dataesr/scientific-paragraphs-categorization huggingface dataset:
The drop in violent events is less striking, yet they also decline by almost 20% in March-April 2020 compared to the previous year. (internal and international), limitations of public gatherings, and stay-at-home requirements. Measures are ranked on a scale ranging from 0 to 4: 0 means no measure is in place, 1 means that the measure is a recommendation, and 2 to 4 denote mandated measures of different scale or intensity. 26 The OxCGRT data also contain information on whether the measure is a national or a regional one. We pursue two strategies to measure Covid-19 restriction policies. As our baseline, we compute a global index which includes all policies and define a "shutdown" as the period during which this index exceeds some threshold value of restriction intensity. In addition to it, we consider each policy separately in our estimations.
We measure democracy using the combined Polity/Freedom House index as available from the Quality of Government dataset (Teorell et al. , 2020). Finally, global institutional quality is proxied by a measure of the rule of law from the Worldwide Governance Indicators (World Bank Group et al. , 2010), again retrieved from Quality of Government dataset. This variable captures the confidence in the rules of society by including perceptions on crime, the judiciary system and enforceability. Since the beginning of Covid-19 pandemic, Reporters Without Borders has been documenting state censorship, disinformation, and negative consequences on the right to reliable news. 31 These effects are argued to be more prominent in countries where media independence and pluralism or respect for freedom of press was rather low before the pandemic.
32 North Korea is a glaring example of how the Reporters Without Borders's evaluation of pre-Covid press freedom across countries correlates with media reporting during the pandemic. The country, which is ranked at the bottom of the 2020 World Press Freedom Index, is yet to report a coronavirus case a year since the disease first emerged. 33 To account for potential bias in the reporting of conflict that could be due to media restrictions, we include the press freedom index from Reporters Without Borders (Quality of Government dataset). 34 Again, we consider the pre-sample average of press freedom to avoid reverse causality concerns.
* This work was supported by  French National Research Agency  Grants  ANR-17-EURE-0020. Mathieu Couttenier  acknowledges financial support from the  IDEXLYON, University of Lyon (French National Research Agency , " Programme Investissements d'Avenir "  ANR-16-IDEX-0005 ) and financial support from the  French National Research Agency   ANR-20-COV4-0005-01. Nathalie Monnet  acknowledges financial support from the  Swiss National Research Foundation  (grant Economics of Conflict and Violence  P0GEP1-175125  and grant Land Concentration and Rural Poverty,  100018/178902 )). Rohit Ticku  acknowledges financial support from the  Institute for the Study of Religion, Economics and Society, Chapman University.
Hats and restrictions on fillings of contact manifolds Hats can give rise to caps via a branched cover construction: given a (suitable) projective hat  for  in ( 3 ,  std ), the -fold cyclic cover of the projective cap branched over  is a cap for the -fold cyclic cover (Σ  (),  , ) of ( 3 ,  std ) branched over. (When  = 2 we omit  from the notation. ) Warning 1. 14. We advise the reader that, in the following two statements, we will abuse notation by denoting a transverse knot by its topological type; for example, we will write   (9 46  ) to denote a transverse knot. What we mean is that we are considering the transverse knot obtained as the closure of the braid representing the knot taken from the KnotInfo database  [54]. We also note that the data we are using might agree with other knot databases or knot tables, as well as with other data on KnotInfo, only up to mirroring.
8 ). The number of connections initiated by each active individual is set to be equal to m c = 7. Such a variable is calibrated on the SocioPatterns primary school dataset  (Stehlé et al. 2011) , by considering the average daily number of contacts between students in different classes that last for a sufficiently long time. Specifically, consistent with the European Union guidelines on the definition of contacts at risk (https:// ec. europa. eu/ health/ eheal th/ covid-19_ en), we have only considered contacts that last cumulatively at least 15 min. Fourth, while we have explored some potentialities of our modeling framework, a fundamental and complete analysis of the proposed model is still missing. Despite its complexity may hinder the analytical derivation of theoretical results, we believe that its amenability to perform fast simulations poses the basis for future numerical studies on the general properties of the model, including the numerical computation of the epidemic threshold.
