dataesr/scientific-paragraphs-categorization huggingface dataset:
The coding script (script. do) allows researchers and organizations to assess the accuracy of our dataset and the quality of the sources. We are aware that collecting data from other sources might lead to the importation of any measurement issues they bring with them. The ACAPS dataset provides a full range of information going from political declarations to the detailed implementation of public health or social measures. Our reading of the ACAPS dataset translates the information reported in the ACAPS dataset and scales the selected measures. The IMF gives short case studies of countries but does not provide a dataset format or a list of measures per country, so the quality of the information relies on their treatment of the information and our understanding of the measures. The dataset on schools closures comes from the UNESCO and does not allow differentiating the different levels of schools which are closed.
Users of the dataset must keep in mind that countries do not have the same institutional settings. A federal country like the United States typically gives greater responsibility to state governments to implement national measures and rarely present singular country-level responses. Moreover, the indexes summarizing the different measures should be interpreted as indicators of governments' reactions to COVID-19. They do not integrate whether the measures were correctly implemented. Furthermore, the level of the indexes should not be interpreted as proxies for good or bad governance. The dataset is based on manual recording of policy measures implemented all around the world. Even though we made the best attempt to report data as accurately as possible, there might be some remaining errors and we apologize in advance for that.
Acknowledgments D. D. was supported by the  Laboratory of Mathematics  ( LAMA UMR #5127 ) and the  University Savoie Mont Blanc  to attend the meeting in Wisła. V. R. acknowledges a partial support of the project  IPaDEGAN  ( H2020-MSCA-RISE-2017 ), Grant Number  778010 , and of the  Russian Foundation for Basic Research  under the Grants  RFBR 18 -01 -00461  and  16 -51 -53034 -716  GFEN. Both Authors would like to thank the  Baltic Mathematical Institute  for organizing this scientific event and the anonymous Referee who helped us to improve the presentation indicating some shortcomings and misprints.
It is thus necessary to rely on computing codes that allow fast calculations of these variations with small numerical uncertainties. The DYTurbo program, which is presented in this paper, aims at providing fast and numerically precise predictions of the Drell-Yan production cross sections, for phenomenological applications such as QCD analyses and extraction of fundamental parameters of the SM. The enhancement in performance over original programs is achieved by overhauling pre-existing codes, by factorising the differential cross section into production and decay variables, and by introducing the usage of one-dimensional and multi-dimensional numerical integration based on interpolating functions. The DYTurbo program is a reimplementation of the DYRes  [36]  and DYqT  [33]  programs for q T resummation, and of the DYNNLO  [19]  program for the finite-order perturbative QCD calculation up to NNLO.
The DYRes  [36]  and DYqT  [33]  programs encode the q T resummed cross sections up to nextto-next-to-leading-logarithmic (NNLL) accuracy by using the resummation formalism proposed in Refs. [69] [70] [71]. The W +jet and Z /γ * +jet predictions at O(α S ) and O(α 2 S ) are reimplemented from the analytical calculations of Refs. [72] [73] [74] , as encoded in DYqT, for the case of the triple-differential production cross sections as a function of rapidity y, invariant mass m, and transverse momentum q T of the lepton pair, and from the MCFM program  [75] , as encoded in DYRes and DYNNLO, for the full kinematical dependence of the decaying leptons. Software profiling was employed to achieve code optimisation. The most successful optimisation strategies leading to significant performance improvement were hoisting loop-invariant expressions out of loops, removing conditional statements from loops to allow the compiler performing automatic loop vectorisation, and manual loop unrolling.
The DYTurbo software is based on a modular C++ structure, with a few Fortran functions wrapped and interfaced to C++. Multi-threading is implemented with OpenMP, and through the Cuba library by means of fork/wait system calls  [76]. A flexible user interface allows setting the parameters of the calculation through input files and command line options. The results are provided in the form of text files and ROOT histograms  [77]. Preliminary versions of the DYTurbo program were used by the ATLAS Collaboration in Refs. [6, 78, 79]. The DYTurbo program is publicly available  [80].
