dataesr/scientific-paragraphs-categorization huggingface dataset:
Software possibilities Tinker-HP is part of the Tinker 8 package and consequently it is fully compatible with the canonical Tinker and the Tinker-OpenMM (GPU) codes. Therefore, all Tinker's analysis and visualization tools are available with Tinker-HP. Details about these possibilities are not described here and can be accessed on the Tinker community website (http://tinkertools. org). The Tinker-HP source code is freely available to the academic community: details and downloading informations can be found on the Tinker-HP website (http://www. ip2ct. upmc. fr/ tinkerHP). In the following section, we detail the possibilities of the code that will be contained in the incoming public releases.
Classical force elds. By construction, the soware is able to perform classical force eld simulations following the canonical Tinker initial implementation of the AMBER, CHARMM and OPLS potentials. Such force elds also benet from the speed up of the massively parallel framework but our objective is to reach comparable performance to the AMBER and CHARMM (Domdec 61 ) CPU implementations. The detailed analysis of such code capabilities being beyond the scope of this paper, fully dedicated to polarizable models, and it will be discussed elsewhere. However, it can be noted that classical MM that requires much less work than PFFs allows for a 5-8 acceleration of the production per day over AMOEBA (depending on the use of TCG vs. PCG solvers) on the same computational platform, and will be used for hybrid simulations with PFFs coupled to non-polarizable parts of the system.
Benchmarks and applications using the AMOEBA polarizable force field The present implementation has been extensively tested and reaches exactly the same accuracy as the canonical Tinker for polarizable force eld when considering analogous algorithms, allowing Tinker-HP to produce reference computations. All the proposed benchmarks use the AMOEBA force eld. We tested the performances of Tinker-HP on various systems. We studied the scalability of the code dealing with homogeneous systems such as bulk water, and inhomogeneous systems ranging from ionic liquids to proteins. Finally we tested our approach on very large biosystems.
Homogeneous systems: water boxes and ionic liquids Water boxes. We rst benchmarked the code on cubic water boxes of increasing size: from 96 000 atoms up to 23. 3 millions atoms. Table  1  summarizes the characteristics of these boxes: their size in Angstroms, the number of atoms they contain, the size of the associated PME grid and the name with which they will be referenced in the rest of the paper. Fig. 6  show the detailed scalability up to almost 1 million atoms. A very good scalability is observed in the three cases. Table  3  displays the best production timings in ns per day. The code appears to be competitive with the GPU numbers extracted from Tinker-OpenMM even for a system such as the smallest waterbox test (Puddle, 96 000 atoms). In this case, Tinker-HP is already 1. 5 faster than a GTX 1080 card (3 times for a GTX 970) but with double precision compared to mixed precision arithmetics used by GPUS.
We started our study by testing Tinker-HP on small proteins were 3D domain decomposition is expected not be fully efficient (our water boxe study started at 96 000 atoms, which is 4 times the size of DHFR and 10 times that of Ubiquitin). Surprisingly, results remain competitive with GPUs which are fully taking advantage of their computing power for such a range of systems with low memory requirements. DHFR allows to study in depth the code behavior in that system size range. Indeed, the best production time for a use of all cores of a node brings us to a 7. 69 ns per day using TCG2. This production time is really close to the 8. 29 ns per day exhibited by Tinker-OpenMM on a GTX1080 (see Table  3 ). If we used the same number of cores distributed on more nodes, to use the same technique we used on the large ocean and sea water boxes, the performance extends to 8.
Acknowledgements This work was made possible thanks to the French state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR-11-IDEX-0004-02 ) and through support of the  Direction Générale de l'Armement (DGA) Maîtrise NRBC  of the  French Ministry of Defense. Funding from  French Centre National de la Recherche (CNRS)  is also acknowledged (PICS international collaboration grant ( Sorbonne U. -UT AUstin ) and  Inniti  fund), as well as support by the  National Institutes of Health  ( R01GM106137 ,  R01GM114237  and  R01GM108583 ),  CPRIT  ( RP160657 ) and the  Robert A. Welch Foundation  ( F-1691 ). This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship  under Grant No. DGE-1610403.
