dataesr/scientific-paragraphs-categorization huggingface dataset:
Some of these plots display distances between prototypes on the grid and can be used as diagnosis plots. An exhaustive description of available graphics is provided in  [10]  and examples of such plots, obtained on the famous iris dataset  [13] , are given in Figure  1. Finally, super-clusters can be obtained by using the function superClass on the output of the trainSOM function. This method performs hierarchical clustering on the prototypes of the map to cluster the units in "super-clusters". (a) what="obs" and type="boxplot" (b) what="prototypes" and type="3d" (c) what="add" and type="pie" (d) what="prototypes" and type="poly" Fig. 1 : Different types of graphics obtained for the iris dataset: distribution of the variables within units (1a), prototype values for the different dimension (1b), additional variable (Species) distribution within units (1c) and distances between prototypes (1d) as proposed in  [14].
Documentation and graphical user interface Additionally to the standard R documentation, SOMbrero also provides five vignettes (documentation files accessible from within the package and at https://cran. r-project. org/ web/packages/SOMbrero/). The vignettes illustrate the functions with three datasets that correspond to the three variants implemented in the package. The first dataset is the iris dataset  [13]. The second dataset (KORRESP variant, see Section 3) is the contingency table of votes for the combinations of "Départements" and "Candidates" during the French presidential election of 2002 4. It provided in the package under the name presidentielles2002. The last dataset (relational variant, see Section 4) is the shortest path length between all nodes in the co-occurrence network obtained from the French novel "Les misérables" (V.
Hugo)  [15]. This dataset (co-occurrence network and dissimilarity matrix) is provided in the package under the name lesmis. For users who are not familiar with R command lines, SOMbrero contains a graphical user interface which has 4. Source: http://www. interieur. gouv. fr/Elections/Les-resultats/ Presidentielles been programmed with the R package shiny  [16]  (see Figure  2 ). The GUI is launched from within R with the function sombreroGUI() and opens in default web browser.
SOM FOR CONTINGENCY TABLES Another very standard type of data that can be handled by SOMbrero is the case of contingency tables in which the dataset T = (n ij ) i=1,. ,p,j=1,. ,q is composed of joint frequencies for a pair of categorical variables (p is the number of levels for the first categorical variable and q the number of levels for the second). Classical correspondence analysis performs a weighted principal components analysis, using the χ 2 distance simultaneously on the row profiles and on the column profiles. The same principle is used in the so-called "KORRESP" algorithm,  [17] ,  [18] , which extends SOM to contingency tables and is implemented in SOMbrero. More precisely, the contingency table is first transformed into a dataset with row and column profiles: for which: • row profiles, (x i ) i=1,.
Acceleration of relational SOM However, the complexity of the assignment and representation steps of stochastic relational SOM are, respectively, O(n 2 U ) and O(nU ), as pointed in  [32]. This leads to a total complexity of O(n 2 U ) for one iteration. To obtain good convergence properties, the algorithm requires a number of iterations of the order of αn, as shown in  [33] , yielding a complexity of O(αn 3 U ). Hence, relational SOM is not adapted to large datasets and cannot be used to analyze more than a few thousands observations. [34]  have proposed two approximate versions to overcome this issue, using sparse representations of the prototypes or DR preprocessing techniques. However, the version implemented in SOMbrero uses the solution proposed in  [35]  which yields to a complexity of O(αn 2 U ).
Thus, the time scale ranges from 10 2 to 10 5. The multi-scale geometry system and multi-scale time signal are challenges the simulation very difficult, especially with the implementation of the hysteresis model. Usually, such computations would be very time-consuming and require a huge memory space. Therefore, a new computation strategy was developed in FEM Flux software code. 5 This method allows performing computation in a fraction of the time and requires less memory space. The strategy consists in dividing the computation in two phases. The LF and HF simulations are performed separately, but remain linked by the magnetic state of the different layers induced by the LF computation. Thus, two models are created with the same geometries and meshes and are run consecutively at each time step of the LF computation.
