dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementational details All numerical experiments were carried out in FreeFEM  [50]  integrated with PETSc  [43]. The codes are downloadable from https://bitbucket. org/sudhipv/mbe covid. The correspondence among the code and relevant equations is detailed on the README. md file in the repository. The two-grid variants of preconditioners differ only in the coarse solver. The preconditioner architecture is implemented by using a single V-cycle multigrid framework from PETSc. The two-grid RAS-V3 makes use of HYPRE [51] which is available through PETSc which runs BoomerAMG  [52]  as preconditioner for the coarse solver. This multilevel coarse solver adopts a V-cycle with Jacobi smoother and Gaussian elimination for coarse correction  [43, 44]. This was specifically chosen to address the convergence bottleneck for high-resolution models.
The DD algorithm lends itself to a data parallel program which utilizes Message Passing Interface (MPI) libraries to exploit distributed memory machines (linux clusters) maintained by Digital Research Alliance of Canada [55] such as Beluga  [56]  or Niagara  [57]. In these machines, each computational node consists of 40 Intel Skylake cores running at 2. 4 GHz connected through an EDR infiniband network. In our parallel implementation, the MPI initializes the same number of tasks/processes as the number of cores giving exactly one task per core called a pure distributed-memory program  [58]. Although no hybrid parallelism is used in the current investigation, it can be implemented by delegating subdomain (local) solves to accelerators such as graphics processing units.
Application: Southern Ontario In this section we apply the two-grid RAS -V3 preconditioner to the region of Southern Ontario. This region, consisting of 27 Public Health Units (PHUs), is densely populated, accounting for 95% of the population of the province of Ontario, while only occupying approximately 13% of area  [60]. The public data on COVID-19 statistics from these PHUs can help to infer the status of COVID-19 infections in time and space. We utilized the open source software QGIS  [61]  to define the geographical boundaries and generate a mesh file  [62]. The meshed domain was then subdivided into different subdomains for the domain decomposition solver. The mesh of Southern Ontario subdivided into 200 subdomains is shown in Figure  8  (overlapping parts not shown for clarity). Note that these subdomains do not correspond to the aforementioned PHUs.
The initial conditions were obtained from publicly reported testing data on September 1, 2020. We generated the densities of each compartment as a sum of 27 Gaussian pulses, centered at the main city of each PHU (following a similar approach as  [7] ): s(x, 0) = 27 i=1 A i exp (x -x i ) 2 + (y -y i ) 2 2B 2 i , (5. 2) where each (x i , y i ) denotes the coordinates of the cities, and B i represents the radius around (x i , y i ) which captures 95 % of the population of the i th PHU. The value of A i is a constant which ensures that the integral of the two dimensional function over the entire domain equals to the total number of people in the respective compartment for that PHU. The parameters reported for the Southern Ontario case involved a two-step manual tuning. First, an ODE-based SEIRD model was initialized with the values used for the square domain.
The following 10 compartments involves a diffusion term, modeling spatio-temporal population movement : S , V, E, F, A, B, C, P, R1, R2. Publicly available data may not permit construction and calibration of stratified SEIRD models based on age, co-morbidity, sex, socio-economic status, etc. [1]. However, private health care databases (e. g. , COVID-19 database from ICES  [21] ) can permit such detailed stratified SEIRD-based modeling.
Acknowledgments Sudhi Sharma  acknowledges the support of the  Ontario Trillium Scholarship for International Doctoral Students. Brandon Robinson  acknowledges the support of the  Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program  from the  Natural Sciences and Engineering Research Council of Canada (NSERC)  and the  Ontario Graduate Scholarship. Abhijit Sarkar  acknowledges the support of a  Discovery Grant  from  NSERC.