Predictions with DYTurbo The DYTurbo program provides predictions for W and Z /γ * -boson (collectively denoted as V -boson) production cross sections, fully differential in the four momenta of the decay leptons, and inclusive over final-state QCD radiation. The cross sections can be computed by performing the resummation of logarithmically-enhanced contributions in the small-q T region of the leptons pairs at leading-logarithmic (LL), next-to-leading-logarithmic (NLL), and NNLL accuracy, and also including the corresponding finite-order QCD contributions at next-to-leading order (NLO) and NNLO. The logarithmically-enhanced terms are resummed by using the resummation formalism of Ref. [70]  in impact-parameter space. The structure of the cross section calculations is summarised in Eqs. (  1 ) and (  4 ), and we refer the reader to the discussion in Refs.
The function Σ V (q T /Q) arises from the finite-order expansion of H V × exp{G}, and it matches the singular behaviour of dσ f. o. in the region q T → 0. An additional feature of the DYTurbo program is the possibility of computing finiteorder cross sections at LO, NLO and NNLO without the resummation of logarithmically-enhanced contributions. At NLO and NNLO, the finite-order cross section for V -boson production is computed by using the q T -subtraction formalism  [81] , and it is expressed as the sum of three components: dσ V (N)NLO = H V (N)NLO × dσ V LO + dσ V+jet (N)LO -dσ CT (N)LO , (4) with dσ CT (N)LO given by dσ CT (N)LO = dσ V LO × ∞ 0 d 2 q T Σ V (q T /m) (N)LO. ( 5 ) The LO cross-section term dσ V LO = d σ V LO (q T )δ(q 2 T ) is evaluated at q T = 0, and dσ V+jet is the V +jet cross section.
The integral transformations are evaluated by means of one-dimensional quadrature rules based on interpolating functions. The numerical integration over the other variables is performed with two different methods. The first method is based on the Vegas algorithm  [82]  as implemented in the Cuba library  [83]. The second method employs a combination of one-dimensional and multi-dimensional numerical integrations based on interpolating functions. The one-dimensional integrations are performed by means of Gauss-Legendre quadrature rules, with nodes and weights evaluated with the Elhay-Kautsky method  [84, 85]. The multi-dimensional integrations are evaluated with the Cuhre algorithm  [86, 87]  as implemented in the Cuba library  [83]  and in the Cubature package  [88] , and with a tensor product of Clenshaw-Curtis quadrature rules as implemented in the Cubature package.
The convolution with PDFs is more efficiently performed by considering double Mellin moments of the partonic functions Ŵab , defined as Ŵ N 1 ,N 2 ab = 1 0 dz 1 z N 1 -1 1 1 0 dz 2 z N 2 -1 2 Ŵab (z 1 , z 2 ), ( 11 ) where z 1,2 = m/ √ ŝe ± ŷ , ŷ = y -1/2 ln(x 1 /x 2 ), ŝ = x 1 x 2 s, W(b, m, y) = 1 2πi 2 c+i∞ c-i∞ dN 1 x -N 1 1 c+i∞ c-i∞ dN 2 x -N 2 2 F N 1 a F N 2 b Ŵ N 1 ,N 2 ab , (12) where x 1,2 = m/ √ s e ±y , c is a real number which lies at the right of all the poles of the integrand, and F N i , with i = a, b, are Mellin moments of PDFs, f i (x), defined as: F i (N ) = 1 0 dx x N -1 f i (x). ( 13 ) The integral transformation of Eq. (  12 ) is computed by means of Gauss-Legendre quadrature, and the PDFs are evolved  [33, 36]  from the factorisation scale μ F to the scale b 0 /b (b 0 = 2e -γ E , and γ E is the Euler number) by using the program Pegasus QCD for the evolution of PDFs in Mellin space  [94].
Indeed most of the non-parallelisable time is spent in the program initialisation, which becomes negligible for long runs with high target precision. We conclude this section reporting typical running times for fast and numerically precise DYTurbo predictions with the numerical integration based on interpolating functions. Figure  10  shows NLL+NLO and NNLL+NNLO predictions for the Z -boson production at 13 TeV in fulllepton phase space, integrated in the range of invariant mass  [66, 116]  GeV and in the range of rapidity |y| ≤ y max ∼ 5. 3. The predictions are computed in 100 equally-spaced q T bins from zero to 25 GeV. The predictions are evaluated with a target in the relative numerical uncertainty of 10 -4 for each term, and using simultaneously 20 parallel threads. The computation time required to perform the full calculation is 4 min at NLL + NLO and 3.
