dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge Synchrotron SOLEIL ( Saint-Aubin, France ) for allocation of beamtime within the scope of proposal #20200258 and we would like to thank the staff for assistance in using beamlines ANATOMIX ( M. Scheel ,  J. Perrin ), SMIS and NANOSCOPIUM ( K. Medjoubi ,  A. Somogyi ). ANATOMIX is an  Equipment of Excellence (EQUIPEX)  funded by the  Investments for the Future program  of the  French National Research Agency (ANR) , project  NanoimagesX , grant no. ANR-11-EQPX-0031. We would like to thank  Clément Tavakoli  ( STROBE ,  Univ. Grenoble-Alpes, Grenoble ;  Univ. Lyon 1, Lyon)  for his help during the acquisitions at SOLEIL. The study was further supported by the  European Synchrotron Research Facility (ESRF, Grenoble, France ; beamline, ID-19; proposals  MD1018  and  IN1041 ) and by the  ALBA Synchrotron Light Source (ALBA-CELLS, Barcelona , Spain; beamline MIRAS; proposal 2018082957).
This study was performed within the framework of  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the  "Investissements d'Avenir" program  ( ANR-11-IDEX-0007 ) of the  French National Research Agency (ANR). We are grateful to  Corinne Perrin  from the  Tumorothèque Est tissue bank, CRB-HCL (Lyon, France ), for managing human brain samples. We also thank  Jérémy Verchère  ( ANSES, Lyon, France ) for the management of APPPS1 brains. p-FTAA was a gift from the laboratory of chemistry at  ENS-Lyon. S. Singh  received support from the  Auvergne-Rhône-Alpes region (FUI   GigaQuant  project).
acknowledgements We thank  M. Delmastro  of  AGCOM  for insightful discussions about the Italian news ecosystem as well as for providing access to the database of Italian news. The database was shared in the framework of the  Task Force on 'Digital Platforms and Big Data -Covid-19 Emergency ' , established by the AGCOM to contribute, among other things, to the fight against online disinformation on issues related to the COVID-19 crisis. The authors received no specific funding for this work.
Methods Searches data. The information demand about a specific subject was obtained from Google Trends, a platform providing access to an anonymous sample of actual search requests made in the Google Search engine, from a selected location and time interval. For each given keyword, Google Search returns a time series with values proportional to the number of times the keyword was searched each day. Since Google Search does not disclose the actual number of searches, the time-series values are rendered as percentages of the maximum number returned. As a result, data consist of integers within the interval 0-100. The time series of one keyword was referred to as the 'Searches' for that keyword and provided a measure of the interest it received. The use of the 'pytrends' library for Python (https://github.
bufale. net/the-black-list-la-lista-nera-del-web/ or https://www. butac. it/the-black-list/). The Authority verified the fact-checking organizations' methodologies and legitimacy through the recognition of international organizations such as the International Fact-checkers Organizations, the Duke Reporters' Lab or the European Digital Media Observatory. The Authority released the list of unreliable sources to its scientific partners only after it was used by independent scientific studies  11, [64] [65] [66] [67]. Also, the list provided by the Authority in 2020 was already adopted in one other study  68. All the sources annotated as questionable in our dataset are listed in Supplementary List 1. The source-based methodology is well-known and well-established in the current literature on disinformation 8,69-71.
As detailed in Sec. 3. 3, this is due to the fact that the band-width of the RRG perturbation is finite and not all the sites in which γ i > 0 effectively produce We obtain the data by extrapolating the system size scaling of the typical resistance R typ (cfr. Eq. (  16 )). Specifically, we consider system sizes L = 8 ÷ 256 and measure the exponent β = log R typ /log L. The critical (white) line W c = W (p c ) signals a transition between a subdiffusive (β > 1) and a diffusive (β = 1) behavior. The black cross markers are examples of extrapolation points, with associated error estimations. The black dashed line p = 1, corresponds to no dephasing. In this limit, the system is always Anderson localized for any W > 0, with a resistance exponentially suppressed in system size (dashed line) The hatched region in the GOE correspond to Anderson localization which only extends in a vanishing region of the phase diagram in the thermodynamic limit where SciPost Physics Submission 1 -p = O(1/L).
Accurate measurements of the masses of neutron stars are necessary to test binary evolution models, and to constrain the neutron star equation of state. In pulsar binaries with no measurable post-Keplerian parameters, this requires an accurate estimate of the binary system's inclination and the radial velocity of the companion star by other means than pulsar timing. In this paper, we present the results of a new method for measuring this radial velocity using the binary synthesis code ICARUS. This method relies on constructing a model spectrum of a tidally distorted, irradiated star as viewed for a given binary configuration. This method is applied to optical spectra of the newly disco v ered black widow PSR J1555-2908. By modeling the optical spectroscopy alongside optical photometry, we find that the radial velocity of the companion star is 397 ± 4 km s -1 (errors quoted at 95 per cent confidence interval), as well as a binary inclination of > 75 •.
