dataesr/scientific-paragraphs-categorization huggingface dataset:
54 GHz processors. The typical node used at the Mésocentre contains 2 sockets, each with 8 cores, 32 GB of local memory and an L3 cache size of 20 MB. The PIC code uses standard parallelization libraries for both, the distributed memory paradigm (using multiple software processes with MPI libraries), and the shared memory paradigm (multiple threads with OpenMP). While using these libraries, care was taken to associate both processes and threads to the underlying hardware to reduce the requests to remote memory. For this purpose, software processes were placed in single processor sockets (the hardware unit that groups a set of cores) and software threads in single cores. In this way, we attempted to minimize the negative impact of non-uniform memory access (NUMA) which can happen when a software process uses cores placed in different sockets, for example.
Landau damping simulations The purpose of this section is to solve numerically the Landau damping test case in order to verify the code (see  [7, 11]  and the references therein). The relevant equations are              ∂ t f + v • ∇ x f -eE(x, t) • ∇ v f = 0, E (x, t) = -∇ x φ(x, t), -∆ x φ = ρ i + ρ e ρ i = 1 and ρ e = -e R 2 f (x, v, t)dv, f (x, v, t = 0) = f 0 (x, v) (8) where -e = -1 stands for the electron charge and the initial condition is given by f 0 (x, v) = 1 2πv 2 th 1 + α cos (kx 1 ) exp - v 2 1 + v 2 2 2v 2 th , (9) where v th = 1, k = 0. 5, and Ω x = 0; 2π k × [0; 1] (10) is the domain in the physical space. We use classical periodic boundary conditions for the physical domain. Even if the data stand in four-dimensional phase space, this test case is mainly two-dimensional since the perturbation α occurs only in the x 1 direction of the physical space.
Code optimization and performance For the sequential implementation of this code we have focused on the same general type of data structures and algorithms as introduced in the work by Bowers (see  [3]  and  [4] ) and also implemented in other codes (see  [12] ). This approach is tailored to the modern commodity CPU architecture as it attempts to minimize and order the memory access as its guiding principle. This acknowledges that memory access is relatively expensive compared with computations. Thus, a minimization of the number of memory accesses and furthermore, an increase of the amount of sequential memory access results in very large performance gains. These principles are accomplished through several key design points: -particle lists organized as arrays of structures: the array of structures obeys the principle of "data that are used together should be stored together"; all the particle information needed to represent a particle can be thus accessed in a single memory stream.
This leads to simulations using from 1 to 16 threads in all, but distributed over 2 processes as described above. In order to assess the efficiency of our code we performed simulations with three data sizes: 2 million, 20 million, and 200 million particles. Thus, the data (particles and fields) size is bigger than the L3 cache size (thus the memory access speed is determined by the access speed to the main memory). The reference grid was set at 512 x 16 cells, the time step to ∆t = 0. 01 and the number of iterations to 1000, when α = 0. 1. The results of using thread affinity are illustrated in Fig. 3  and Table  1  where the best run times among several runs are given. The best speedup of 9. 43 was reached on 16 cores for the biggest data size. As expected, Fig. 3  (at right) shows the linear growth of the run time with respect to the increase of the data size.
Conclusion and outlook The aim of this paper was to efficiently implement a Particle-In-Cell method for solving some nonlinear Vlasov-like models in a four-dimensional phase space. The organization of the code follows an existing work (see  [3] ) and mainly focuses on optimization of the memory access. We demonstrated the performance of our code within some numerical experiments on the Landau damping test case. We also verified the code on a simulation of the Kelvin-Helmholtz instability. In addition, this test case allowed us to numerically illustrate some convergence result from the Vlasov-Poisson model with a strong magnetic field towards the guiding center model. In future work, we intend to implement the time stepping scheme introduced in [8]. This method allows to solve multiscale Vlasov-Poisson models by treating the stiff terms as an exponential integrator.
