dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected from a non-preferential dissociation model that is used in both solvers, the maximum in vibrational temperature occurs at an instant of time where T tr = T ve. This model assumes that the molecules are destroyed or created at the average vibrational energy of the cell, which causes the vibrationalto-translational energy transfer to govern the thermochemical relaxation process. Mathematically, the vibration-chemistry coupling term ˙ c:v-e is much smaller than the term ˙ t-r:v-e provided by the Landau-Teller model, for which dT ve dt = 0 at the same time that T tr = T ve. The slight discrepancies also are expected, since there are some differences in the modelling, namely Arrhenius law coefficients in the reaction mechanism as well as different thermodynamic database and handling of the electronic excitation.
Acknowledgements The authors wish to acknowledge the support of the  EPSRC  funded  ARCHIE-WeSt High Performance Computer  (www. archie-west. ac. uk), EPSRC  Grant No. EP/K000586/1. The authors wish to thank  K. Hanquist , from  University of Arizona , for his contribution to the results obtained with the  LeMANS code , presented in Sect. 2. 3.
†   The research of OFB was supported by the  EPSRC  grant  EP/R012008/1. † † The research of SN was supported by  ANR  grant " ANR-14-CE34-0002-01 " for the project " Dynamics and CR  geometry". K of restrictions of functions in the unit ball of H ∞ (D) is a compact subset of C(K). Kolmogorov and Tikhomirov [KT61] showed that, under natural assumptions on D and K, the asymptotic order of n+1 < M (∀ǫ > 0).
So, for instance and given a topological space X, C(X, L(H) * -str ) is the space of continuous functions from X to L(H) endowed with the * -strong topology. Given a group Γ which acts on a set A, we shall denote as it is customary by A Γ the subset of A composed of the Γ-invariant elements. K 0 (C(X) ⋊ red Γ) → S 1 (X ⋊ Γ) → RK 1 (X ⋊ Γ) → K 1 (C(X) ⋊ red Γ) Acknowledgements. The authors wish to thank P. S. Chakraborty, T. Fack, N. Higson, V. Mathai, P. Piazza, B. Saurabh, G. Skandalis, R. Willett and V. Zenobi for many helpful discussions. We are especially indebted to the referee for having read carefully this manuscript, and for her/his comments to improve it, especially the important suggestion of removing the cocompactness condition in all the statements of the first three sections. MB thanks the French National Research Agency for support via the ANR-14-CE25-0012-01 (SINGSTAR).
The present investigation aims primarily at the assessment of a novel approach to take into account inhomogeneity. The lock-down period for a number of similar occidental countries seems the correct starting point, since despite differences in detailed measures, the parameters governing the dynamics of the epidemic are expected to become constant during lock-down. The universal features captured by the model can therefore be disentangled from other effects, and the results can be compared to the data within the limits of the quality of the measurements. In the following we will first present the SBIR model and its difference with the classical SIR approach (section II). We will analytically determine the exponential decay time-scale for the SIR model with and without cross-immunity, as well as for the SBIR model.
We show, by comparison with data from the Johns Hopkins University data-base  16  , that the SBIR model allows to simultaneously fit the data in the increasing and decreasing phase of the epidemic without the need of adjusting the model-parameters during the epidemic (section III). We further estimate the reproduction number and show that it reaches a stable minimal value during lock-down. The appendices contain the mathematical procedure to analytically estimate the decay exponents of the different models and the initial and final reproduction numbers. 1. Diagrammatic representation of three compartmental models, SIR, CSIR and SBIR. In SIR and CSIR three compartments interact, with the only difference in CSIR that those three compartments do not contain the entire population, since part of the population is supposed to be protected by cross-immunity.
ACKNOWLEDGMENTS The authors thank  Samuel Alizon  ( MIGEVEC -CNRS ) and  Loïc Méès  ( LMFA -CNRS ) for stimulating discussions, as well as  Benjamin Pillot  ( UMR Espace-Dev 228 ) for developing the Pycovid-19 package 25 used to access data of the JHUdatabase and for useful advices. We also thank  Pascal Roy  ( LBBE UMR 5558 ) and  Vitaly Volpert  ( ICJ UMR 5208 ) for their helpful advices.
