dataesr/scientific-paragraphs-categorization huggingface dataset:
Rubin Observatory Legacy Survey of Space and Time (LSST 4  ,  Ivezić et al. 2019 ), Euclid 5   (Laureĳs et al. 2011) , the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx 6  ,  Doré et al. 2014) , and the Nancy Grace Roman Space Telescope (WFIRST 7  ,  Spergel et al. 2015; Eifler et al. 2020a,b). The increased cosmological information encoded in these datasets will require a new level in accuracy of modeling cosmological observables. One of the fundamental quantities for making theoretical predictions is the matter power spectrum   (, ), which quantifies the amount of matter clustering at the second-order level and its evolution as a function of time. Previous studies have estimated that   (, ) needs to be predicted to ∼ 1% level out to  10 ℎMpc -1 for the future era of Stage IV cosmological experiments (e.
The 'baryonification' model contains prescriptions for the density profiles of the stellar, gas, and the redistributed DM components to correct the particle positions in DMO simulations so as to more accurately approximate what they would look like in the presence of baryons  (Schneider & Teyssier 2015; Schneider et al. 2019; Aricò et al. 2020). Dai et al. (2018)  propose using the potential gradient descent model to displace particles to improve the modeling of non-linear matter distribution. In this paper, we aim to utilize the information from small-scale cosmic shear data to place constraints on the strength of baryonic effects, and to compare the results with existing hydrodynamical simulations. We will also explore the potential for achieving more precise cosmological constraints with the inclusion of small-scale data.
We adopt the PCA baryon mitigation framework  (Eifler et al. 2015)  to perform our analyses. In  Huang et al. (2019) , we have validated and improved the performance of the PCA method using simulated analyses of cosmic shear mock data under an LSST-like survey configuration. Here we delve into its application to the observational data of DES Y1, which includes two-point correlations of cosmic shear, galaxy-galaxy lensing and galaxy clustering. With the ability to model small-scale cosmic shear, we push the cosmic shear observables down to 2. 5 and perform a combined analysis with galaxy-galaxy lensing and galaxy clustering data (subjected to the original conservative Y1 scale cuts). We begin with an overview of the data products, the theoretical modeling, and the analysis approaches in §2.
2018a). For galaxy-galaxy lensing and galaxy clustering, the scale cuts are defined using a specific comoving scales of ( ggl ,  clustering ) = (12, 8) Mpc ℎ -1 to avoid parameter biases due to non-linear biasing or non-locality of  t , and converted to their corresponding angular scales in each tomographic bin  (Krause et al. 2017). After scale cuts are applied, there are a total of 457 elements for the fiducial DES Y1 3×2pt cosmological analysis  (Abbott et al. 2018). In this analysis, we utilize the DES Y1 cosmic shear correlation function measurements down to scales of 2. 5. Together with the galaxy-galaxy lensing and galaxy clustering measurements (subjected to the original DES Y1 scale cuts), our extended 3×2pt data vector has a total of 630 data points (400 elements for cosmic shear, 176 elements for galaxy-galaxy lensing and 54 elements for galaxy clustering).
In Appendix B of  Huang et al. 2019 (hereafter H19) , we have discussed the convergence of power spectrum ratios in detail and provide an upper limit for their uncertainties due to cosmic variance. We have used the power spectrum ratio for MB2, Illustris, Eagle from H19. We extracted power spectrum measurements from the publicly released IllustrisTNG100 snapshot data  (Nelson et al. 2019 ) and added the corresponding baryonic scenario to our power spectrum library. The Horizon-AGN   () data are computed in  Chisari et al. (2018). The cosmo-OWLS and BAHAMAS   () sets are taken from the power spectra library released by  van Daalen et al. (2020). Specifically, as listed in Table  1  of  van Daalen et al. (2020) , for the cosmo-OWLS baryonic scenario sets, we use the   () data from files: • AGN_Mseed800_WMAP7_L100N512, • AGN_Mseed800_Theat 8p5_WMAP7_L100N512, • AGN_Mseed800_Theat 8p7_WMAP7_L100N512, for the BAHAMAS sets, we use files: • AGN_CALIB_nu0_WMAP9_L400N1024, 10 -1 10 0 10 1 10 2 k [Mpc -1 h] 0.
