dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample 1: Global scheduling The first sample architecture models are built by extending the example 5. 1. 1, using a larger number of tasks and pes. It is therefore a multiprocessor system without any hardware shared resource and a Proportionate-Fair global scheduling. The software architecture is made of periodic tasks scheduled on a runtime environment including several processors, and complies with LOG_UPG. All processors are identical and independent. All tasks are allowed to execute on all pes. As confirmed by the execution of our dp_check algorithm, the architecture model actually complies with dp1 and dp3 patterns. The first experiment measures the impact of the number of tasks on the execution time of DP_cheker and on the size of the Prolog model. The results are shown Fig. 11. The second experiment is nearly the same except we analyze the effect of the number of processors (Fig.
Sample 3: Instruction cache impact on scheduling The last sample architecture models are built from the 5. 1. 2 example. In this sample, the architecture presented Section 5. 1. 2 has been scaled from a dual-processor to a multiprocessor architecture (with a  _ processor number ranging from 1 to 100). The software architecture consists of  _ (ranging from 10 to 1000) tasks and complies with LOG_UPG. In the sample, the architecture models are built with  _ = 10 ×  _ in order to assign to each processor a group of 10 tasks. Each processor has a level 1 private instruction Fig. 13. Execution Time and Prolog Model LoC depending on the number of tasks and processors. The ratio between the number of tasks and the number of processors is fixed to around 10 (but the actual load of each processor depends of the global scheduling algorithm).
The purpose of this article is to formalize the applicability constraints of the analysis methods implemented in Cheddar, and in particular the interference due to the runtime environment hardware resources. From this formalization, we developed a tool implemented in the Prolog language, that checks the compliance of an application model (including the specification of the runtime environment), with a catalog of design patterns. The outcome is a list of analysis methods that can be applied on the input system. Today, 5 design patterns are available and recognized by the tool, but the way we express the constraints on the system allows for a rather simple extension by appending new Prolog inference rules. Our approach has been applied on 3 representative examples, and also on large automatically generated systems.
In addition, an infrared thermal imaging camera monitoring the sample surface and Jtype thermocouples welded on neighboring samples were used to actively track sample temperature. The typical standard deviation for average sample temperatures was AE 5 °C. Damage and ion implantation profiles given in Fig. 1  were calculated with the Stopping and Range of Ions in Matter (SRIM-2013) program using the quick Kinchin-Pease method. A displacement energy of 40 eV was used for iron, chromium, and nickel. Damage was calculated using the VACANCY. txt output file. Doses reported are for an approximate mid-range depth of 1 -1. 4 lm, which was used for microstructure characterization.
50 %. APT volumes were reconstructed using Cameca's Integrated Visualization and Analysis Software (IVAS). A field factor (k) of 4. 5 was used, and since pole structures could not be well resolved, an image compression factor (g) of 1. 65 was assigned. Volumes were then imported into our recently developed Open Source Characterization of APT Reconstructions (OSCAR), an iso-position corelinkage algorithm written in Python, to perform the cluster analysis. Since IVAS tends to over identify clusters when there is low contrast between matrix and cluster solute concentrations  [71] , OSCAR was used to improve the accuracy of cluster identification. Further details on OSCAR are available in Refs. [72, 73]. In this study, Cu-rich clusters were identified by Cu local concentration, while Ni-Si-Mn rich clusters were identified by Si local concentration.
Funding Data analysis, manuscript writing, and part of characterization experiments sponsored by  Office of Fusion Energy Sciences, U. S. Department of Energy  under grant # DE-SC0006661  with the  University of Tennessee  (SML, SJZ) and contract  DE-AC05-00OR22725  with  UT-Battelle, LLC (PDE, AB). In addition, this work is supported by the  U. S. Department of Energy Nuclear Energy University Program (NEUP)  and  Nuclear Science User Facilities (NSUF)  under grant  DE-NE0008520. This material is based upon work supported under an  Integrated University Program Graduate Fellowship  and the  Graduate Advanced Training and Education (GATE) program  of the  University of Tennessee  (SML). Part of the APT experiments were performed at the  Center for Nanophase Materials Science , which is a  DOE User Facility.