3. 10. In order to fully determine the shape of the NIR-optical-UV continuum, we also retrieved archival spectra using the HST Space Telescope Imaging Spectrograph (STIS)  (Woodgate et al. 1998) , which extends the spectral coverage to 10255 Å. The STIS spectrum was scaled by a factor of 0. 68 to match the 2019 COS flux at their overlapping energy bands. These data were also processed with the standard data reduction pipelines. The observation log of the HST COS and STIS observations, and the XMM-Newton and NuSTAR observations that were used in our SED modelling, are provided in Table  1. In our SED modelling we made use of XMM-Newton OM  (Mason et al. 2001)  data. The data from OM, which was operated in the Science User Defined mode, were taken with the primary photometric filters and the optical grism.
X-ray data For our SED modelling in the X-ray band, we fitted spectra from XMM-Newton RGS and EPIC-pn, and NuSTAR. The XMM-Newton data were processed using the Science Analysis System (SAS v18. 0. 0). The RGS  (den Herder et al. 2001)  instruments were operated in the standard Spectro+Q mode. The data were processed through the rgsproc pipeline task; the source and background spectra were extracted and the response matrices were generated. We filtered out time intervals with background count rates >0. 1 count s -1 in CCD number 9. The first-order RGS1 and RGS2 spectra were fitted simultaneously over the 6-37 Å band in our spectral modelling. The XMM-Newton EPIC-pn instrument  (Strüder et al. 2001 ) was operated in the Small-Window mode with the Thin Filter. Periods of high-flaring background for EPIC-pn (exceeding 0.
4 count s -1 ) were filtered out while applying the #XMMEA_EP filter. We extracted a single event (PATTERN==0), high-energy (PI>10000 && PI<12000) light curve from the event file over the entire chip to identify intervals of high-flaring background. The XMM-Newton EPIC-pn spectra were extracted from a circular region centred on the source with a radius of 40. The background was extracted from a nearby source-free region of radius 40 on the same CCD as the source. The pileup was evaluated to be negligible. The single and double events were selected for the EPIC-pn (PATTERN <= 4). Instrumental response matrices were generated for the spectrum using the rmfgen and arfgen tasks. The fitted spectral range for EPIC-pn is 0. 3-10 keV. The NuSTAR observations were reduced using the NuSTAR Data Analysis Software (NUSTARDAS) and CALDB calibration files of HEASoft v6.
27. The data were processed with the standard pipeline script nupipeline to produce level 1 calibrated and level 2 cleaned event files. The data from the South Atlantic Anomaly passages have been filtered out and event files were cleaned with the standard depth correction, which reduces the internal background at high energies. The source was extracted from a circular region (radius ∼90 ), with the background extracted from a source-free area of equal size on the same detector. Then the nuproducts script was run to create level 3 products (spectra, lightcurves, ARF and RMF response files) for each of the two hard X-ray telescope modules (FPMA and FPMB) onboard NuSTAR. The spectra and corresponding response files of the two telescopes were combined for spectral modelling using the mathpha, addrmf, and addarf tools of the HEASOFT package.
NIR-optical-UV continuum and reddening The NIR-optical-UV spectrum of NGC 3227 is shown in Fig. 3. To fit the continuum emission extending from NIR to UV we started with a disk blackbody model (dbb in SPEX). This model is based on a geometrically thin, optically thick, Shakura-Sunyaev accretion disk model  (Shakura & Sunyaev 1973). This model is appropriate for the accretion disk of the Seyfert-1 galaxy NGC 3227 and fits well the continuum of HST COS and STIS spectra over 1067-10255 Å, after including in the model the effect of reddening described below. The 2016 unobscured observation does not have any HST data and its XMM-Newton OM exposure was taken only with the UVW1 filter. Therefore, in our SED modelling we assume the shape of the NIR-optical-UV continuum in the 2016 observation was the same as in the 2019 one, and simply scale the normalisation of the dbb model (derived from the 2019 observation) to match the 2016 OM flux.