6 0. 8 1. 0 1. 2 1. 4 P hydro δ (k)/P DMO δ (k) z = 0. 0 BAHAMAS (T7. 8) BAHAMAS (T7. 6) BAHAMAS (T8. 0) 10 -1 10 0 10 1 10 2 k [Mpc -1 h] 0. 6 0. 8 1. 0 1. 2 1. 4 z = 0. 3 MB2 Eagle Horizon-AGN Illustris TNG100 10 -1 10 0 10 1 10 2 k [Mpc -1 h] 0. 6 0. 8 1. 0 1. 2 1. 4 z = 0. 8 cOWLS-AGN (T8. 0) cOWLS-AGN (T8. 5) cOWLS-AGN (T8. 7) Figure  1. Baryonic effects on the 3D matter power spectrum at different redshifts. We plot the power spectrum ratio for 11 hydrodynamical simulations with respect to their corresponding DMO simulation setting at the same initial condition: IllustrisTNG, MB2, Eagle, Horizon-AGN, Illustris, the cosmo-OWLS sets, and the BAHAMAS sets, at redshifts 0. 0, 0. 3, 0. 8. The gray vertical lines delineate regions where the data points come from direct measurement ( < 30 ℎ Mpc -1 ) and from extrapolation ( > 30 ℎ Mpc -1 ) with a quadratic spline fit based on data points at  ∈ [10, 30] ℎ Mpc -1.
In H19, we validate this method assuming an LSST-like cosmic shear experiment. We further improve the efficiency of this method by imposing a covariance-driven weighting factor when performing PCA, which is referred to as method C in H19. Below we briefly summarize the formalism of this method. Let M be a DMO-based theoretical 3 × 2pt model vector, and B  be a model vector contaminated with baryonic scenario , computed by replacing the matter power spectrum via Eq. (  1 ) (see §2. 1. 3 for detail). We first build a difference matrix 𝚫 𝚫 = B 1 -M B 2 -M. B 𝑁 sim -M 𝑁 data ×𝑁 sim. (15) Each column of  is a difference vector, B  -M , with 630 elements ( §2. 1. 1), computed with the cosmology and the nuisance parameters being set to the fiducial values listed in Table  1. Next we use the Cholesky decomposition on the data vector covariance matrix, to find the square root of the covariance C = LL t.
Input hydrodynamical scenarios for PC construction We will use the Illustris and the Eagle scenarios as the conservative and optimistic validation scenarios for our PCA baryon mitigation model. As mentioned before, we exclude the considered scenario in the baryon PC basis set, hence we are building two PC bases for this exercise, one excluding Eagle and the second one excluding Illustris. The first PC set is constructed with 10 hydrodynamical scenarios: MB2, Horizon-AGN, TNG100, Eagle, three variants of cosmo-OWLS, and three variants of BAHAMAS scenarios with different AGN feedback strength. We will use this basis set to mitigate baryonic effects for our Illustris and DMO mock data vectors (see §2. 1. 3), and for the real DES Y1 observational data vector. The second PC basis set is constructed with the same scenarios as the first, with the Eagle scenario being excluded.
2017; Hikage et al. 2019; Abbott et al. 2018) , where the weak lensing results show lower values in the  8 constraints. As shown in the right panel of Fig. 13 , although the two datasets are largely in agreement to within the 95% confidence level in their 2D posterior constraints, the marginal 1D  8 constraints differ by more than 1 (see summary in Fig. 9 ).
Adopting cosmological parameter priors from external datasets As discussed in §4. 4, we adopt the Planck EE+BAO likelihood as the primary source of prior information on cosmological parameters, due to its consistency with DES Y1 data. With the inclusion of the Planck EE+BAO information, the constraints on cosmology improve by ∼ 38% in the 68% confidence interval of  1 (see the red curve in Fig. 15 ), which is further quantified as: -0. 06 <  1 < 3. 04 (68%, DES+Planck EE+BAO) -1. 68 <  1 < 4. 66 (95%, DES+Planck EE+BAO). With the tighter constraining power, the cosmo-OWLS 10 8. 7 K scenario is disfavored by ∼ 2. 8. We note that when claiming the cosmo-OWLS (10 8. 7 K) scenario is ruled out at > 2 significance with DES Y1 data, this does not mean that we are ruling out setting the subgrid physical parameter of Δ heat at 10 8.
2019 ) leads to a slight shift of ∼ 0. 5 in  8 , but overall the resulting likelihoods are still in agreement within Y1 errors  (Troxel et al. 2018a; Samuroff et al. 2018). The improved data quality in forthcoming datasets will likely mean that discrepancies induced from these small-scale systematics will become non-negligible, and will require extra efforts to extract precise joint constrains on both cosmology and baryonic physics. The ongoing KiDS and HSC analyses provide an excellent dataset to get additional insights into discriminating between different baryonic physics scenarios. Moreover, future datasets from DES Year 3 and Year 6 will provide improved joint constraints on baryonic physics and cosmological parameters. In the regime where effects of baryonic physics are causing the suppression of clustering power ( few 10 Mpc -1 ℎ), the properties of halo gas contain a wealth of information on baryon feedback mechanisms.