Implications of all the available evidence Rigorous estimates of the impact of childhood vaccination programmes on morbidity and mortality inform public health investment decisions made by countries and global donors. The results highlight the importance of maintaining and increasing vaccine coverage to sustain gains made in reducing infectious disease-related mortality in LMICs. pathogen transmission intensity is not modified by vaccination coverage. Dynamic models simulate infectious disease transmission dynamics and model both the direct effect of vaccination on vaccinated cohorts and the indirect effect of vaccination on unvaccinated populations. Standardised demographic data (live births per year and death rates) based on the UN World Population Prospects (UNWPP) 2017 was used for all countries.
Furthermore, in the absence of vaccination, we estimate that all-cause mortality among children younger 5 years would be 45% higher than currently observed. These impacts are a testament to both the public health benefit of vaccines overall and the sustained investment in increasing global vaccination coverage in the past two decades. They also highlight what might be lost if current vaccination programmes are not sustained, and thus provide quantitative evidence supporting both donor and country investments in vaccination programmes. When compared with mortality estimates for measles from the Global Burden of Disease (GBD) Study 2017, we find some differences between 2000 and 2010. These are likely to be driven by differences in source data and model types. For example, the GBD measles estimates are based on static models, whereas the two VIMC models are both dynamic.
Contributors VIMC secretariat: XiaL and CM contributed to study design, data analysis, figures, and writing. ZMC contributed to study design, data analysis, and data interpretation. SEL contributed to data analysis, data interpretation, and writing. HLJ contributed to study design, data collection (input data for models), data interpretation, and writing. NMF and TG led on study design and writing and contributed to figures, data analysis, and data interpretation. NCG reviewed the models for HPV, rotavirus, and N meningitidis serogroup A and contributed to manuscript revision. WH contributed to figures, data analysis, and data curation. KW contributed to writing and data coordination. KAMG contributed to writing and data interpretation. Cambridge N meningitidis serogroup A model: AK and CLT contributed to the study (model) design, interpretation, and writing; AK also contributed to the study (model) implementation.
Center for Disease Analysis Foundation hepatitis B model: IG, HR, and DR-S contributed to study design, data analysis, and data interpretation; DR-S also contributed to literature search and data collection. Goldstein hepatitis B model: XiL contributed to analysing hepatitis B vaccine impact. Harvard HPV model: SR contributed to study design, data analysis, and data interpretation; SSw contributed to study design, data analysis, and model design; SSy contributed to study design and model design. Imperial yellow fever model: KAMG and KJ contributed to data analysis, data interpretation, and critical revision of the draft manuscript. Imperial hepatitis B model: MJdV wrote the model description for the paper and contributed to running and design of the model; TBH and SN contributed to design of the model.
Johns Hopkins model for H influenzae type B, S pneumoniae, and rotavirus (LiST): YT ran the analysis to produce impact estimates using the Lives Saved Tool, and contributed to drafting the model descriptions in the supplementary information; EDC supported vaccination impact modelling functionality within the Lives Saved Tool, and contributed to drafting the model descriptions in the supplementary information; NW primarily worked on developing assumptions used in the LiST model, and also worked with the team in setting up the approach used in the models. Johns Hopkins rubella model: JL, ST, and AW contributed to the data collection and data analysis. Kaiser Permanente Washington N meningitidis serogroup A model: MLJ contributed to study design, data analysis, data interpretation, and critical review of the manuscript.
Notre Dame Japanese encephalitis model: SMM contributed to data collection, data analysis, data interpretation, and writing. Oxford University Clinical Research Unit Japanese encephalitis model: HEC, DMN, and QMT contributed to study design and data interpretation; DMT and QMT also contributed to data analysis; QMT also contributed to the literature search; HEC also contributed to writing. Public Health England rubella model: EV commented on rubella programming, interpreted output, and commented on the draft manuscript; TP contributed to programming and running the rubella model, collating and analysing output, and commenting on drafts of the manuscript. Pennsylvania State University measles model: KE and MJF contributed to data analysis and drafting text on model description. XiaL and CM contributed equally, as the VIMC science team throughout 2019.
