dataesr/scientific-paragraphs-categorization huggingface dataset:
Organisation The DPAC is a large entity in charge of a set of complex, large, and interdependent data processing tasks. The DPAC is internally organised into a set of relatively independently operating units, called coordination units (CUs), which roughly correspond to the cyclic data processing tasks outlined above, with some units also in charge of daily data processing. The actual processing takes place on hardware spread around six data processing centres in Europe (located at ESAC, Toulouse, Cambridge, Genève, Barcelona, and Torino), which communicate through the main database located at ESAC (hub-and-spokes topology). The main database also houses the DPAC end data products from which the public data releases are produced. The DPAC is managed at the top level by an executive body consisting of the leaders of the coordination units and representatives from the data processing centres.
End-of-mission scientific performance The end-of-mission science performance of Gaia, based on CCD-level Monte Carlo simulations calibrated with measurements and then extrapolated to end-of-life conditions, has been published prior to launch in de Bruijne (2012) and was updated in de Bruijne et al. (  2014 ) following the in-orbit commissioning phase of the mission. Here, we summarise the most upto-date end-of-mission performance estimates based on in-orbit experience collected to date (mid 2016). An associated Python toolkit is available from https://pypi. python. org/pypi/ PyGaia/. The actual scientific quality of Gaia Data Release 1 is described in  Lindegren et al. (2016)  for the astrometry and in  Evans et al. (2016)  for the photometry. All performance estimates presented here include a 20% scientific contingency margin to cover, among other things, scientific uncertainties and residual calibration errors in the on-ground data processing and analysis, for example uncertainties related to the spacecraft and solar system ephemeris, estimation errors in the sky background value that needs to be fed to the centroiding algorithm, the contribution to the astrometric error budget resulting from the mismatch between the actual and the calibrating point spread function, template mismatch in the spectroscopic cross-correlation, and residual errors in the derivation of the locations of the centroids of the reference spectral lines used for the spectroscopic wavelength calibration; the fact that the sky does not contain, as assumed for the performance assessments, perfect stars but normal stars, which for example can be photometrically variable, have spectral peculiarities such as emission lines, have (unrecognised) companions, and can be located in crowded fields.
g. σ α * = σ α cos δ). The expected astrometric correlations between the five astrometric parameters were discussed in detail by The performance equations presented here refer to the standard errors, i. e. the precision of the astrometry. An assessment of (residual) systematic errors in the astrometry, linking to its accuracy and in particular to the parallax zero point, is much more difficult to provide. For astrometry, a potential contributor to systematic parallax errors are unmodelled, Sun-synchronous basic angle variations  (Sect. 3. 3. 4). The metrology data derived from the basic angle monitor should ultimately allow, after careful calibration  (Lindegren et al. 2016) , the limitation of possible systematic effects in the final data release to µas levels.
Spectroscopy Spectroscopy is being collected for a subset of the astrometric and photometric data to derive radial velocities and to perform stellar parametrisation. For the vast majority of stars, namely those that are faint, the individual transit spectra are too noisy to derive transit-level radial velocities. As a result, a single, end-ofmission composite spectrum is first reconstructed by co-adding all spectra collected during all RVS CCD crossings throughout the mission lifetime. A single, mission-averaged radial velocity is then extracted from this end-of-mission composite spectrum by cross-correlation with a synthetic template spectrum. The Gaia Collaboration et al. : The Gaia mission Table  1. Numerical factor to be applied to the sky-averaged astrometric standard errors of Eqs. (  4 ) and (  7 ) for the five astrometric parameters as a function of ecliptic latitude β, including the effect of the variation of the end-of-mission number of transits over the sky.
The quantity N obs in column 4 denotes the end-of-mission number of focal plane passages for AF, BP, and RP (both fields of view combined; recall that Gaia Data Release 1 is based on 14 months of data, corresponding on average to 16 field-of-view transits). For RVS, the number of focal plane transits is a factor 4/7 = 0. 57 smaller (Sect. 3. 3. 7). The transit numbers in column 4 are based on an assumed 6% dead time (data loss). For the faintest objects (G 20 mag or G RVS 14 mag), the actual losses are larger (Sect. 5. 3. 1) cross-correlation method finds the best match of the observed spectrum within a set of predefined synthetic spectra with different atmospheric parameters and subsequently assigns the astrophysical parameters of the best-fit template to the observed target. For the few million brightest targets, single field-of-view transit spectra will be used to derive associated epoch radial velocities.
