dataesr/scientific-paragraphs-categorization huggingface dataset:
It also provides the contribution of children and adults age classes, thus including the effects of non-homogeneous travel behaviors and mixing patterns. The numbers of infectious individuals of each type flying from a subpopulation with degree ′ and arriving to a subpopulation with degree during the entire duration of the outbreak are given by: 1 -) , i. e. the final proportion of the , hosts who contract the infection and diffuse with rate , during their infectious period , with. and indicate the attack rates in the children and adult age classes, respectively, and they are given by the solution to 1 -= exp -∑ [52]. Figure 3A shows the behavior of the final attack rates and as a function of the reproductive number , considering the partition of the population in children and adults observed in the 8 European countries of the Polymod dataset [22] and their mixing properties.
Variations between countries, depending on the age profile and the mixing patterns, are observed. Countries are generally predicted to have significantly larger epidemic sizes in children, as shown, for example, by the case of Italy. This would correspond, on average, to a larger number of individuals in the children class that could potentially seed other subpopulations andm -1 i = c,asustain the spatial invasion. However this effect is counterbalanced by the age-specific traveling probabilities that are much lower in the children class. In the case of Belgium the final epidemic sizes in the two classes are found to be almost equal, with the size of the epidemic in the adult population being slightly larger than the one in the children population. This is the only country in the dataset under consideration that has a ratio larger than 1, indicating a larger average number of contacts established by adults with respect to children, likely induced by the specific survey methodology adopted [22].
In both plots, Belgium is a standalone example, with > and > , differently from all other countries and due to the fact that it is the only population in the dataset to have > 1, as discussed in the main text. The dashed line represents the case of homogeneous mixing when no partition of the population is considered (in panel B it corresponds to the function ). C: Global invasion threshold * as a function of the reproductive number , for different values of the parameters describing the mobility process. Air mobility networks having degree distributions ( ) ∝ with = 2 and = 3 are shown to consider different levels of heterogeneity. The results obtained in the two cases are compared to the scenarios with homogeneous diffusion rates = ( ) obtained for = 0. All curves are obtained by setting the fraction of passengers in the children class equal to the observed data, i.
Acknowledgments The authors would like to thank  Niel Hens  for providing us the daily contact data of the POLYMOD study and for his useful comments;  Caterina Rizzo  for the age profile of the laboratory confirmed cases in Italy during summer 2009;  Stephen Eubank  for providing us the synthetic contact data for Portland; and  Jose J Ramasco  and  Pablo Jensen  for useful discussions. This work has been partially funded by the  ERC Ideas  contract no. ERC-2007-Stg204863 (EPIFOR) and the  EC-Health  contract no. 278433  (PREDEMICS) to VC and CP; the  EU-FP7  contract no. 231807  (EPIWORK) to AA; the  ANR  contract no. ANR-12-MONU-0018 (HARMSFLU)  to VC.
A Mathematica package for quiver invariants It should be clear from the above that the Coulomb branch formula produces a parametrization of the Poincaré-Laurent polynomial of any quiver in terms of single-centered BPS invariants in a completely combinatoric way. However, even for moderately complicated quivers the necessary computations quickly become tiresome, and are best implemented on a computer. We have implemented the Coulomb branch formula as well as Reineke's formula for stack invariants (and many related other routines) in a mathematica package "CoulombHiggs" available from arXiv and described in Appendix A, which we hope will facilitate studies of single-centered BPS invariants. This package has been successfully tested on the examples investigated in  [14]  and many more.
Inclusion of households started in December 2009, at the end of the first H1N1pdm season in metropolitan France. We studied factors associated with the post-pandemic H1N1pdm titer from blood samples collected at inclusion. Previous studies showed that post-pandemic titer was linked to age classes  [2, [14] [15] [16] [17] [18] [19] [20]  and to pandemic vaccination status  [21]. Relying on the massive amount of data collected at entry in the cohort, we tried to find other independent associations with this titer. In a complementary study, we carried out a nested case-control analysis in these subjects to identify risk factors for probable infection during the first H1N1pdm season.