Acknowledgments The authors would like to acknowledge  Caleb Massey  from  Oak Ridge National Laboratory  for running APT tips at the CNMS facility,  Yajie Zhao  from  University of Tennessee  for fruitful scientific discussion and help in the development of OSCAR software, and  Ovidiu Toader ,  Fabian Naab , and  Thomas Kubley  at the  Michigan Ion Beam Laboratory  for their assistance with the ion irradiations. The authors acknowledge the  University of Michigan College of Engineering  for financial support and the  Michigan Center for Materials Characterization  for use of the instruments and staff assistance.
Context. Disc-halo decompositions z = 1-2 star-forming galaxies (SFGs) at z > 1 are often limited to massive galaxies (M > 10 10 M ) and rely on either deep integral field spectroscopy data or stacking analyses. Aims. We present a study of the dark-matter (DM) content of nine z ≈ 1 SFGs selected among the brightest [O ii] emitters in the deepest Multi-Unit Spectrograph Explorer (MUSE) field to date, namely the 140 h MUSE Extremely Deep Field. These SFGs have low stellar masses, ranging from 10 8. 5 to 10 10. 5 M. Methods. We analyzed the kinematics with a 3D modeling approach, which allowed us to measure individual rotation curves to ≈3 times the half-light radius R e. We performed disk-halo decompositions on their [O ii] emission line with a 3D parametric model. The disk-halo decomposition includes a stellar, DM, gas, and occasionally a bulge component.
(2021). This paper aims to show that a disk-halo decomposition can be achieved for individual low-mass SFGs at intermediate redshifts (0. 6 < z < 1. 1) using the GalPaK 3D algorithm combined with the deepest (140 h) Multi-Unit Spectroscopic Explorer (MUSE;  Bacon et al. 2010 ) data obtained on the Hubble Ultra Deep Field (HUDF) and presented in  Bacon et al. (2021). We show that rotation curves can be constrained up to 3 R e thanks to the 3D modeling approach on these deep IFU data. This paper is organized as follows. In Sect. 2, we present the sample used here. In Sect. 3, we present our methodology. In Sect. 4, we present our results. Finally, we present our conclusions in Sect. 6. Throughout this paper, we use a 'Planck 2015' cosmology  (Planck Collaboration XIII 2016)  with Ω M = 0. 307, Λ = 0.
2021, for details). Thanks to the AO, the resulting point-spread function (PSF) full-width-at-half-max (FWHM) ranges from ≈0. 6 at 5000 Å to 0. 4 at 9000 Å. The sample of [O ii] emitters was selected from the mosaic catalogue  (Inami et al. 2017)  where we chose galaxies with the highest S/N per spaxel in [O ii] that were not face-on (using the [O ii] inclinations estimated in  Bouché et al. 2021). From the catalog, we found 9 galaxies matching these criteria, listed in Table  1 , with some reaching S /N pix ∼ 100 in the central spaxel. All galaxies but one are contained within the deepest 140 h MXDF circle of 31. One galaxy, ID3, has only 24 h of integration in the MXDF dataset, but is fortuitously located in a deep stripe of the UDF10 region  (Bacon et al. 2017 ) leading to a total of 42 h integration.
These galaxies have redshifts ranging from 0. 6 to 1. 1, and have stellar masses from M = 10 8. 9 M to M = 10 10. 3 M with SFRs from 1 to 5 M yr -1. The stellar masses and SFR were determined from spectral energy distribution (SED) fits with the Magphys  (da Cunha et al. 2015)  software on the HST photometry (as in  Maseda et al. 2017; Bacon et al. 2017 Bacon et al. , 2021) )  using a  Chabrier (2003)  initial mass function (IMF). Uncertainties on these quantities are obtained from the marginalized posterior probability distributions given by Magphys under the assumption of smooth star formation histories with additional random bursts  (da Cunha et al. 2008, and references therein). The main properties of these galaxies are listed in Table  1. In Fig. 1 , we show the HST/F160W images of the nine SFGs where the background and foreground objects have been masked.