Conclusions The DYTurbo program provides fast and numerical precise predictions of Drell-Yan processes, through a new implementation of the DYqT, DYRes and DYNNLO numerical codes. The cross-section predictions include the calculation of the QCD transverse-momentum resummation up to nextto-next-to-leading logarithmic accuracy combined with the fixed-order results at next-to-next-to-leading order (O(α 2 S )). They also include the full kinematical dependence of the decaying lepton pair with the corresponding spin correlations and the finite-width effects. The enhancement in per-formance over previous programs is achieved by code optimisation, by factorising the cross section into production and decay variables, and with the usage of numerical integration based on interpolating functions. The resulting cross-section predictions are in agreement with the results of the original programs.
Acknowledgements We thank  Sven Moch  for fruitful discussions on Mellin transformations. M. S. acknowledges support from the  Volkswagen Foundation  and the  German Research Foundation (DFG). G. B. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement Nos. 647981 ,  3DSPIN ).
Study design. The objective of this longitudinal study was to assess the onset and the persistence of anti-SARS-CoV-2 antibodies in sera from mild COVID-19 healthcare workers from Strasbourg University Hospital. Among the 507 individuals of the cohort who donated samples, 308 individuals with RT-qPCR-confirmed COVID-19 diagnostic were enrolled in the study, with samples collected at two timepoints: Month 1 (M1) (median 31 days, range 11-58 days) and M3-6 (median 107 days, range 78-172 days) POS. Primary data are provided in the figures or the Supplementary Materials. Participants. Since April 2020, a prospective, interventional, monocentric, longitudinal, cohort clinical study enrolling hospital staff from the Strasbourg University Hospitals is on-going (ClinicalTrials. gov Identifier: NCT04441684).
At enrolment (From April 15 to 29 2020) written informed consent was collected and participants completed a questionnaire which covered sociodemographic characteristics, virological findings (SARS-CoV-2 RT-PCR results including date of testing) and clinical data (date of symptom onset, type of symptoms, hospitalization). The symptoms included in the survey were: myalgia, shortness of breath or difficulty breathing, fever, asthenia, rhinitis/pharyngitis, cough, headache, anosmia/dysgeusia, diarrhea, other. Laboratory identification of SARS-CoV-2 was performed at least 10 days before inclusion by RT-PCR testing on nasopharyngeal swab specimens according to current guidelines (Institut Pasteur, Paris, France; WHO technical guidance). The assay targets two regions of the viral RNA-dependent RNA polymerase (RdRp) gene with a threshold of detection of 10 copies per reaction.
Cells. 293T cells (ATCC® CRL-3216™ ) were grown in complete DMEM medium (10% Fetal Calf Serum, 1% Penicillin/ streptomycin). 293T cells were transduced to express SARS-CoV-2 spike protein (GenBank: QHD43416. 1) or were transduced with an empty lentivector to assess background staining. The transduced cells were selected with 2. 5 ug/mL of puromycin. 293T cells stably expressing ACE2 and inducible TMPRSS2 (293T-ACE2-iTMPRSS2) were made by lentiviral transduction and selection with puromycin (1 ug/mL) and blasticidin (10 ug/mL). Cells were tested for absence of mycoplasma contamination using Mycoalert™ Mycoplasma Detection Kit (Lonza).
The mix was incubated for 30 minutes at 4°C. The cells were then washed with PBS and stained with anti-IgG Alexa Fluor 647 secondary antibody (dilution 1:600; Thermo Fisher Scientific) for 30 min at 4°C. Cells were fixed for 10 min with 4% paraformaldehyde and data was acquired on an Attune NxT instrument (Life Technologies). Results were analysed with FlowJo 10. 7. 1 (Becto Dickinson & Company). After testing 253 pre-pandemic samples, the positivity of a sample was defined as a specific binding above 40%. The specific binding was calculated as follow: 100 x (% binding 293T Spike -% binding 293T Empty)/ (100 -% binding 293T Empty). Binding units (BU) were calculated to standardize the results. A standard curve established with serial dilutions of a human anti-S monoclonal antibody (mAb 48) was acquired on the same day as the S-Flow assay.