Study design This study relies on 601 households (1450 subjects) included in the study between December 2009 and July 2010, according to a stratified geographical sampling scheme in the French general population. More details on this sampling procedure, the representativeness of the sample and the global study design are available in a previous publication  [11]. A total of 575 households (96%) were included after the first pandemic season (September 7 to December 27, 2009  [22] ). During the inclusion visit, nurses collected detailed data from all subjects with questionnaires and blood samples for serological analyses. As 73 of these samples (5. 0%) were either too difficult to obtain (young children especially) or of insufficient quality or quantity to be analyzed, the analyses presented here focused on the 1377 subjects for whom haemagglutination inhibition (HI) titer was measured.
All pandemic vaccine recipients were excluded from these analyses. Covariates. All covariates used in the analysis are listed elsewhere  [11]  and detailed in Tables S1-S6 in File S1. The relation with HI titer was studied for 310 covariates, gathered according to 6 main dimensions: 1) sociodemographic characteristics, smoking habits and medical history, 2) vaccination and preventive measures against the virus, 3) indoor housing, 4) attitudes, beliefs and risk perception, 5) nature of meetings with other people and characteristics of contacts and 6) ecological data regarding the surrounding environment. For dimensions 1 to 5, we used data collected from questionnaires completed by the household members, with the help of the visiting nurse. For geographic data, we geocoded the addresses of households and used information on the surrounding demographic and socioeconomic context provided by the French Institut national de la statistique et des etudes economiques (Insee) regarding statistical block groups of about 2000 inhabitants (IRIS)  [27].
Statistical methods All collected covariates likely to be associated with post-seasonal elevated HI titer were studied. Comparison tests between subgroups were Fisher's exact test (for binomial covariates) and Kruskal-Wallis rank sum test (for continuous covariates). Estimation of geometric mean titers (GMTs). GMTs were estimated for HI assays with the use of regression models for interval-censored data  [29, 30]  accounting for the within-household correlation. Post-stratification was used to compute representative post-seasonal GMT in the French general population. Calculation and use of sampling weights were detailed elsewhere  [11]. We defined the ''GMT ratio'' (GMTR) as the multiplicative factor between the GMT in exposed versus non-exposed (for a binary covariate) or for each unit increase (for a quantitative covariate).
Model selection. The same selection process was used for both analyses. GMTRs and ORs were estimated for all covariates individually. Since a large number of covariates were tested, we adjusted the p-value to control the alpha inflation associated with multiple hypothesis testing and to account for the false discovery rate (FDR) for all covariates  [32]. All covariates with an adjusted P,0. 30 in univariable analyses were included in a multivariable analysis. Thirty datasets were imputed via multiple imputations by chained equations (MICE)  [33]. Covariates related to attitudes, beliefs and risk perception for children were sampled from subjects over 15 years in the same household. The criterion for model selection was the mean residual sum of squares with 10-fold cross-validation -aimed at avoiding overfitting and controlling FDR -run over the 30 imputed datasets and considering only models with P,0.
05 for all covariates. Resulting coefficients and standard errors were combined to obtain the reported results  [34]. Additional multivariable models were estimated separately stratified by pandemic vaccination status. Statistical analyses were performed with R version 2. 15. We estimated GMTRs with the function ''survreg'' (package ''survival'' version 2. 36). Multiple imputation was done with the package ''mice'' and we ran alternating logistic regression with the package ''orth''.
