dataesr/scientific-paragraphs-categorization huggingface dataset:
D I S C U S S I O N In this paper, we have described the saturation of the MRI in a plasma dominated by the Hall effect. This situation is of particular importance to protoplanetary discs, which are poorly ionized and in which one expects a particularly strong Hall effect near the disc mid-plane (e. g. Wardle 2007). A strong Hall effect is considered to be particularly important to the disc dynamics, as it can destabilize plasmas which would otherwise be stabilized by Ohmic losses and could therefore eliminate (or at least significantly reduce) purported 'dead zones' in such discs  (Wardle & Salmeron 2012). Using the 3D spectral code SNOOPY, we recovered the linear properties of the Hall-dominated MRI. We also demonstrated analytically that MRI 'channel' modes remain non-linear exact solutions notwithstanding the complicating influence of the Hall effect, and we explored their stability with respect to secondary parasitic modes.
AC K N OW L E D G E M E N T S Support for MWK was provided by NASA through Einstein Postdoctoral Fellowship Award Number PF1-120084, issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. GL acknowledges support by the European Community via contract PCIG09-GA-2011-294110. This work was granted access to the HPC resources of IDRIS under allocation x2013042231 made by Grand Equipement National de Calcul Intensif (GENCI). Some of the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the Rhone-Alpes region (GRANT CPER07_13 CIRA: http://www. ci-ra. org). The Texas Advanced Computer Center at the University of Texas at Austin also provided HPC resources under grant number TG-AST090105.
5 and 7 m. In two dimensions, the desired contour can then be extracted using, for instance, Matlab's © function contourc, the accuracy of the solution being dependent on the grid increment, hence on the grid size. Such an approach may, however, become rapidly prohibitive when using computational, time-consuming models (with run times varying from hours to days). This issue has given rise to numerous studies either relying on appropriate grid computing architecture (e. g. Boulahya et al. , 2007)  or on the use of meta-models (also named response surfaces or surrogate models). The latter technique consists in replacing the true model F by a mathematical approximation that predicts the model responses with a negligible computation time cost. In the coastal domain, the application of the meta-modelling approach to the specific case of contour estimation of computationally intensive numerical codes is illustrated by  Rohmer and Idier (2012) , but it still remains a matter of continuing research.
For that, the method of joint probability analysis can be used. By definition, this method computes the probability of occurrence of events in which two or more partially dependent variables simultaneously exceed given values. A joint probability analysis software package for coastal applications named JOIN-SEA was developed during a Defra-funded research programme  (Hawkes et al. , 2002). Details of the theory, development, testing and validation of JOIN-SEA are given in  (HR Wallingford, 2000a, b). There are five main stages in the analysis: i. preparation of input data consisting of independent combinations (ξ e ; H S ). These combinations are simultaneous observations of the two variables at the offshore analysis point (before the breaking of waves); ii. fitting of marginal distributions for ξ e and H S (Generalized Pareto Distribution or GPD); iii.
, 1999). It solves the wave action equation and provides wave spectra and wave set-up. At the offshore boundary, uniform sea level and wave height are imposed. The bathymetric and topographic data used here have been obtained through lidar measurements (data provided by DREAL LR), as well as bathymetric surveys (data provided by SHOM). The grid size is 2 m (Fig. 5 ). Second, to compute the swash-induced elevation S, we use the formulation of  Stockdon et al. (2006). Based on the assumption of reflective beach, the analysis of the equations proposed in  Stockdon et al. (2006)  shows that we can, as a first approximation, assume that S is equal to the set-up. Thus the total maximum water level ξ ec at the beach is calculated as follows: ξ ec =ξ e + 2η. (2) The inversion is based on this model (SWAN and the swash formula of  Stockdon et al.
