dataesr/scientific-paragraphs-categorization huggingface dataset:
Total magnitudes are determined from surface brightness profile fitting. A second data source is the 6dFGS Fundamental Plane (6dFGSv) catalogue of nearly 9000 early-type galaxies in the local (z < 0. 055) universe  (Campbell et al. 2014). In this data set, velocity dispersions are derived from 6dF V -band spectral data, whereas the photometric FP parameters are inferred from seeingconvolved two-dimensional surface brightness fitting of the 2MASS images. The published catalogue includes only spectral measurements with σ > 100 km s -1 and lists velocity Figure  13. Edge-on view of the FP of massive ETGs. The solid black line represents the best-fit to the mass FP defined by our mock first-ranked group galaxies (big dark-blue circles) in global parameter space [log Re, log σe, µe]. The distribution of our mock second-ranked galaxies is also shown (big red circles) together with four public data sets of nearby ETGs: a homogeneous set of 85 BCGs extracted from clusters (big yellow circles), the ETGs in the Norma and Coma clusters (big orange circles), the 6dFGSv catalogue (green dots), and a volume-limited sample of ETGs based on SDSS-UKIDSS observations (small grey circles).
All subsets, simulated and real, show a strikingly similar tilt and normalization. Note that we plot three independent points for each mock galaxy corresponding to the estimates from the projections onto the three Cartesian planes. dispersions taken within 6. 7 arcsec diameter fibre aperture, that we also correct to 1Re. Another data set of similar characteristics is the sample of 1430 ETGs defined in La  Barbera et al. (2008). These are galaxies extracted from a complete volume-limited catalogue of the SDSS-DR5 database in the redshift range 0. 05-0. 095 which have ETG-like SDSS spectroscopic and photometric parameters, reliable central velocity dispersions between 70 and 420 km s -1 , and available K-band photometry from the second data release of the UKIRT Infrared Deep Sky Survey (UKIDSS).
ACKNOWLEDGMENTS The authors wish to thank the referee,  Dan Taranu , for his thorough revision of the manuscript and insightful comments and suggestions, which have helped to improve both the justification of the initial conditions of our simulations and the presentation of the results. This work was supported by the  Program for Promotion of High-Level Scientific and Technical Research of Spain  under contracts  AYA2010-15169  and  AYA2013-40609-P. The simulations of the present work have been performed with the  Computer Service Infrastructure of the Spanish Instituto de Astrofísica de Andalucía (IAA). This research is part of the  IDILICO (Investigation of the DIffuse LIght COmponent in compact groups of galaxies)  project, an international collaboration that runs simulations of the formation of groups of galaxies using high-performance computing resources (see our website at http://www.
However, this EPR-I information needs to be merged with the one gathered from other sources, be it X-rays or optical photography in order to provide a single object to which we may attach an interface for subsequent manipulations. This merging would be greatly simplified if we could unify the different pipelines that take raw data and transform them into images. At the moment, there are different pipelines (programs and associated algorithms) for each imaging technique, and even the data formats are different. This actually stands as a major global challenge : many domain specific techniques exist to solve particular problems in imaging, yet the final results obtained after applying each technique are difficult if not impossible to combine. The PCMTH team, in association with the C2RMF imaging team, is thus developing a generic approach which would allow a single pipeline to process all the different kinds of data, with a single unifying data structure and mathematical model founded on Chebyshev technology [9], as championed by the Oxford University Numerical Analysis Group through the development of Chebfun  [13] , an open-source software system for numerical computing with functions.
It is quite straightforward to transform Equation 15 into an algorithm amenable to a Chebfun compatible implementation : 1. Prefactor the raw spectra r(t) by 1/ √ 1t 2 ; 2. transform each prefactored spectrum into a chebfun with the FUNQUI function ; 3. apply the square brackets part of equation 15 ; 4. apply the naive backprojection B that constitutes the remaining part of equation 15. Part 4 of our algorithmic implementation above requires the computation of the naive backprojection, and here also we took advantage of the Chebfun approach. Figure  4  shows that our reconstruction is at least as good as the standard procedure using the vanilla Matlab imaging toolbox solution that uses the IRADON function. Original Shepp-Logan phantom. Standard Fourier reconstruction. Our Chebfun reconstruction, cf.