2009; Wisnioski et al. 2015; Übler et al. 2019). Altogether, this 3D model (hereafter 'URC' model) has 13 parameters: x c , y c , z c , f O2 , R e|O2 , n O2 , i O2 , PA |O2 , r t , V 2 , β, σ 0 and the [O ii] doublet ratio r O2. We use flat priors on these parameters and fit them simultaneously with a Bayesian Monte-Carlo algorithm. GalPaK 3D can use a variety of Monte-Carlo algorithms and here we use the python version of MultiNest  (Feroz et al. 2009 ) from  Buchner et al. (2014)  because it is found to be very robust and insensitive to initial parameters. Moreover, it also provides the model evidence Z. There are several advantages to note here. The 3D algorithm GalPaK 3D allows to fit the kinematics and morphological parameters simultaneously and thus no prior information is required on the inclination 3.
Parameter optimization and model selection Having constructed disk-halo models in 3D(x, y, λ) within GalPaK 3D , we optimize the 14 parameters simultaneously with GalPaK 3D using the python pyMultiNest package  (Buchner et al. 2014)  against the MUSE data where the stellar continuum was removed taking into account the PSF and LSF. As in Sect. 3. 1, we use flat priors on each parameter. We also do not use the stellar mass from SED as input/prior on the disk mass (via log X) because the traditional disk-halo degeneracy is broken from the shape (α -βγ) of the DM halo profile Eq. (  3 ) which depends on the disk-to-halo mass ratio X = M /M vir as discussed in the previous section. We do not use priors on the inclination 7 because the i-V degeneracy is broken from the simultaneous fit of the kinematics with the morphology, as discussed in Sect.
The distances are simply calculated from the redshifts and our assumed Planck 2015 cosmology. As we assume that the stellar component is distributed in a disk, we use the axial ratio of galaxies measured from the HST/F160W images to derive the inclinations of galaxies. An alternative approach would be to use the inclinations returned from the GalPaK 3D models, which lead to almost identical results. We estimate the mass-to-light ratios of galaxies combining the stellar masses obtained from photometric SED fits (see Sect. 2) and the total light obtained from the MGE models. Finally we use the module mge_vcirc from the JAM code  (Cappellari 2008 ) to calculate the circular velocity in the equatorial plane of each galaxy.
The disk-halo decomposition We now turn to our disk-halo decomposition using the method described in Sect. 3. 2. For each SFG, we ran several combinations of disk-halo models, such as different halo components (DC14/NFW), different disk components (Freeman/MGE), with or without a bulge, with various asymmetric drift corrections and chose the model that best fit the data for each galaxy according to the model evidence. We find that the DC14 halo model is generally preferred over a NFW profile and the resulting model parameters are listed in Table  2. The evidence for the DC14 models is discussed further in Sect. 4. 6. Before showing the disk-halo decompositions, we compare the disk stellar mass M (M being one of the 14 free parameters) obtained from the 3D fits with the SED-derived M. This comparison is performed in Fig.
Conclusions Using a sample of nine [O ii] emitters with the highest S/Ns in the deep (140 h) MXDF  (Bacon et al. 2021 ) dataset, we measure the shape of individual RCs of z ≈ 1 SFG out to 3 × R e with stellar masses ranging from 10 8. 5 to 10 10. 5 M , covering a range of stellar masses complementary to the analysis of  Genzel et al. (2020) , whose sample has M > 10 10 M. We then performed a disk-halo decomposition on the [O ii] emission lines using a 3D modeling approach that includes stellar, dark-matter, gas (and bulge) components (Fig. 4 ). The dark-matter profile is a generalized  Zhao (1996)  profile using the feedback prescription of Di  Cintio et al. (2014) , which links the DM profile shape to the baryonic content. Our results are as follows. We find that: -The 3D approach allows to constrain RCs to 3R e in individual SFGs revealing a diversity in shapes (Fig.
In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework.