Although simultaneously sequencing a pool of libraries can lower costs, this strategy must therefore be carefully planned. Several tools have been developed to check index compatibility. For instance, Illumina's Experiment Manager (IEM) software checks the compatibility of indexes provided by the user. However, IEM is available only for computers running Windows operating systems, and cannot build combinations that fulfil complex criteria (such as the use of single indexes in several lanes). sicTool (http://amaryllisnucleics. com/sicTool/) also provides a web interface with advanced options but does not handle the different Illumina chemistries. Here, we describe the checkMyIndex web-based application. When given a set of available indexes, checkMyIndex generates compatible combinations that meet the idiosyncratic experimental conditions imposed by both the research objective and the user's preferences.
Results We used the popular R package shiny  (Chang et al. , 2017)  to develop a user-friendly application (available free of charge at checkmyindex. pasteur. fr) dedicated to searching for compatible combinations of indexes. The corresponding R source code is hosted on GitHub (https://github. com/PF2-pasteur-fr/checkMyIndex) so that researchers can also run the application on their own computer. Note that checkMyIndex uses only native R code, and shiny is the only additional package required. An R script is also available for advanced users willing to run checkMyIndex from the command line. Our application supports single-and dual-indexing in order to achieve higher multiplexing rates and decrease error rates  (Kircher et al. , 2012). checkMyIndex is also compatible with the various chemistries used by the Illumina HiSeq, MiSeq, NextSeq and iSeq devices.
Acknowledgements We thank  Odile Sismeiro ,  Caroline Proux ,  Juliana Pipoli da Fonseca ,  Christiane Bouchier ,  Sean Kennedy  and  Thomas Cokelaer  for assistance and helpful suggestions. We also acknowledge  Celine Trebeau and Raphae ¨l Etournay  for their constructive discussions, and  Emmanuel Guichard  and  Nicolas Maillet  for their help during the virtual machine set-up.
Animals, phenotypes and genotypes The dataset used in this study was provided by the French national milk records system and included animals from the two main French dairy goat breeds, Alpine and Saanen. Phenotypes for protein content, pedigree data, genotypes and environmental fixed effects used in the ssGBLUP method were obtained from the official genetic evaluation of January 2016  [36]. Analyses were performed with a multi-breed dataset (Alpine and Saanen animals combined) and in two separate within-breed analyses. The trait analyzed was protein content (g/kg) with measurements from 6,767,490 lactations and 2,458,453 females recorded between 1980 and 2010. Descriptive statistics (animal and record numbers, minimum, mean, maximum, coefficient of variation) for each breed are in Table  1.
The pedigree consisted of 2,543,789 animals (1,449,991 Alpine and 1,093,798 Saanen). In addition, it was completed with 36 unknown parent groups. Unknown parent groups were defined for each breed and for animals born before 1975, and then for cohorts born in 2-year windows up to 2010. Animals that were genotyped with the Illumina goat SNP50 BeadChip (50K SNP)  [37]  were also used in the analysis. Quality control (QC) for a dataset of 3347 genotyped animals (2020 Alpine and 1278 Saanen) and 53,347 SNPs was performed independently for each breed. SNPs with a minor allele frequency (MAF) lower than 1% and a call rate lower than 95% were removed. Hardy-Weinberg equilibrium was also tested and the associated Chi squared statistic was calculated for each SNP. SNPs with a Chi squared statistic higher than 24 were removed.
Genomic prediction with and without considering information on the α s1 casein genotypes ssGBLUP was implemented in 2017 in the official genetic evaluations for the two main French dairy goats. This method and pedigree-based BLUP were used as the reference method in our study and compared with Wss-GBLUP, two alternatives of the WssGBLUP method, TABLUP and the gene content method. Analyses were performed using the blupf90 software  [38].
X is the incidence matrix relating phenotypes to the fixed effects ( β). Z is the design matrix allocating phenotypes to breeding values ( u ) and W is the incidence matrix relating phenotypes to permanent environmental effects ( p). (1) y = Xβ + Zu + Wp + e, Matrix H is the genetic relationship matrix combining SNP information and pedigree data, implemented as in Legarra et al. [12] : where A is a pedigree-based relationship matrix with indices 1 for ungenotyped animals and 2 for genotyped animals, and G is the genomic relationship matrix derived as in Christensen and Lund  [11] : where m is the number of SNPs, p i is the estimated allele frequency at locus i and M is a centered matrix of SNP genotypes. Variance components were estimated by using the restricted maximum likelihood (REML) method in the remlf90 software  [38].