In the on-ground processing, however, the full resolution of the astrometric instrument, combined with the window size (at least 12 along-scan pixels of 0. 06 each), allows one to systematically resolve double stars down to separations around 0. 1. A special deblending treatment of close binaries will be performed in the BP/RP data processing. Moving objects: The majority of main-belt and near-Earth asteroids, at least up to speeds of 100 mas s -1 , are properly  detected (de Bruijne et al. 2015). Moving objects may, after successful detection, leave the window (and even the CCD) at any moment during the focal plane transit because the window propagation assumes every object is a fixed star. In BP/RP, an additional window is assigned to bright objects (G = 13-18 mag) that have a large across-scan motion  (Sect.
Summary Gaia is the space-astrometry mission of the European Space Agency which, after successful commissioning, started scientific operations in mid-2014. The primary science goal of Gaia is to examine the kinematical, dynamical, and chemical structure and evolution of our Milky Way. In addition, the data of Gaia will have a strong impact on many other areas of astrophysical research, including stellar evolution and physics, star formation, stellar variability, the distance scale, multiple stars, exoplanets, solar system bodies, unresolved galaxies and quasars, and fundamental physics. With a focal plane containing more than 100 CCD detectors, Gaia surveys the heavens and repeatedly observes all objects down to G ≈ 20. 7 mag during its five-year nominal lifetime. The science data of Gaia comprise absolute astrometry (positions, proper motions, and parallaxes), broadband photometry in the unfiltered G band, low-resolution blue and red (spectro-)photometry (BP and RP), and integrated G BP and G RP photometry for all objects.
Medium-resolution spectroscopic data are collected for the brightest few hundred million sources down to G RVS ≈ 16. 2 mag. The concept and design of the spacecraft and the mission ultimately allows, after five years, stellar parallaxes (distances) to be measured with standard errors less than 10 µas for stars brighter than G ≈ 13 mag, around 30 µas for stars around G ≈ 15 mag, and around 600 µas around G ≈ 20 mag. End-of-life photometric standard errors are in the milli-magnitude regime. The spectroscopic data allow the measurement of (mission-averaged) radial velocities with standard errors at the level of 1 km s -1 at G RVS ≈ 11-12 mag and 15 km s -1 at G RVS ≈ 15-16 mag, depending on spectral type. The Gaia Data Processing and Analysis Consortium (DPAC) is responsible for the processing and calibration of the Gaia data.
Acknowledgements. This work has made use of results from the  European Space Agency (ESA) space mission Gaia , the data from which were processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Gaia mission website is http: //www. cosmos. esa. int/gaia. The authors are current or past members of the  ESA and Airbus DS Gaia mission teams and of the Gaia DPAC. This work has financially been supported by: the  Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory ; the  Austrian FWF Hertha Firnberg Programme  through grants  T359 ,  P20046 , and  P23737 ; the  BELgian federal Science Policy Office (BELSPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grants; the  Brazil-France exchange programmes FAPESP-COFECUB  and  CAPES-COFECUB ; the  Chinese National Science Foundation  through grant  NSFC 11573054 ; the  Czech-Republic Ministry of Education, Youth , and Sports through grant  LG 15010 ; the  Danish Ministry of Science ; the  Estonian Ministry of Education and Research  through grant  IUT40-1 ; the  European Commission 's  Sixth Framework Programme  through the  European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network  ( MRTN-CT-2006-033481 ), through  Marie Curie  project  PIOF-GA-2009-255267  ( SAS-RRL ), and through a  Marie Curie Transfer-of-Knowledge (ToK) fellowship  ( MTKD-CT-2004-014188 ); the  European Commission's Seventh Framework Programme  through grant  FP7-606740  ( FP7-SPACE-2013-1 ) for the  Gaia European Network for Improved data User Services (GENIUS)  and through grant  264895  for the  Gaia Research for European Astronomy Training (GREAT-ITN) network ; the  European Research Council (ERC)  through grant  320360  and through the  European Union 's  Horizon 2020 research and innovation programme  through grant agreement  670519  (Mixing and  Angular Momentum  tranSport of massIvE stars -MAMSIE); the  European Science Foundation (ESF) , in the framework of the  Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF ); the  European Space Agency  in the framework of the  Gaia  project; the  European Space Agency Plan for European Cooperating States (PECS)  programme through grants for Slovenia; the  Czech Space Office through ESA PECS  contract  98058 ; the  Academy of Finland ; the  Magnus Ehrnrooth Foundation ; the  French Centre National de la Recherche Scientifique (CNRS)  through action ' Défi MASTODONS '; the  French Centre National d'Etudes Spatiales (CNES) ; the  French L'Agence Nationale de la Recherche  ( ANR) investissements d'avenir Initiatives D'EXcellence (IDEX) programme PSL * through  grant  ANR-10-IDEX-0001-02 ; the  Région Aquitaine ; the  Université de Bordeaux ; the  French Utinam Institute of the Université de Franche-Comté , supported by the  Région de Franche-Comté  and the  Institut des Sciences de l'Univers (INSU) ; the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Mobility  fellowship; the  United Kingdom Rutherford Appleton Laboratory ; the  United Kingdom Science and Technology Facilities Council (STFC)  through grants  PP/C506756/1  and  ST/I00047X/1 ; and the  United Kingdom Space Agency (UKSA)  through grants  ST/K000578/1  and  ST/N000978/1. The GBOT programme uses observations collected at (i) the European Organisation for Astronomical Research in the Southern Hemisphere with the  VLT Survey Telescope (VST) , under  ESO  programmes  092. B-0165 ,  093. B-0236 ,  094. B-0181 ,  095. B-0046 ,  096. B-0162 , and  097. B-0304 , (ii) the Liverpool Telescope, which is operated on the island of  La Palma  by  Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias  with financial support from the  United Kingdom Science and Technology Facilities Council , and (iii) telescopes of the  Las Cumbres Observatory Global  Telescope Network.