Declaration of interests Consortium members received funding from Gavi and the Bill & Melinda Gates Foundation via VIMC during the course of the study. In addition, MJ reports grants from WHO and the Gates Foundation. KE and MJF report grants from the Gates Foundation, outside of the submitted work. IG, HR, and DR-S report grants from the John C Martin Foundation during the conduct of the study; and grants from AbbVie, Gilead, Intercept, Pan American Health Organization, and Association of State and Territorial Health Officials, outside of the submitted work. IG, HR, and DR-S are employees of the Center for Disease Analysis Foundation, which had no role in study design, data collection and analysis, interpretation of data, or preparation of the manuscript. KAMG reports personal fees from Wellcome Genome Campus advanced courses and scientific conferences.
Data sharing This study does not involve any patient data or participant data. Data collected for the study consisted of demographic data from UNWPP and coverage data from WHO-UNICEF Estimates of National Immunization Coverage, which are freely accessible. Further coverage projections were obtained from the Gavi operational forecast, which is prepared to provide an aggregate, long-term strategic picture of the portfolio of Gavi vaccines and, as such, is highly uncertain at the country level. Due to programmatic reasons, the operational forecast is sensitive. As such, interested parties should contact Hope L Johnson (hjohnson@gavi. org) for more information. Estimates of vaccine impact and disease burden for 2000-19 (specified as deaths, deaths averted, DALYs, and DALYs averted) are presented in a publicly available data visualisation tool.
Acknowledgments VIMC is jointly funded by  Gavi , the  Vaccine Alliance , and the  Bill & Melinda Gates Foundation  (grant number  OPP1157270 ). Funding from  Gavi  is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper:  Imperial College London, London School of Hygiene & Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers ). Funding from the  Gates Foundation  was used for salaries of the Consortium secretariat (authors represented here: XiaL, CM, ZMC, SEL, MJ, NCG, TBH, KW,  NMF , and TG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors).
We also acknowledge funding to support aspects of VIMC's work from the  Medical Research Council (MRC)   Centre for Global Infectious Disease Analysis  (reference  MR/R015600/1 ), which is jointly funded by the  UK MRC  and the  UK Foreign, Commonwealth & Development Office (FCDO) , under the  MRC/FCDO  Concordat agreement, and part of the EDCTP2 programme supported by the  EU. Development of the London School of Hygiene & Tropical Medicine  models for HPV, measles, S pneumoniae, H influenzae type b, and rotavirus was funded by  WHO ,  Gavi , and the  Gates Foundation  under several grants, past and current. The  Gates Foundation  supported the development and maintenance of the Lives Saved Tool. The views expressed in this Article are those of the authors and not necessarily those of the VIMC or its funders.
Screening wild fauna for viral genomes (metagenomics), and implementing open-access databases is necessary to trace the thread of transmission and viral evolution within the intermediary hosts of SARS-CoV-2. This information will also be needed for future emerging viruses. Modelling the spread and course of the pandemic proved to be critical for decision making throughout the course of the pandemic. It is therefore important to improve data integration and processing and to increase modelling capacities across Europe and the world. This could include models that better describe infection spread accounting for both the climatic conditions and their effect on transmissibility  (Sarigiannis, 2020) , as well as efforts that describe the interactions among community members to help estimate the effective contact rate.
Procedure This study will follow a retrospective, observational, patient-level prediction design (https://ohdsi. github. io/TheBookOfOhdsi/PatientLevelPrediction. html). We de ned the 'patient-level prediction' as a modeling process wherein an outcome is predicted within a time at risk relative to the target cohort start and/or end date. Prediction will be performed using a set of covariates derived using data prior to the start of the target cohort. Figure  2  (from (https://ohdsi. github. io/TheBookOfOhdsi/PatientLevelPrediction. html) illustrates the prediction problem we will address. Among a population at risk, we aim to predict which patients at a de ned moment in time (t = 0) will experience some outcome during a time-at-risk. Prediction is done using only information about the patients in an observation window prior to that moment in time.
