work (see below)</li> <li>field_v2_OBSVALUE.nc - observed superob brightness temperature at each of 10 AMSR2 channels.</li> <li>field_v2_TSFC.nc - skin temperature computed by the ECMWF forecast model</li> <li>field_v2_WINDSPEED10M.nc - 10m wind speed computed by the ECMWF forecast model</li> <li>field_v2_EMIS_WATER.nc - Ocean water surface emissivity at 10 AMSR2 channels, simulated from the ECMWF forecast fields using the FASTEM-6 model</li> <li>field_v2_CLOUD_FRACTION.nc - Effective cloud fraction used in the atmospheric radiative transfer model at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC_CLD.nc - Surface to space transmittance in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TUP_CLD.nc - Upwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TDOWN_CLD.nc - Downwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC.nc - Equivalently for the clear column</li> <li>field_v2_TUP.nc - Equivalently for the clear column</li> <li>field_v2_TDOWN.nc - Equivalently for the clear column</li> <li>field_v2_SEAICE.nc - Sea ice concentration from the ECMWF OCEAN5 analysis, for validation only (not used in training)</li> </ul> <h3>Grid space data: initial data for training; validation sea ice data</h3> <p>A number of properties are provided to the hybrid physical-empirical model that is being trained, on a special map grid defined in this project, including all 62,499 of the reduced Gaussian 40km grid points that have at least one superob at some point during the year of training data. These are:</p> <ul> <li>ifs_seaice_initials_year.nc - sea ice concentration from OCEAN5, monthly averaged on the grid, and then provided on all days of the relevant month as initial conditions (technically, first guess) for the training. This includes an additional day before the beginning of the training, used for time-lagging (see draft paper).</li> <li>ifs_tsfc_year_dailyx.nc - skin temperature from ECMWF forecast fields at observation locations, averaged onto the daily grid, to help provide constraints on the likelihood of sea ice as part of a sea ice loss function.</li> </ul> <p>For diagnostic and validation purposes, the ECMWF OCEAN5 analysis is also provided on the grid:</p> <ul> <li>ifs_seaice_year.nc - sea ice concentration from OCEAN5 at observation locations, averaged onto the daily grid</li> </ul> <p>All these fields are provided on the following dimensions:</p> <ul> <li>LON - the longitude of the grid point in degrees</li> <li>DAY - the day through the training year (0-364, 1st July 2020 to 30th June 2021) or through the training year extended forward by one day (30th June 2020) for the sea ice (0-365). In practice the days are offset by 3 hours from the UTC day to match the ECMWF data assimilation windows, which start at 21 UTC the day before.</li> </ul> <p>The latitude is also provided</p> <ul> <li>LAT - the latitude of the grid point in degrees</li> </ul> <p>Note that the observation location IGRID is on the custom grid of the ML model that is defined implicitly in these gridded files. The LON and LAT vectors in these files are the longitude and latitude points of the grid and are of 62499 in length. The IGRID number for an observation is the index into these arrays from 0-62498.</p> <h2>Outputs from training</h2> <p>The following files are the output and diagnostics from the year-long training. The python code and the draft paper are the primary documentation for these:</p> <ul> <li>models_year.nc - settings of the model are recorded here, along with the trained values of the smaller empirical components/layers within the hybrid model. For example, the layer weights of the wind speed bias correction, the observation space bias correction, and the empirical surface emissivity model are recorded here. The values of the loss function at each epoch are also recorded here.</li> <li>properties_year.nc - trained values of each of 3 empirical properties of sea ice on the map grid (3 properties by 62499 locations by 365 days from 1st July 2020)</li> <li>seaice_year.nc - inferred values of sea ice fraction on the map grid (62499 locations by 365 days from 1st July 2020, discarding the additional day at the start)</li> <li>tbsim_year.nc - simulated AMSR2 brightness temperatures from the trained network</li> <li>tbsim_initial_year.nc - simulated AMSR2 brightness temperatures using the untrained network</li> </ul> <p>The longitude and latitude of the map grid is found in any of the initial data files described in the previous section. The days are 0-364 corresponding to 1st July 2020 to 30th June 2021.