Acknowledgments. We thank the patients for participation to the study,  Maaran Michael Rajah  for critical reading of the manuscript, the  DRCI , the  CIC  and Médecine du travail teams for the management of the cohort,  Sophie Bayer  from the  UCBEC team ,  Anne Moncolin ,  Veronique Sohn  and  Axelle Grub  from the  Virology laboratory  for management and distribution of the samples,  Pierre Charneau  for the kind gift of pseudoviruses.
Asymptotic results of gradient iterations The goal of the first experiment is to illustrate how the spectral properties described earlier can be used for providing a rough estimate of parameter γ *. We have implemented steepest descent and minimal gradient iterations for several real matrices of size 1000 generated by MATLAB routine sprandsym. The right-hand side is chosen to be a vector of ones. In Figure  3 , parameter γ is plotted versus iteration number, under which a red dotted line marks out the position of γ *. It is clear that γ tends to γ * asymptotically as expected. As can be seen, steepest descent with limits (31) and (32) turns out to be a better strategy than minimal gradient in all cases. The indirect approximations based on (32) and (34) yield faster convergence for both steepest descent and minimal gradient.
Materials and Methods Our study employs daily data of a wide range of FX future markets consisting of Canadian Dollar, Brazilian Real, Norwegian Krone, Mexican Peso, Russian Ruble, Euro, Japanese Yen, Swedish Krona, South African Rand, South Korean Won, Great Britain Pound, and the Indian Rupee. We sampled all these currencies with respect to the US Dollar. Data for the WTI oil prices and sampled FX market is based on the daily frequency and ranges from 3rd January 2000-30th August 2019. Data for both the series, i. e. , WTI oil and FX market, was taken from Thomson Reuters DataStream. The descriptive statistics for WTI Crude oil and sample currencies is presented in Table  1. Among the FX market, South African Rand has a maximum daily return value of 0. 0176%, whereas Euro exhibits a daily loss of 0.
Acknowledgements The authors thank two anonymous reviewers and one associate editor for their useful comments. This project was written when  S. Courtin  was visiting the  University of the Basque Country. The first author would like to thank this University for its hospitality. This work has also benefited from comments made by a number of conference and seminar participants. This research has been developed within the  CoCoRICo-CoDEC research program  ( ANR-14-CE24-0007-02 ). The second author acknowledges the support of the  Spanish Ministerio de Economía y Competitividad  under project  ECO2015-67519-P , and of the  Departamento de Educaci ón, Política Ling üística y Cultura of the Basque Government  (Research Group  IT568-13 ).
Malm (1996)  confirms the extent of free riding. He investigates the impact of subsidies on the purchase of high-efficiency heating systems and estimates free riding at 89%. More recently,  Nauleau (2014)  uses the same database as in this paper and studies the effect of the same tax credit on the decision to invest in opaque and glazed insulation, using a difference estimator. She estimates the share of free riders as between 61% and 85% a year according to the year considered. Contrary to her approach, this paper focuses on all renovations eligible for the tax credit (insulation and heating system changes) and we use a complementary methodology in order to contribute additional elements on the effectiveness of this measure. We also go further to assess the extent to which the tax credit increases renovation expenditures.
Data We use ADEME-SOFRES Maîtrise de l'Energie surveys from 2001 and 2008. It is an unbalanced panel dataset. Each year, approximately 7,000 households are surveyed. Information is available about whether households undertook energy-saving renovations, the type of renovations undertaken (e. g. , improvement of insulation, modification of heating system, adoption of renewable energy) and the renovation expenditures. In addition, we have information on household characteristics (e. g. , income, occupational status, household size, age of reference person, occupancy status) and housing characteristics (type of dwelling [i. e. , house or apartment], surface area, year of construction, type of heating system) as well as geographic area (climatic area, size of the urban area) and energy bills. A zero-interest bank loan for energy-saving renovations was introduced in 2009.
To avoid capturing the effect of this policy and isolate the impact of the tax credit, we do not take into account data after 2008. Therefore, we observe energy-saving renovations four years before and four years after the introduction of the tax credit in 2005. After accounting for missing observations, our final sample comprises 41,102 households. Amongst renovations eligible for the tax credit, the survey considers renovations aiming at reducing heat loss (opaque and glazed surface insulation) and improving heating, hot water, or ventilation systems (installation or replacement of the boiler, adoption of renewable energy, and/or installation of a heating regulation or programming system) 2. Taking into account only these renovations, we observe a slightly higher renovation rate in the four years following the introduction of the tax credit (9.