We follow the PROGRESS best practice recommendations for model development and the TRIPOD guidance for transparent reporting of the model results. (8, 9). For all data sources, we refer to the appendices. In all models we estimated the risk after 1, 2, and 5 years after diagnosis. Our population setting comprises patients with a time-at-risk window between 0 and 365 days, 0 and 730 days, and 0 and 1826 days. In all settings, the minimum lookback period applied to the target cohort is 365 days, without removing patients without time at risk or removing patients with an outcome prior to diagnosis. We included only the rst exposure per patient.
Statistical Analysis Method(s) Algorithms Lasso logistic regression belongs to the family of generalized linear models, where a linear combination of the variables is learned and nally a logistic function maps the linear combination to a value between 0 and 1. The lasso regularization adds a cost based on model complexity to the objective function when training the model  (10). This cost is the sum of the absolute values of the linear combination of the coe cients. The model automatically performs feature selection by minimizing this cost. We use the Cyclic coordinate descent for logistic, Poisson and survival analysis (Cyclops) package to perform largescale regularized logistic regression: https://github. com/OHDSI/Cyclops Model Evaluation Model evaluation will be based on the calibration plot and the discrimination of the internal and external validation.
Covariate Settings A covariate included in the model needs to contain at least 0. 001 times. In all models we speci ed medium term as 180 days and long term as 365 days. In the model development we will split the data in a train set (75%) and a test set (25%) for internal validation. The optimal lambda for the lasso regression will be assessed by 3-fold cross validation on the train set. Discriminative ability between models will be assessed by the area under the receiver operating characteristic curve (AUC). The discrimination of the clinical model will be compared against the conceptbased model.
Acknowledgements Ron Herrera Flavio Camarrone Vasileos Sakalis Maria Escala Garcia Christos Chatzichristos Billy Franks Anke Schulz Alex Asiimwe Asieh Golozar Principal investigators :  Ron Hererra ,  Nazanin Kermani , Sebastiaan Remmers The study is supported by the  IMI2 initiative  ( PIONEER  project).
Background: We aimed to assess the role of different setting and activities in acquiring SARS-CoV-2 infection. Methods: In this nationwide case-control study, cases were SARS-CoV-2 infected adults recruited between 27 October and 30 November 2020. Controls were individuals from the Ipsos market research database matched to cases by age, sex, region, population density and time period. Participants completed an online questionnaire on recent activity-related exposures. Findings: Among 3426 cases and 1713 controls, in multivariable analysis, we found an increased risk of infection associated with any additional person living in the household (adjusted-OR: 1Á16; 95%CI: 1Á11-1Á21); having children attending day-care (aOR: 1Á31; 95%CI: 1Á02-1Á62), kindergarten (aOR: 1Á27; 95%CI: 1Á09-1Á45), middle school (aOR: 1Á30; 95%CI: 1Á15-1Á47), or high school (aOR: 1Á18; 95%CI: 1Á05-1Á34); with attending professional (aOR: 1Á15; 95%CI: 1Á04-1Á26) or private gatherings (aOR: 1Á57; 95%CI: 1Á45-1Á71); and with having frequented bars and restaurants (aOR: 1Á95; 95%CI: 1Á76-2Á15), or having practiced indoor sports activities (aOR: 1Á36; 95%CI: 1Á15-1Á62).
Study design and participants In this case-control study, cases and controls were selected from two different national databases. Cases were obtained through the database from the Caisse Nationale d'Assurance Maladie (CNAM), a national health insurance agency which receives notification of all cases of COVID-19 in France. Potential cases for our study were all those diagnosed with COVID-19 and with an e-mail address with the national health insurance agency (55% of the adult French population). Based on the characteristics of cases, Ipsos, a French market research and public opinion specialist company, selected controls at regular intervals from a panel representative of the French population using frequency-matching with cases on age (18-28, 29-58, 59+ years), sex, region, population density, and period of infection (before or during stay-at-home orders).
Data collection Cases and controls were invited by email and received information online about the study before filling a questionnaire if they agreed to participate. The questionnaire covered sociodemographic characteristics (age, sex, region of residence, composition of the household, profession), exposure information related to place of work and means of transportation, places visited, type of leisure or sporting activities. Questionnaires covered the 10 days preceding symptom onset for cases (or testing if asymptomatic), and the 10 days preceding inclusion for controls. The Figure shows the timeline of the epidemic curve of positive SARS-CoV-2 cases in France, as well as the periods covering the different control measures, and the exposure period covered by the questionnaires sent to study participants.