Inversion results We inverse the system to provide offshore conditions in the variable space (ξ e ; H S ). The wave period and the wave direction are fixed at their most frequently observed values. For the present case, the analysis of a few years' worth of wave data from a buoy located in the south-east of the area indicates that the dominant wave incidence angle is 105 • (corresponding to a nautical wave direction of 135 • N), for a wave period of 7 s. Thus, the return period of the combination (ξ e ; H S ) is computed for a wave dataset restricted to this fixed wave period and direction. Given the relatively moderate computational time cost of the considered numerical model (on average one simulation requires 15 min to run), we focus on a "grid-based" approach and simulated 30×30 different configurations of the offshore forcing conditions (ξ e ; H S ) using a cluster of ten computer units, which enables the overall computation to take less than one day.
Offshore time series generation Ideally, the inversion step would provide water levels and wave heights at a location where continuous measurements are made (permanent buoys or sensors). In some places, this is indeed the case. In the present study, there are no observations of water levels or wave conditions at the selected site. Thus, there is a need to find a time series dataset, corresponding to the conditions at the offshore boundaries of the SWAN implementation (Fig. 5 ). For this purpose, we use numerical model outputs. Concerning the waves, outputs of the WW3 model, implemented in this area by  Ardhuin et al. (2010) , are used. The closest node of the WW3-grid having the same water depth as the offshore boundary of the SWAN model is used. This point is located 1 km south-west of the DEMO site.
The quality of the WW3 implementation in Mediterranean Sea has been investigated by  Ardhuin et al. (2007)  who show that the best fit slope between the modelled and observed wave heights is about 0. 84-0. 87 for the WW3 model forced by ALADIN wind data (Météo-France). Thus, for the demonstration, we corrected the WW3 outputs to compensate for this error, by multiplying the significant wave height by a factor of 1. 18 (i. e. 1/0. 85). Furthermore, for the water level, numerical computations are performed using the MARS2DH model (Lazure and Dumas, 2008). The background of the implementation of the MARS2DH model for the study area as well as its validation is presented in  Pedreros et al. (2011). Here, the simulations are based on FES2004  (Lyard et al. , 2006)  for the tidal boundary conditions and on the Climate Forecast System Reanalysis (CFSR,  Saha et al.
Return period of R ≥ R C The preparation of input data (selection of independent events) for the joint probability analysis was performed in two stages. First, one event per day was selected taking the daily maximum of H S with a minimum 12 h duration between two successive events. Then, to each of these H S peaks, the maximum of ξ e in a temporal window of 12 h centred on the peak of H S were associated. This is a conservative approach that allows capturing the highest values of the water level signal. In all, 231 events per year were selected in this way. For the calculation of marginal distribution of ξ e , instead of using the default JOIN-SEA distribution that is based on waves and water levels data covering the same temporal period (i. e. 5 yr), we fitted a new GPD for ξ e using all available data (13 yr).
However, as for direct approaches, we should be careful when using models outside the regimes for which they have been validated on site. Second, attention should be paid to input parameters, which can be associated to uncertainty (e. g. bathymetry varying in time, friction coefficient varying in space and time), such that for a given configuration of offshore conditions, the model gives different results. Integrating such parameter uncertainty in the inversion procedure remains a matter of ongoing research. The recent advances in optimisation of stochastic functions  (Picheny et al. , 2013 ) could be a starting point for such investigations. Finally, the offshore dataset quality can influence the results on exceedance return period. In the present study, it has been constructed using a modelling approach.
Acknowledgements. The authors thank the  ANR VMC 2006  project  VULSACO  no. ANR-06-VULN-009 , the  ANR   VMC 2007  project  MISEEVA  no. ANR-07-VULN-007  as well as the BRGMfunded VULNERISK project, for contributions to the financial support of the present work. A. Magnan ,  M. Poumadère ,  M. Garcin ,  C. Oliveros ,  E. Foerster  and  A. L. Gautier  are acknowledged for fruitful discussions and support. The authors are also grateful to  R. Pedreros ,  V. Turpin  and  C. Vinchon  for their contribution, and to  P. Hawkes , resp. Ifremer, for providing the JOIN-SEA, resp. MARS2DH, software. The MISEEVA team working on the public flooding risk representation (specifically  C. Meur-Ferec ,  B. Rulleau ,  H. Rey-Valette , and their team) is also acknowledged for providing the results of their survey.