Course of the Study We performed a single-center, prospective, non-interventional study. All patients meeting the inclusion criteria and presenting at the day hospital for infliximab infusion between January 1, 2018 and March 1, 2018 were included. In accordance with French and European recommendations,  4, 6, 7  infliximab treatment was initiated immediately in cases of severe, or serious disease, as well as in cases of classical treatment failure and of prior biotherapy treatment failure or escape. On the day of inclusion, just before the scheduled perfusion of infliximab, a blood sample was collected to determine the residual levels of infliximab and to test for anti-infliximab antibodies in an enzyme-linked immunosorbent assay (ELISA). 11 The patients were then examined by a doctor from the department, and a data collection form was completed, including demographic data, the site and classification of the disease according to the Montreal scale, a description of fistulous anoperineal lesions according to the UFS classifica-tion (Ulceration/Fistula/Stenosis classification also called CARDIFF classification)  12  and American Gastroenterological Association (AGA)  13  classification, surgical history, previous and concomitant treatments.
Acknowledgements This study was supported by the  French scientific interest group "Obépine"  for the surveillance of SARS-CoV-2 in wastewater of France. The steering committee of "Obépine" is composed of  I. Bertrand  and  C. Gantzer  ( Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement, LCPME, UMR 7564 CNRS-Université de Lorraine, Nancy, France ),  S. Le Guyader  ( Ifremer Nantes, France ),  Y. Maday  ( Institut Carnot Smiles, Sorbonne Université, Laboratoire Jacques-Louis Lions ,  UMR 7598  and  Institut Universitaire de France) ,  V. Maréchal  ( Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, 75012, Paris, France ),  J. -M. Mouchel  ( Sorbonne Université, CNRS, EPHE, UMR 7619 Metis, Paris, France, e-LTER Zone Atelier Seine ),  L. Moulin  and  S.
Funding This study was financially supported by  SUEZ (France). It has partially benefited from the financial support of the  French Government's Ministry of Higher Education and Research and Innovation (Ministère de l'enseignement supérieur, de la recherche et de l'innovation [MESRI])  for the development of the SARS-CoV-2 quantification method. All the authors' salaries were funded by either  Lorraine University (France) , the  University Hospital of Nancy (France)  or SUEZ (France).
Introduction Spatially and temporally resolved atmospheric emission inventories are key to investigate and predict the transport and chemical transformation of pollutants, as well as to develop effective mitigation strategies (e. g. Pouliot et al. , 2015; Galmarini et al. , 2017). During the last decade, global and regional inventories have substantially increased spatial resolution from ~50 by 50 km (e. g. MACCity;  Granier et al. , 2011; EMEP-50km, Mareckova et al. , 2013)  to ~10 by 10 km or less (e. g. EMEP-0. 1deg, Mareckova et al. , 2017 ; TNO-MACC;  Kuenen et al. , 2014). Several datasets even provide emission maps for selected pollutants or study regions with resolutions as fine as 1 by 1km (e. g. ODIAC2016,  Oda et al. , 2018; Hestia-LA; Gurney et al. , 2019; Super et al. , 2020).
This improvement is largely due to the emergence of new detailed, satellite-based and open-access spatial proxies such as the population maps at 1 by 1 km proposed by the Global Human Settlement Layer (GHSL) project  (Florczyk et al. , 2019) , the global land cover maps at 300mx300m provided by the European Spatial Agency Climate Change Initiative (ESA CCI, https://www. esa-landcover-cci. org/) or the georeferenced road traffic network distributed by Open Street Maps (OSM, www. openstreetmap. org). While a clear evolution is observed in terms of spatial resolution, the improvement of the temporal representation in current state-of-the-art emission datasets has not been addressed much  (Reis et al. , 2011). Using global and regional emission inventories in atmospheric chemistry models requires the original aggregated annual emissions to be broken down into fine temporal resolutions (ideally hourly) using emission temporal profiles (e.
g. Borge et al. , 2008; Bieser et al. , 2011; Mues et al. , 2014). In practice, temporal profiles are normalized weight factors for each hour of the day, day of the week and month of the year. At the global scale, the most commonly used emission temporal profiles are the monthly factors provided by the Air Pollutants and Greenhouse Gases Emission Database for Global Atmospheric Research inventory (EDGARv4. 3. 2; Janssens-Maenhout et al. , 2019)  and the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants inventory (ECLIPSEv5. a; Klimont et al. , 2017). Also at the global level, the Temporal Improvements for Modeling Emissions by Scaling (TIMES) dataset was produced to represent the weekly and hourly variability for global CO2 emission inventories  (Nassar et al. , 2013).