Accelerating code execution time Because we target real time imaging, we need to decrease the processing time. The accelerations currently only rely on the vectorization of the algorithms, and in order to reap the full benefits we decided to buy Graphical Processing Units (GPU) hardware (Figure  7 ) and we describe that in sections 3. 4 to 3. 2. The implementation of the most important part of the code is described in section 3. 1 and the whole code will be made available through a Gitlab repository as a literate program  [20]  written with the inhouse developed Vim literate programming plugin 11  called Kosmogram [6]. Further improvements are expected with the use of a novel programming language, Pony [10], that puts actor programming to the forefront as we shall explain in section 3. 3.
MATLAB Distributed Computing Server The image construction is done pixel by pixel, which is an embarrassingly parallel process which quiet naturally led us to use the MATLAB Distributed Computing Server  [12]  on the cluster HPU4Science which we designed accordingly as we shall see in section 3. 4. The construction of the pixels is distributed on the appropriate target machines which we selected thanks to the MATLAB Job Scheduler (MJS) -Table  1  explains the MJS configuration for each construction. We then replace each FOR loop by a PARFOR loop.
HPU4Science in the beginning Servers room For the operating system we use Linux for its stability, the ability to pick from a wide variety of file systems, the large existing code base for high performance computing, and the ease of tailoring the OS to the specific hardware requirements. Availability of open-source projects with code that can be configured for highly parallelized processing was also an important advantage. The master and all workers run Ubuntu server edition, installed with the minimal OpenSSH server profile. All machines are headless to minimize the OS memory footprint, so all interactions with the cluster happens at the command prompt through ssh. The only closed source software used on the cluster is Matlab and it is because of the power of the Chebfun toolbox that we are re-coding some of its function using open source languages (3.
Authors' contributions Mariem El Afrit is a PhD candidate working on the development of novel approaches based on Chebfun for (tomographic) imaging and data fusion, and she worked specifically on the implementation of the algorithms and on the details of the mathematical derivations and their algorithmic formulation, together with the subsequent parallelization, while at the same time being responsible for most of the cluster assembling and management. Yann Le Du is a CNRS research scientist at Chimie-ParisTech specialized in Electron Paramagnetic Resonance Imaging, and he initiated the use of Chebfun for imaging, devised the Tricomi transform approach to backprojection and worked on the whole mathematical framework, while also contributing to the code and launched the work on the re-coding of the pipeline in the Pony language.
Rafaël Del Pino, now a PhD candidate in cryptography, was a summer intern in the team under the guidance of Yann Le Du, and worked on the algorithm implementation, helped with the mathematical derivations and specifically with the finalization of the mathematical form of the Tricomi expression used in the pipeline. Guolin Zhang was a summer intern in the team, and worked on the acceleration of the code on the Matlab source code of the imaging pipeline under the guidance of Mariem El Afrit. As for the paper itself, it was written by Mariem El Afrit and Yann Le Du.
In this context, it has been recently demonstrated that the MadGraph5 aMC@NLO framework  [15]  can provide a general platform for computing (differential) observables within many beyond the Standard Model theories  [16]. This approach relies in particular on the use of the FeynRules  [17]  and NLOCT  [18]  packages for (automatically) generating a UFO library  [19]  containing the vertices and the needed counterterms for NLO computations. Within this framework, we match for the first time NLO QCD matrix-element-based predictions to parton showers for gluino pair-production. Virtual contributions are evaluated following the Ossola-Papadopoulos-Pittau (OPP) formalism as implemented in MadLoop  [20] [21] [22]  and combined with real emission contributions by means of the FKS subtraction method as embedded in Mad-FKS  [23, 24] ; these two modules being fully incorporated in MadGraph5 aMC@NLO.