</p> <h3>Sea ice surface emissivity at grid locations</h3> <p>A packaged version of the sea ice surface emissivity is provided at grid locations, alongside the surface emissivity model, the sea ice concentration and the four inputs to the model, i.e. the normalised skin temperature and the three empirical variables:</p> <ul> <li>emissivity_grid_year.nc</li> </ul> <p>Note that in the training, the surface emissivity is computed at observation locations and has not been stored due to memory limitations. For easier comparison to other datasets, the surface emissivity has been recomputed on grid locations in this package, using the year-long trained emissivity model and its trained inputs. The sea ice surface emissivity is only physically meaningful for sea ice concentrations above around 0.25. Also be aware of the "hole at the pole" which is the small region of the Arctic ocean that is sometimes not covered by an AMSR2 overpass, and which is found from 88 degrees N. On days where the hole or part of the hole exists, the sea ice emissivity on the grid is not valid at these locations. These locations can be identified by having all values of the empirical properties zero (because the empirical properties were never constrained by any observations on that day, and remain at their initial values before training).</p> <h2>Sensitivity tests</h2> <p>Extensive sensitivity tests were carried out, as described in the appendices of the draft paper and as documented in the Python code, using the month of August 2020 as an example. These required equivalent month-long training and initial data similar to those described above, but all observation space fields are contained within the same file in this case. Output files follow similar principles to those described above. The full package is provided as a tar file:</p> <ul> <li>sensitivity.tar</li> </ul> <p>This contains the training and initial files:</p> <ul> <li>amsr2_v2_202008.nc</li> <li>ifs_tsfc_dailyx_202008.nc</li> <li>ifs_seaice_202008.nc</li> </ul> <p>as well as directories containing the trained model outputs and diagnostics at each of the sensitivity tests, using the same formats as described for the yearly training, with these names:</p> <ul> <li>nprop - number of empirical properties</li> <li>epoch - number of epochs</li> <li>deep - configuration of the empirical sea ice emissivity model, including multiple layers of nonlinear dense neural network</li> <li>bseaice - background error for the sea ice physical bounds background error (loss) term</li> <li>bemis - background error for the sea ice emissivity background error (loss) term</li> <li>bbias - background error for the bias correction background error (loss) term</li> <li>batchsize - batch size used in training</li> <li>bbatchsize - extended epochs testing of batch size used in training</li> </ul> <h2>Licensing</h2> <p>This data product is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>You are free to:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Under the following terms:</p> <ul> <li>You must give appropriate credit (attribution) to ECMWF as outlined below, provide a link to the licence, and indicate if changes were made.</li> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits.</li> </ul> <p>The following wording shall be attached to the use of this ECMWF data product: </p> <ol> <li>Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source <a href="http://www.ecmwf.int/">www.ecmwf.int </a>and <a href="https://doi.org/10.5281/zenodo.10009497">https://doi.org/10.5281/zenodo.10009497</a></li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, an indication if the material has been modified and an indication of previous modifications.</li> <li>DOI: 10.5281/zenodo.10009498</li> </ol> <p>Original data for this value-added product was provided by Japan Aerospace Exploitation Agency (JAXA). Specifically, this dataset builds on the Advanced Microwave Scanning Radiometer 2 (AMSR2) level 1B data available from the JAXA G-Portal, https://gportal.jaxa.jp/gpr/, which has the following attribution and licensing:</p> <ol> <li>Give credit for the original data to JAXA, i.e. "Original data for this value added data product was provided by Japan Aerospace Exploration Agency"</li> <li>DOI for original JAXA data is L1B-Brightness temperature (TB) GCOM-W/AMSR2 L1B Brightness Temperature: <a href="https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h">https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h</a></li> <li>Original terms of data service from JAXA, with highlighted extracts: <ul> <li><a href="https://gportal.jaxa.jp/gpr/index/eula?lang=en">https://gportal.jaxa.jp/gpr/index/eula</a> <ol> <li>The user is entitled to use G-Portal data free of charge without any restrictions (including commercial use) except for the condition about acknowledgement of data credit as stipulated in Article 7.(2). (see above)</li> <li>JAXA is collecting results (papers, theses, reports, etc.) using G-Portal data. If you have any results using G-Portal data, please mail/e-mail a copy of the result to G-Portal Support Desk (Contact Information written at the end of the Terms of Use). We appreciate your cooperation very much.</li> </ol> </li> </ul> </li> </ol>
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