This zeropoint was then used to calculate the u s mags of the reference stars in the field of PSR J1555-2908, and these calculated u s reference magnitudes were then used to calibrate the u s data of PSR J1555-2908 from each of the other nights of data. The calibrated g s and u s fluxes agree across every night of observation to within < 0. 05 magnitudes, with the exception of data taken on 2018-06-16, when the data had a shift of 0. 2 magnitudes relative to the rest of the observations. The cause of this discrepancy is unclear, but may be related to the diffraction spike from a nearby star, whose angle changes as the rotator angle of ULTRACAM changes o v er the course of an observation, passing directly o v er the target at this time. There are systematic offsets between every night of i s which arise due to the effects of a nearby bright star on the i s CCD.
Priors The optical light curves and spectra were modeled using the binary stellar synthesis code ICARUS  (Breton et al. 2012 )  along with stellar atmosphere grids computed using ATLAS9  (Castelli & Kurucz 2003 )  (see Appendix A for further details). In the following, we have assumed that the companion is tidally locked. The parameters which were fit for using ICARUS were the inclination of the system i , the Roche lobe filling factor f RL (measured as the ratio of the stars radius to the L1 point along the line joining both), the base temperature of the star T base , the irradiation temperature T irr , 2  the systemic velocity of the system γ , the radial velocity amplitude of the companion star K 2 , the distance to the source d , and the optical reddening in the direction of the source E (g-r).
This range, in combination with the pulsar's projected semi-major axis and the binary orbital period, equates to a restriction on the neutron star mass of 0. 5 < M NS < 3. 5 M ⊙. The parameter space was explored to ensure the fit had found the global minimum when considering the photometry data and to estimate parameter errors using MULTINEST  (Feroz & Hobson 2008 ; Feroz, Hobson & Bridges 2009 ; Feroz et al. 2019 )  as implemented in PYTHON through PYMULTINEST  (Buchner et al. 2014 ). MULTINEST provides the Bayesian evidence, ( Z ), which is used to calculate the Bayes factor and compare models between each other. The Bayes factor is given by B 1 , 2 = Z 1 Z 2. ( 3 ) Following the criteria set out in  Jeffreys ( 1939 ) , a ratio in evidence of < 1 suggests model 2 is preferred o v er model 1, a ratio of 1-10 is weak evidence, a ratio of 10-150 is moderate, and a ratio > 150 is strong evidence that model 1 is preferred o v er model 2.
While assessing this dependence is difficult, stellar atmospheres for high temperatures tend to be relatively reliable. With only a grid made using the ATLAS9 suite of programs available to us to generate the specific intensities which are in turn used to create spectroscopy grid, we are unable to better quantify how strong this dependency may be. As a test, the photometry was modeled using an atmosphere grid constructed using the PHOENIX stellar atmosphere code  (Husser et al. 2013  ) which extends down to 2,300 K. This grid is not of specific intensities, but rather of integrated spectra, making them unsuitable for spectroscopic modeling. The posterior distributions of all parameters from the MULTINEST analysis of the photometry using this grid were identical to those found using the ATLAS9 grid.
CONCLUSIONS In this paper, we have presented detailed multi-band optical light curves and spectra of the black widow system PSR J1555-2908, and modeled these data using the binary synthesis program ICARUS. Modeling of the multi-band light curves of PSR J1555-2908 constrains the binary inclination to be > 75 •. The model which best describes the observed data contains a companion in which a significant amount of the pulsar's energy that is absorbed by the companion is redistributed o v er its surface. There is a large temperature difference of > 6000 K between the day side and night side of the companion, in line with the very high spin-down energy of the pulsar. The companion also seems to be very close to filling its Roche lobe, with the Roche lobe filling factor constrained to be > 0. 95.
A single likelihood e v aluation requires the flux from 3072 surface elements to be extracted from the atmosphere models and a 4D interpolation performed at 12 854 wavelengths for each of the 26 spectra, for 16 billion memory look-ups per likelihood e v aluation. To achieve reasonable computation times, we store the degraded atmosphere models on an NVIDIA TITAN RTX graphics card, whose 24GB of memory is sufficient to store the 11GB set of atmosphere models, and have implemented the atmosphere model retrie v al and interpolation using the CUDA framework. This is called by ICARUS using the PYCUDA module  (Kl öckner et al. 2012 ). With this setup, a full likelihood e v aluation takes ∼30s. We do not use information from the continuum of optical spectroscopy when calculating the log likelihood of a given model, as flux calibration typically requires fitting a high order polynomial to the observed spectrum of a standard star.