Once the patient is informed, and provided he/she has not expressed refusal, the CRA initiates data collection from the medical file. At each new consultation, the CRA collects the new data on the consultation and on hospitalisation provided in the medical file. For patients with GFR <60 ml/min at inclusion who have not consulted again 18 months later, the CRA investigates the reasons from the healthcare teams and, if required, from the person's birthplace (to ascertain vital status). Patients are considered lost to followup if this procedure does not enable any information to be obtained concerning the patient. The CRAs transfer all relevant information on the cohort to the medical teams. The data is collected using an electronic CRF developed by SLC Expertise. The CRF is created under Microsoft Access, a relational database management system.
Statistical analyses Patient numbers and characteristics will be described for the overall cohort, and for each centre. Data analysis will concern the variables collected, and descriptive statistical analyses will be performed each year on all variables. With 5,000 patients, assuming equal groups, an average follow-up of 5 years, and a censoring rate of 0. 1 per year, the minimum detectable hazard ratios (with twosided α = 5 % and 80 % power) for a survival analysis are 1. 20, 1. 15 and 1. 12 for baseline event rates of 0. 05, 0. 10 and 0. 20 per year  [12]. Depending on their nature, quantitative variables will be described by total numbers, numbers of missing data, numbers of items of data collected, median, mean and standard deviation, 95 % confidence interval and extreme values (max/min).
Qualitative variables will be described in terms of total numbers, numbers of missing data, numbers of items of data collected, percentages for each modality of response (calculated from completed data alone) and 95 % confidence interval. The statistical tests will be unilateral and the significance threshold is set at 5 %. Inter-group comparisons will be performed using: the Chi 2 test or the Fischer exact test if the theoretical numbers are below 5 for qualitative variables, Student's test for Gaussian quantitative variables Wilcoxon's non-parametric test for semi-quantitative or non-Gaussian quantitative variables. The statistical analyses will be performed on SAS® software, version 9. 4, SAS Institute, NC, Cary, USA. The evolution of the main variables will be calculated on the basis of data from as close as possible to January 1 st of year N and year N + 1.
A complete report presenting all the variables described in the CRFs will also be sent to the centres once a year. There will be regular publications of results in scientific journals. Quality control procedures will be implemented yearly in each participating centre. This will include a complete audit of ten randomly-selected medical files. The data collected via this cohort is indirectly identifiable, thus requiring the authorisation of the French advisory committee on information processing in the area of health (CCTIRS), equivalent to an ethics committee  [16] , and of the CNIL (French data protection authority). The CCTIRS issued approval on July 27 th 2015 and the CNIL on November 26 th 2015. For this cohort, an information sheet is issued to the patient by the physician.
Discussion The need to develop epidemiological cohorts in France has been underlined by the Government and by health product manufacturers  [17]. Their value and function, and the quality criteria to be met for them to yield tools for public health monitoring, were described in a document issued by InVS in 2010  [18]. The ND-CRIS is a large cohort that complements the existing epidemiological tools available in France in the area of kidney disease, in particular the REIN registry  [10]  and the CKD-REIN Cohort  [12]. It takes its place among the large international cohorts on CKD, such as those reviewed in the Chronic Kidney Disease Prognosis Consortium  [19]. While the REIN registry collects national data in France concerning patients with end-stage renal disease (ESRD), the ND-CRIS concerns the same population as the CKD-REIN cohort on regional scale.
2391 patients have been included and their follow-up has been initiated. The number of patients that can be reasonably expected given the turnover of patients followed by the nephrologists concerned (at least 5000 patients by 2017), in addition to providing routine descriptive data from the CRFs, will also enable ad hoc studies. The ND-CRIS cohort also has certain limitations. As indicated earlier, the ND-CRIS does not a priori claim national representativeness. The management of patients with kidney disease varies from country to country and region to region. Thus the regional character of the ND-CRIS will preclude any extrapolation of the data, which will be considered as dependent on the region or on the care management implemented. The confrontation of the characteristics of the ND-CRIS patients with those in the CKD-REIN cohort could however provide information on the degree of national representativeness of the ND-CRIS cohort.