More recently,  Crippa et al. (2020)  developed a new set of high-resolution temporal profiles for the EDGAR inventory, which allows producing monthly and hourly emission time series and grid maps. At European level, the temporal factors provided by the University of Stuttgart (IER) as part of the Generation of European Emission Data for Episodes (GENEMIS) project are still considered as the main reference  (Ebel et al. , 1997; Friedrich and Reis, 2004). The original GENEMIS profiles were later used as a basis to derive two independent datasets: (i) the EMEP temporal profiles, which provide monthly, weekly and hourly weight factors that vary per emission sector, country and pollutant  (Simpson et al. , 2012) , and (ii) the TNO temporal profiles, which provide monthly, weekly and hourly weight factors that vary per emission sector (Denier van der  Gon et al.
Overview The CAMS-TEMPO dataset consists of a collection of global and regional temporal factors that follow the domain description and sector classification reported by the CAMS-GLOB_ANT and CAMS-REG_AP/GHG emission inventories. In order to better distinguish between the two sets of profiles, we refer to them as CAMS-GLOB-TEMPO (https://doi. org/10. 24380/ks45-9147, global temporal profiles associated to the CAMS-GLOB_ANT inventory) and CAMS-REG-TEMPO (https://doi. org/10. 24380/1cx4-zy68, regional European temporal profiles associated to the CAMS-REG_AP/GHG inventory). Depending on the pollutant source and temporal resolution (i. e. monthly, weekly, daily, hourly), the resulting profiles are reported as spatially invariant (i. e. a unique set of temporal weights for all the domain) or gridded values (i.
e. temporal weights vary per grid cell). Similarly, depending on the characteristics of the input data used and approaches to compute the profiles, these can be yearly dependent and/or pollutant dependent. The spatial resolution of the gridded profiles is 0. 1x0. 1 deg for CAMS-GLOB-TEMPO and 0. 1x0. 05 deg for CAMS-REG-TEMPO. In the case of CAMS-REG-TEMPO, the domain covered by the dataset is: 30° W -60° E and 30° N -72°N. Table  1  and Table  2  summarise the characteristics of each temporal profile included in CAMS-GLOB-TEMPO and CAMS-REG-TEMPO datasets, respectively. The sector classification for each case corresponds to those used in CAMS-GLOB_ANT and CAMS-REG_AP/GHG. For both temporal profile datasets, the sum of all weight factors is equal to 12 for monthly profiles, 7 for weekly profiles, 365 or 366 (in case of a leap year) for daily profiles and 24 for hourly profiles.
The following subsections describe the input data and methodologies used to compute the emission temporal profiles for each targeted sector: (i) energy industry (Sect. 2. 2), (ii) residential/commercial combustion (Sect. 2. 3), (iii) manufacturing industry (Sect. 2. 4), (iv) road transport (Sect. 2. 5), (v) aviation (Sect. 2. 6) and (vi) agriculture (Sect. 2. 7). The specificity of the computed profiles depends upon the degree of sectoral disaggregation used in the original CAMS inventories. For example, the CAMS-GLOB_ANT dataset reports emissions from power and heat plants and refineries under the same sector ("ene", see Table  1 ) and therefore a common set of temporal profiles had to be assumed for the two types of facilities. In contrast, the CAMS-REG_AP/GHG inventory reports power and heat plants under the GNFR_A category and refineries under sector GNFR_B, together with all manufacturing industries (Table  2 ).
Energy industry The temporal profiles computed for the energy industry are reported under the ene sector in CAMS-GLOB-TEMPO and the GNFR_A category in CAMS-REG-TEMPO. The temporal variability of emissions from this sector was estimated from electricity production statistics under the assumption that it largely depends upon the combustion of fossil fuels in power and heat plants. This approximation is consistent with the definition of the GNFR_A sector in the CAMS-REG_AP/GHG dataset. The representativeness of the computed profiles is likely lower in CAMS-GLOB_TEMPO because the ene sector also includes other facilities such as refineries. As shown in Table  1  and Table 2 , the profiles reported for this sector include pollutant and country dependent monthly, weekly and hourly factors. The electricity production dataset compiled to derive profiles for this sector were as follows: • The ENTSO-E Transparency Platform  (Hirth et al.