The present contribution is organized as follows. First, the analytical formulation, describing the general case of cracks embedded in different layers, is introduced as a general extension of the previous work. The derivation of the associated Green operators is then explained in some detail. Subsequently, the treatment of arbitrary crack orientations in PMS is presented. Comparisons of simulation results with experimental data in laboratory-controlled conditions and numerical results obtained with the FEM code Comsol Multiphysics™ v3. 5a [17] follow, before conclusions are drawn.
High-throughput sequencing is a common approach to discover SNP variants, especially in plant species. However, methods to analyze predicted SNPs are often optimized for diploid plant species whereas many crop species are allopolyploids and combine related but divergent subgenomes (homoeologous chromosome sets). We created a software tool, SNiPloid, that exploits and interprets putative SNPs in the context of allopolyploidy by comparing SNPs from an allopolyploid with those obtained in its modern-day diploid progenitors. SNiPloid can compare SNPs obtained from a sample to estimate the subgenome contribution to the transcriptome or SNPs obtained from two polyploid accessions to search for SNP divergence.
Typically, the reads from a given allopolyploid are aligned to a reference transcriptome. Then, if the allele sequences of the diploid progenitor species can be sampled, it is possible to infer the genome origin of the identified SNPs and to estimate the contribution of the homoeologous genes to the total transcript level. Here we present a new tool, SNiPloid, that can tackle the many aspects involved in the analysis of SNPs in the context of allopolyploidy. Based on the coassembly of homoeologs, SNiPloid compares either putative SNPs detected from an allopolyploid to those obtained in its parental genomes, or putative SNPs derived from two allopolyploid accessions to search for polymorphism. SNiPloid web server and source code (downloadable under the CeCILL public license) are accessible at http://sniplay.
Methods 2. 1. Data Preprocessing. Before interpreting the results of RNA-Seq data using SNiPloid, data preprocessing is required. Biologists can preprocess their data through the Galaxy public server (https://main. g2. bx. psu. edu/) as described in Figure  1. SNiPloid assumes that short reads datasets (i. e. , samples) derived from unique single genotype or distinct accessions (diploid or polyploid) are separately aligned against a single diploid transcriptome reference corresponding to one of the parental diploids using dedicated mapping software such as BWA  [2] , Soap  [3] , or Bowtie  [4]. Mapping alignment is a key step in data preprocessing and mapping parameters need to be adjusted and optimized to best fit the single diploid genome used as reference. Actually, since the reference diploid transcriptome is more closely related to one of the two subgenomes in the tetraploid, it might have collateral effects on the mapping efficiency and indirectly cause biases in the interpretation of the SNP, notably when analyzing the relative homoeologous gene expression represented by the contribution of subgenomes to total gene expression.
The SNiPloid utility uses the power of the Variant Call Format (VCF) which lists SNP variations and assigns alleles for each sequenced sample, by comparison with a reference sequence  [5]. The VCF format is now widely recognized and is a standard format output of numerous SNP calling softwares. In this perspective, we suggest using the UnifiedGenotyper module in the GATK toolkit  [6]  for SNP discovery. A second type of input required by SNiPloid corresponds to a coverage depth file outputted by the Depth Of Coverage module of GATK. Optionally, SNP discovery and subsequent SNiPloid analysis can be improved by running the GATK ReadBacked-Phasing utility to determine potential associations between alleles and produce phasing.
SNiPloid Utility. Inputs to the SNiPloid software consist of two different GATK outputs for each sample: (i) a VCF file listing putative SNPs and (ii) a coverage depth file (Figure  1 ). For each sample, the user can set the minimum depth coverage required to consider a position in the output statistics and the minimum minor allele frequency (MAF) required to consider the position as a variant. SNiPloid comprises three main steps (Figure  2 (a)). The first step of the utility consists in extracting regions that meet a minimum coverage depth threshold for each sample (previously set by the user) and then in identifying overlapping regions between samples. Subsequent analysis will be restricted to these regions for variant comparison. As a consequence, if putative SNPs show sufficient depth coverage in the allopolyploid but not in the diploid, or reciprocally, the position will not be processed.
