dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The work underlying this paper began during "Hackout 3: analysis and modelling tools for emergency outbreak response" (http:// hackout3. ropensci. org/). The authors would like to thank the organisers of the event, particularly  Thibaut Jombart  and  Karthik Ram. Thanks  to  Nicholas Reich  for integrating some of our code into CoarseDataTools, and to  Isabel Rodriguez-Barraquer  and  Noemi Picco  for helpful discussions about this project. Thanks to all the people and organisations that provided data for this project, in particular the  Food and Agriculture Organization of the United Nations (FAO)  for authorisation to use the MERS data via the  EMPRES-I (Global Animal Disease Information System ) available at http://empres-i. fao. org/eipws3g/ and  Dr Mosoka Fallah  ( National Public Health Institute of Liberia ) for permission to use the data from Liberia from the 2013-16 Ebola epidemic.
Fashion sketch drawing requires professional knowledge on fashion design; for example, aesthetics, ergonomics, and painting. People without related skills find difficulty drawing fashion sketches. Currently, there are two methods to draw fashion sketches. One is drawing by hand, which leads to what is called ''manually drafted'' fashion sketches. The other method is the use of special design software, like Adobe Illustrator and CorelDraw, which is called ''computer aided drafting''. Both types of fashion sketch drawing methods require superior drawing skills. To avoid distraction, fashion sketch drawing does not require much movement or shading. A neatly detailed fashion sketch simply means how well in detail one can illustrate design requirements to related production departments. Generally, the fashion sketch is drawn on white paper using black lines, because black lines make it easier for people to follow visual guidelines and provide a clearer representation of designers' ideas.
C. JEAN FASHION SKETCH AND PATTERN AUTO-GENERATION SYSTEM We developed a software application named Associate Design System for jean Fashion sketch and Pattern 2016 (ADSFP 2016) to generate jean fashion sketch and pattern automatically and rapidly. In the system, all the dimensions of fashion sketches and patterns in the height direction were calculated based on (11), (  12 ), (  13 ) and  (14) ; likewise, all the dimensions of fashion sketches and patterns in the width direction were calculated based on Eq. (  15 ), (  16 ), (  17 ), (  18 ), (  19 ), (  20 ), (  21 ) and  (22). As shown in Fig. 5 , ADSFP 2016 has six input parameters: jean length, silhouette, waist height category, human body stature, human body waist girth and human body hip girth; meanwhile, the outputs of this system are jean fashion sketches and their corresponding patterns.
The work of  K. Liu  was supported in part by the  National Natural Science Foundation of China  under Grant  61806161 , in part by the  Special Scientific Research Plan Projects of Shanxi Education Department  under Grant  18JK0352 , in part by the  Social Science Fund Project of Shanxi Province  under Grant  2018K3 , in part by the  China National Endowment for the Arts  under Grant  2018-A-05-(263)-0928  and in part by The  Youth Innovation Team of Shaanxi Universities.
We fit the data with CIGALE exploring different configurations for dust attenuation and star formation histories, considering either the full dataset or one that is reduced to the stellar and dust emission. We compare estimates of the dust luminosities, star formation rates, and stellar masses. Results. The fit of the stellar continuum alone with the starburst attenuation law can only reproduce up to 50% of the total dust luminosity observed by Herschel and ALMA. This deficit is found to be marginally consistent with similar quantities estimated in the COSMOS field and is found to increase with the specific star formation rate. The combined stellar and dust SEDs are well fitted when different attenuation laws are introduced. Shallow attenuation curves are needed for the galaxies whose cold dust distribution is very compact compared to starlight.
2000)  or built to reproduce average observed trends  (Charlot & Fall 2000; Lo Faro et al. 2017; Cullen et al. 2018). These methods are very efficient at fitting very large samples of galaxies. Hayward & Smith (2015)  showed that the SED modeling code MAGPHYS recovers physical parameters reasonably well for most of their simulated galaxies. It has recently been shown that effective attenuation laws are far from universal but actually vary in local and more distant galaxies (e. g. ,  Salmon et al. 2016; Salim et al. 2018; Buat et al. 2018). The attenuation law appears to flatten out when the amount of obscuration increases, a trend that is predicted by radiation transfer models in dusty media with idealized geometries  (Chevallard et al. 2013, and references therein)  or more realistic distributions from hydrodynamical simulations  (Jonsson et al.