To investigate the causes of the marked differences between the predictive power of the ME models inferred using either the naive approach or the SAEM algorithm, we compared the corresponding parameter distributions. In both cases, the mean values of the parameters were comparable and within the expected ranges (see S1 Table for parameter values and S1 Text for literature values). However, the distribution obtained with the SAEM algorithm was significantly more compact (i. e. it had a smaller volume in the parameter space) and was more The SAEM approach provides parameter distributions that capture the population behavior because of cross-correlations between parameters. A. Representation of the experimental dataset. B. Simulated behavior obtained when using the parameters of each observed cell in the dataset (325 cells) inferred with the SAEM approach.
C. Simulated behavior obtained when using the parameters of each observed cell in the dataset (325 cells) inferred with the naive approach. D. Simulated behavior of 10000 cells when resampling the population joint distribution inferred with SAEM, (pink). E. Simulated behavior of 10000 cells when resampling the population joint distribution inferred with the naive approach. F. As an illustration we show the simulated behavior of 10000 cells when resampling the population parameter distribution as in D but without preserving the covariance between parameters (i. e. , using marginal distributions). For E and F, note that the y-axis has been scaled differently. Shaded areas represent the fluorescence values of 95% of the population and the dashed lines represent the median. Experimental data is represented in blue.
Black bars indicate the presence of osmotic shocks. Note that unlike actual cells, all simulated cells are represented during the whole experiment (ie from 0 to 10hrs). doi:10. 1371/journal. pcbi. 1004706. g002 structured (i. e. it had higher cross-correlations on average; S3 Fig). This strongly suggested that capturing the structure of the parameter distribution is essential in order to explain the population behavior. Both the individual statistics of each parameter, and their covariance, describing mutual relationships, contain essential information to properly account for the cell-cell variability observed in the dataset. And indeed, when using a parameter distribution with the same individual parameter statistics (mean and variance) as the distribution inferred using SAEM but with null cross-correlations (i.
All of these components were driven by the open-source software μManager which was interfaced with Matlab. Images were taken using a 100× oil immersion objective (PlanApo 1. 4 NA; Olympus). The fluorescence exposure time was 200 ms, with fluorescence illumination intensity set to 50% of maximal power. The fluorescence exposure time was chosen such that the fluorescent illumination did not cause noticeable effects on cellular growth over extended periods of time. Importantly, illumination, exposure time, and camera gain were not changed between experiments, and besides background and auto-fluorescence subtraction (defined as the minimum intensity in the first frame), no data renormalization or processing was done. Imaging was performed at a frequency of one frame every 3 min for bright-field and one frame every 6 min for fluorescence measurements.
Inference of single-cell and ME population models Two methods were proposed to infer ME population models: a naive approach and SAEM. The naive approach used the local optimization algorithm fminsearch from Matlab to maximize the (log-)likelihood of the parameters tested, given the observed data for the considered cell. The parameter distribution for the ME model is then defined based on the set of single-cell parameters. The SAEM approach aims directly at maximizing the likelihood of the population (high-level) parameters describing the distributions of the model parameters, given all the single-cell data. We used the SAEM implementation of Monolix software. Lastly, having inferred a distribution for the model parameters of a population of cells, one could estimate the most likely parameter values for each single cell (ME single-cell models).
We used the local optimization tool fminsearch from Matlab to implement a maximum a posteriori approach. For more details on the parameter inference approach see S1 Text. Relating the specific intracellular processes involved in gene expression with other, non-modeled cellular properties The analysis of the correlations between the perceived shocks or the single-cell measured features and the estimated parameters was performed using the Spearman coefficient of correlation. The significance of the correlations (p-values) was assessed using the standard two-tailed test implemented in the Matlab statistics toolbox. To test whether parameters of mother and daughter cells were statistically closer than on average, we constructed pairs of cells that differed solely by whether they were direct relatives (mother/daughter pairs, MD pairs) or not (non-related mother/daughter pairs, nMD pairs).