Even if (1. 2) does not have a Lax pair formulation it still has an Hamiltonian formulation, with conserved Hamiltonian H(u): H(u) = 1 2 T (∂ x u) 2 dx + 1 p + 2 T u p+2 dx. That H is preserved along the flow of (1. 2) and some of its truncations will play a key role in our analysis. Equation (1. 2) is the so called defocusing model. The focusing model can be obtained by a change of sign in front of the nonlinear term in (1. 2) (which also changes the sign in front of the second term defining H). Most of the results that we will obtain below can be extended to either odd p or to the focusing gKdV for even p, provided that a uniform bound on the H 1 norm of the solution is assumed. These extensions do not Date: July 6, 2021. The first author was supported by ERC grant ANADEL no 757996, the second author by ANR grant ODA (ANR-18-CE40-0020-01), the third author acknowledge the Gruppo Nazionale per l' Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituzione Nazionale di Alta Matematica (INDAM).
First, the initial capsule membrane has to be discretized by using a finite element triangular mesh made of nodes {X i } I i=1. Regarding time discretization, in  [2] , an explicit time scheme is used and the velocity field is determined by the use of a boundary integral method (BIM) coupled with a finite element method (FEM). A numerical stability condition imposes the use of small time steps. For a given parameter , the time evolution of capsule dynamics on the time intervals of interest generally requires hours of CPU time. To better understand the membrane behavior with respect to , a design of computer experiment (DoCE) is done: from a set of J parameter samples of  j ∈ D, j = 1,. , J, a spatio-temporal solution is computed for each  j , leading to a database of shape solutions under the form of a third-order tensor T x = x(X i ,  j , t n ) i=1,.
,I, j=1,. ,J, n=0,. ,N, ∈ R 3I×J× (N+1) (4) using a triangular finite element discretization of the membrane, a time discretization t n = nΔt (assuming that the time step is constant) and the parameter samples  j. Typically, for practical computations, I = O(1000), N = O(10000) and J = O(100), so that the tensor database becomes rather huge (about O(10) gigabytes). Of course, one can only store the solutions at coarser times steps and reduce N to O(100) but the database remains rather big in this case. From this data tensor, one can imagine different use cases leading to different tools: 1. Data exploration and knowledge extraction; 2. Real-time rendering of capsule dynamics for better understanding; 3. Data-driven modeling of capsule dynamics in the whole parameter domain. First and second items can be achieved by means of data dimensionality reduction.
1, 0. 9) for the sake of simplicity and brevity. Figure  1  shows both membrane shapes and positions in the channel at different instants. At each time t n = nΔt, where the time step is equal to Δt = 0. 04, a snapshot is saved and stored in the database. Note that all time quantities are adimensionalized by the factor ℓ/V. The resulting generated data matrix is used as the entry matrix for the ROM learning process. Then a truncated SVD is applied to get the spatial POD modes. In Figure  2 , the four first eigenmodes Φ k are plotted (more precisely this is a combination of each mode with the original spherical shape for better visualization and understanding of their influence). In Figure  3 , the graphics k ↦ → 1 -RIC(k) in log scale is plotted. We decide to use a truncation rank K equal to K = 10, returning a relative information content of about 1 -3.
HOSVD on the whole data tensor, error measurements on the whole database Now the consider the whole database made of 55 samples in the parameter domain. In Figure  8  we plot the location of the 55 chosen samples in the plane (Ca, a/ℓ). The design zone of interest corresponds to capsule shapes that can reach a steady state after a certain time. A SVD is first performed on the -flattening of the data tensor T x. In Figure  9 , we plot the four first parameter (normalized) eigenmodes in the parameter domain. These modes give an idea on the dependency of the capsule shapes with respect to the parameters. To complete the analysis, on Figure  10  we show the spectrum of the singular values for the -flattening matrix. One can observe a rather fast decay of the singular values especially for the ten first modes.