ACKNOWLEDGMENTS This work was supported by  European  project  SIMPOSIUM  under Grant Agreement 285549. The authors would like to especially thank  M. Kern Rolf  and  M. Christian Conrad , experts in presshardened steel characterization from  Fraunhofer IZFP, Saarbrucken , for their technical support and participation in the entitled project ' Simulation Platform for Non Destructive Evaluation of Structures and Materials.
Observations The LIFE observations we present in this paper were taken using the ESPaDOnS (an Echelle SpectroPolarimetric Device for the Observation of Stars) instrument at the Canada France Hawaii Telescope (CFHT) and are summarised in Table  1. We observe over a wavelength range from about 3700 Å to 10500 Å with a resolving power of ∼ 68000. Each spectrum is spread over 40 echelle orders. The observations are corrected for the bias and flat-field, and a ThAr spectrum is used to calibrate the wavelengths to the pixel values. The data were reduced using Libre-Esprit  (Donati et al. 1999)  and Upena, a software pipeline available at the CFHT. The data were taken in circular spectropolarimetric mode, measuring Stokes I and V. Each observation sequence consists of four sub-exposures, taken with different orientations of the Fresnel rhombs.
Normalisation of stellar spectra We used a semi-automatic Python program  (Martin et al. 2017, and Appendix A)  to determine the continuum shape of the Stokes I spectrum, fitting each ESPaDOnS order individually. The Python program allows the user to fit a third-order spline to the continuum. This is achieved by initially fitting a third-order spline to the reduced unnormalised spectrum. Points from the spectrum are then iteratively σ-clipped about the spline, until only continuum points remain. For the majority of spectra the σ-clipping is asymmetric with more points clipped below than above the fit (e. g. , σ upper = 3 and σ lower = 1 ). This is because, to obtain a good continuum fit, all spectral lines must be removed and for the stars in this sample we mainly observe absorption lines.
To start, we extract a stellar line list from the VALD3 database  (Piskunov et al. 1995; Kupka et al. 1999). This line list is calculated with the T eff and log g which we determine for each of our stars (see Section 3. 1). However, it will contain lines which are not present in the stellar spectrum and other lines which will add noise to the LSD profile. Therefore, we comb through each list, first removing lines with a depth smaller than 0. 01 (relative to a continuum level of 1), and those which we do not see in our observed spectra. We remove hydrogen lines, because their shape is different from metal lines, and we remove any lines which blend with either H, interstellar, or strong telluric lines. Finally, we adjust the depth of each remaining line in the line mask so that it is consistent with the ob-   2.
Fundamental parameters For each star we calculated the effective temperature (T eff ) and surface gravity (log g) using the uvbybeta code  (Napiwotzki et al. 1993 ). We also used grids of synthetic spectra to visually check that the values accurately reproduce primarily the Hα, Hβ, Hγ and Hδ lines, but also regions of metal In the case of atlas9 model atmospheres  (Kurucz 1993b) , the synthetic spectra are calculated assuming plane parallel geometry, local thermodynamic equilibrium (LTE) and opacity distribution functions (ODFs) for solar abundances  (Kurucz 1993a ). The synthetic spectra were computed with cossam simple  (Martin et al. 2017 ) and synthe  (Kurucz & Avrett 1981; Bohlin et al. 2017). For the tlusty model atmospheres the synthetic spectra are calculated assuming non-local thermodynamic equilibrium, plane-parallel geometry and hydrostatic equilibrium using synspec  (Hubeny & Lanz 2011).
Luminosity and evolutionary status To determine the evolutionary status of each star, along with its radius (R) and mass (M), we use the luminosity, L, and the quantity L = T 4 eff /g. We calculate log L/L using parallaxes from the Hipparcos database (van Leeuwen 2007) or where available the GAIA data release 1  (Gaia Collaboration et al. 2016b,a)  and the Johnson visual magnitudes given by  Perryman et al. (1997). We calculate the bolometric correction for each star following  Flower (1996)  and  Torres (2010)  using the temperatures given in Table  2. We calculate L/L following  Langer & Kudritzki (2014) , log L L = log T 4 eff g -log T 4 eff g , (4) where T eff and log g are given in   3. For each star, we plot log L/L on the theoretical Hertzsprung-Russell (HR) diagram and log L/L on the spectroscopic HR diagram shown in the left and right panels of Fig.