In this paper, we analyze a sample of z ∼ 2 galaxies observed with ALMA and complemented by a rich multi-wavelength dataset from the rest-frame UV to the submm. We explore which SFHs and attenuation laws are needed to fit the SED of these galaxies and to what extent the SFR and stellar masses determined from the fit, with and without the IR emission emitted by dust, are reliable. With this aim in mind, we use data from the Hubble Ultra-Deep Field (HUDF)  (Dunlop et al. 2017 ) GOODS-South field  (Elbaz et al. 2018)  surveys. We use CIGALE  (Boquien et al. 2019)  to fit the SED of these objects with three different attenuation laws that are representative of the very different shapes of effective attenuation laws used in the literature: the starburst law of  Calzetti et al. (2000) , and the recipes of  Charlot & Fall (2000)  and Lo  Faro et al.
(2018) , except for UDF 10 and 15 which are not included in the  Elbaz et al. (2018)  sample. The data from the Herschel GOODS-South field catalog were produced by the ASTRODEEP team 2  (Wang et al. , in preparation). Postage-stamp HST images with ALMA contours for each source are shown in  Dunlop et al. (2017) ,  Rujopakarn et al. (2016)  and  Elbaz et al. (2018).
The code CIGALE The SED fitting is performed with CIGALE 3. We refer to  Boquien et al. (2019)  for a detailed description of the code. CIGALE combines a stellar SED with dust attenuation and emission components. A critical aspect is that it conserves the energy between dust absorption in the UV-to-NIR domain and emission in the mid-IR and far-IR. The quality of the fit is assessed by the best χ 2 (and a reduced best χ 2 defined as χ 2 r = χ 2 / (N -1), with N being the number of data points). The value of the physical properties and their corresponding uncertainties are estimated as the likelihood-weighted means and standard deviations. In this work, we introduce different attenuation models. To compare them we use the Bayesian inference criterion defined as BIC = χ 2 + k × ln(N ), with k being the number of free parameters and N the number of data points used for the fit  (Ciesla et al.
5 µm: CF00 is much flatter than C00  (Chevallard et al. 2013; Lo Faro et al. 2017; Buat et al. 2018). The average LF17 law is flatter than the two previous laws over the full wavelength range. Table  1. CIGALE modules and input parameters used for all the fits. The IMF is that of  Chabrier (2003)  with Z = 0. 02. -0. 7 A ISM V /(A ISM V + A BC V ) µ 0.
Fitting the stellar continuum We model the stellar continuum using all the HST, VLT, and IRAC data available. We run CIGALE with the three attenuation laws and with and without a burst added to the delayed SFH. First we test the requirement to add a burst to the delayed SFH. The comparison of the BIC is used for this purpose. Two additional free parameters are introduced when a burst is added to the delayed SFH. The difference between the two BICs corresponding to the models with and without a burst is calculated as ∆BIC = χ 2 (burst) + 2 × ln(N)χ 2 (no burst), with N being the number of fitted bands. We adopt the limit of |∆BIC| > 6 to put a preference on a model: this represents strong evidence against the model with the higher BIC  (Kass & Raftery 1995; Salmon et al. 2016). In all cases, ∆BIC is found between +5 and -3.
Fitting the dust IR SED We model the dust emission using all the IR data available (Spitzer/MIPS, Herschel/PACS and SPIRE, and ALMA) to mea- sure L dust independently of the stellar continuum. This quantity is commonly used to measure obscured star formation using simple assumptions about the SFH and dust absorption (e. g. ,  Kennicutt & Evans 2012). In this case no energy budget is performed and the IR data are fitted to the  Draine & Li (2007)  models. No AGN is introduced. Without IRAC data which are not considered for the fits, the constraint on the AGN component would be very weak. We plot the resulting values in Fig. 3  and compare them to the measurements of  Elbaz et al. (2018)  for the 15 galaxies in common. We find very good agreement between both values. The L dust values are listed in Table  3.
