dataesr/scientific-paragraphs-categorization huggingface dataset:
As we try to check that the paths are implemented correctly, we focus on specific traffic generation. For generating traffic, we propose to use the ICMP echo request / echo reply packets through the known ping utility 3. The ICMP request / reply is performed for each pair of hosts that correspond to the head and tail nodes of the test cases. Later, the passing traffic is inspected at all node interfaces via a simple network sniffer. The network traffic of all switches can be obtained in different ways, starting from a simple Unix-like sniffer as the tcpdump utility and finishing with non-software-based (physical / vendor) switches via protocols such as Net-Flow  (Claise, 2004)  or sFlow  (Phaal et al. , 2001). As an example, consider the requested path (h 1 , s 1 )(s 1 , s 3 )(s 3 , s 4 )(s 4 , s 2 )(s 2 , h 2 ) with respect to the RNCT in Fig.
Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets, characteristic of genetic isolates and of unrelated individuals, before analysing the isolate of Cilento for various commonly studied traits. We show the strengths of using genetic relationship matrices for variance decomposition over identity-by-descent based methods in a population isolate and that heritability estimates in isolates will avoid the downward biases that may occur in studies of samples of unrelated individuals; irrespective of the simulated distribution of causal variants. Yet, we also show that precise estimates of dominance in isolates are demonstrably problematic in the presence of shared environmental effects and such effects should be accounted for.
Aside from these studies, dominance heritability estimation using GRMs has rarely been carried out, and the authors who are more interested in dominance tend to rely on family data  35, 36. Of particular note is the observation that significant non-additive genetic components for many traits have been found in some studies on population isolates: Abney et al. 37  , Pilia et al. 38  , and Traglia et al. 39  (Table  1 ). An isolate is characterized as a population arising from a small group of founders and experiencing subsequent demographic growth in isolation. Such populations will include pairs of distantly related individuals who nonetheless share long haplotypes IBD, and may even share both haplotypes IBD in some regions. The presence of both pairs of closely related individuals and pairs of cryptically related individuals suggests that isolates could be ideally suited to heritability analyses.
41  who performed a study on extended genealogies from the Icelandic populations, itself a moderate isolate. Genetic dominance has often been considered in the study of various animal species (mammals, poultry, and fish are most commonly studied). Here, by design, confounding with shared environmental factors can often be avoided and extensive and highly accurate pedigree data can be recorded. For many traits, dominance heritability is often found to be significantly different from zero and the inclusion of dominance has been shown to give improved performance of prediction models in animal studies  [42] [43] [44] [45] [46] [47]. Negative results regarding the improvement of prediction given by including genetic dominance have also been presented (eg. Heidaraitabar et al. 48 ) and indeed debate continues in regards to the practical value of non-additive variation; for recent reviews we refer the reader to Varona et al.
Analysis of the Cilento Isolates. We first calculated the matrices K and D using different approaches and then compared the resulting values. We calculated K and D using either the pedigree information, or as GRMs using genotype data before or after imputation. Results were in accordance with those from the simulated population isolate Isolated(1444) (Supplementary Fig. 9 ). However, we observed greater differences between the off-diagonal elements calculated with the pedigree and those in the GRMs when analysing the real Cilento data as compared to Isolated(1444). This is likely to stem from the explicit use of the pedigree information within the simulation. The inclusion of imputed variants led to similar estimates for the matrices K and D (Supplementary Fig. 10 ). Following quality control and imputation (full details are given in the Supplementary Materials); we fitted LMMs to the data in Cilento having estimated matrices K and D as GRMs (using all variants with MAF > 0.