The UVW1 flux in the 2016 observation was higher by a factor of 1. 82 than in the 2019 observation, thus we use this flux ratio for scaling the dbb model. NGC 3227 displays significant internal reddening. This is evident from both the shape of the observed optical-UV continuum and the flux ratio of the AGN emission lines. As shown in the optical-UV spectrum of Fig. 3 , and later in the SED plot of Fig. 7  (top panel), the observed optical-UV continuum drops steeply towards higher energies. This is characteristic of strong reddening. The Milky Way reddening in our line of sight is too small to account for this, as it has a colour excess E(B -V) = 0. 02 mag  (Schlafly & Finkbeiner 2011) , reported by NASA/IPAC Extragalactic Database (NED). Therefore, the additional reddening in our line of sight is intrinsic to NGC 3227.
(2007)  using archival HST Near Infrared Camera and Multi-Object Spectrometer (NICMOS) observations, taken with the F160W (16050 Å) and F222M (22180 Å) filters on 6 April 1998. These data are not included in our spectral fitting, but rather are overplotted for comparison with our SED model. As shown in Figs. 6  and 7  (right middle panel), the measured continuum at 16050 Å is consistent with our dbb model. However, at the longer wavelength of 22180 Å, the flux of the measured continuum is higher than that of the dbb model (see Fig. 7 , right middle panel). This excess above the dbb model is likely the high-energy tail of a thermal emission component from the hottest regions of the AGN torus. Indeed by going to longer wavelengths the SED becomes dominated by emission from the torus rather than the accretion disk, as shown for example in  Mehdipour & Costantini (2018)  for IC 4329A.
The data are from the 2016 unobscured (left panels) and 2019 obscured (right panels) observations. The data in the top panels include the effects of reddening and X-ray absorption, while those in the middle panels are corrected for these effects, revealing the underlying continuum. The best-fit continuum model is shown in dashed black line in the top panels and solid black line the middle panels for comparison with the data. Residuals of the fit, defined as (data-model)/model, are displayed in the bottom panels. The archival HST NICMOS data at NIR are not included in our spectral fitting, but rather are over-plotted for comparison with our SED model as described in Sect. 3. 1. The displayed spectra have been binned for clarity of presentation. The displayed OM data are taken with the UVW1 filter.
(2011 ), Done et al. (2012) ,  Petrucci et al. (2013 Petrucci et al. ( , 2018 Petrucci et al. ( , 2020)) ,  Kubota & Done (2018). We note that this is only one plausible explanation proposed in the literature for the origin of the soft X-ray excess in AGN (relativistic ionised reflection is another explanation), yet it is sufficient for our purpose of deriving a broadband SED for NGC 3227 in this paper. The parameters of the comt model are its normalisation, seed photon temperature T seed , electron temperature T e , and optical depth τ of the upscattering plasma. In our modelling the T seed parameter of comt is coupled to the T max parameter of the dbb model that fits the NIR-optical-UV data (Sect. 3. 1). Because in the 2019 observation the soft X-ray spectrum is obscured, it can be challenging to constrain the parameters of the soft X-ray excess.
Acknowledgements. This work is based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and the  USA (NASA). This research has made use of data obtained with the  NuSTAR mission , a project led by the  California Institute of Technology (Caltech) , managed by the  Jet Propulsion Laboratory (JPL)  and funded by  NASA. We thank the  Swift team  for monitoring our AGN sample, and the  XMM-Newton, NuSTAR , and HST teams for scheduling our ToO triggered observations. This work was supported by  NASA  through a grant for HST program number  15673  from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555.
SRON is supported financially by  NWO , the  Netherlands Organization for Scientific Research. J. M. acknowledges the support from  STFC (UK)  through the  University of Strathclyde UK APAP  network grant  ST/R000743/1. S. B. acknowledges financial support from  ASI  under grants  ASI-INAF I/037/12/0  and n. 2017-14-H. O. B. D. M. acknowledges support from  Ramán y Cajal Fellowship   RYC2018-025950-I. S. G. W. acknowledges the support of a PhD studentship awarded by the  UK Science and Technology Facilities Council (STFC). P. O. P. acknowledges financial support from the  CNES French space agency  and the  PNHE high energy national program  of  CNRS. G. P. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  865637 ).