SNiPloid Outputs. The Web application allows the export of the detailed list of classified SNPs in a tabulated format. At the end of the process, the program summarizes the analysis by counting the different SNP classes for each gene/contig of the reference dataset and by reporting the results in a dynamic sortable table (Figure  3 (a)) so that users can easily classify and retrieve SNP classes of interest. For genes presenting at least one SNP class 5, an average ratio is given to obtain a global estimate of the subgenome contribution of the gene to the transcriptome. In addition, when the objective is to calculate general statistics or SNP frequencies along the transcriptome, the counting of SNP categories can be reported to the number of positions taken into account for the analysis, that is, positions that had met the minimum coverage depth threshold defined by the user.
SNiPloid Map Viewer. Finally, SNiPloid includes a viewer that allows a graphical overview of the distribution of SNP categories and of subgenome contributions along the chromosomes (Figure  3(b) ). This functionality can only be applied on species for which a complete and fully annotated reference genome sequence is available and requires a structural genome annotation in General Feature Format (GFF) format as additional input, supplying the viewer program with the coordinates of gene models used as reference on the genome. The aim is to rapidly localize potential highly bias-expressed regions, introgressed genes, or homogenized regions within the genome.
Examples of Use Case. A whole transcriptome analysis was conducted on the allotetraploid Coffea arabica by using the SNiPloid software for the analysis of the contribution of subgenomes to the transcriptome  [10]. This study enabled to characterize genome-wide homoeologous expression gene expression in C. arabica, a recent allopolyploid combining two subgenomes that derive from two closely related diploid species: C. canephora and C. eugenioides. Different samples of C. arabica obtained at contrasted temperatures and one C. eugenioides sample were mapped against the C. canephora reference transcriptome, analyzed for SNP discovery, before being compared with SNiPloid in order to estimate homoeologous gene expression and to highlight potential variation between growing conditions. Additionally, by mapping reads against the C.
Performance and Limitations. The main functionality of SNiPloid is dedicated to RNA-Seq data and to polyploid species for which a diploid transcriptome reference is available for at least one of the parents. One limitation of the use of RNA-Seq for SNP detection and subsequent interpretation is that the transcript sequences represent only the expressed part of the genome and that the sequencing depth varies considerably across the genome due to the different gene expression levels. Thus, only SNPs in well-expressed genes can be detected and allele or homoeolog expression bias could make the detection of certain SNP difficult due to their low frequency in the transcriptome. However, NGS technologies and the use of appropriate read cutoffs allow to detect and interpret SNPs for a large number of genes distributed across the genome.
Theoretically, even though the allele expression quantification would not be performed, a genome wide analysis would be also possible on genomic data. However from a technical point of view, whole genome analysis would be difficult to perform through our Web server, since it requires uploading VCF and depths file inputs that would be sizeable and should be computed by command line after having downloaded the SNiPloid package or through Galaxy. In terms of performance, in our practical experience two RNA-Seq samples derived from a polyploid and a diploid species first mapped against a complete reference transcriptome and then generating 600 000 putative SNPs each can be successfully compared by SNiPloid Web server in less than five minutes.
Comparison with Other SNP Bioinformatics Tools. Even though numerous SNP bioinformatics tools or pipelines exist for SNP calling (GATK  [6] , VarScan  [11] , WEP  [12] , and MiST  [13] ) or SNP annotation (SNPEff  [14] ) at a whole genome scale, only a few software packages allow to automatically categorize and interpret putative SNPs from polyploid species. An example of pipeline reported by Hand et al. [15]  predicts the subgenome-specific origin of SNPs using a phylogenetic approach based on comparison with orthologous sequences from predicted progenitor species. More recently a new pipeline called PolyCat  [16]  has been developed for mapping and categorizing NGS reads produced from allopolyploid organisms. Having the same aim as SNiPloid, the approach is a little bit different. PolyCat uses reads from diploids to generate preindexed homoeoSNPs that will be then used to assign reads from tetraploids to a subgenome.