, 2018; ENTSO-E, 2018)   The compiled data was first analysed to assess whether interannual variability is important for this sector. Seasonal cycles were computed for different years (2010 -2017) and countries using the IEA statistics. In the majority of the countries analysed, the monthly profiles were found to be consistent through the different years and to present small interannual variations (Fig. S1 ). Although some studies have pointed out a temperature dependence of the monthly electricity generation in power plants  (Thiruchittampalam, 2014) , we neglected it at present. Consequently, we assume the monthly temporal profiles for this sector to be the average over all the available years of data.
Monthly profiles For European countries, monthly profiles were derived using the ENTSO-E dataset. The analysis of the data showed that the seasonality of electricity production varies significantly by fuel type (Fig. S1 ). The different use of energy sources (i. e. lignite, hard coal, natural gas, biomass and oil) implies that temporal patterns will also vary from one pollutant to the other. For each month, country and pollutant, profiles were calculated following Eq. (  1 ): ! !,#,$ = ∑ ! !,#,% &' !,# * )& #,$ ∑ &' !,# * )& #,$ % #&' + %,- (1) Where ! !,#,$ is the monthly factor for month m, country c and pollutant p; ! !,#,% is the is the monthly factor for month m, country c and fuel f; %& #,% is the fuel share factor for country c and fuel f and '% %,$ is the emission factor for fuel f and pollutant p.
Fuel share factors were obtained by averaging the ENTSO-E production data for years 2010 to 2017 per country, and the emission factors were taken from the EMEP/EEA 2016 emission inventory guidebook for the priority air pollutants (EMEP/EEA, 2016; 1. A. 1 Energy industries, Table  3 -2, 3-3, 3-4, 3-5 and 3-7) and from the IPPC guidelines  (IPCC, 2006;  Volume 2: Energy, Table  2. 2) for GHGs (Table  3 ). We note that only fuels with shares larger than 10% were considered. For instance, in the case of Austria, only hard coal (25%) and natural gas (65%) were used, and the original shares were normalised so that their sum equalled 100%. This was done to avoid introducing errors due to residual fuels, which may be related to few (or even just one) power plants. For other countries, monthly factors by pollutant could not be developed as both the IEA and the MSB datasets do not report electricity production split by fuel type.
Hence, monthly factors were derived by averaging the available production data per month and relating them to the total production in the year. For the US, NOx and SOx monthly profiles were derived from the corresponding hourly measured emissions reported by the EPA. The seasonality for the other pollutants (i. e. NMVOC, NH3, CO, PM10, PM2. 5, CO2, CH4) was linked to the measured heat input, following EPA's recommendations.
Weekly profiles Weekly profiles were developed for Europe and the US using the hourly electricity production data reported by ENTSO-E and US EPA, respectively. As in the case of the monthly profiles, weekly scale factors were found to significantly vary according to the type of fuel (Fig. S1 ). These results are in line with the conclusions of Adolph (1997), which identified three generic weekly profiles-base, medium and peak load-as a function of the type of power plant. Pollutant-related weekly profiles were developed following the same methodology applied for obtaining the monthly weight factors (Sect. 2. 2. 1). For countries with no information on daily electricity production data, we used the weekly profile reported in the TNO dataset for the energy sector.
Hourly profiles Hourly profiles were developed for Europe and the US using the hourly electricity production data reported by ENTSO-E and US EPA, respectively. As previously seen, large differences are observed between fuels. Profiles related to the so-called base peak load power plants (i. e. annual useful life of more than 4000 hours) present a rather flat distribution, whereas in other cases the change in energy production between day and night is relatively high (Fig. S1 ). Pollutant-related hourly profiles were developed following the same methodology applied for obtaining the monthly weight factors (Sect. 2. 2. 1). For countries with no information on hourly electricity production data, we assumed the hourly profile reported in the TNO dataset. Some studies have suggested that the hourly variation of power plant activities may vary according to the season of the year  (Thiruchittampalam, 2014).
Monthly and daily profiles Gridded daily temporal profiles were derived according to the heating Degree Day (HDD) concept, which is an indicator used as a proxy variable to reflect the daily energy demand for heating a building  (Quayle and Diaz, 1980). This method has been proven to be successful in previous emission modelling works (e. g. Mues et al. , 2014; Terrenoire et al. , 2015). The heating degree day factor (())(+, -)) for grid cell x and day d is defined relative to a threshold temperature (/. ) above which a building needs no heating (i. e. heating appliances will be switched off), following Eq. (  2 ): ())(+, -) = max (/. -/ /! (+, -), 1) (2) Where / /! (+, -) is the daily mean 2m outdoor temperature for grid cell x and day d [ºC]. This information was obtained from the ERA5 reanalysis dataset for the period  2010 -2017 (C3S, 2017).