ACKNOWLEDGEMENTS M. R. K. , R. P. B. , C. J. C. , D. M-S. , G. V. , and J. S. acknowledge support from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 715051 ; Spiders). The proposal to obtain spectroscopy, data acquisition, reduction, and initial manuscript preparation were performed when M. R. K. was a Ne wton International Fello w funded by the  Royal Society  (Grant number  NF171019 ). M. R. K also acknowledges support from the  Irish Research Council  in the form of a  Go v ernment of Ireland Postdoctoral Fellowship  ( GOIPD/2021/670 :  Invisible Monsters ) which commenced during the late stages of manuscript preparation. V. S. D. , T. R. M. , and ULTRACAM acknowledge the support of the  STFC. D. M. S. also acknowledges the  Fondo Europeo de Desarrollo Regional (FEDER)  and the  Canary Islands go v ernment  for the financial support received in the form of a grant with number  PROID2020010104.
DATA AVAILABILITY The raw spectra of PSR J1555-2908 are available through the ESO archive, while the raw ULTRACAM images and associated calibration frames may be obtained by contacting M. R. Kennedy or the ULTRACAM team (V. S. Dhillon or T. R. Marsh). The reduced spectra and reduced light curves are located at a permanent Zenodo repository: https://zenodo. org/r ecor d/5653061.
MNRAS 512,3001-3014 (2022)nautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation Grant No. AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. This work made use of ASTROPY (Astropy Collaboration 2013 , 2018 ). MNRAS 512, 3001-3014 (2022).
Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [49]. The online event selection is performed by a trigger  [50] , which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. Simulation is required to model the effects of the detector acceptance and the imposed selection requirements. In the simulation, pp collisions are generated using Pythia  [51]  with a specific LHCb configuration  [52]. The final state radiation is generated using Photos  [53]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit  [54]  as described in Ref.
Results The datasets that were collected in 2016, 2017 and 2018 are considered as independent datasets, which are used to perform cross-section measurements and combined to get results of the full dataset. In the combination of integrated cross-section measurements obtained from the different datasets, the systematic uncertainties from FSR, background modelling, luminosity, and closure test, are treated as 100% correlated between different datasets, and other systematic uncertainties are assumed to be uncorrelated. The combination is performed using the Best Linear Unbiased Estimator (BLUE) method  [64, 65].
Integrated cross-section results The measurements of integrated cross-section from different datasets are shown in Appendix C. The χ 2 per degree-of-freedom of the combination is determined to be 0. 9/2, using the BLUE method. In the LHCb detector fiducial region, the Z boson integrated production cross-section is measured to be σ(Z → µ + µ -) = 196. 4 ± 0. 2 ± 1. 6 ± 3. 9 pb, where uncertainties are statistical, systematic, and due to the luminosity measurement, respectively. In Fig. 10  the determined integrated cross-section is compared with different theoretical predictions and the previous LHCb measurement  [33]. In the comparison, the predictions are calculated using Powheg with NNPDF3. 1NLO [76] , Powheg with CT18NLO  [37] , ResBos with CT18NNLO, Fewz with CT14NNLO  [34] , Fewz with NNPDF3.
Conclusion In summary, the most precise measurement to date of the Z boson production cross-section in the forward region at √ s = 13 TeV is presented, using pp collision data collected with the LHCb detector. The dataset corresponds to an integrated luminosity of 5. 1 ± 0. 1 fb -1. The integrated cross-section in fiducial region is measured to be σ Z→µ + µ -= 196. 4 ± 0. 2 ± 1. 6 ± 3. 9 pb, where the first uncertainty is statistical, the second is systematic, and the third is due to the luminosity determination. The single differential and the double differential crosssections are measured. This is the first measurement of the double differential cross-section in the forward region. Overall, reasonable agreement between measured results and the theoretical predictions are seen. However, there are sizable disagreements in the large p Z and φ * η regions, which need more investigations in future.
C Numerical results The measured total cross-sections using different data sets are presented in Table  13. The measured single differential cross-sections in interval regions of y Z , p Z T and φ * η are presented from Table  14  to 16. The measured double differential cross-section as a function of p Z T and φ * η in interval regions of y Z are presented in Tables  17  and 18. The summarized systematic uncertainties for single differential cross-sections are shown in Table  19  to 21, and in Tables  22  and 23  for double differential cross-section measurements. Table  13 : Measured total Z-boson cross-section for different datasets. The first uncertainty is statistical, the second systematic, and the third is due to the luminosity. Table  15 : Measured single differential cross-sections in interval regions of p Z T.