The subgenome attribution is made at the read level whereas SNiPloid manages the subgenome attribution by considering SNPs position by position, counting homoeoSNPs for each transcript of a whole transcriptome analysis. This approach is relevant and more advanced but can appear slightly more fastidious to operate. The main advantage of SNiPloid is its ease to be applied since it does not require preliminary work to establish homoeoSNPs database that can be time-consuming, and offers to non-bioinformaticians a ready-to-use Web server allowing to rapidly obtain subgenome attribution thanks to a "one click" analysis. In addition, our approach seems to be more appropriate for allopolyploid species for which the polyploidization event is relatively recent in the evolution such as Coffea or Spartina.
The control chunks are Init 1  , InitAck, SACK, Heartbeat, HeartbeatAck, Abort, Shutdown, ShutdownAck, Error, CookieEcho, CookieAck and ShutdownComplete. Control chunks are used to setup and shutdown the association, selectively acknowledge, report error messages, monitor reachability of the peer, etc. Association setup uses a four-way handshake comprising four control chunks: Init; InitAck; CookieEcho and Cook-ieAck. Graceful closing uses a three-way handshakes comprising three control chunks: the Shutdown; ShutdownAck and ShutdownComplete chunks. The Data transfer phase involves Data and SACK (Selective Acknowledgement) chunks. Further detail of the structure of chunks can be found in  [8]. Figure  2  shows a typical procedure of association establishment. An association between two nodes, A and Z, is initiated by an SCTP user on node "A" issuing an "ASSOCIATE" command.
Modelling Approach Coloured Petri Nets  [5]  are well known for modelling and analysing concurrent and complex systems including validating various transport protocols such as Wireless Application Protocol (WAP)  [3] , TCP  [4] , and Datagram Congestion Control Protocol (DCCP)  [11]. Our model has been created and maintained using CPN Tools  [2]  which is a software package for the creation, editing, simulation and state space analysis of CPNs. It supports the hierarchical construction of CPN models  [5] , using constructs called substitution transitions. These transitions hide the details of subnets and allow further nesting of substitution transitions. This technique allows a complex specification to be managed as a series of hierarchically related pages. According to  [1] , the hierarchical structure of the CPN model can be classified into three modelling styles: state-based; event-processing and procedure-based.
A further challenge is that NGS data for any given genome project sometimes exists as a mixture of reads produced by different technologies. The need to assemble genomes from NGS data has led to an explosion of novel assembly software. A new generation of assemblers such as EULER  [5] , ALLPATHS  [6] , Velvet  [7]  and ABySS  [8]  have utilized de Bruijn graphs to attack the problem. The de Bruijn approach was also used by the SOAPdenovo assembler  [9]  in generating the first wholly de novo assembly of a large eukaryotic genome sequence (the giant panda, Ailuropoda melanoleuca  [10] ). More recent assemblers such as SGA  [11]  and fermi  [12]  have capitalized on the increasing length of sequence reads, and utilize string graph approaches, recalling the previous generation of overlap-layout-consensus assemblers.
For the sake of brevity, these species will henceforth be referred to as simply 'bird', 'fish', and 'snake'. Teams were invited to participate in the contest by submitting assemblies for any or all of these species; in many cases, participating teams were themselves the authors of the assembly tools that they used. As in the first Assemblathon contest (henceforth, Assemblathon 1) we have attempted to assess the performance of each of each the participating teams by using a variety of metrics. Unlike Assemblathon 1, we do not know what the correct genome sequence should look like for any of the three species. Because of this we make use of various experimental datasets such as Fosmid sequences and optical maps by which to validate the assemblies. A secondary goal of the Assemblathon is to assess the suitability of different metrics by which to assess genome assembly quality, and we employ some novel statistical methods for assessing each assembly Overall, we find that while many assemblers perform well when looking at a single metric, very few assemblers perform consistently when measured by a set of metrics that assess different aspects of an assembly's quality.