Acknowledgements We acknowledge discussions with  Z. Ren ,  C. Xu ,  M. Kimura ,  M. Lyu ,  Y. Ye ,  Z. Yang ,  Q. Zhao ,  X. Cao ,  T. Myo , and  H. Toki. This work was supported in part by the  National Natural Science Foundation of China  under contract Nos. 12175042 ,  11890710 ,  11890714 ,  12047514 , and  12147101 ,  Guangdong Major Project of Basic and Applied Basic Research  No. 2020B0301030008 , and  China National Key R&D Program  No. 2022YFA1602402. This work was partially supported by the  111 Project. This work was supported in part by  Shanghai "Science and Technology Innovation Action Plan " Project No. 21ZR1409500. This work was supported by  RCNP Collaboration Research Network (COREnet). This work was supported by  MEXT/JSPS KAKENHI  Grant Number  JP21H00127. Numerical computation in this work was partly carried out at the  Yukawa Institute Computer Facility.
PIMD simulations were performed at a temperature of T ¼ 1 K (using the latest techniques as recently reviewed  [23]  and implemented in the CP2k simulation package  [24, 25]  ), essentially corresponding to the ground state, to investigate SDFs (see Sec. SI2). In addition, converged variational nuclear-motion computations were carried out, using the code GENIUSH,  [26] [27] [28]  to compute eigenenergies and eigenfunctions (and thus nuclear densities, NDs) of rovibrational eigenstates not only below but also above the first dissociation limit of HHe þ 3 (see Sec. SI3-SI7). These highly accurate calculations provide the basis for investigating the delocalization of the solvating He atom of HHe þ 3.
Acknowledgements IS and  AGC  are grateful to  NKFIH  for support (grants  ÚNKP-20-3 ,  ÚNKP-21-3 , and  K138233 ). The Bochum research has been partially funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy - EXC 2033-390677874 -RESOLV  and via grant  MA 1547/19-1  with computational resources provided by  HPC-RESOLV , HPC@ZEMOS, and  BoViLab@RUB. CS acknowledges partial financial support from the  Alexander von Humboldt Stiftung  and the  DFG -500244608. The Köln team has been supported by  DFG  via  SFB 956  (project  ID 184018867 ) and grants  AS 319/2-2  and  SCHL 341/6-2. This publication supports research performed within the  COST Action   CA21101  " Confined molecular systems : from a new generation of materials to the stars" (COSY), funded by the  European Cooperation in Science and Technology (COST).
Search for pair-produced scalar and vector leptoquarks decaying into third-generation quarks and first-or second-generation leptons in 𝒑 𝒑 collisions with the ATLAS detector The ATLAS Collaboration A search for pair-produced scalar and vector leptoquarks decaying into quarks and leptons of different generations is presented. It uses the full LHC Run 2 (2015-2018) data set of 139 fb -1 collected with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of √  = 13 TeV. Scalar leptoquarks with charge -(1/3) as well as scalar and vector leptoquarks with charge +(2/3) are considered. All possible decays of the pair-produced leptoquarks into quarks of the third generation (, ) and charged or neutral leptons of the first or second generation (, , ) with exactly one electron or muon in the final state are investigated.
Neural network training Simulated signal and background events in the training region are used to train several NNs for the various signal hypotheses. The NNs are implemented using the NeuroBayes package  [100, 101]  which combines a three-layer feed-forward NN with a complex and robust preprocessing of the input variables prior to their presentation to the NN. The purpose of the preprocessing is to facilitate optimal network training by ordering the input variables according to their ability to discriminate between signal and background, taking correlations into account, and removing all but the most powerful ones. NeuroBayes uses Bayesian regularisation techniques for the training process to improve the generalisation performance and to avoid overtraining. In general, the network infrastructure consists of one input node for each input variable plus one bias node, an arbitrary, user-defined number of hidden nodes, and one output node which gives a continuous NN output score ( out ) in the interval (0, +1), where large values indicate signal-like events and small values background-like events.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR;  MES of Russia  and  NRC KI , Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ; MOST, Taiwan; TENMAK, Türkiye; STFC,  United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG.