Fitting the full SED We now fit the full datasets with the same models and parameters defined in Section 3. First, as for the stellar continuum, we test the requirement to introduce a recent burst by calculating the BIC difference between the scenarios with and without a burst. We apply the same criterion: there is strong evidence for a burst when ∆BIC < -6. Using C00 and CF00 we find strong evidence for a burst for respectively 13 and 11 of the 17 galaxies. We find ∆BIC 6 (i. e. , close to strong evidence for no burst) for UDF13 with both C00 and CF00 and also for UDF7 and GS4 with CF00. With LF17 we find strong evidence for a burst for only three galaxies (UDF3, GS3, and GS6). For all the other cases, ∆BIC 6. The requirement for a burst for C00 and CF00 is essentially due to the fact that the delayed SFH model alone is most of the time unable to produce enough IR emission.
Dust emission L dust is an output of the three fits performed in Section 4. Here we compare the different estimates. CIGALE outputs L dust due to absorption of starlight, as the result of the energy budget. We correct the measure of dust emission with IR data (Section 4. 2) for the AGN contribution estimated with the full SED in order to compare the three measurements of L dust. While we expect a large difference between the results from the fits of the stellar continuum and the other measurements, the L dust values measured from IR data and from the full SED should be similar. We show in Fig. 6  that the agreement between both measurements is indeed very good.
Dust emission from the fit of the stellar continuum We find a substantial attenuation when fitting the stellar continuum alone with an average value of A V = 1. 6 ± 0. 5 mag. In Fig. 6  we compare L dust induced by this attenuation to the values measured with IR data only in Section 4. 2. L dust values obtained with the fit of the stellar continuum are found to be lower than the luminosities measured with IR data. The median value of the ratio between both measurements is 0. 41 for the whole sample, and 0. 56 and 0. 28 for the UDF and GS galaxies respectively. In one case (UDF13) the IR emission is over-predicted from the fit of the stellar continuum alone, with an excess of 30%. In section 4 we show that both the stellar and full SEDs of UDF13 can be fitted without adding a burst, for any attenuation law.
Comparison of the HUDF, GOODS-S and COSMOS/HELP surveys As part of the HELP project 6 ,  Małek et al. (2018)  analyzed the SED of more than 40 000 galaxies of the pilot field ELAIS-N1 and performed SED fitting with CIGALE. They estimated L dust from the stellar continuum for 686 galaxies in the ELAIS-N1 field detected with Herschel with a signal-to-noise ratio (S/N) larger than two. They found a very good agreement and a tight relation between both estimations (their figure 12. c). The ELAIS-N1/HELP field is not very deep with a mean redshift of 0. 93 and not suitable for a comparison with our current study. In order to compare samples in the same range of redshift and L dust , we consider the COSMOS/HELP field for which similar data products are available. As for the ELAIS-N1 study, we select galaxies with Herschel detections (at least two SPIRE fluxes with an SNR>3 and at least one PACS flux with SNR > 2), at least six photometric measurements of the stellar continuum, and good-quality fits of the stellar continuum and IR SED (for more details of the selection see  Małek et al.
(2018) ). We select 3365 galaxies with z < 3 and 694 with 1. 5 < z < 2. 5. In Fig. 8  we plot the two estimations of L dust (from the stellar continuum alone and with IR data) for the COSMOS sample, the 6 The Herschel Extragalactic Legacy Project (https://herschel. sussex. ac. uk/). HELP data products are distributed on HeDaM (http://hedam. lam. fr/HELP/ ) correlation is good with a correlation coefficient of 0. 90. We can see on the plot that the dispersion around the linear fit increases with redshift and luminosity. The dispersion for the full sample is σ = 0. 26 and increases to σ = 0. 36 for the subsample of galaxies with 1. 5 < z < 2. 5 and L dust > 10 11 L. The values for the GS and UDF galaxies are over-plotted. All of them lie on or above the linear regression line found for the COSMOS sample.
Star formation rate In Fig. 10 , we compare the SFR measured from the full SED with the simple approach of adding the SFR obtained from the total dust and UV emissions: SFR(IR)+SFR(UV). We adopt here the calibrations of  Buat et al. (2008)  between luminosities and SFR. We use L dust values obtained with the fit of the IR data and corrected for the AGN contribution as explained in Section 5. The SFRs(IR) agree within a factor two with the same quantity measured by  Dunlop et al. (2017)  for the UDF galaxies (SFR FIR1 in their Table  4 ) except for UDF3 for which our measurement is 2. 5 higher than the value they reported. The restframe UV luminosity (without dust correction) is an output of CIGALE already used in section 5. 2: it comes from the fit of the stellar continuum alone. We find a satisfying agreement between the two estimates of the SFRs.