Accuracy of genomic predictions Genomic evaluations were performed from all phenotypes recorded until January 2010, but we were also interested in the prediction of genotyped animals that constituted our reference population. This reference population was composed of 905 sires born between 1993 and 2012 and genotyped with the 50K SNP chip (Table  2 ) and was split into a training population of 554 sires born from 1993 to 2007 (307 Alpine and 247 Saanen) with phenotypes of their daughters recorded until January 2010), and 351 validation sires born from 2008 to 2012 (205 Alpine and 146 Saanen) with no daughters in January 2013 (daughters of these animals were removed from the dataset). Then, GEBV and DYD computed from the official genetic evaluation of January 2016 were compared for the 351 animals in the validation set.
Acknowledgements This study would not have been possible without the goat SNP50 BeadChip developed by the  International Goat Genome Consortium (IGGC) : www. goatg enome. org. The authors thank  Ignacy Misztal  ( University of Georgia, USA ) for the blup90iod2 program. We sincerely thank the two anonymous reviewers and  Julius Van der Werf  (editors for Genetics Selection Evolution) whose comments/suggestions helped improve and clarify this manuscript.
Funding The authors thank the  French Genovicap and Phenofinlait programs  ( ANR, Apis-Gène, CASDAR, FranceAgriMer, France Génétique Elevage ,  French Ministry of Agriculture Agrifood, and Forestry)  and the European  3SR  project, which funded part of this work. The first author also received financial support from the  Midi-Pyrénées region  and the  French National Institute for Agricultural Research (INRA) SELGEN program (INCoMINGS).
A wider exploitation of deep geothermal reservoir requires the development of Enhanced Geothermal System technology. In this context, drilling and stimulation of high-enthalpy geothermal wells raise technical challenges. Understanding and predicting the rock behavior near a deep geothermal wellbore are decisive to implement stimulation strategies to reach the couple temperature/flowrate target. Numerical modeling can contribute to enhanced stimulation processes thanks to a better understanding of impact of stress release, pressure changes and rock cooling in the near-wellbore area. In this paper, we use Discrete Element Method (code PFC2D, © Itasca Consulting Group), and more specifically bonded-particle model to capture the thermo-mechanical processes at metric scale. The application case corresponds to the beginning of thermal stimulation at Reykjanes in well RN-15/IDDP-2 (Iceland, IDDP-2 project and H2020 project DEEPEGS).
Acknowledgements We would like to thank  Kati Tänavsuu-Milkeviciene  (Equinor) and  Claudia Kruber  ( Equinor ) who helped with the geology, mineralogy and porosity analyses;  Théophile Guillon  (BRGM) and  Arnold Blaisonneau  ( BRGM ) for fruitful discussion on modeling issues. The authors are grateful to the editor and to the two anonymous reviewers for their helpful comments and advice.
Then, the horizontal turbulent transport resulting from 3D turbulent motions sustained by the instability of the radial differential rotation is derived. We implement this framework in the stellar evolution code STAREVOL and quantify its impact on the rotational and structural evolution of solar metallicity low-mass stars from the pre-main-sequence to the red giant branch. Results. The anisotropy of the turbulent transport scales as N 4 τ 2 / 2Ω 2 , N and Ω being the buoyancy and rotation frequencies respectively and τ a time characterizing the source of turbulence. This leads to a horizontal turbulent transport of similar strength in average that those obtained with previously proposed prescriptions even if it can be locally larger below the convective envelope. Hence the models computed with the new formalism still build up too steep internal rotation gradients compared to helioseismic and asteroseismic constraints.
This is a good motivation to also consider the horizontal transport induced by 3D turbulent motions sustained by the instability of the radial differential rotation, which has been ignored until now. In this work, we derive a new prescription for the corresponding turbulent transport coefficients (i. e. ν h,v and D h,v ). This allows us to propose for the first time an expression that depends explicitly on stratification, rotation, and their ratio. In Sect. 2, we generalize the spectral model introduced by  Kitchatinov & Brandenburg (2012)  to study the anisotropy of turbulent flows in differentially rotating stably stratified layers and we derive scaling laws allowing us to establish our new prescriptions. In Sect. 3, we implement them in the stellar evolution code STAREVOL. We study their impact on the rotational and structural evolution of a 1.