This showed that the SAEM approach described the population with a smaller distribution. (PDF) S4 Fig. Average  parameter distance of Mother-Daughter pairs against random pairs from the same experiment. The blue bar represent the average distance in parameters between 55 mother-daughter pairs from experiment D I. The red distribution is obtained by bootstrapping 20000 sets of 55 random pairs of cells (from the same experiment). We see that the distance is very significantly smaller for mother-daughter pairs. (PDF) S1 Table. Parameter estimates for the mixed-effects model using the naive inference approach (A), using SAEM on the identification dataset D I (B) and using SAEM on the validation dataset D V (C). (A) Initial values for the search have been obtained by global optimization (CMAES) on the mean behavior starting from literature-based parameters.
The value of the delay τ has been fixed for all cells to its mean-cell. Therefore, statistics on its variability have been shaded. The dataset used is the identification set D I (B and C) The parameter search is initialized with parameter means extracted from the literature and a diagonal covariance matrix. The parameter search has been adapted to account for the structural non-identifiability relation of k m and k p (only their product is relevant in single-cell models): the mean of k p is kept at a constant value during the search. No constraints are placed on its variance though. The value of the delay τ is estimated but is set identical for all cells. The dataset used for identification is D I (B) and D V (C). The relative standard errors of the estimated moments are typically less than 2%, with the exception of the estimate of SD[k m ] where it was 8%.
This work was supported by the  Agence Nationale de la Recherche  ( ICEBERG-ANR-10-BINF-06-01 ), the  Who am I? Laboratory of Excellence  ( ANR-11-LABX-0071  and  ANR-11-IDEX-0005-01 ), the  Interdisciplinary Program of Sorbonne Paris Cité (SPC) ,  the C'Nano program  from the  region Ile de France , and the  EU   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement n•  257462 HYCON2  Network of excellence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Construction of the calculating tool To create a human-computer dialog system with an easy-operating and visualized interface, we decided to construct the calculating tool (programme) for PMGF frequencies using the Visual Basic (VB) language (Microsoft Corporation). Numerical integrations for PMGF calculation followed the Simpson integration method  (Davis and Rabinowitz 1979). The outputs of calculated PMGF frequencies were described either as a value (frequency) at a given spatial distance interval, or as a curve (frequencies) at a series of spatial distance intervals in plotting field after all the required parameters were included.
Case studies: calculating PMGF frequencies Three wind-pollination crop species were selected for case studies: rice, wheat and maize. This is because published data from the PMGF experiments are available for the three crop species. These data were used to examine the predicting power of the PMGF calculating tool by comparing the results generated by the model simulation and those obtained from the PMGF experiments. In addition, all the biological parameters required by the calculating tool are accessible for the three crop species. For the required parameters, pollen diameter (the diameter of pollen grains) of the three species was determined based on data from published studies  (Goss 1968; Chaturvedi et al. 1998). The parameter of relative pollen release height was determined following the method described in  Wang et al.
Electronic structure calculations have become a powerful foundation for computational materials engineering. Four major factors have enabled this unprecedented evolution, namely (i) the development of density functional theory (DFT), (ii) the creation of highly efficient computer programs to solve the Kohn-Sham equations, (iii) the integration of these programs into productivityoriented computational environments, and (iv) the phenomenal increase of computing power. In this context, we describe recent applications of the Vienna Ab-initio Simulation Package (VASP) within the MedeA ® computational environment, which provides interoperability with a comprehensive range of modeling and simulation tools. The focus is on technological applications including microelectronic materials, Li-ion batteries, high-performance ceramics, silicon carbide, and Zr alloys for nuclear power generation.
This is truly exciting and one can expect new algorithms, new computational procedures, and new forms of data analytics to be developed. Large databases of computed results are being created for existing as well as hypothetical structures. Mining this richness of information will undoubtedly reveal new insights and novel materials. While this large volume of data is valuable, it will not resolve other major challenges of computational materials engineering, namely (i) the multi-scale and multi-physics aspects, (ii) the inherently non-equilibrium character of materials, and -last but not least -(iii) the accuracy of the ab initio calculations. Let us consider these aspects as they provide a perspective on necessary future developments. In the overwhelming number of cases, materials engineering has a multi-scale and multi-physics character.
Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an additional way of providing informative features about fire regimes through the analysis of patch size distribution. We evaluated the ability of global BA products to accurately represent morphological features of fire patches, in the fire-prone Brazilian savannas. We used the pixel-level burned area from LANDSAT images, as well as two global products: MODIS MCD45A1 and the European Space Agency (ESA) fire Climate Change Initiative (FIRE_CCI) product for the 2002-2009 time period. Individual fire patches were compared by linear regressions to test the consistency of global products as a source of burned patch shape information.