, 2013) , the role of spike timing and firing rate in neural activity  (Brette, 2015) , the stimulus-response dynamics  (Pospischil et al. , 2011) , and individual cells and circuit functions  (Migliore et al. , 2018; Egger et al. , 2020). Depending on the scientific principles underlying the model construction, different levels of neural details can be implemented and, to cope with the wide range of computational neuroscientists' needs, a number of simulation environments are already available, such as NEURON  (Hines and Carnevale, 1997) , GENESIS  (Bower and Beeman, 2007) , Brian  (Goodman and Brette, 2008; Stimberg et al. , 2019)  and NEST  (Gewaltig and Diesmann, 2007; Eppler, 2008) , to cite a few. Also, modelers can rely on a growing ensemble of neural datasets, built in the framework of large-scale brain initiatives  (Insel et al.
, 2010)  and PyNN  (Davison, 2008 ) model description languages. The number of platforms created for neuroscience data sharing is also increasing, e. g. , DANDI Archive, 5 Zenodo, 6 Dryad, 7 Figshare, 8 1 www. nsgportal. org 2 cscs. ch 3 juelich. de 4 cineca. it 5 dandiarchive. org 6 zenodo. org 7 datadryad. org 8 figshare. com Hippocampome. org, 9 NeuroMorpho. Org 10 and a number of initiatives foster the development of tools for collaborative data visualization and analysis, such as Neurodata Without Borders  (Teeters et al. , 2015)  and the Allen Institute for Brain Science, 11 which, in addition to a large dataset of neuroscience images and data, provides a dedicated Software Development Kit (SDK) for data analysis. Also the EBRAINS Knowledge Graph (KG), built in the framework of the Human Brain Project (HBP)  (Amunts et al.
Hodgkin-Huxley Neuron Builder frontend and backend The EBRAINS HHNB is implemented via a Python-based Django project 12  consisting of two Django applications, the efelg, which implements the EBRAINS NeuroFeatureExtract (NFE) web application and provides the algorithms and interface for the feature extraction procedures in the HHNB workflows and the hh-neuron-builder, which implements the frontend and backend for the model selection/upload, HPC job configuration, optimized model fetching, running and integration with the Model Catalog (see the Table  1 ). To guarantee seamless and fast communication and data management between the two web apps, they are hosted on the same Ubuntu 20. 04 Virtual Machine (VM), in the CINECA supercomputer center (see Table  1 ). The CINECA VM consists of 60 GB RAM, 8 VirtualCPUs (VCPU) and is accessible via Openstack.
13 The frontend has been developed in HTML, Javascript and CSS code and the backend in Python; frontend and backend interact via the Django routing system and users' requests, performed via the HHNB GUI, are handled via the HTTPS protocol and managed by an NGINX web server coupled with a uWSGI web interface. Requests from different users are handled asynchronously and independently via the creation and management of userdedicated folder trees based on the file system and system resources. A system user with limited access and privileges to the VM resources performs the HHNB system operation, in order to limit security vulnerabilities. The code is publicly available  (Gleeson et al. , 2017)  on GitHub, under the LGPLv3 license (see Table  1 ) and the use of the application is free of charge.
Electrophys Feature Extraction Library and Blue Brain Python E-feature extraction library The feature extraction functionalities of the HHNB are available to the users via the integration of the NFE web application  (Bologna et al. , 2021)  in the HHNB GUI. This tool leverages the Electrophys Feature Extraction Library (eFEL) and Blue Brain Python E-feature extraction (BluePyEfe) libraries (see Table  1 ) that provide the core feature extraction software package and its convenient Python wrapper, respectively. More specifically, the eFEL accepts electrophysiological traces as input and extracts several activity features from the time signals. The features available for extraction belong to three different categories, depending on their properties (i. e. , spike event features, spike shape features and voltage features) and can be selected via the HHNB interface.
While the feature extraction code is optimized for the analysis of individual traces in the eFEL, higher level operations like feature grouping (e. g. , by stimulus amplitude) and averaging are not implemented. For this reason, we integrated, in the HHNB backend, the BluePyEfe software library that provides a wrapper around the eFEL and allows to read different file formats, group the electrophysiological traces by cell and stimulus amplitude, average the feature values by cell and again by ensemble of cells. Finally, the feature extraction output files are appropriately formatted for the Blue Brain Python Optimization Library (BluePyOpt) (see Table  1 ) optimizer and are transparently integrated in the file package to be sent to the HPC system for the model optimization. For more details on the integration of the eFEL and BluePyEfe packages in the HHNB/NFE, see  (Bologna et al.