Next, we perform the SVD of the time t-flattening matrix of T x. The SVD provides us temporal eigenmodes. In Figure  11 , the four first temporal eigenmodes  m , m = 1,. , 4 are plotted. The first one appears to be the linear function while the others add details especially in the transient zone of the capsule dynamics. The spectrum of the singular values again shows a fast decay especially for the six first modes. To conclude this section, we have tested the accuracy of both ROM-A and ROM-B on the whole database. For each sample, we have derived a ROM model, i. e. a low-order dynamical system formulated in the PCA space. Then we have compared the ROM solution to the FOM solution by calculating  Shape between the two capsule shapes. In Figure  12 , the heat maps of  Shape are plotted for ROM-A and ROM-B.
Acknowledgements B. H. and D. S. 's research is partially supported by the  ANR   GRAAL ANR-14-CE25-0014. C. G. 's research is supported by  EPSRC  Fellowship  EP/N004833/1. She is grateful to LIX, École polytechnique for two professeur invité positions in November 2016 and November 2017, and for funding from  Université Sorbonne Paris Nord  as part of the Projet MathStic to pay for a week's visit in March 2018 and for a professeur invité position in September 2018, all of which facilitated work on this project.
Study design We conducted a prospective observational study, including both attendees to the French Intensive Care Society (FICS/SRLF) annual meeting held in Paris between June 9 and June 11, 2021 and matched controls. Indeed, given the specific exposure of ICU healthcare professionals, inclusion of a control grouped seemed mandatory. Matched controls were colleagues of attendees who stayed in ICU throughout the medical conference. Data were collected during the meeting and up to day 21 after the meeting. This report follows the STROBE guidelines  [7].
Data collection Standardized forms were used to record the following data: participant's characteristics (age, gender, height, weight) and COVID-19 status (previous COVID-19 infection and date of occurrence), risk factors of developing a severe form of COVID-19 (see Additional file 4: Table  S1 ), anti-SARS-CoV-2 vaccination status (number of doses, date of injection, type of vaccine), results of the day 7 selfadministered LFT and occurrence of COVID-19 symptoms and/or need of medical assistance for a COVID-19 infection over a three week period following the congress. Follow-up was obtained at day 21 for each participant and control.
Statistical analysis Qualitative variables were described as number (%) and quantitative variables as mean ± SD if normally distributed and as median [25th-75th percentile] otherwise. Matching factors between attendees and controls were compared by means of Fisher tests. All tests were two-tailed with a significance level of 0. 05. Confidence interval for the probabilities of being infected for attendees and controls was performed by means of one-tailed exact 95% confidence interval for binomial proportions. For the difference in proportions, we used the simple "add 2 success, add 2 failures" method  [9]. All statistical analyses were performed using R statistical software version 4. 0. 3.
Acknowledgements We thank  Alexandra Bernard  ( SRLF, Paris, France ),  Damien Le Marec  ( Europa Grou ) and  Mathieu Lloung  ( SRLF, Paris, France ). SRLF/FICS Trial group writing committee:  Pierre-Yves Boelle, Institut Pierre Louis d'Epidemiologie et de Santé Publique Paris (France) ,  Guillaume Decormeille, Service de Réanimation polyvalente, Toulouse (France) ,  Bertrand Hermann ,  Service de Médecine Intensive Réanimation, Hôpital Européen George Pompidou Paris (France) ,  Nicholas Heming ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire, Garche (France) ,  Gwenaelle Jacq ,  Medecine Intensive Reanimation, Centre Hospitalier, Versailles (France) ,  Toufik Kamel ,  Medecine Intensive Reanimation, Centre Hospitalier Regional, Orleans (France) ,  Jean-Baptiste Lascarrou ,  Service de Médecine Intensive Réanimation, Centre Hospitalier Universitaire Nantes (France) ,  Eric Maury ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire Saint Antoine, Paris (France) ,  Laurent Papazian ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire, Marseille (France) ,  Gael Piton ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire, Besancon (France) ,  Laurent Poiroux ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire, Angers (France) ,  Julien Ramillon ,  Institut Pierre Louis d'Epidemiologie et de Santé Publique Paris (France) ,  Anahita Rouze ,  Medecine Intensive Reanimation, Centre Hospitalier Universitaire, Lille (France).