As proposed by  Zahn (1992) , rotation then can become "shellular" with related simplifications of 2D transport equations  (Zahn 1992; Maeder & Zahn 1998; Mathis & Zahn 2004 ) that have been successfully implemented in several stellar evolution codes: the Geneva code (e. g. Talon et al. 1997; Meynet & Maeder 2000; Eggenberger et al. 2008; Ekström et al. 2012) , the code STAREVOL (e. g. Palacios et al. 2003; Decressin et al. 2009; Charbonnel & Lagarde 2010; Amard et al. 2016) , the code CESTAM (e. g. Marques et al. 2013) , and the code FRANEC (e. g. Chieffi & Limongi 2013). The control parameter of such dynamics is the ratio between the vertical and horizontal turbulent transport coefficients D v ≡ D ∝ u l = u 2 τ = l 2 τ (2) and D h ≡ D ⊥ ∝ u ⊥ l ⊥ = u 2 ⊥ τ = l 2 ⊥ τ , ( 3 ) where τ is a dynamical time scale characterizing the turbulence and its source, when assuming a diffusive description for each of them  (Chaboyer & Zahn 1992).
Method We implement Eqs. (  48 ), (  49 ), (  52 ) and (  54 ) in the stellar evolution code STAREVOL as an additional source of turbulence in the horizontal direction. The total horizontal turbulent diffusion coefficient D h is then defined as the sum of D h,v as given by these equations (we test the different expressions for τ) and of D h,h from  Mathis et al. (2004, Eq. 19). The treatment of rotation-induced processes in STAREVOL is described in detail e. g. in  Decressin et al. (2009)  and  Amard et al. (2016) , and we refer to this latest paper for the information on the basic input physics (i. e. , equation of state, nuclear reactions, opacities, etc. ) used here (see also  Siess et al. 2000; Palacios et al. 2003; Lagarde et al. 2012). The angular velocity profile in the radiative interior derives from the redistribution of angular momentum by shear-induced turbulence and meridional flows, which are treated using the formalism derived by  Zahn (1992) ,  Maeder & Zahn (1998) , and  Mathis & Zahn (2004) ; uniform rotation is assumed in convective regions.
Acknowledgements. We dedicate this work to the memory of our friend and mentor  Jean-Paul Zahn , in the steps of whom we walk. Pursuing the path he opened is a great honor. We warmly thank the referees for their careful reading and constructive reports that allowed us to improve our work. S. M. and V. P. acknowledge support by  ERC   SPIRE 647383  and  FP7 SpaceInn  grants. S. M. , V. P. and A. P. acknowledge support by  CNES  PLATO funding. C. C. and L. A. thank the  Equal Opportunity Office of the University of Geneva. The authors acknowledge financial support from the  French Programme National de Physique Stellaire PNPS of CNRS/INSU , the grant  ANR 2011  Blanc  SIMI5-6 020 01  Toupies (Towards understanding the spin evolution of stars) and the  Swiss National Science Foundation (SNF).
I. INTRODUCTION Traditionally, cloud system designers have used purposespecific storage services for their data storage needs. These include key-value stores  [1] -  [3] , wide-column databases  [4] -  [6] , or streaming message brokers  [7] ,  [8]. On the contrary, high-performance computing (HPC) platforms rest on on a much more constrained set of storage primitives, typically limited to parallel file systems  [9] ,  [10]  or transient burst buffers  [11] ,  [12]. The low availability of local storage on the compute nodes of most supercomputers today and the lack of administrative access give little opportunity for users to deploy the storage system they need. As the boundaries between HPC and big data analytics (BDA) continue to blur  [13] , new challenges arise. A critical objective set in this convergence context is to foster application portability across platforms  [14].