Implications of all the available evidence The places and activities that appear to have facilitated SARS-CoV-2 infection despite the public health and social measures implemented at the national level are those in which adherence to infection prevention and control measures is conceiveably more difficult. In these settings, ongoing reinforcement of hand and respiratory hygiene, physical distancing, mask wearing and adequate ventilation of indoor settings remains critical to reducing viral circulation. minimize the impact of random variation in the selection of cases for available controls, we performed 1000 random sampling of two cases per control with replacement (bootstrapping)  [26]. We then calculated the mean number of cases and controls for each exposure category over the 1000 databases.
Ethical considerations This study received ethical approval by the Comit e de Protection des Personnes Sud Ouest et Outre Mer 1 on 21 September 2020. The data protection authority Commission Nationale de l'Informatique et des Libert es (CNIL) authorized the processing of data on 21 October 2020. Informed consent was obtained from all participants. The study is registered with ClinicalTrials. gov under the identifier NCT04607941.
Role of the funding source The study was funded by Institut Pasteur and Research & Action Emerging Infectious Diseases (REACTing). AF's laboratory receives support from the Labex IBEID (ANR-10-LABX-62-IBEID) and the INCEPTION project (PIA/ANR-16-CONV-0005) for studies on emerging viruses. TC is funded by the Fondation de France (Alliance "Tous unis contre le virus").
Results From 27 October to 30 November 2020, 694,298 individuals with a diagnosis of COVID-19 were contacted by e-mail by the insurance company, of which 50,847 (7. 3%) replied. Of note, only individuals aged 18 years and older (roughly 85% of all cases) were eligible for the study, so that the true participation rate may be slightly higher. Among these, 8,976 were health care workers and not considered further in this study, leaving us with 41,871 cases. At regular intervals, controls were frequency-matched to the cases, so that 1713 controls were available out of the 16,850 contacted by the end of the study period (see Fig. 1 ). Table  S1  describes the socio-demographic characteristics of the study participants. When compared to the 1,076,284 adult (18 years and older) patients registered in the national COVID-19 database during the period 20 October À 30 November 2020, cases in our study were more likely to be females (65% compared to 55% in the national database), from the Eastern part of France -Grand Est and Bourgogne-Franche-Comt e regions (29% versus 14%), in the age group 39-59 years (48% versus 34%), and less likely older than 69 years (4% versus 18%).
Acknowledgment This paper is originated in questions asked to the author by his Ph. D. advisor Professor Indira Chatterji. He wish to thank her for many valuable discussions, help and useful comments. He is also grateful to Professor François Labourie for providing a proof for Proposition 3. 1 and to Professors Vincent Delecroix, Ursula Hamenstädt, Yair Minsky and Alain Valette for discussions related to this work. Finally, the author would like to thank the referees for their valuable comments which helped to improve the manuscript. The author is supported by China Scholarship Council (NO. 201706140166).
Experimental setting The TensCol algorithm was implemented in Python 3. 5 with Pytorch 1. 1 library for tensor calculation with CUDA 10. 0 5. It is specifically designed to run on GPU devices. In this work a Nvidia RTX 2080Ti graphic card with 12 GB memory was used. A Nvidia Tesla P100 with 16 GB memory was used only for the two very large graphs (C2000. 5. col and C2000. 9. col instances). For a small graph such as r250. 5. col colored with k = 65 colors for an equi- To identify appropriate values for TensCol's parameters, a grid search is applied on a set of representative instances (DSJC250. 5. col, DSJC250. 9. col, DSJC500. 5. col, DSJC500. 9. col, DSJR500. 1c. col, DSJR500. 5. col, R250. 5. col, R1000. 1. col). For the grid search, each parameter is set within a reasonable range (λ ∈ {10 -4 , 10 -5 , 10 -6 }, µ ∈ {10 -5 , 10 -6 , 10 -7 } (for the GCP), ν ∈ {10 -4 , 10 -5 , 10 -6 } (for the ECP), α ∈ {1.