The nature of the ND-CRIS cohort and its scale mean that it would be difficult to collect and store samples. In addition, the immediate objectives of the ND-CRIS differ from those of the CKD-REIN, and do not in fact require a system of this sort. It would nevertheless be possible to envisage the creation of a biobank in an ancillary protocol. For the moment, the ND-CRIS cohort will provide nephrologists with regular data on the epidemiology of the population they are managing, and on their own practices. Each participating centre will receive the data on its own practices, but only aggregated data will be used in communications and publications. With the follow-up of included patients, the routine data contained in the CRFs will enable the trends in the characteristics of the population managed for ND-CRIS to be documented, and also, allowing for an adequate time-lapse, the impact of management on renal function, on complications and on recourse to renal replacement therapies.
The IRIP method has been compared with others model outputs such as SHYREG  [24] , CINECAR and EXZECO  [25]  showing the complementarity of these models. Other works based on the comparison of the IRIP method with surface runoff coefficient from discharge data  [26] ,  [27]  highlight the fact that surface runoff phenomenon is too much complex to be represented with only one runoff coefficient. Large scale comparison has been made with the Gaspar French database of natural disaster claims  [13]  showing promising correlation. Statistical comparisons has been made with surface runoff impact locations on the transportation network, such as roads and railways  [12] ,  [14] ,  [28]  showing good correlation between the high susceptibility levels of the transfer and accumulation maps and the occurrence of surface runoff impacts.
A software version, called iRIP© has been developed by SNCF (French National Train Company) for its own purpose and is used for this study. The IRIP method produces susceptibility maps and is independent of the meteorological hazard. To get a surface runoff hazard map, the IRIP maps must be combined with the rainfall. From previous studies conducted to assess the relevance of the IRIP maps, several points have been learnt and some hypotheses have been raised. When comparing the IRIP maps with risk regulatory zonings, there are agreements between the IRIP map of accumulation and the dry talweg network and between the IRIP map of transfer and soil erosion risk areas. However, some parts of the IRIP map of accumulation also correspond to soil erosion risk areas. The hypothesis is that the map of accumulation also shows the sediment deposit areas.
Acknowledgements We thank ANRT (Agence Nationale de la Recherche et de la Technologie) for funding the PhD grant of the first author. We thank Judicaël Dehotin who developed the software implementing the IRIP method, Oumaïma Benarina for her useful reflexions on the transportation network vulnerability in the Gard basin, all the colleagues who contributed to the IRIP method development and evaluation, Olivier Payrastre for fruitfull discussions about this work.
Background: Recent Cochrane reviews on falls and fall prevention have shown that it is possible to prevent falls in older adults living in the community and in care facilities. Technologies aimed at fall detection, assessment, prediction and prevention are emerging, yet there has been no consistency in describing or reporting on interventions using technologies. With the growth of eHealth and data driven interventions, a common language and classification is required. Objective: The FARSEEING Taxonomy of Technologies was developed as a tool for those in the field of biomedical informatics to classify and characterise components of studies and interventions. Methods: The Taxonomy Development Group (TDG) comprised experts from across Europe. Through face-to-face meetings and contributions via email, five domains were developed, modified and agreed: Approach; Base; Components of outcome measures; Descriptors of technologies; and Evaluation.
2 ). By providing detailed information they can better classify a study, but they are not obliged to dig into the taxonomy, if they are not seeking a detailed classification. The online, web-based application has been built using industrial standards for distributed web applications, and in particular by exploiting the technical framework Spring (https://spring. io/). All the relevant data are stored in a robust database accessible by researchers. Future extensions will allow users to query for studies that have been tagged with respect to the taxonomy. The online application allows authorised users to upload new versions of the taxonomy, allowing for future extensions, updates, and clarifications. The system keeps track of all the versions of the taxonomy to guarantee consistency and to allow researchers to inspect the changes.