4, 72. 0) years (P,0. 0001). Nine hundred and thirteen subjects (66. 3%) were living in a house and the median area per inhabitant was 36. 7 (IQR: 25. 0, 52. 5) m 2. Detailed data on meetings was collected in 1360 out of the 1377 subjects. The median number of reported daily meetings was 6 (IQR: 3, 10) and the median summed duration was 963 (IQR: 503, 1646) mn/day, with significant differences according to age groups and locations (Figure  1 ). Subjects aged less than 15 years had a higher daily duration of meetings than older ones: 1847 (IQR: 1147, 2564) mn vs. 848 (IQR: 440, 1378) mn, P,0. 0001. Children at school reported a large amount of meetings with children of the same age. Working adults aged 20 to 60 years had many meetings with persons of their age. At home, subjects had meetings with people of their age and with persons from the previous or next generation.
As inclusions were delayed, data regarding ILIs were collected retrospectively and recall bias may be important in subjects with late inclusion. Moreover, we found a decreasing GMT according to time since vaccination in pandemic vaccine recipients, and we cannot exclude an antibody loss in the months following an infection, although we did not find any association between GMT and date of inclusion in unvaccinated subjects. Such limitations may have biased the association between GMT and other covariates. In the case-control analysis, cases were defined serologically, yet we know that an elevated titer can sometimes be explained by cross-reactions, especially in the elderly  [16] , and that infected subjects can show a low titer a few months after infection  [70]. This lack of specificity and sensitivity to identify infections must be considered in light of the sensitivity analyses results, which often showed similar results with different case definitions.
Acknowledgment. This work was initiated during the 10 th McGill -INRIA Workshop on Computational Geometry at the Bellairs institute. The authors wish to thank all the participants for creating a pleasant and stimulating atmosphere, in particular  Sariel Har-Peled  and  Raimund Seidel  for useful discussions at the early stage of this work. The authors also thank  Imre Bárány  and  Pierre Calka  for helpful discussions.
Acknowledgment. The authors wish to thank  Institut Henri Poincaré (Paris, France)  for providing a very stimulating environment during the " Control of Partial and Differential Equations and Applications" program  in the Fall 2010. The first author was partially supported by the  ANR  project  GAOS "Geometric analysis of optimal shapes ". The third author was partially supported by the Grant  MTM2011-29306-C02-00  of the  MICINN (Spain) , project  PI2010-04  of the  Basque Government , the  ERC Advanced  Grant  FP7-246775  NU-MERIWAVES, the  ESF   Research Networking Program OPTPDE. The authors warmly thank  Aline Bonami ,  Giuseppe Buttazzo  and  Michel Crouzeix  for useful discussions.
Important information concerning a multivariate data set, such as clusters and modal regions, is contained in the derivatives of the probability density function. Despite this importance, nonparametric estimation of higher order derivatives of the density functions have received only relatively scant attention. Kernel estimators of density functions are widely used as they exhibit excellent theoretical and practical properties, though their generalization to density derivatives has progressed more slowly due to the mathematical intractabilities encountered in the crucial problem of bandwidth (or smoothing parameter) selection. This paper presents the first fully automatic, databased bandwidth selectors for multivariate kernel density derivative estimators. This is achieved by synthesizing recent advances in matrix analytic theory which allow mathematically and computationally tractable representations of higher order derivatives of multivariate vector valued functions.
E. coli data The E. coli data set is provided by the UCI machine learning database repository  (Frank and Asuncion, 2010). The original data were contributed by Kenta Nakai at the Institute of Molecular and Cellular Biology of Osaka University. The data represent seven features calculated from the amino acid sequences of n = 336 E. coli proteins, classified in eight classes according to their localization sites, labeled imL (2 observations), omL (5), imS (2), om (20), pp (52), imU (35), im (77), cp (143). A more detailed description of this data set can be found in  Horton and Nakai (1996). Since two of the original seven features are binary variables, only the remaining five continuous variables (d = 5), scaled to have unit variance, were retained for the cluster analysis. The number of groups identified by the mean shift procedure with correction for insignificant groups (using α = 5% as a default) was 5 for PI and SCV bandwidths, which is the natural choice if the insignificant clusters imL, omL and imS are merged into bigger groups.