In our phenomenological study, loop-calculations are performed numerically in four dimensions by means of the MadLoop package and therefore require the extraction of rational parts that are related to the ǫ-pieces of the loop-integral denominators (R 1 , which are automatically reconstructed within the OPP reduction procedure) and numerators (R 2 ). For any renormalizable theory, the number of R 2 terms is finite and they can be seen as counterterms derived from the bare Lagrangian  [29]. In the context of the L SQCD Lagrangian, all necessary R 2 counterterms can be found in Ref. [30]. The setup described above has been implemented in the FeynRules package and we have made use of the NLOCT program to automatically calculate all the ultraviolet and R 2 counterterms of the model. The specificity of the renormalization of the stop sector has been implemented via a new option of NLOCT, SupersymmetryScheme->"OS", that allows to treat all scalar fields that mix at the loop-level as described above.
We have validated the output against our analytical calculations, and these results represent the first validation of NLOCT in the context of computations involving massive Majorana colored particles. We have finally generated a UFO version of the model that can be loaded into MadGraph5 aMC@NLO and which we have made publicly available on http://feynrules. irmp. ucl. ac. be/wiki/NLOModels. We have further validated the model, together with the numerical treatment of the loop-diagrams by MadLoop, by comparing MadGraph5 aMC@NLO predictions to those of the code Prospino  [31] , using a fully degenerate mass spectrum due to the limitations of the latter. I , we compute gluino pair-production total cross sections for proton-proton collisions at a center-of-mass energy of √ s =13 TeV and for different gluino masses.
In order to achieve realistic simulations of LHC collisions, we first handle gluino decays into two colored partons and a neutralino via an off-shell squark by using tree-level decay matrix-elements. For the fixed-order results presented below, we have analytically calculated those decay matrix elements and integrated them over the phase space, after checking our results with the Mad-Spin  [36]  and MadWidth  [37]  programs. These latter two programs have been used in the computation of the NLO predictions matched to parton showers. Due to the three-body nature of the gluino decays, spin correlations are here omitted as MadSpin can only handle them for two-body decays. We then interface the partonic events obtained in this way to a parton showering and hadronization description as provided by the Pythia 8 package  [38] , and use the anti-k T jet reconstruction algorithm  [39]  with a radius parameter set to 0.
Finally, our results also shows that all technical obstacles for automating the matching of fixed-order calculations for inclusive supersymmetric particle production at the NLO in QCD to parton showers have been cleared, up to the ambiguity issue of the double counting arising in real emission resonant contributions  [32]. Research Fellow, VH is supported by the SNF grant PBELP2 146525 and JP by a PhD grant of the Investissements d'avenir, Labex ENIGMASS.
ACKNOWLEDGMENTS We are grateful to  R. Frederix ,  S. Frixione ,  F. Maltoni  and  O. Mattelaer  for enlightening discussions. This work has been supported in part by the  ERC  grant  291377  LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the precision frontier, the  Research Executive Agency of the European Union  under Grant Agreement  PITN-GA-2012-315877  ( MC-Net ) and the  Theory-LHC-France initiative of the CNRS  ( INP/IN2P3 ). CD is a  Durham International Junior.
For the present study we have constructed 2D and 1D models of the heat storage container responsible for the complex heat transfer phenomena, using COMSOL Multiphysics simulation platform  [20]. It is a powerful code for modelling and solving various sciences and engineering problems based on the Finite Element (FE) method. The software is particularly well suited for the treatment of multiphysical and non-linear problems where several phenomena are involved, giving rise to non-linear behaviour, as in the case for the present study. The apparent heat capacity of the PCM was modelled according to Figure  5  and its thermal conductivity is illustrated in Figure  6. For the aluminium components of the system the model assumes constant thermophysical properties: ρalu = 2700 kg•m -3 and cp alu = 897 J•kg -1 •K -1.