Furthermore, it would appear that only a relatively small number of SNPs are required to compute such GRMs in an isolate as using far denser sets of variants (either in our simulation or through imputation in the Cilento dataset) did not noticeably affect the fitting of the LMM. The advantage observed for GRMs could be because they can capture similarities between all types of pairs of individuals in the isolate; including similarities not described by the recorded pedigree structure or originating before the founding event of the population. Therefore this approach combines the classical interpretation of heritability regarding closely related individuals with the more recent approaches involving samples of unrelated individuals. Foreseeably, the simulated outbred populations led to underestimation of both h A 2 and h D 2 in most of the settings of phenotype simulation.
Higher order non-additive genetic variance components could be contributing to our estimates of dominance in Cilento  5. Indeed, we recognise that ignoring the presence of epistatic effects has been shown to lead to overestimations of H 2 by Zuk et al. 57  who also proposed a non-parametric method for estimating heritability in a population isolate. Such approaches require large samples of pairs of individuals with identical expected relatedness coefficients. Similar approaches include those based on Haseman-Elston regression  58  and studies focusing on populations of siblings or nuclear families. However, for the data of Cilento such methods proved not to be applicable due to the variety of relationships between pairs, such that looking at each pair type separately resulted in sample sizes too small to provide realistic estimations.
The high quality of the reconstructed genealogy in Cilento makes it an appropriate tool for simulating a realistic example of data from an isolated population. Individuals from Campora and Cardile have been genotyped on an Illumina 370 K array, whilst individuals from Gioi have been genotyped on an Illumina HumanOmniExpress array. Deep phenotyping has been performed in Cilento for a range of anthropometric, cardiometabolic, and haematological traits. For the purposes of this study, we have concentrated on phenotypes that have been often analyzed in the literature of both other population isolates and in samples of unrelated individuals (Supplementary Table  1 ).
Simulation of genotypes and phenotypes. To create simulated datasets, we created mosaic haplotypes using the same stochastic recombination model as in the generation of control individuals by the software HapGen2  65. We took the UK10K imputation panel as reference haplotypes having first removed one individual from every pair of twins present in the panel. To simulate unrelated individuals we sampled 22 pairs of mosaic chromosomes, where each section of their mosaics is copied from a randomly sampled haplotype from the pool of UK10K haplotypes. In this manner, we created a sample of 8,664 (6 × 1,444) unrelated individuals. To create isolate type data, for each chromosome, we randomly selected 200 UK10K haplotypes, from which 2,940 mosaic haplotypes were simulated in order to simulate the 1,470 founders of the combined pedigree of Cilento.
This set of founder haplotypes were supplied to the software Genedrop (part of the MORGAN 66 package) along with the pedigree of Cilento in order to simulate phased genetic data for the 1,444 genotyped members of Cilento. Our gene-dropping approach was identical to the methods used in Herzig et al. 67  We have made comparisons on four potential populations: the 1,444 individuals from Genedrop with isolate type data, labelled "Isolated(1444)", and three possible sets of the 8,664 simulated unrelated individuals: "Outbred(1444)", "Outbred(4332)", "Outbred(8664)", that represent outbred populations of the same size as Cilento, three times the size, and six times the size, respectively. We chose this range of samples sizes based on an analysis of the variance of eigenvalues  68  of GRMs estimated on the populations Isolated(1444) and Outbred(1444) (Supplementary Materials and Supplementary Table  2 ).
The choice of 200 haplotypes for the generation of founder haplotypes for Cilento stems from the previous work which estimated that 96. 7% of the genetic diversity in Campora is accounted for by 17 female and 20 male lineages  63. This would suggest that 74 (37 × 2) autosomal haplotypes would be appropriate for the generation of simulated data for Campora and we decided to scale this up to 200 for the generation of simulated data for the three villages. We checked that this created simulated data with a similar structure as the observed data in Cilento (Supplementary Table  2  and Supplementary Fig. 13 ). Our method for simulating isolate-type data requires a pedigree for gene-dropping. To create larger datasets with isolate characteristics, we used the Cilento pedigree multiple times. In detail, we simulated six populations of size 1,444, each using the Cilento pedigree but with different random draws of founding haplotypes.