Here, we compare the significant curvature regions obtained using a usual r = 0 bandwidth selector to those with an r = 2 optimal bandwidth in Figure  5  on the earthquake data from  Scott (1992). The recorded measurements are the latitude and longitude (in degrees) and depth (in km) of epicenters of 510 earthquakes. Here, negative latitude indicates west of the International Date Line, and negative depth indicates distances below the Earth's surface. The depth is transformed using -log(-depth). For these transformed data, we use PI selectors H PI,0 and H PI,2 and SCV selectors H SCV,0 and H SCV,2. As expected from asymptotic theory, bandwidths for Hessian estimation are larger in magnitude than bandwidths for density estimation. Moreover only the central modal region is present using H PI,0 , whereas with H PI,2 , the three local modal regions are more clearly delimited from the surrounding space, confirming the three modes obtained with subjective bandwidth selection by  Scott (1992).
Acknowledgements I would like to thank  Luca Carlevaro ,  Amit Giveon ,  Stefan Groot-Nibbelink ,  Nick Halmagyi ,  Josh Lapan  and  Jan Troost  for enlightening discussions. This work was supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02.
The data used in the paper come from a graduate tracer study (GTS) of a representative sample of over 4,700 university graduates who qualified in 2004 and were interviewed in both 2005 (around one and a half years after graduation) and 2007 (around three and a half years after graduation). This dataset also contains a "calendar" recording monthly activities over the entire period since graduation (44 months). The data are not experimental and, as will become clear from the descriptive analysis, selection into the programme is not random. Ordinary Least Squares (OLS) and Propensity Score Matching (PSM) will be used to try and compare treated and untreated individuals. The data allow the investigation of a wide range of outcomes, including joblessness, unemployment, sector of employment, as well as job quality and earnings.
The paper proceeds as follows. Section 'Employment subsidies' reviews the literature on employment subsidies and summarises their effectiveness in generating employment, as well as the design features associated with the most successful programmes. Section 'The SIVP programme' then explains the workings of the Tunisian SIVP programme at the time the graduates in the dataset were observed. Section 'Data and descriptive statistics' discusses the data and describes the characteristics of SIVP beneficiaries. Section 'Unemployment and participation in the SIVP programme' looks in more detail at the attributes associated with selection into the programme, and Section 'SIVP participation and employment outcomes' provides a brief overview of the methodologies used and presents the results from the analysis.
Data and descriptive statistics The graduate tracer study (GTS) data used in this paper consists of a random and representative sample of 4,763 individuals who qualified in 2004 and were surveyed on two occasions: once in 2005 (around 1. 5 years after graduation) and then again in 2007 (around 3. 5 years after graduation). Sample attrition was relatively low: 89% responded to the first survey (n = 4,250) and 79% (n = 3,751) to the second. The survey contains a range of sociodemographic and labour market information on the graduates as well as month-by-month calendar data on their main activity. 12 igure  1  below shows the main activity of graduates over the 44-month period covered by the calendar. As shown by the graph, the first six months of the calendar fall halfway through the academic year, so many students are still inactive/studying (month 1 of the calendar coincides with February 2004).
Acknowledgements I am very grateful to the  Observatoire National de l'Emploi et des Qualifications  for granting me access to the data as well as for clarifications about the SIVP programme itself. I would also like to thank the research department at the African Development Bank where a good deal of this paper was drafted. Tahar Abdessalam ,  Loubna Bourkane ,  Ousman Gajigo  and  Amadou B. Diallo  provided useful comments on earlier drafts. The paper also benefited enormously from comments and suggestions by  Núria Rodriguez-Planas ,  David Neumark ,  Amelie Constant  and other participants at the IZA workshop on  Labor Markets  and  Labor Policy  in MENA Countries, as well as two anonymous referees. All remaining errors are, of course, my own. The views expressed in this paper do not necessarily represent those of the OECD,  AfDB , or ONEQ.