Conclusion In this paper, we evaluate the impact of a French tax credit on renovation rate and expenditures. We use French household-level databases on energy conservation from 2001 to 2008 and econometric approaches. We estimate the tax credit effect with regression discontinuity design; in order to ensure the robustness of the results, results from matching method are presented in the Appendix. The effectiveness of the tax credit is mixed. The measure has a significant and positive effect on renovation rate and expenditures. However, the effect on renovation rate is low: We find that 1. 09% of the dwellings were renovated as a results to the tax credit, according to regression discontinuity design, ceteris paribus. The impact on renovation expenditures is much higher, leading to an increase of expenditures of 21.
B. 4. Sensitivity analysis Matching is based on the CIA: Given the observable characteristics, the renovation rate (or expenditures) is independent of the probability of being eligible for the tax credit. This assumption is not satisfied when unobserved characteristics of the treatment group differ from unobserved characteristics of the control group, and the results may thus be biased. In this section, we observe the sensitivity of the results to a deviation from this assumption. The control group is in a different time period than the treatment group, and we are not able to take into account time information because we pooled the database. An unobserved factor (e. g. , renovation prices, household preferences) could have an impact on the decision to renovate and may change over the period.
the 14th EASA Biennial Conference  (Milan, 2016) , the 28th SASE conference  (Berkeley, 2016) , and numerous research seminars at the EHESS (Paris) and the Museo Nacional (Rio de Janeiro). I am grateful for all the comments I received. I received fi nancial support for linguistic corrections from the Initiative de Recherches Interdisciplinaires Stratégiques (IRIS) Etudes Globales of Paris Sciences et Lettres (PSL) (ANR-10-IDEX-0001-02PSL). Hadrien Saiag is Permanent Research Fellow (chargé de recherché) in Economic Anthropology at the CNRS (research unit IIAC-LAIOS, UMR 8177). His research focuses on the Argentine working class's monetary practices and, more recently, on the incorporation of the subproletariat into consumer credit. He is the author of Monnaies locales et économie populaire en Argentine (Paris: Karthala), and several articles published in Economy and Society and the British Journal of Sociology, among others.
We therefore use the characteristics of the treated group to define the control group. As complementary analyses, we use the cross-sectional variation in measures of bank exposure to NIRP to identify the effects of this policy and the propensity-score matching approach to build a control group in order to cross-validate our results. This paper differs from the existing literature in two respects. First, the granularity of our dataset enables us to precisely identify the channels of banks' response to the implementation of NIRP at a fine disaggregated level. We thus explore the dynamic of banks' response and assess whether keeping this policy for a prolonged period of time might have negative consequences for banks. It also assesses whether the channels of NIRP transmission to banks' balance sheet differ according to banks' characteristics (such as deposit reliance, size and leverage).
This also enables us to use the variability of the policy rate below zero (the deposit facility rate in the euro area, for instance) to obtain a more precise inference of the effect of negative interest rates. Third, we include a proxy for central bank balance sheet policies in our specification to control for the implementation of other unconventional monetary policies that happen at around the same time. Fourth, our dataset has an annual frequency but several central banks introduced the negative interest rate policy in the middle or end of a year -the European Central Bank (ECB), for instance, reduced its deposit facility rate from 0 to -0. 10% in June 2014. We therefore redefine the NIRP dummy such that it takes the value of one the year after the NIRP implementation -2015 in the case of the ECB.
Breakdown of interest and non-interest flows The analysis of banks' responses to negative interest rates requires the most disaggregated profit and loss account data possible. For this purpose, we use the Fitch Solutions database. This commercial database is the most comprehensive one providing balance sheet and income statement information for thousands of banks around the world. We can then disaggregate banks' revenues and expenses into interest and non-interest flows. In the interest flows category, we decompose net interest income between interest income and interest expenses. On the one hand, gross interest income is broken down into interest income on loans (related to loans, advances and leasing) and other interest income which refers to interest income generated from all non-loans assets (such as debt securities, trading book items, short-term funds and investment securities).