<p>We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials is a nascent field with substantial fragmentation largely driven by the great variety of datasets used to develop machine learning models. This fragmentation makes comparing the performance and generalizability of different methods difficult, thereby hindering overall research progress in the field. Building on top of open-source datasets, including large-scale datasets like the OpenCatalyst Project, OQMD, NOMAD, the Carolina Materials Database, and Materials Project, the MatSci ML benchmark provides a diverse set of materials systems and properties data for model training and evaluation, including simulated energies, atomic forces, material bandgaps, as well as classification data for crystal symmetries via space groups. The diversity of properties in MatSci ML makes the implementation and evaluation of multi-task learning algorithms for solid-state materials possible, while the diversity of datasets facilitates the development of new, more generalized algorithms and methods across multiple datasets. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties, such as energy and forces. Using MatSci ML, we evaluate the performance of different graph neural networks and equivariant point cloud networks on several benchmark tasks spanning single task, multitask, and multi-data learning scenarios. Our open-source code is available at https://github.com/IntelLabs/matsciml.</p>
The magnetic recording stability of vortex state irregularly shaped natural iron oxides: raw data, processing and micromagnetic modelling results
<p>Magnetic minerals, especially those existing within the vortex domain state, serve as the primary natural archives of ancient magnetic fields. In this investigation, we introduce an innovative method to examine the magnetic stability of remanence-bearing minerals. This method involves integrating <strong>Synchrotron-based Ptychographic X-ray Computed Nano-tomography (PXCT)</strong> <strong>with micromagnetic modelling</strong>. PXCT, a tomographic technique, is a non-destructive resource, which enables its application to valuable (unique) samples. When applied to a microscopic sample of weakly magnetic carbonate rock, PXCT revealed numerous nanoscopic grains of magnetite/maghemite, each exhibiting diverse morphologies, alongside various non-magnetic minerals present in the rock matrix. Subsequently, micromagnetic models were employed to predict the properties of these grains and investigate the potential impacts of irregular morphologies.</p>
On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models
<p>In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.</p>
Prediction of COVID-19 case numbers using state-space modeling and wastewater virus datasets
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Supplementary files from: Improving inference and avoiding over-interpretation of hidden-state diversification models: Specialized plant breeding has no effect on diversification in frogs
<p>The hidden-state speciation and extinction (HiSSE) model helps avoid spurious results when testing whether a character affects diversification rates. However, care must be taken to optimally analyze models and interpret results. Recently, Tonini et al. (2020; TEA hereafter) studied anuran (frog and toad) diversification with HiSSE methods. They concluded that their focal state, breeding in phytotelmata, increases net diversification rates. Yet this conclusion is counterintuitive, because the state that purportedly increases net diversification rates is 14 times rarer among species than the alternative. Herein I revisit TEA's analyses and demonstrate problems with inferring model likelihoods, conducting post-hoc tests, and interpreting results. I also re-evaluate their top models and find that diverse strategies are necessary to reach the parameter values that maximize each model's likelihood. In contrast to TEA, I find no support for an effect of phytotelm breeding on net diversification rates in Neotropical anurans. In particular, even though the most highly supported models include the focal character, averaging parameter estimates over hidden states shows that the focal character does not influence diversification rates. Finally, I suggest ways to better analyze and interpret complex diversification models – both state-dependent and beyond – for future studies in other organisms.</p>
TESTAR State Model
<p>TESTAR extracted State Model dataset with TESTAR tool using CODEO desktop application as System Under Test (SUT). This State Model has been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>CODEO (https://www.sysgo.com/codeo) is an Eclipse-based IDE that facilitates embedded applications development, by providing all the components software engineers need. The architecture configurations can be esaily done through the CODEO graphical tools.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will use the Document Object Model (DOM) information extracted from MyThaiStar SUT, to generate and save a TESTAR State Model in the OrientDB graph database. This model contains information about the Widgets, States and Actions, that were found in the SUT.</p> <p>- ArtefactStateModel_codeo_74_1dwbc1338198529501_2021-12-14_13h38m39s.zip: For DECODER project purposes, the knowledge extracted with TESTAR in the generation of the State Model has been summarized and referenced in an artifact JSON file to be adapted to PKM input requirements.</p>
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