Data source We used data from the Registre de Dialyse Péritonéale de Langue Française (RDPLF),  24  a peritoneal-dialysis registry with an estimated 85% coverage of all patients in France, in 2011 (C Verger, personal communication, 2013). We identified all adult (≥18 years old) patients in the RDPLF database who started peritoneal dialysis between 1 January 2000 and 31 December 2009 in metropolitan France and determined their treatment status at 31 December 2010. We then excluded patients whose treating centre joined RDPLF less than 2 years before their date of starting treatment (to ensure sufficient data to calculate centre volume, described below); patients treated in private clinics as the volumes of these centres was restricted compared with other centre types and patients who changed centres during follow-up.
Centre volume measure We calculated the number of patients on peritoneal dialysis per day in each patient's treating centre over the 12 months prior to each patient's date of starting peritoneal dialysis ('prevalent measure') and the total number of patients initiating peritoneal dialysis in each patient's centre over the same period ('incident measure'). As the two measures were highly correlated (Pearson's r=0. 82), we used the prevalent measure as the centre volume variable but included the incident measure in the missing-data imputation models, which are described below.
The Cox models combined results from the five imputed data sets; the Fine and Gray models used one imputed data set. We reported HRs with 95% CIs calculated using robust variances to account for clustering of patients within centres. The supplementary material explains the choice of confounders and presents additional model results (30-day and 1-year follow-up) as well as the cs-HRs and sd-HRs at 5-year follow-up from a complete-case analysis (ie, excluding all patients with any missing data). Sex, diabetes, previous treatment and assistance were included as stratification variables in the final Cox models as they appeared not to meet the proportionalhazards assumption based on the Schoenfeld residual plots. Age, sex and previous treatment showed small deviations from proportional hazards in the Fine and Gray models; however, the regression coefficients for centre volume did not change upon introducing timeinteraction terms  32  (data not shown), and therefore we only used main covariate terms in the final models.
41 owever, another Canadian study found no association between distance to care and outcomes. 12 These data are from countries where distance to care can be considerable (>100  40  and >300 km  41  ), different from the French (and most European) settings, and we have doubts about whether the distance-outcomes association can be estimated from observational data in European settings (see arguments in online supplementary material). Overall, we therefore consider the predicted change in cumulative incidence of the outcomes in the intervention simulations to be an upper limit of the impact of a real-life implementation. Several limitations of this study must be borne in mind. Approximately 15% of peritoneal-dialysis patients in France are not included in the RPDLF database and 13. 2% of eligible patients were excluded from the study, both of which may have introduced bias and limited the generalisability if these patients or their treating centres differed from those included in the analysis.
What is probably less intuitive is that even the sheer expansion of the firm by means of replication of already existing activities, might incur in difficulties that are related to the impediments of transfer of knowledge, also within the same organization  (Szulanski, 1996). The French database enables us to investigate this issue, as it is possible to observe the increase in the firms' number of plants. A few other studies before have addressed similar research questions on French or Italian firms. However, either they had to resort to surveys to access actual investment data, thus much limiting the scale of the analysis  (Parisi et al. , 2006  for Italy) or, in another stream of literature, they had to compute investment as the (adjusted) difference of capital stock over two consecutive years  (Bontempi et al.
As a result, a great deal of attention is devoted to contribute to the methodology for identifying these exceptional episodes of investment, often referred to as spikes in this stream of literature. We then investigate which characteristics make it more likely for a company to undertake an investment project, and relatedly, how the episode of investment impacts on firm performance in the following periods. The rest of the paper is organized as follows. In Section 2 we describe the database for France and Italy. After documenting the lumpy nature of firm-level investment, in Section 3 we consider and compare different measures of investment spikes in order to account for large capital purchases, and we also introduce our own measure. In Section 4, first we analyze the determinants of the probability to observe a spike, and then we study the effects of such events on firm performance.