Yet, important functions are still lacking such as high performance computing capacity to process whole genome data, and the integration of spatial statistics to support a distinction between selection and demographic signals. Here we present the software Samβada, which aims at filling these gaps by offering an open source multivariate analysis framework to detect signatures of selection. Samβada's use is illustrated with a case study dedicated to the detection of potentially adaptive loci in 813 Bos taurus and Bos indicus individuals in Uganda genotyped for ∼ 40,000 SNP. Lastly, Samβada's performance is described with respect to other state of the art software to detect signatures of selection.
Samβada measures the global spatial autocorrelation in the whole dataset with Moran's I, as well as the spatial dependency of each point with Local Indicators of Spatial Association (LISA)  (Moran, 1950; Anselin, 1995). In practice, LISAs are computed by comparing the value of each point with the mean value of its neighbours as defined by a specific weighting scheme based on a kernel function (see supplementary material). Both a spatially fixed kernel type relying on distance only, and a varying kernel type considering point density can be used. Samβada includes three fixed kernels (moving window, Gaussian and bisquare) and a varying one (nearest neighbours). The sum of LISAs on the whole dataset is proportional to Moran's I  (Anselin, 1995). Significance assessment relies on an empirical distribution of the indices.
For Moran's I, values (genotype occurrences) are permutated among the locations of individuals in the whole dataset and a pseudo p-value is computed as the proportion of permutations for which I is equal to -or more extreme (higher for a positive Moran's I or lower for a negative Moran's I) -than the observed I. For LISA, the pseudo p-value is separately computed for each point (individual), by keeping the individual of interest fixed and permuting its neighbouring points with the rest of the dataset.
Discussion The key features of Samβada are the multivariate modelling and the measure of spatial autocorrelation. Both can support the interpretation of results in case the dataset features population structure. Bivariate models may indeed include the global ancestry coefficients provided by a preliminary analysis. This setup can detect which loci are correlated with the environment while taking demography into account. Additionally, the introduction of measurements of spatial autocorrelation into these analyses integrates spatial statistics with landscape genomics. Contrary to most current and non-spatial approaches (e. g. Frichot et al. , 2013; Coop et al. , 2010) , Samβada allows the determination of whether the observed data reflects independent samples, a requirement of the underlying statistical model.
uniprot. org). Moreover, genotype HM-28 (GG) shows spatial autocorrelation in the North-Western part of Uganda and this area overlaps with one of those where the higher load of tse-tse fly (Glossina spp. ) occur in the country  (Abila et al. (2008); MAAIF et al. , 2010). Hence the risk of cattle trypanosomiasis is high in this region and the detected mutations may be involved in parasite resistance. The increasing availability of large molecular datasets raises challenges regarding their analysis. Correlative approaches in landscape genomics enable fast detection of candidate loci to local adaptation. However these methods must take into account the effect of population structure  (Frichot et al. , 2013; Joost et al. , 2013; De Mita et al. , 2013). Limited dispersal of individuals leads to spatial autocorrelation of marker frequencies, which may cause spurious correlations with the environment.
, 2002; Joost et al. , 2013). Thus GWR enables building a null model where the constant term may vary in space and then refining it by adding a global environmental effect for all locations. Comparing these two models would enable an assessment of whether the global environmental effect is needed to describe the distribution of the genotype. The key advantage of allowing the constant term to vary in space is to take spatial autocorrelation into account in the models. This way, GWR allows an investigation of the spatial behaviour of loci showing selection signature with standard logistic regressions and may help to distinguish between local adaptation and population structure in landscape genomics. However GWR models require a finetuning of the weighting scheme from the user, which restrains their application to very large datasets.
The recent availability of whole genome sequence (WGS) data also raises issues regarding the statistical assessment of multiple comparisons. Indeed, while many individuals and few genetic markers were available ten years ago, the current high costs of WGS limit the number of sequenced samples. Therefore standard procedures for multiple comparisons, such as the Bonferroni correction, are over-conservative and may lead to discard some adaptive loci. In this context, alternatives procedures focus on controlling the ratio of false positives in a set of significant results. Among them, Storey and Tibshirani's false discovery rate (  2003 ) was especially designed for large molecular datasets and suits any detection method relying on significance tests. Its implementation in Samβada is ongoing. Computation time is critical when processing large datasets.