Yet, in scientific applications, distributed logs could play many roles, for example for in situ visualization of large data streams, collection of telemetry events for computational steering, or data aggregation from arrays of physical sensors. A shared log enables multiple processes to append data at the end of a single byte stream. Unfortunately, in such a case, the write contention at the tail of the log is among the worst-case scenarios for parallel file systems, yielding problematically low append performance  [18]. Thus, efficient streaming log middleware is needed that can leverage the primitives available on HPC platforms for persistence. We present the SLoG 1  shared log middleware. It provides high append velocity over distributed file systems by leveraging I/O parallelism and proxying, circumventing the inherent limitations of HPC file systems.
A. Leveraging a high-end experimental platform All experiments carried out in this paper are based on the Theta supercomputer  [19] , hosted at the Argonne Leadership Computing Facility (ALCF) of Argonne National Laboratory. Theta is a 9. 63 petaflops system, ranked 16 in the TOP500 as of June 2017. It is composed of 3,624 nodes, each containing a 64-core Knights Landing Xeon Phi CPU with 16 GB of high-bandwidth in-package memory (MCDRAM), 192 GB of DDR4 RAM, and a 128 GB SSD. Networking leverages a Cray Aries interconnect with Dragonfly configuration. The Theta storage system is based on Lustre. Its 10 TB parallel file system spans 170 storage servers. Theta is also connected to the GPFS file system of the Mira supercomputer colocated in the same facility using high-speed interconnects. This configuration allows for experiments on both Lustre and GPFS from the same experimental platform.
ACKNOWLEDGMENT This work is part of the " BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data " project ( H2020-MSCA-ITN-2014-642963 ), funded by the  European Commission  within the  Marie Skłodowska-Curie Actions framework. This work was supported by the  U. S. Department of Energy, Office of Science, Advanced Scientific Computing Research  (Contract  DE-AC02-06CH11357 ). It is also supported by the  ANR   OverFlow  project ( ANR-15-CE25-0003 ). We thank  Quincey Koziol  (LBNL),  Noah Watkins  ( UC Santa Cruz ),  Robert Jacobs  (ANL), and  Jay Lofstead  ( SNL ) for their expert assistance. The experiments presented were carried out by using resources of the  Argonne Leadership Computing Facility , which is a  DOE Office of Science User Facility  (Contract  DE-AC02-06CH11357 ).
By augmenting both C to C ^and γ to γ ^, we can still write the model as a composite link model, which allows us to estimate the complex decomposition using reliable algorithms to estimate cause-specific contributions to each component while constrained to sum to the overall components. As we demonstrate in the Application section, more hump-related causes can be incorporated by further augmentation of the model elements. An implementation of these methods is available in the MortHump R package on the CRAN repository  (Remund et al. 2018). Figure  3  illustrates the application of this technique to our toy example. The black solid line represents the all-cause age-specific fitted rates, and the black dashed and dotted lines represent the estimated hump and senescence components, respectively.
Application Data We use an early release of data produced by the Human Mortality Database (2017) on cause-and age-specific death rates for the United States between 1959 and 2015, covering ICD versions 7-10. These data are aggregated from National Center for Health Statistics deaths microdata into 92 cause categories. We first graduate the cause-specific death rates from abridged age groups to single ages using P-splines implemented in the MortalitySmooth R package  (Camarda 2012) ; we then constrain them to sum to single-age all-cause mortality rates from the Human Mortality Database. We make no adjustments to smooth potential coding ruptures, but none of the three ICD revisions  (in 1968, 1979, and 1999 ) generate a visible rupture in the patterns we report, except the introduction of the HIV code in 1987.
Acknowledgments This study was conducted in the framework of the project " From Disparities in Mortality Trends to Future Health Challenges (DIMOCHA) " funded by  Deutsche Forschungsgemeinschaft (DFG) (Germany)  ( JA2302/1-1 ) and  Agence Nationale de la Recherche (ANR) (France)  ( ANR-12-FRAL-0003-01 ). Adrien Remund  is affiliated with the  Swiss National Center of Competence in Research LIVES -Overcoming Vulnerability: Life Course Perspectives , which is financed by the  Swiss National Science Foundation  (Grant No. 51NF40-160590 ). The authors are grateful to the  Swiss National Science Foundation  for its financial assistance. We thank the Human Mortality Database and  Magali Barbieri  for providing us with an early release of their U. S. cause-of-death data, which are based on data from the  National Center for Health Statistics of the Centers for Disease Control and Prevention.