05 and those passing a quality control threshold on the Hardy-Weinberg p-values (>10 -5 ). In the case of Isolated(1444), we also estimated K and D from the pedigree structure of Cilento using software IdCoefs 4 that calculates Δ … Δ , , 1 9 through the recursive algorithm described by Karigl  71. Furthermore, we were able to record the origin of every mosaic segment simulated during the HapGen based and gene-dropping stages. This allowed us to calculate true proportions of IBD-sharing between every pair of individuals in the Isolated(1444) population. We also tested the software IBDLD  9  and GIBDLD  52  which directly estimate Δ … Δ , ,. For IBDLD, we used the LD-RR mode, default parameters, and we supplied the software with the expected values of Δ … Δ , , 1  9   between all pairs from the pedigree (calculated by IdCoefs).
Conversely, GIBDLD used only the genotypes; we also ran this software with default parameters. For both IBDLD and GIBDLD, we used only the SNPs present in both genotyping arrays in Cilento as the software were not designed for sequence data. Here we introduce the sibship matrix, denoted as S, which has values of 1 on the diagonal and at every off-diagonal element corresponding to pairs of siblings in the sample; all other entries are zero. To simulate phenotypes for the population Isolated(1444) with additional correlation between pairs of siblings, approximating an effect of shared environmental exposure, we simulated phenotypes under the same model as Equation 3 except that the environmental components were no longer drawn independently from normal distributions, but from a multi-variate normal distribution with zero mean and a covariance structure of Analysis of Cilento Data.
After quality control on both phenotypes and genetic data (details in the Supplementary Materials), we used the same approach as with the simulated data to estimate the heritabilities of the seven traits considered in this study. The only difference being that for the analyses of Cilento data, we added the following covariates to the LMM: age, sex, age × sex, and indicators of village membership (Campora, Cardile, or Gioi). For one trait (Triglycerides) we transformed the phenotype to a logarithmic scale, whereas other traits were left untransformed after excluding very small numbers of outliers. LDL and Total Chol were both pre-adjusted for use of lipid-lowering medication. Matrices K and D were again estimated on the basis of pedigree or genetic information. To calculate GRMs from genetic data, we were restricted to using the set of variants on the of the two arrays used for genotyping of Cilento data.
Acknowledgements We address special thanks to the people of  Campora, Cardile, and Gioi  for their participation in the study. We kindly thank the  European Genome-phenome Archive  at the  European Bioinformatics Institute  for making available to us the UK10K imputation panel ( EGAD00001000776 ) and  HRC imputation panel  ( EGAD00001002729 ). A. F. H. was funded by an international Ph. D. fellowship from  Sorbonne Paris Cité  (convention  HERZI15RDXMTSPC1LIETUE ) and by the  Fondation Recherche Médicale  (convention FRM  FDT201805005384 ).
From 2012 to 2016, the first requirement was successfully addressed within the Broader Approach (BA) activities under the framework of collaboration between Japan and Euratom (Ref. 2). The Computer Simulation Centre was set up within the BA and its computational resources (HELIOS supercomputer) were actively and efficiently used by the European Union (EU) fusion community. Following the shutdown of HELIOS at the end of 2016, EUROfusion took a strategic decision to implement a supercomputer for fusion applications in Europe. A high-level support team (HLST) was established within the European Fusion Development Agreement (EFDA) back in 2009 to address the second requirement, and it successfully continues its activities in providing support with code optimization and parallelization and sharing its expertise with a broad community of developers of firstprinciple codes for magnetic fusion applications.