ACKNOWLEDGEMENTS SM acknowledges support from  ERC   SPIRE grant  ( 647383 ). WM acknowledges support from  CNPq , grant  307152/2016-2. GAW acknowledged support from the  Natural Sciences and Engineering Research Council (NSERC) of Canada  in the form of a  Discovery Grant. This research has made use of the SIMBAD database operated at  CDS, Strasbourg (France) , and  NASA 's Astrophysics Data System (ADS). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
As the hybrid bus' electric motor and engine have different dynamic characteristics (the power supplied, the response time, etc. ), the dynamic constraints linked to the dynamic motors are taken into account. An online sub-optimal speed profile and related power split generation using a multidimensional interpolation is developed to deal online with the current road profile and driver velocity demand. This is carried out using the Optimal Profile Database based on DP (OPD-DP). The battery SOC is estimated and predicted, using Kalman Filter estimation, in order to guarantee that at the end of operational cycle (in the end of a course of a day) the electric battery charge is not below its permitted minimum level. The rest of the paper is organized as follows. In section 2, the studied bus powertrain and its dynamical model are presented.
OFFLINE AND ONLINE OPTIMIZATION STRATEGIES BASED ON DP ALGORITHMS This section is dedicated to the development of a simultaneous speed profile optimization and an energy management strategy in offline and online mode. The procedure of the decision tree construction is described in details in section 4. 1. The developed offline algorithm permits to move to the online implementation by means of the Optimal Profiles Database (cf. section ??), which generates the speed profile and its energy management strategy, depending on the road profile, the bus current bus state and velocity setpoint. The battery SOC estimation and prediction procedure is described in section 4. 1. 2.
The Optimal Profiles Database based on DP (OPD-DP) was built for all the possible combination of velocities from 0 to 15 m/s with ∆v = 1 m/s, the bus weight varies from 13 to 15 tonnes with ∆M = 0. 5 tonne, and the slope angle value ranges from -5 • to 5 •. Based on the offline optimal solutions, OPD-DP will be used to generate the speed profiles and the related power split in a real-time system. The multi-dimensional OPD-DP is used for online speed profile and its power split generation. The scheme of the online control implementation is presented in Figure  8. The inputs of the multidimensional OPD-DP are the road profile (x,y, θ), the bus weight, the current speed and the driver's reference (desired) speed, and estimated SOC value (this block is detailed later). Each 21 meters this block generates the sub-optimal speed profile reference and torque split depending on the current state of the vehicle and the road profile.
The bus weight is considered constant to travel from one bus station to another, thus the weight is susceptible to change only after each bus station. The driver's command must be followed as precise as possible due to the safety measures, ensuring the collision avoidance. The OPD-DP generates the sub-optimal profile references, as well as T EM and T HM set points. The set point torques sent to EM and HM local controllers, which generate the control input to the motors (I EM EM current, D HM HM displacement). Knowing that the multidimensional OPD-DP has only finite set of values for the parameters (slope θ, weight M, etc. ), it is very important to have a mean to use this Database even for values not belonging to it. To solve this problem, below it is proposed a Multidimensional interpolation method.
CONCLUSIONS AND PROSPECTS In this paper, the DP technique is used to simultaneously generate the optimal velocity profile and its power split strategy in order to ensure the electric energy and fuel economy, respecting passengers comfort (by limiting the acceleration/deceleration). As the dynamics of the electric motor and engine are different, not all the energy management configurations are This aspect is taken into account in the DP-SEO algorithm. Thereafter, the offline optimal solutions of the DP based optimization were collected into a multidimensional OPD-DP for the online implementation for a priori unknown traffic conditions for instance. To cope with the problem of the finite set of sample points in the database, a Linear Multidimensional interpolation was used to obtain the values at all other points.