The SFR(IR)+SFR(UV) calculation is based on very simple assumptions on the SFH and dust heating. We therefore Table  3. Physical quantities estimated from the fits performed in section 4 with the 1σ error given by CIGALE. Column 1: Target names of the sources. Column 2: L dust from the fit of the IR data (section 4. 2). Columns 3 and 4: SFR and stellar masses from the fit of the full SED (section 4. 3). Column 5: stellar masses from the fit of the stellar continuum alone (section 4. 1). do not expect a perfect agreement with our measurements coming from SED fitting. In Fig. 10  we also plot the SFRs measured from the stellar continuum alone. We find they are much lower than the ones based on the full SEDs by a factor similar to the one found for the estimation of L dust. Once again, UDF13 departs from the general trend.
Another one (GS6) has an SFR 3. 5 times higher than the MS. If we now use the stellar masses measured from the full SED, five galaxies are found above the MS but close to the boundaries of the relation, and no galaxy is starbursting. UDF13 is still well below the MS and UDF3 very close to starbursting. 2015 ) relations using either the stellar mass measured with the fit of the stellar continuum only (empty circles) or with the fit of the full SED (filled circles, red for C00, blue for LF17, and black for CF00). Vertical lines connect the two SFR ratios calculated with the different stellar mass estimations for each galaxy. Dunlop et al. (2017)  concluded that their data were completely consistent with the published MS relations at z 2. Elbaz et al. (2018)  also introduced the variation of the MS with redshift by using the relations of  Schreiber et al.
(2015)  as we did above. They also found that the UDF and GS galaxies are located in the upper part of the MS and only UDF3, GS6, and GS7 are starbursting, with a SFR more than four times above the MS. In our analysis, GS6 and GS7 appear less extreme but still well above the MS for a stellar mass estimated with the stellar continuum. The difference found between L dust estimations with the fit of either IR data or stellar continuum discussed in section 5. 3 is found to increase with star forming activity. In Fig. 12  the same quantities as in Fig. 8  are plotted but this time color coded with the specific SFR defined as the SFR from the fit of the full SED divided by the stellar mass from the fit of the stellar continuum. For COSMOS, UDF, and GS galaxies the excess of dust emission measured with IR data increases with sSFR.
Conclusions Here, we analyze the SEDs of 17 dust-rich galaxies at z ∼ 2 detected with ALMA from the blind survey conducted by  Dunlop et al. (2017)  in the HUDF and the massive Herschel galaxies in the GOODS-South field by  Elbaz et al. (2018). We performed several fits with CIGALE with different attenuation laws, considering only the stellar continuum, the dust emission, and the full dataset. The fit of the stellar continuum with the starburst law of  Calzetti et al. (2000)  reveals an obscuration which corresponds on average to one-third (UDF galaxies) to half (GS galaxies) of the total dust emission detected with Herschel and ALMA. Qualitatively, the analysis of the slope of the UV continuum confirms this obscuration of the stellar continuum. We compared our estimations of dust emission to the same quantities measured for a sample of galaxies of similar L dust and redshift in the COS-MOS field.
Acknowledgements. VB thanks  David Elbaz  for very fruitful discussions which helped to improve the paper and  Tao Wang  for providing access to his Good South Herschel data before their publication. MB acknowledges funding from the  FONDECYT  regular project  1170618. KM has been supported by the  National Science Centre  ( UMO-2018/30/E/ST9/00082 ). The project has received funding from  Excellence Initiative of Aix-Marseille University -AMIDEX , a  French 'Investissements d'Avenir' programme.
For instance, a few million particles distributed globally are often enough to investigate the statistical properties of air mass transport (e. g. , monthly average residence times in a particular area that is not too small) but would not be enough for a case study of airstreams related to a particular synoptic situation (e. g. , describing flow in the warm conveyor belt of a particular cyclone). FLEXPART is an offline model that uses meteorological fields (analyses or forecasts) as input. Such data are available from several different numerical weather prediction (NWP) models. For the model version described here, v10. 4, data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS) can be used.
