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414 results for “Generative Model”
Figure 1: Asymmetric representation for the ¯rst four generations, in its elec- trical equivalent-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>These changes<br> might not seem signi¯cant, but one should recall that they are originated by<br> the symmetric geometry of the respiratory tree. However, when asymmetry<br> is considered, one deals with several homothety factors, i.e. as schematically<br> drawn in ¯gure 1.</p>
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Transfer learning with generative models for object detection on limited datasets
<p>The provided datasets are used for the analysis in the work "Transfer learning with generative models for object detection on limited datasets" (https://doi.org/10.1088/2632-2153/ad65b5). The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine. The provided datasets are built with the help of Gligen and the already existing NuImages, Ozfish and Deepfish datasets. The file "CarGenerated.zip" contains images generated with Gligen and with provided bounding boxes around cars in an urban environment. The file "fishes_on_bkg.zip" provides fish images generated with fishes from Deepfish inpainted with Gligen on generated backgrounds. The file "fish_text.zip" contains images completely generated with Gligen containing fishes with annotated bounding boxes. Finally, the file "oz_masked_512.zip" contains a simpler dataset of copy paste images of Deepfish fishes on Ozfish backrounds. All the files contains the images saved in different folders for training and validation, plus an index file called gt_fish.csv for the bounding boxes.</p>
Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>Formally, the theory section (see line 11) contains only static data. Here the educator places<br> theoretical information about the learned concept in HTML format.<br> The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix<br> of static data and dynamic values referring the previously computed symbol values. The semantic of<br> these sections is to create a dynamic content to be presented to the learner. Formalisms for specific<br> details are also present, like:<br> i) correctness - because we need to know which is the correct answer if they are multiple;<br> ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and<br> showed to the learner only in certain conditions.</p>
Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The proposed model is defined using the EBNF meta-language resulting in the concise<br> grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and<br> space reasons.<br> Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question;<br> v) answers; vi) feedbacks. Next, we will explain each section in details.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 10. Room model generated with Autodesk 123D Catch - the 3D model (screen capture from GLC Player)
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 9. Room model generated with Autodesk 123D Catch - the 2D model
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
Structural modelling results to accompany the paper "Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing: an update"
<p>This repository contains the results of modelling missense mutants in KRAS, NRAS and BRAF observed in our study in the corresponding protein structures. Modelling was performed using FoldX.</p>
Logistics Transport Label Data - 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts'
<p>Example dataset described in ICMLA2019 Paper 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts' (Dörr, Brandt, Meyer, Pouls).</p>
Evaluation data used in "An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists"
<p>This repository contains all data of the evaluation questionnaire used to assess modifications in pupils’ perceptions of same water resources concepts and science and scientist resulting from the application of OH-Kids outreach model in six Mexican primary schools (n=344 pupils).</p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Generation of tissue-like models of human bilayered tissues functionalized with olive oil components"
<p>This file contains the raw dataset generated in the work entitled "GENERATION OF NOVEL TISSUE-LIKE MODELS OF HUMAN BILAYERED TISSUES FUNCTIONALIZED WITH BIOACTIVE COMPONENTS OBTAINED FROM OLIVE OIL". These results correspond to the quantification of the histological results obtained in this work.</p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Online example database generated representing nuclear astrophysics models predictions of correlations between stable/stable abundances of specific isotopes.
<p>Library of figures created using the SIMPLE code (Stellar Interpretation for Meteoritic data and PLotting). The SIMPLE stellar database includes 18 core-collapse supernova models with 3 different initial masses of 15, 20, 25 solar masses, all of solar metallicity and non-rotating stars. The 6 sets are the following:</p> <ul> <li>Rauscher et al. 2002 [Ra02]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract">https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract</a>),</li> <li>Pignatari et al. 2016 [Pi16]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract">https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract</a>),</li> <li>Sieverdin et al. 2018 [Si18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract</a>),</li> <li>Limongi & Chieffi 2018 [LC18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract</a>),</li> <li>Ritter et al. 2018 [Ri18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract">https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract</a>),</li> <li>Lawson et al. 2022 [La22]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract</a>)</li> </ul> <p>The figures can be divided into two types. The first shows the structure of the ejecta and the abundance of the selected isotopes. The layers are automatically detected using SIMPLE based on the abundances of the main fuels (H-1, He-4, C-12, O-16, Ne-20, Si-28) from the supernova model ejecta. The code names the different layers based on the schematic diagram in Schofield et al. 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract</a>). <br>Ni and Fe isotopes are plotted in the figures. The abundances shown include the radiogenic contribution from unstable isotopes.</p> <p>The SIMPLE code is designed to compare stellar data with measurements from meteorites. To achieve this, abundances in mass fractions need to be converted into isotopic ratios using specific units. See Lugaro et al. 2023 (<a href="https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract">https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract</a>) for details. For the specific case of Ni64 ratios, in comparison with model data we report the measured meteoritic anomaly by Steele et al 2012 (<a href="https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract">https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract</a>) as a continuous horizontal line. The same is done for the Fe54 ratios, with reference measurements by Hopp et al 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract">https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract</a>). </p> <p>In the database the abundance plots are identified as <strong>structure_<model refe<em>rence>_<initial mass>_<element>_<decayed or undecayed>.png</em></strong><em>. In particular, the available reference model options are Ra02, Pi16, Si18, LC18, Ri18, La22; the initial mass of the progenitors are 15, 20 or 25 (solar masses). The third part of the filenames are the plotted elements (in this case Ni or Fe) and then if they are decayed or undecayed. In this database we only consider the decayed species, which means that the radioactive isotopes whose decay can add to the abundance of the selected isotopes were considered. In this plots the x-axis represents the total mass from the core and the y-axis is the mass fraction on a logarithmic scale. For the slopes the same name scheme applies, but they are identified as <strong>slopes_<model reference>_<initial mass>_<element>_<decayed or undecayed></strong></em><strong>.png,</strong> and on the y-axis there are the slope values insteas of abundances.</p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
<p>Availability of highly parallelized immunoassays has renewed interest in the discovery of serology-based biomarkers for infectious diseases. Protein and peptide microarrays now provide a high-throughput platform for immunological screening of potential antigens and B-cell epitopes. However, there is still a need to prioritize relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize antigenic targets starting from a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We applied this method to the prioritization of potential diagnostic antigens and peptides in a number of pathogen proteomes and human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). After training a linear regression model the method achieves good to excellent performance on most species, measured by the enrichment of validated antigens at the top of the ranking. An unbiased validation using independent data sets shows APRANK is successful in predicting antigenicity for all pathogen species tested. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
ScienceDex guides
Understand access before you commit
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.