Blue Brain Python Optimization Library The model optimization process is carried out via the BluePyOpt, which is open source software implementing multiobjective model parameter optimization based on a genetic evolutionary algorithm  (Van Geit et al. , 2016). The latter implements the evolution of a population of parameters through consecutive generations. For each iteration a set of offspring individuals is generated from selected parents (following the principles of the genetic algorithm) belonging to the previous generation. Each individual contains a set of passive properties and peak ion channel consistent with the electrophysiological trace features provided. The cost function of the process is a score defined by the total error associated with each individual and calculated as the sum of the absolute deviations of the features observed in the simulated activity from its experimental counterpart.
In terms of implementation, while the BluePyOpt library is agnostic with respect to the model folder and file organization, the models in the dataset (and the model files the users can upload) are organized following a specific folder structure. The mechanisms folder contains the NEURON (see Table  1 ). mod files that describe the kinetics of the ion channels (these files contain instructions formalized in the NMODL language). The morphology folder contains the. asc file describing the cell morphology. These files are ASCII-encoded text files handled by the Neurolucida neuron reconstruction software. They contain tree-structured text building blocks appropriately formatted to represent the neural morphology in terms of compartment coordinates in the 3D space. Neuron somas are represented as contours named "cellbody.
Additionally, it is possible to upload one's own feature and/or model files so as to leverage the HHNB functionalities to perform analysis and optimization on one's own scientific results (either combined with the datasets available in the HHNB or consistently and fully provided by the users). The HHNB GUI has been designed to be simple and intuitive. The main/overview page allows users to initiate a new workflow or upload one that has been previously saved (the "Save" button is made available once the workflow is initiated); each workflow is uniquely identified via a workflow ID. The main workflow page reflects the context separation between the operations to be performed before and after the model optimization via two panels, the "Cell Optimization" and the "Single Cell Simulation Run" (see Figures  2A, B ).
Feature extraction To optimize the model parameters and behavior against the observed electrophysiological functioning of the neuronal cell of interest, a feature extraction procedure that allows the extraction of relevant characteristics of neural signals is available in the HHNB. The feature extraction process is enabled through the integration of a web-application, the NFE, that allows visual inspection of electrophysiological traces (see Figure  3A ), feature extraction parameter setting, feature selection and results download. The tool has been thoroughly described in  Bologna et al. (2021)  and is hosted in the same VM as the HHNB, guaranteeing a seamless interaction between the two frameworks (see the "Methods" section). Briefly, the NFE allows to select electrophysiological traces from a dataset of recordings hosted in the EBRAINS KG or contributed by research collaborators and made available via the application GUI.
The provided data are labeled according to their cellular and experimental characteristics (e. g. , species, brain area, electrical type, stimulus amplitude) and can be explored via a visualization interface that allows one to zoom the traces in and out and selectively choose the signals from which the features will be extracted (the users can also upload and appropriately label their own data). The GUI also allows the feature extraction parameter configuration and the selection of features of interest to be performed before the extraction process is launched. The latter uses the eFEL and BluePyEfe Python libraries that offer advanced functionalities for the data analysis  (3) choose the HPC system to be used for the model optimization process and set the job and optimization algorithm parameters.
(A) Service Account workflow: once logged in to the EBRAINS authentication system, a user token is generated and sent to the Service Account server. Here, the EBRAINS OIDC engine is queried to check the validity of the token. Information on the job to be submitted (e. g. , quota, job id, time requested) are stored in the Service Account database. The job is then submitted on behalf of the user to the HPC system. Once the job is completed, the Service Account APIs allow to fetch the results and provide them to the user in a transparent way. (B) Architecture of the Service Account database: Six tables are used to keep track of the submitted jobs' details, users' quotas and project used. For each table property, the data type is indicated; primary and foreign keys are noted as PK and FK, respectively.