Common spatial resolutions for IFS depending on the application include 1 • ×1 • at 3 h (standard for older products, e. g. , ERA-Interim), 0. 5 • × 0. 5 • at 1 h (standard for newer products, e. g. , ERA5) and 0. 1 • ×0. 1 • at 1 h (current ECMWF operational data). The ECMWF IFS model currently has 137 vertical levels. NCEP GFS input files are usually used at 1 • × 1 • horizontal resolution, with 64 vertical levels and 3 h time resolution. NCEP GFS input files are also available at 0. 5 • × 0. 5 • and 0. 25 • × 0. 25 • horizontal resolution. Other FLEXPART model branches have been developed for input data from various limited-area models, for example the Weather Research and Forecasting (WRF) meteorological model  (Brioude et al. , 2013)  and the Consortium for Small-scale Modeling (COSMO) model  (Oney, 2015) , which extend the applicability of FLEXPART down to the mesogamma scale.
Notice that the turbulence parameterizations of FLEXPART are valid at even smaller scales. Another FLEX-PART model version, FLEXPART-NorESM/CAM  (Cassiani et al. , 2016) , uses the meteorological output data generated by the Norwegian Earth System Model (NorESM1-M) with its atmospheric component CAM (Community Atmosphere Model). The current paper does not document these other model branches, but most share many features with FLEX-PART v10. 4 and some are briefly described in Appendix C. A key aspect of these model branches is the ability to read meteorological input other than that from ECMWF or NCEP.
2 (for ECMWF) and v6. 4 (for GFS). Version 8. 0 unified the model branches based on ECMWF IFS and NCEP GFS input data in one source package but still required the building of two different executables. Importantly, Fortran 90 constructs were introduced in parts of the code, such as initial support for dynamic memory allocation. Furthermore, a global land use inventory was added, allowing for more accurate dry deposition calculations everywhere on the globe (before, land use data were provided only for Europe). The reading of the -at the time -newly introduced GRIB-2 format with the ECMWF grib_api library was implemented in v8. 2. An option to calculate the sensitivity to initial conditions in backward model runs (in addition to the emission sensitivity calculations) was also implemented in v8.
All the output is written in a directory unique for each run. There are also other directories containing the model testing environment and example runs, as well as preprocessing and post-processing software (see Table  1 ). Sensu stricto FLEXPART consists of the (Fortran) source files required to build an executable, not including external libraries such as those needed for input reading. The make-files and the sample input as provided in the "options" may also be included under this term. However, in order to do real work with FLEXPART, one also needs to obtain meteorological input data (in the case of the ECMWF this is not trivial, so the flex_extract package is provided), and one needs to do post-processing. This is the reason why we include a selection of such tools here.
Boundary layer turbulence Subgrid-scale atmospheric motions unresolved by the meteorological input data need to be parameterized in FLEX-PART. This is done by adding stochastic fluctuations based on Langevin equations for the particle velocity components  (Stohl et al. , 2005). In the ABL, the stochastic differential equations are formulated according to the well-mixed criteria proposed by  Thomson (1987). Until FLEXPART version 9. 2, the Eulerian probability density functions (PDFs) for the three velocity components were assumed to be three independent Gaussian PDFs. However, for the vertical velocity component, the Gaussian turbulence model is well suited only for stable and neutral conditions. In the convective ABL (CBL), turbulence is skewed since a larger area is occupied by downdrafts than by updrafts (e.
Since the data amounts stored in the grid_time_date_nnn files can be much larger than in the grid_initial_nnn files, this is a highly efficient way to save storage space. This setup also interfaces directly with the inverse modeling package FLEXINVERT  (Thompson and Stohl, 2014 ). An application can be found in  Thompson et al. (2017)  wherein initial conditions were taken from a gridded observation product. A further output option, which was also introduced for practical considerations of inverse modeling, is the LINVERSIONOUT option in the file COMMAND. If LINVERSIONOUT is set to 1, then the grid_time_date_nnn and grid_initial_nnn files are written per release with a time dimension of footprints instead of the default per footprint with a time dimension of releases. Since inverse modeling assimilates atmospheric observations and each observation is represented by a single release, it is computationally more efficient to read in the grid files separated by release.