Acknowledgments The taxonomy was developed as part of the project  FARSEEING , funded by the  European Commission  (Grant agreement no. :  288940 ). The authors were participants in the  FP7  funded  FARSEE-ING  project, or were invited to contribute to the development of the taxonomy due to their expertise in the field (NG and AC). The authors would like to thank  Rachel Potter  ( RP ) for her input in the initial stages of developing the taxonomy.
So,  Rodrigues et al. (2006)  propose a taxonomic model of the main components of data visualization methodologies. They consider that elements to be taken into account are: the shape, the color, and the position. In other words, it is necessary for data visualization methodologies to allow a visual stimulation (shape / color) and a spatialization of the data (position). However, the color and shape are elements which are sometimes difficult to make clearly interpretable. The positioning has a much more important impact  (Skupin and Fabrikant, 2003). Interpreted in the broad sense, data spatialization implies transforming something which is not spatial into something which is spatial. The result is a geometrical representation in a smalldimension space, generally in two or three dimensions, which is intended to make it possible to detect trends and relations which are invisible in a large-dimension database.
Results Identification of the export best practices. The first phase of this study consisted of reviewing dimensions and capabilities related to export within the scientific literature. This bibliographical research was conducted on about fifty scientific articles. After a descriptive analysis of these publications and the structuring of the collected data, eight dimensions concerning export were put forward and characterized in the form of capabilities and observable phenomena (Tab. 1). Then, several interviews (Tab. 2) were conducted with managers of international SMEs, as well as with experts in the internationalization of SMEs (researchers, consultants, advisors…). These interviews made it possible to validate the theoretical data identified in the 1 According to the classification by technological level, source:  Hatzichronoglou (1997)  literature so as to obtain as realistic a model as possible.
Data spatialization by using force-based algorithms The similarity matrix (Fig. 8 ) gives an indication of the capacities which can be considered as similar, though its interpretation remains difficult. Indeed, this matrix puts forward pairs of capacities having a more or less strong degree of similarity and this degree of similarity is important to take into account for this study. However, this representation does not enable the identification of clusters taking into account existing links between the data and strength of these links. But this visualization is necessary for the definition of a common space between the SMEs' innovation and export capabilities. Therefore, the decision was made to realize a "structuring" data spatialization through the Gephi software, so as to highlight the links between the considered data.
The links can be considered as springs between two nodes (the stronger the weight of the link, the stiffer the spring). At every step of the algorithm, the sum of the forces is applied to every node. These nodes move until they reach a stable state. The force-based algorithms position nodes with regard to the others. Graphs still do not converge on the same final configuration. So it is not possible to read the position of a node as such, and it is necessary to compare its position with the others. The Gephi software proposes several force-based algorithms, so it was necessary to choose the algorithm most suited to this study. Table  4  proposes a comparison of these various algorithms. This overview puts forward some differences between the considered algorithms. Yifan Hu is rather intended to handle a significant number of data, which is not the case for this study.
Discussion The objective of this study is to identify the common spaces between the capabilities of innovation and the capabilities of export. The similarity analysis relying on the frame of reference describing separately the innovation and export capabilities highlighted the pairs of practices which can be considered as similar, and the intensity of this similarity. Then, a structural data spatialization was realized, through the use of a force-based algorithm. This data visualization methodology has in particular the advantage to show each datum only once, which facilitates the interpretation of the results. It also takes into account the strength of the similarity between the data. Through the use of the Gephi software and the ForceAtlas2 algorithm, the findings show clusters of capabilities which can be considered as similar (Tab.
Acknowledgements This research work has been cofinanced by the  Regional Council of Lorraine (France)  and the Research Chair PRINCIP, Granted by the  French "Innovation and Industry Fund"  ( F2I ) and the  Union of Metal Industries and Professions (UIMM). The authors would also underline that this work is on line with the  IDEFI InnovENT-E project  ( Initiatives of excellence in innovative training ) related to the innovation and export capabilities of SMEs.