Background: The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region has become possible but remains relatively costly. In order to obtain the HLA information for the millions of samples already genotyped by chips in the past ten years, efficient bioinformatics tools, such as SNP2HLA or HIBAG, have been developed that infer HLA information from the linkage disequilibrium existing between HLA alleles and SNP markers in the MHC region. Results: In this study, we first used ShapeIT and Impute2 to implement an imputation method akin to SNP2HLA and found a comparable quality of imputation on a European dataset.
Imputation algorithms typically use a reference panel to infer statistical patterns from linkage disequilibrium, that allows them to impute missing data in other datasets, usually using Hidden Markov Models on haplotypes  [9]. The HLA typing technologies have evolved in the past few years from Sequence-Specific Primers (SSP) and Sequence-Specific Oligonucleotide Probes (SSOP) to Next-Generation Sequencing (NGS)  [10]. SSP and SSOP were until fairly recently the best way to detect variations in the MHC but required known constant primers which could fail in the HLA region since some genes can have almost all of their nucleotides display polymorphisms (Single Nucleotide Polymorphism, hereafter SNP)  [11]. These old methods also focused mostly on exons 2 and 3 for class I HLA genes (which code for the binding site), or just exon 2 in class II HLA genes, so many recorded HLA alleles are only known from these exons.
We'll for instance denote an allele of the HLA-A gene at the one-field level as HLA-A*02 and at the two-field level as HLA-A*02:01. Thanks to the availability of large reference panels being genotyped both by genotyping chips (Illumina, Affymetrix, other) and by NGS in the HLA region, several imputation methods have been developed in the past few years: SNP2HLA  [13]  (modeling HLA alleles as binary SNPs when running imputation software beagle), HIBAG  [14]  (R package using attribute bagging), or HLA*IMP  [15]  (Web service now discontinued). They exhibit a fairly good imputation accuracy level in the tests performed, ranging from 90 to 97% according to the HLA gene at stake  [13]. Of course, this performance may greatly vary with the reference panel provided, as some studies have shown for instance that using a European panel for a Finnish population may lead to poor results  [16].
Data material We primarily used the T1DGC (Type 1 Diabetes Genetics Consortium) cohort as our reference panel  [17, 18]  of 5225 European unrelated individuals. Genotype data included 7135 SNPs within the MHC region obtained with the Illumina Immunochip platform, and classical HLA allele typing for HLA-A, HLA-B, HLA-C, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1 and HLA-DRB1 at a twofield resolution. The T1DGC reference panel can be obtained from the NIDDK repository at https://www. niddkrepository. org/niddk/home. do. This panel was the reference also used in previous studies  [13] , and it will allow for an easier comparison with state-of-the-art tools. We used this panel as provided originally with the SNP2HLA package. As a testing panel for our imputation method, we used the British 1958 Birth Cohort (1958BC)  [19]  composed of 2434 individuals genotyped on Illumina Human-Hap550 and also typed by gold-standard methods at two-field or one-field levels for HLA-A, HLA-B, HLA-C, HLA-DQB1 and HLA-DRB1.
Access to this data was obtained through the Wellcome Trust Case Control Consortium Data Access Committee and could be done from the European Genome-phenome Archive (EGA) at https://www. ebi. ac. uk/ega/. 1958BC was also used as a testing panel in previous studies  [13]. These panels cover a variety of existing common alleles in European population: for 1958BC and T1DGC respectively, we have 25 and 51 alleles for HLA-A, 44 and 98 for HLA-B, 21 and 34 for HLA-C, 34 and 52 for HLA-DRB1, and 18 and 19 for HLA-DQB1. The method used in these panels to attribute HLA alleles has since their publication been shown to cause some systematic errors, for instance HLA-DRB1*14:54 vs DRB1*14:01:01 in  [20]. We have also used the panel of 5008 haplotypes from various origins  [21] , assembled by the 1000 Genomes project in which the SNP/indels were phased thanks to the ShapeIT software  [22].