II. EUROfusion COMPUTATIONAL CAPABILITIES (HPC MARCONI-FUSION) Following the increasing computational needs of firstprinciple simulations and IM, the growing number of HPC users, success in code optimization, and the ability to scale to a large number of cores, the EU extended its computational capabilities by acquiring a new supercomputer for fusion applications under EUROfusion. This supercomputer, called MARCONI-FUSION, is a dedicated part of a larger supercomputer hosted at the Inter-University Computing Consortium (CINECA) (Bologna) under a EUROfusion Project Implementing Agreement with the National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)/CINECA. It consists of two parts: a conventional processor part and a many-cores processor part. The first phase of the conventional part [based on Intel Xeon-Broadwell processors for a total peak performance of 1 petaflop (Pflop)] of this machine has been operational since mid-2016 and its replacement in a second phase [5 Pflop of Intel Xeon-Skylake processors] is now in progress.
To provide the facility for IM which can run massively parallel codes, a new Gateway is embedded into MARCONI-FUSION hosting the IM tools developed by EUROfusion. A cluster of 24 computer nodes has been configured in a flexible environment, allowing graphical interactive remote sessions and batch job submissions as well. Thanks to a common Omni-Path low latency network, a GPFS storage system, and a portable batch system (PBS) batch queue system, Gateway users can exploit the HPC computing resources of the whole MARCONI. The Gateway software resources are based on the system and application software of MARCONI on which the CPT and Gateway support team have developed an ad-hoc user environment based on a modules tool. The Gateway enables users to perform distributed workflows (WFs), i. e. , WFs executing partly on a distributed HPC architecture and partly on the Gateway (for example, the WF for transport-turbulence simulations where the first-principle turbulence simulations can be done on the HPC architecture).
The codes recently supported by HLST in various physics areas are briefly described in Table  I. These codes have been selected based on their potential contribution to the execution of the EUROfusion work program. As it is not possible to present the detailed improvements obtained for each code in a short overview, only the most significant HLST computational achievements helping to extend physics application domains of the first-principle codes are shown in Secs. III. A through III. D. problem, computationally challenging for modern five-dimensional gyrokinetic codes. The dimension of the problem can be reduced to four dimensions [threedimensional (3D) configuration space and velocity parallel to the magnetic field] in the low-electron β e limit β e , m e /m i , where m e and m i are the electron and ion mass, respectively, while retaining key physics such as phase mixing and electron Landau damping, ion finite Larmor radius effects, electron inertia, electron collisions, and ohmic resistivity.
This approach has been implemented in VIRIATO (Ref. 7), a novel fluidkinetic code which solves four-dimensional (4D) The original VIRIATO code is parallelized with MPI using domain decomposition over two directions in the configuration space, one in the plane perpendicular to the magnetic field where a pseudo-spectral approach is used, and the other along the magnetic field, where a highorder upwind scheme is employed. With this parallelization involving the standard MPI algorithms, VIRIATO performance is close to the ideal strong scaling at small to medium numbers of cores (up to 2048 cores in the analyzed case). However, it strongly deviates from this scaling when a large number of cores is used, due to the negative effect of the network latency on the data transposition involved in the standard bi-dimensional (2D) fast Fourier transform (FFT).
Within the activities of the HLST, the parallel scalability of VIRIATO has been improved by developing new efficient transpose algorithms for the 2D FFT (Ref. 8). The main idea of these algorithms is the data aggregation before carrying the allto-all communication patterns to avoid penalizations due to network latency accumulation. The execution time of the 2D FFT has been reduced by one order of magnitude with these algorithms as compared to the original algorithm. The deployment of the new algorithms in VIRIATO allows one to achieve speedup factors close to 3 with small numbers of cores and of about 1. 5 with large numbers of cores (Fig. 2 ). While the former eases future developments of the code by making smaller test cases more affordable, the latter is of particular importance for physics studies.
It is useful, for example, for the investigation of the dependencies and parameter space for new physics phenomena, such as electron heating caused by reconnection in strongly magnetized, weakly collisional plasmas, or the effect of Landau damping on the turbulence energy spectra. HLST support has been provided to a new stellarator neoclassical code SFINCS that can be used for neoclassical transport studies in nonaxisymmetric magnetic configuration. 9 SFINCS solves the 4D drift-kinetic equation for the distribution function, retaining coupling in two spatial independent variables and two velocity independent variables. It allows for 3D magnetic configurations, multiple plasma species, and employs the full linearized Fokker-Planck (FP) collision operator with no expansion made in mass ratio.