This analysis builds from previously published work (see Ref. [12]  and Ref. [14] ) using the same experiment and employing similar analysis procedures. Similar approaches were also employed by the CLAS Collaboration for the determination of the beam helicity asymmetry measurements using polarized neutrons  [5, 15, 16]. Here, we have extracted the beam-target (E) observable for neutral pion photoproduction from data taken with the CLAS FROzen Spin Target (FROST)  [17]. This observable is valuable both in multipole analyses and in providing a contribution to the Gerasimov-Drell-Hearn (GDH) and related sum rules  [18]. Our extraction uses a different beam and polarized target apparatus as well as reaction identification methodology from the single previous measurement (see Ref. [19, 20] ). Where the data overlap with the previous measurement it provides an important independent check on systematics in the extraction of double-polarization observable E, while improving the statistical quality of the world dataset.
The new data also provide first information for energies W < 1. 42 GeV. As described below, the observable E is measured using a longitudinally polarized target and a circularly polarized photon beam. The difference of cross sections for helicity states 3/2 and 1/2, that is, (dσ/d ) = (dσ 3/2 /d -dσ 1/2 /d ) , for γ N → N π , is given in terms of helicity amplitudes: dσ 3/2 d = q k |H 1 | 2 + |H 3 | 2 , ( 1 ) dσ 1/2 d = q k |H 2 | 2 + |H 4 | 2 , ( 2 ) where q and k are the pion and photon center-of-mass momenta, respectively. Helicity amplitudes are labeled following Ref. [21]  with H 1 and H 3 having initial helicity 3/2 and final helicities 1/2 and -1/2, respectively; H 2 and H 4 have initial helicity 1/2 and final helicities 1/2 and -1/2, respectively. An integral involving (dσ/d ) gives the single-pion production part of the GDH sum rule  [18]  (see Eq.
The paper is organized in the following manner. We give a brief background of the experimental conditions for this study in Sect. 2. An overview of the method used to extract the double-polarized asymmetry results is given in Sect. 3 and the uncertainty estimates for the data obtained are given in Sect. 4. The resulting data are summarized and compared to various predictions in Sect. 5 and a new partial wave analysis (PWA), where we compare multipoles obtained with and without including the present dataset is presented in Sect. 5. 1. A summary and outlook are presented in Sect. 6.
Results and discussion The current work enabled the determination of E between center-of-mass (c. m. ) energies W = 1. 25 GeV and W = 2. 23 GeV for a wide angular coverage of the π 0. These results extend the kinematic reach of the world dataset for the E observable to lower energies, while significantly improving the statistical precision in all energy bins. The newly obtained values agree well with previously published data from CBELSA  [19, 20] , as shown in Fig. 2. Specifically, Fig. 2  shows the CLAS results on E (black open circles) for six c. m. energy bins and how they compare to CBELSA (red open squares) results. We note here that the CLAS results at backward angles for the energy bin W = 1. 47 GeV are somewhat smaller in magnitude than the CBELSA data, but these don't correspond to identical W bins.
Some deviations also are observed at higher energies and forward angles,   [19, 20]  (red open squares) for six kinematic bins. Statistical and point-to-point systematic uncertainties are combined in quadrature. Systematic uncertainties for CLAS results are indicated by the shaded regions at the bottom of each plot. PWA predictions for Scattering Analysis Interactive Database (SAID) SM22  [46] , MAID PIONMAID-2021  [47] , and Bonn-Gatchina BnGa-2022-02  [48]  are shown in solid blue, magenta dashed-dotted, and green dashed curves, respectively. The CBELSA data were included in the SAID, MAID, and Bonn-Gatchina fits as seen for example in the energy bin W = 2. 11 GeV and cos θ c. m π 0 ∼ 0. 3. The figure also provides predictions, (i. e. , PWA solutions where the newly obtained data were not included in the fits) from the SAID (solid blue line), the MAID (magenta dashed-dotted line), and the Bonn-Gatchina (green dashed line) PWA solutions.