This panel was required to extend (by imputation with IMPUTE2  [23] ) the SNP coverage of genotypes obtained by chips into the HLA exonic regions, since this step is needed for HLA-check. We also used the HLA reference database, called IPD-IMGT/HLA Database (http://hla. alleles. org/), version 3. 22 (Oct 2015). This database defines at all levels (protein, cDNA, and gDNA) all the known HLA alleles.
HLA typing by imputation To impute HLA from SNP data, we included all HLA alleles, each of which was represented as a biallelic SNP marker: present or absent. We used the ShapeIT software to phase haplotypes, and IMPUTE2 software to impute the HLA alleles. Then, we kept only alleles with a post-probability dosage of more than 0. 5, thus defining the individuals for which HLA was "called". In 99. 9% of the cases this indeed gave us exactly two alleles.
HLA-check: the approach The principle of the method is to compare the SNP genotypes of an individual obtained by any experimental (i. e. chip) or computational method (i. e. imputation) with the SNP genotypes defined by all the known combinations of HLA alleles from the IPD-IMGT/HLA Database (http:// hla. alleles. org). We then check if the attributed HLA allele pair is among the best matches by computing a discrepancy measure D. Our approach is straightforward: we first try to impute as many SNPs as possible in the HLA exonic regions, using the best reference set at our disposal. As of today, the 1000 genomes project has the best coverage of SNPs with a large panel of already phased genotypes from various populations all over the genome, but similar results will likely be obtained with the Haplotype Reference Consortium  [24]  in the near future.
This SNP extension phase needs to be done as precisely as possible, in order to get a coverage as precise and as complete as possible in the exonic sequences of the HLA genes. For an individual, for each SNP in the exonic segments of a given HLA gene, we then compare the post-probabilities genotype obtained after its imputation with the genotypes from all possible allele pairs derived from the HLA reference genome, obtained through the available exonic HLA cDNA sequences of IPD-IMGT/HLA Database (http://hla. alleles. org). We do a per-SNP evaluation of the discrepancy measure where D, for a pair of HLA alleles, at a given SNP marker, is the probability of this HLA pair to be incorrect. Then, D obtained for an HLA allele combination for a given individual is simply obtained by summing it for all SNP markers available in the gene 1 : D(genome, HLA 1 , HLA 2 ) = rs D(rs) After computing D for all the pairs of HLA alleles of the IPD-IMGT/HLA Database, we compare the discrepancy measure of the attributed HLA of an individual with the best one obtained among all HLA pairs tested: the final D we compute is simply the difference between those.
A difference of 0 indicates that we reached the least possible discrepancy for the attributed HLA allele pair, making us highly confident in the validity of this HLA typing, while a high discrepancy indicates a mismatch between the best possible HLA fitting with the genotype and the attributed HLA, suggesting that these HLA alleles are unlikely to be well attributed. Interestingly, our approach depends mainly on the definitions of the HLA alleles from the IPD-IMGT/HLA Database. All HLA allele combinations are tested, and only the quality of imputation in the HLA exons and the SNP coverage will have an impact.
Replication of SNP2HLA results with ShapeIT and IMPUTE2 We first replicated the SNP2HLA method for imputation of HLA alleles, by phasing the same reference panel with the ShapeIT  [22]  software developed by our group and by imputing SNPs and HLA in the region with the IMPUTE2 software. The results were quite similar to those obtained by SNP2HLA for the quality of imputation (Table  1 ).
HLA-check: detection of spurious alleles We also developed a tool, HLA-check, to detect spurious attributed HLA alleles, as described in Material and Methods. This tool relies both on the precise SNP imputation in the exonic parts of HLA genes currently using the 1000 genome reference panel and on the genetic description in the exonic regions of all known HLA allele pairs using the HLA reference database (see pipeline Fig. 1 ). We first evaluated the soundness of HLA-check on the T1DGC reference panel that contains 5225 individuals typed at a two-field resolution, who were Fig. 1  HLA-check pipeline: We start by augmenting the panel to test to get more SNP using an imputation phase with the 1000genomes data, then we compare those SNP alleles with their theoretical values for all possible HLA allele pairs also genotyped using an Illumina chip.