Results Quantification of Homologous Recombination, HGT, and Their Covariates. We analyzed a dataset of 79 core genomes and pangenomes (SI Methods) corresponding to a total of 884 complete genomes. These clades were based on taxonomy, i. e. , the genomes of a named species were put together. They spanned many different bacterial phyla (Fig. 1A  and SI Methods). The pangenomes varied between 466 and 18,302 gene families (Dataset S1), and correlated with genome size (Spearman's ρ = 0. 89, P < 10 -4 ) and phylogenetic depth, defined as the average root-to-tip distances in the clade phylogenetic tree (SI Methods and Dataset S2) (Spearman's ρ = 0. 42, P < 10 -4 ). Hence, our dataset represents a large diversity of bacteria in terms of taxonomy, genome size, and intraspecies diversity. HR is notoriously difficult to quantify accurately  (24).
We used five different programs to detect HR in the core genome (SI Methods). These programs detect different types of signals, and together they should provide a thorough assessment of HR. Among the 79 core genomes, we found an average of 329 (NSS), 374 (MaxCHI), 264 (PHI), 504 (Geneconv), and 1,035 (Clonal-FrameML, CFML) HR events per core genome (Datasets S1 and S3). Even if the different methods provided different numbers of events, their results were highly correlated (average Spearman's ρ = 0. 84, all comparisons P < 10 -4 ). Accordingly, we focused our analysis on the results of Geneconv, which provides the positions of recombination tracts and directions of transfer necessary for the last part of this study. We used Count  (25)  to infer the events of HGT from the patterns of presence and absence of gene families in the species' trees (SI Methods and Dataset S4).
We identified 236,894 events of gene transfer in the 79 pangenomes (Dataset S1). These events were very      Analysis of HR and HGT events. (A) 16S rRNA phylogenetic tree of the 79 bacterial species. The tree was drawn using the iTOL server (itol. embl. de/ index. shtml) (40). The innermost circle layer indicates the species and associated clade. The six subsequent layers correspond (in an outwardly direction) to the average number of HGT events per genome computed using Count; the number of recombined genes per genome given by NSS, MaxChi, and PHI; and the number of recombination events per genome given by Geneconv and CFML (outermost layer), respectively. These values are given in Dataset S1. (B) Distribution of the average number of horizontal gene transfer (HGT) events and homologous recombination (HR) events (inferred by Geneconv) per clade according to genome size (G S ).
1B  and Fig. S1 ). The remaining variables had significant, but less important, explanatory roles. HR also depended weakly on core genome size (Table  S1 , part B). Hence, studying the effect of R-M systems on HR and HGT requires control for phylogenetic depth, the number of genomes in the clade, and especially the genome size. Association Between R-M Systems and Genetic Transfer. We identified 1,352 R-M systems among the 79 clades using a previously published methodology (4) (SI Methods and Dataset S1), including 233 Type II R-M systems (excluding Type IIC). The number of HGT events was higher in genomes with more R-M systems (Fig. 2A ), and especially in those with Type II systems (Fig. 2B ). The number of HR events increased with the number of R-M systems (Fig. 2C ) and especially in the presence of Type II R-M systems (Fig.
3 A  and B ). The analysis of the Specificity (S) and target recognition domains (TRDs) led to similar conclusions (SI Methods and Fig. S3A ). The small number of such systems in REBASE gold standard resulted in small statistical power for this analysis, but adding more recent data from REBASE PacBio database did not change these conclusions (SI Methods and Fig. S3 B-E ). The rapid evolution of sequence target specificity precludes the identification of systems with similar restriction sites from the alignment of REases or MTases in both Type I and Type III R-M systems. In contrast, homologs of Type II REases and MTases, which are much more numerous in the database, have different target motifs only when their sequence similarity is low (typically less than 50% for MTases and 55% for REases; Fig.