Sampling design Sampling was designed to cover the whole country, including each eco-geographic region, and to obtain a homogeneous distribution of individuals across the country. A regular grid made of 51 cells of 70 x 70 km was produced to this end. The field campaign took place between March 2011 and January 2012. On average, four farms were visited in each cell and four unrelated individuals were selected from each farm, for a total of 917 biological samples retrieved from 202 farms. Recorded information also included the location of the farm, the name of the breed, a picture and morphological information on each individual. These elements were stored in a database accessible through a Web interface, enabling real-time monitoring of the sampling campaign.
Molecular data Out of the 917 individuals, 813 samples were genotyped with a medium-density SNP chip (54,609 SNPs, BovineSNP50 BeadChip, Illumina Inc. , San Diego, CA). Only markers located on the autosomal chromosomes were considered in the analyses. The dataset was filtered with PLINK  (Purcell et al. , 2007)  with a call rate set to 95% for both individuals and SNPs, and a minimum allele frequency (MAF) set to 1%. The resulting dataset contains 804 samples and 40,019 SNPs.
Population structure Population structure was analysed with Admixture  (Alexander et al. , 2009) , which estimates maximum likelihood of individual ancestries from multilocus SNP genotype datasets. This approach assumes that samples descend from a predefined number of ancestor populations that are mixing. Admixture estimates both the fraction of each sample coming from each population and the marker frequencies in these populations. The optimal number of populations is assessed by a k-fold cross-validation procedure. The best partition of the dataset consisted of four populations, although the vast majority of the samples were allocated to two clusters (almost 96%) on the basis of the ancestry coefficients (Figure  S1 ). Mapping these coefficients revealed the two clusters (340 and 431 individuals out of 804) occurred in the South-West and North-East of Uganda respectively.
Environmental data The geographical coordinates of the individuals sampled enabled the characterisation of their habitat with the help of the WorldClim dataset containing monthly values of precipitation, minimum, mean and maximum temperature as well as 19 derived variables, at 1km resolution  (Hijmans et al. , 2005). This dataset provides appropriate data as it consists of representative climate information collected during 30 years (WMO standard climate normal,  Arguez and Vose, 2010)  and its high resolution suited the scale of our study. These environmental variables were originally stored in four tiles (portions of map) which were pasted using the Geospatial Data Abstraction Library (GDAL Development Team, 2013) and a customized Python script. The topography is described by the 90m resolution SRTM3 (Shuttle Radar Topography Mission) digital elevation model  (Farr et al.
Parallel computing version (CoreSAM) When processing large datasets, primary analysis usually focuses on univariate models. Multivariate models and spatial autocorrelation may be considered as a second step, but are too computationally intensive to be applied to the whole dataset. In order to speed-up the process, CoreSAM is a light version of Samβada, written in C, which focuses on univariate analysis. Compared with Samβada, fewer options are available, but the computation is up to seven times faster. Combining Samβada and CoreSAM, large datasets may be analysed by steps: Univariate logistic models identify candidate loci exhibiting selection signatures. These loci may be then investigated in the light of spatial autocorrelation measures and multivariate models. The former may point out whether the observed correlation is due to similarities between neighbours, while the latter allows including the population structure, if any, in the model in order to assess whether the environmental variable provides supplementary information on the marker frequency when taking the demography into account.
Modules Samβada includes several modules that enhance interfacing with other programs. Geovisualization of spatial statistics Samβada provides an option to save the spatial autocorrelation results as a shapefile (. shp), a common format for storing vector information in Geographic Information Systems (GIS). This feature relies on the shplib open source library (http://shape-lib. maptools. org). Recoding molecular data Samβada is distributed with a utility for recoding molecular data into binary information. Currently RecodePlink handles ped/map files, a format for SNP data used by PLINK  (Purcell et al. , 2007). Supervision For very large molecular datasets, Samβada provides a module to share workload between computers. Supervision splits the input data in several files that can be processed separately, even on heterogeneous computers.