Acknowledgements We thank  C. -P. Yuan  for frequent and interesting discussions on the PDFs. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  NERSC (USA).
The distribution of ΔΦ   is probed in various disjoint kinematic regions, optimised for each analysis specifically. (a) (b) The structure of this article is as follows. Section 2 gives a short summary of the theoretical frameworks used to study the CP properties of the Higgs boson's coupling to top quarks and gluons, as well as its coupling to polarised electroweak bosons. The ATLAS detector and the Monte Carlo and data samples used in these studies are discussed in Section 3 and Section 4, respectively. The event selection and categorisation requirements are presented in Section 5, while the estimation of the various background processes is detailed in Section 6. The theoretical and experimental uncertainties are presented in Section 7. (c) V V V H V q q q q (d) Finally, the results are discussed in Section 8.
Datasets and Monte Carlo predictions Candidate events in data are selected from the combined 2015 and 2016 ATLAS √  = 13 TeV   collision dataset in which all ATLAS subdetectors were fully operational  [35]. The corresponding total integrated luminosity  [36]  is 36. 1 ± 0. 8 fb -1. The modelling of the gluon-induced production of Higgs bosons in association with jets was realised using the M G 5_aMC@NLO 2. 4. 2 generator  [25, 26] , which provides a calculation of the matrix element at next-to-leading-order (NLO) precision for ggF events with up to two additional partons in the final state. The calculations of the matrix element are based on the predictions of the HC model, while the parton shower, hadronisation and underlying-event activity were simulated with the P 8. 212  [33]  generator using the A14 set of tuned parameters  [37].
Results A maximum likelihood (ML) fit is used for the statistical interpretation of the results from both the ggF + 2 jets and VBF analyses. Fits are simultaneously performed on all considered SRs and CRs of each analysis independently in order to constrain the normalisation of backgrounds and the nuisance parameters (NPs) describing the systematic uncertainties. Each systematic variation enters the fit as an individual nuisance parameter. Correlations between systematic uncertainties are maintained across processes and channels. Due to the presence of bins with low event yields, all the NPs describing the systematic uncertainties are incorporated into the fit using a log-normal constraint, such that expected event counts remain positive for all values of the corresponding NPs. Asimov datasets  [84]  are used to study the expected performance of each fit.
The observed and expected likelihood curves corresponding to scans over tan() are presented in Figure  5. The values of the NLL are evaluated in steps of Δ (tan()) = 0. 2, and the minima of all fits are consistent with zero, i. e. the SM hypothesis. If only the shape information is used, the data are not sensitive enough to provide 68% confidence level (CL) intervals on tan(). The ML fit configuration that uses both shape and rate information provides a best-fit value of tan() = 0. 0 ± 0. 4(stat. ) ± 0. 3(syst. ) for both the fits to data and to Asimov data. The observed sensitivity is slightly worse than the expected sensitivity due to a lower than expected signal yield (consistent with a signal strength value below unity). Hence, the fit to this dataset does not provide a 95% CL interval.
[28] ). The expected sensitivity to  T comes predominantly from the shape information, as the likelihood ratio increases only slightly when adding the normalisation information. The resulting best-fit values and their uncertainties computed at 68% CL are presented in Table  9. All measurements are consistent with the SM expectations. The dominant sources of uncertainty are related to the limited data yields and to the uncertainties in modelling the top-quark and  backgrounds. The results of the ML scans of pseudo-observables are shown in Table  10 , with the uncertainty breakdown given in Table  11. Figure  9  shows the expected and observed likelihood curves for scans over one pseudo-observable with the other one profiled. Both shape and rate information are employed. Table  10 : Best-fit values and their uncertainties as obtained from the shape-only and shape-plus-rate likelihood fits to the Asimov dataset and to ATLAS data.
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 ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Funding: This work was partly supported as part of the  HoliFAB  project funded by the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 760927 ) and as part of the  MultiFAB  project funded by  FEDER European Regional Funds and French Région Occitanie  (grant agreement number  16007407/MP0011594 ). This work also received support from the  French National Agency for Research (hiPS-Adipospheres   ANR-18-CE 18-0006  and Printiss  ANR-21-CE19-0041-01 ). This work was also partly supported by the  French RENATECH network.
Computational Details The presented algorithms have been implemented in the massively parallel Tinker-HP molecular dynamics package  47. Atomsin-molecule quantities were computed with the Multiwfn software  48  by partitioning DFT electron densities generated with the Gaussian quantum chemistry software  49  at the PBE/cc-pVQZ  50, 51  level. Free-atom parameters are taken from reference  52, 53  except for noble gases' van der Waals radii which have been taken as the half of the equilibrium distance obtained from accurate reference potential energy surfaces. [54] [55] [56].