The code retains terms in the drift-kinetic equation which are often neglected in other numerical tools but can be important, e. g. , when studying plasma impurities. A multifrontal massively parallel sparse direct solver (MUMPS) has been identified as the most time-consuming element of the SFINCS code and the performance of the alternative solvers implemented in SFINCS for testing (MKL-CPARDISO and HYPRE) has been investigated using 22 test cases. 10 ll 22 tests were passed by MKL-CPARDISO with higher memory consumption (by 33% on average) than MUMPS, and 20 of these cases had better execution time with MUMPS (by 123% on average). None of the test cases was successfully terminated with HYPRE, possibly because HYPRE is more efficient for diagonal dominant problems while SFINCS is advection dominated, with small diagonal elements.
III. B. Edge, SOL and Divertor Physics Comprehensive and accurate modeling of scrape-off layer (SOL) and divertor plasma requires a self-consistent treatment of multiple charged species in real 2D or 3D geometry including their turbulence-driven as well as collisional transport, neutral and atomic physics, and plasma-wall interactions. This is an extremely challenging task for a single computational tool. Present codes are focused on advanced physics descriptions of some of these phenomena while using simplified approaches for others. Limiting this overview to the codes supported by HLST, the SOLPS package  11  is presently a main tool for understanding the plasma behavior in the SOL and divertor on existing European machines, including the effects of plasma transport and radiation, distribution of heat and particle loads on the divertor plates, poloidal asymmetries, and the role of drifts.
SOLPS is based on 2D fluid Braginskii equations solved with ad-hoc transport coefficients for multiple charged species (B2 code  12  ) combined with the Monte-Carlo (MC) technique for neutral simulations (EIRENE code  13  ) in realistic 2D geometry. SOLPS modeling can be complemented with more detailed studies of kinetic effects of parallel plasma transport as included in KIPP (Ref. 14) and first-principle turbulence-transport simulations based on drift-reduced Braginskii equations solved under different assumptions for neutrals and magnetic topology [BOUT++ (Ref. 15), GBS (Ref. 16), GRILLIX (Ref. 17), and TOKAM3X (Ref. 18)]. Finally, the BIT family of codes  19  based on the PIC-MC technique represents an advanced firstprinciple approach where atomic physics, electrostatic turbulence [BIT2 (Ref.
20) and BIT3] and transport are treated self-consistently. The extended physics applications of the SOLPS package, one of the edge turbulence codes (GBS), and the BIT2-BIT3 codes which became available to users following the HLST code development work are described below. Significant efforts were aimed at the OpenMP parallelization of B2. First, several B2 subroutines were optimized to reach a speedup close to the bandwidth limit leading to the reduction of the computation time of the sequential B2 version by 20% for the selected test case as compared to the original code. Second, more than 25 subroutines have been parallelized reaching 90% of parallelism in the whole B2 code. With these changes, a factor 6 speedup has been achieved for the ITER test case when executed on a single compute node (Fig.
3a ). (Ref. 21). The improvement of EIRENE was further required to get an advantage from the parallelized B2 code in the coupled B2-EIRENE system. A simple and balanced parallelization strategy (all MPI processes calculate all strata and distribute particles evenly between the processes) was implemented in the ITER version of EIRENE to avoid a load imbalance when a limited number of cores (up to 16 cores) is used leading to an important speedup of the simulations (Fig. 3b ). The coupled OpenMP B2 and the improved EIRENE codes will be of particular interest for time-consuming studies with SOLPS, such as parameter scans aimed to the optimization of the operational scenarios toward a detached divertor. Within HLST activities, a parallel multigrid (MG) solver has been developed for the GBS code using finite differences.