Numerical Simulations To visualize the difference between the dynamics of the vector model only (i. e. , a model that considers the transmission between vector and human only) and our model, we perform numerical simulations using MATLAB. e values of the parameters used in the simulations are reported in Table  1. Figure  2  shows the dynamics of disease spread with the SEIR-SEI model with vector contamination alone. e susceptible population decreases from the start to day 80. e size of the infected population increases until quite rapidly it reaches a peak of 15% of the people. en, it gradually decreases. is process ends after 75 days when the entire host population recovers. Figure  3  shows the dynamics of the SEIR-SEI model with two transmission routes (vector-to-human and human-tohuman transmission).
Parameter Estimation and Model Validation: The Colombia Scenario In this section, we estimate the values of β hh (human-human transmission rate) and π (vertical transmission rate) parameters related to the population dynamics of ZIKV spread through human and mosquito populations. Mosquito-to-human interaction is set at a � 0. 2. For the mathematical model of ZIKV (1), some parameter values are known and available in scientific journals. In order to simulate the dynamics of the Zika virus in the Colombian population, it is necessary to identify some of the parameter values of the mathematical model. us, the unknown values of the parameters must be estimated using real data regarding the prevalence of the Zika virus. e objective of this section is to explain the behavior of the population-level Zika disease dynamics in Colombia for the year 2016, which also has been used in literature  [22].
e Zika prevalence data were collected from the data available from the National Institute of Health in Colombia. In e only parameters we estimated are the human-tohuman (β hh ) transmission and the vertical transmission rate (π) in mosquitoes. e rest of the parameters remain unchanged. In order to fit the mathematical model (1) of ZIKV to the time-series data of Zika prevalence in Colombia, we found the best-fit model parameters for the considered model  (1)  with the abovementioned data using MATLAB's fmincon minimization software. As indicated in  [24] , we use a total population of 19,471,223, since this is the number of people living below the 1,400 meters where the mosquitoes that transmit the Zika virus live. In addition, we considered the initial number of infected populations as 2,173 as shown in the data in Table  5.
e last part of this study is devoted to the numerical simulation of different scenarios based on the official data published by the National Institute of Health-SIVIGILA, Colombia, from the 1st to the 36th week. We estimated by fitting epidemiological parameters such as human and vertical transmissions in mosquitoes using existing discrete real data. Acceptable agreement between data analysis and numerical solutions is established. is study aims to provide a general overview of the impact of combining a few types of transmission in the spread of ZIKA disease. We use a model that takes into account both human-to-human transmission and vector transmission in mosquitoes. Although the combination of  these two types of transmission accelerates the incidence of ZIKA, it may be necessary to understand the difference between transmission in women and men to examine the risks of infection in both sexes, particularly in pregnant women, which may increase the incidence of secondary infections.
Motivating example Schetelig et al. 12  assessed long-term outcomes of patients with myelodysplastic syndromes (MDS) or secondary acute myeloid leukemia (sAML) after an alloHCT. MDS is characterised by the production of deficient clonal blood cells in the bone marrow and can rapidly progress to more severe sAML. 13 AlloHCT is the only treatment that can offer long-term remission of the disease. Therefore, alloHCT is recommended for disease stages at high risk of transformation into acute myeloid leukemia (AML) or death from other complications. However, this procedure is associated with a high risk of adverse outcomes, either due to relapse of MDS or sAML, or due to side effects of the (pre-)treatment. This leads to the competing risks outcomes relapse and non-relapse mortality. The data set contains 6434 patients transplanted between 2000 and 2012, and registered with the European Society for Blood and Marrow Transplantation (EBMT).
Software All analyses were performed using R version 3. 6. 2  28. The substantive model compatible imputation was performed using the SMC-FCS package version 1. 4. 1,  29  and MICE was performed using the mice package version 3. 8. 0. 30 The cause-specific Cox models were run and subsequent predicted cumulative incidences were obtained using the mstate package version 0. 2. 12. 31.
49. Based on an 95% upper-bound for this FMI, and for a desired coefficient of variation of 0. 05, we would require approximately m = 84 imputed data sets. We rounded this upwards, and performed our final analysis with m = 100. We conserved n iter = 20 as convergence was generally observed from 10 iterations onwards. Figure  4  summarises the exponentiated point estimates (hazard ratios, HR) and associated 95% confidence intervals (CI) from the cause-specific model for REL. The CIs for CH 12 and SMC-FCS are based on the pooled standard errors and the t-distribution. First, we observed a clear gain in efficiency across all coefficients for both imputation methods relative to CCA. Second, there was general agreement between the estimates obtained from both CH 12 and SMC-FCS; a finding which was also reported in the illustrative analysis in the work by Bartlett and Taylor.
Such differences between the MI methods and CCA do question the validity of the M(C)AR assumption made. In the EBMT registry, many missing values can be considered MCAR, for reasons relating to data management. Variables such as comorbidity score, cytogenetic classification and donor age became more frequently collected over time as their clinical relevance grew clearer. Missingness may also be related to the transplant centre, i. e. particular measurements not being recorded in certain clinics. In the current analysis, both calendar date and transplant centre (categorical, large number of levels) were not included in the imputation model for simplicity. An option would have been to include them as auxiliary variables (added as predictor to X | Z, but not to substantive model), however, the use of auxiliary variables was not a focus of this manuscript, and both MICE and SMC-FCS make different assumptions with respect to the inclusion of these variables in the imputation model.
On the x-axis are the hazard ratios, which is plotted on the log scale where the confidence intervals are symmetric. Variables and their descriptions can be found in the data dictionary. Per level of factor and for continuous variables, we show the observed counts (n) and the number of NRM events (# Events) in the full dataset. do acknowledge that given the longitudinal nature of survival data, a missingness mechanism that depends on the observed event time may be rare. To the best of our knowledge, our work is the first systematic assessment of the performance of MI for missing covariates with regard to the prediction of cumulative incidences. In this respect, the imputation methods performed comparably, which may be attributed to a solid estimation of both the baseline hazards and of the regression coefficients from the complete covariates.
Figure5. Forest plot with point estimates and 95% confidence interval for the cause-specific Cox model for non-relapse mortality (NRM). On the x-axis are the hazard ratios, which is plotted on the log scale where the confidence intervals are symmetric. Variables and their descriptions can be found in the data dictionary. Per level of factor and for continuous variables, we show the observed counts (n) and the number of NRM events (# Events) in the full dataset.
Acknowledgements The authors would like to thank  EBMT  for the use of the MDS long-term data set, as well as the patients and centres involved in the original study. We thank  Linda Koster  ( EBMT Leiden Study Unit ) for the continued support with the data set. We also acknowledge  Jacques-Emmanuel Galimard  ( EBMT Statistical Unit, Paris Team ) for his input regarding the interpretation of simulation results, and  Bart Mertens  ( Leiden University Medical Center ) for his help concerning design of the simulation study and choice of missingness mechanisms.
Methods: To reach that goal, we use an integrated approach at the crossroads of geography, spatial epidemiology, and public health science. To eliminate any ambiguity relevant to the scope of the study, attention focused at first on data quality assessment. The data used originate from official databases (Santé Publique France) and the analysis is performed at a departmental level. We then developed spatial autocorrelation analysis, thematic mapping, hot spot analysis, and multivariate clustering.
Modeling is an excellent option when the conditions of collection and processing of data are fully specified and mastered, which is not the case with many COVID-19 official databases. When these conditions are not met, modeling -and especially the array of methods used in the area of epidemiology-often produces errors, hence the inability of COVID-19 models to "predict" future disease developments, particularly in spatial terms  [15]. To put it otherwise, data quality assessment is a firm condition for studying the epidemic further. A number of studies used heterogeneous data resources erroneously, particularly at an international scale level, which produced inconsistent results despite the introduction of highly efficient algorithms and modeling techniques. We hypothesize that the debate has been biased in different ways.