dataesr/scientific-paragraphs-categorization huggingface dataset:
With this solver, GBS is now capable to run both the simple electrostatic and more sophisticated electromagnetic simulations with respective gain factors of 5 and 10 compared to using the MUMPS direct solver. One important application for the improved GBS is the investigation of SOL turbulence self-consistently with the neutral dynamics implemented recently in this code. 22 resently, the BIT2 code simulates multiple plasma species and neutrals in the 2D space using ad-hoc anomalous cross-field diffusion coefficients. Although it is capable to simulate electrostatic turbulence selfconsistently with plasma profiles, these simulations would be extremely time consuming taking into account the typically small simulation time step (≈10 -12 s) needed to resolve the Debye shielding and Larmor rotation, while the characteristic SOL turbulence time is much larger (10 -4 to 10 -5 s) and its accounting requires long simulations.
III. D. Magnetohydrodynamics Large-scale plasma instabilities affected by currents in conducting vessel structures can be modeled by solving the MHD equations in realistic toroidal X-point geometry coupled to a model for vacuum region and resistive conducting structures. This can be done with the coupled JOREK-STARWALL codes,  31  which are currently being extended to include the halo currents in collaboration with the ITER Organization. Although JOREK (Ref. 32) is MPI + OpenMP parallelized and STARWALL (Ref. 33) is partially OpenMP parallel, the coupled codes did not allow resolving realistic wall structures with a large number of wall elements (triangles) due to the consumption of wall clock time and memory. With HLST support the STARWALL code has been efficiently MPI parallelized enabling faster production runs (Fig.
III. E. Development of Multicode Applications and General User Support Modern parallel codes that address the physics issues mentioned above are becoming more and more sophisticated. They are composed of a growing set of submodels, including complex numerical and parallelization schemes, and accept an increasing number of input parameters. Tests are to be devised to detect bugs, cross-check the parallel algorithms and numerical schemes against available verification procedures, and check the parallel performance of these codes. These tests should be automated and triggered either each time the code repository is modified or periodically. With this purpose in mind, the Non-Regression Testing Suite (NRTS), including a minimal set of unit and regression tests, was developed by the Institute for Magnetic Fusion Research/CEA in collaboration with Maison de la Simulation/CEA and the Institute for Development and Resources in Computer Science/ CEA computing facility for GYSELA (Ref.
35), taking this code as an example. With HLST support, NRTS was ported from Jenkins on the Poincare HPC to Buildbot on the HELIOS HPC to make it available to HELIOS's GYSELA users. 36 The deployment strategy (launching the automated tests when required and performance tests-small strong/weak scaling tests-to monitor/report the execution time) was defined and implemented. 36 uture developments will include an extension of NRTS to other codes. In addition to the code optimization, HLST provides support to European scientists running their codes on HELIOS at the International Fusion Energy Research Centre-Computational Simulation Centre (Japan), such as porting the codes and their adaptation to new architectures (e. g. , Many Integrated Core or Graphic Processing Unit), and the analysis of the performance of the most advanced hardware by means of different microbenchmark tests.
38), a multilanguage interface used for exchange of CPOs between IM codes (UAL) (Ref. 39), an automated physics actor generator for KEPLER software presently used as a graphical WF engine for the IM tool, and other infrastructure functionalities. Such an infrastructure is by essence completely generic and can treat any problem which can be described by the data model, in contrast to integrated transport codes  [40] [41] [42]  which are typically focused on solving a specific physics problem (mainly, solving time-dependent core transport equations coupled to a variety of sources and transport components). Recently, the performance of the complex CPS17. 177-5c Fig. 5. Scaling of the total wall clock time in the MPI parallel STARWALL code (solid curve with squares). The numbers next to the symbols indicate the number of processes and computation time.
All WF components are tagged in a specific way under software versioning and then automatically integrated into the KEPLER WF engine as a given self-consistent version of the released physical software. Following the progress in the development of the IM tools and the start of their installation in EUROfusion experiments, the CPT extended its activities beyond the technical support to the developers of the IM tool to include support to users applying the released IM WFs on physics issues. The EU IM infrastructure described above has been used as a prototype for the development of the ITER Integrated Modelling and Analysis Suite (IMAS) infrastructure. 44, 45 The key new feature in IMAS is a more flexible data model (the ITER physics data model) allowing the description of multiple time bases within a given interface data structure (IDS), thus enabling direct representation of experimental data.
Moreover, the GIT-based version control of the IMAS infrastructure and physical components allows an easier local deployment of the whole infrastructure on various computers. CPT activities have been recently geared toward an increased cooperation with the ITER Organization on IMAS including the installation of IMAS in EUROfusion experiments, its upgrade with the tools used for data analysis within EUROfusion, and development of the conversion tools from CPO to IDS in a short term, as well as direct transition to code integration under IMAS. In this frame, the Access Layer has been connected to the Identity and Access Management (IDAM) tool to enable on-the-fly access to remote databases from local experiments which are not natively using the ITER physics data model. The mapping between the native experiment data model and the ITER physics data model is described via an experiment-specific plug-in connected to the IDAM client-server architecture.
V. SUMMARY Within EUROfusion code development and IM efforts, recent achievements through its HLST and CPT in support to multiscale theory-based simulations in various physics areas created a basis for the extension of the application domain of first-principle codes and enabled time-consuming parameter scans as well as application of the optimized computational tools to large devices like ITER and DEMO. This was not possible previously due to unreasonably large computational time demands. The code optimization and speedup have led to a more efficient use of the HPC resources by increasing the number of running codes and physics problems addressed. The support to the IM infrastructure via its further optimization based on the most recent achievements of computer science is efficiently provided by CPT.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014 to 2018 under grant agreement  633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission or ITER. ORCID I. Voitsekhovitch http://orcid. org/ 0000-0003-4077-7474.
Detection of wells The program is prepared to auto-detect circular wells, regardless of their spatial arrangement. To detect the wells we use the circular Hough transform (we have modified the code of Tao Peng distributed by Matlab Central under BSD license). In order to estimate the diameter of the wells, it computes the image's Hough transform for 100 radii different in 5 pixels and a rough estimate of the largest possible radius (length of the longer side of the image divided by the square root of the number of wells) (Additional file 1: Figure  S1D ). The system selects the highest point of this measure as an estimate of the radius of the wells (r est ). It is possible to skip this first step and instead manually specify a value for r est. This may be advisable when many videos are recorded with the same set-up and the same wells.
Afterwards, peaks from the different samples were merged and quantified separately as fragments per kilobase per million reads (FPKM) using the DiffBind package  [45] , obtaining 27,310 peaks in control samples and 33,649 peaks in NaBu samples, with 12,419 peaks shared by both conditions. Finally, EdgeR  [46]  was applied to detect differential binding between control and NaBu samples. The candidate peaks with higher histone H4 acetylation in NaBu compared to control (P < 0. 001) were further filtered using the random samples. Specifically, we removed the regions with higher variability (measured with the coefficient of variation) in random samples, as this variability is not associated with behavior.
Statistical analysis In the case of linear correlation between behavioral parameters or across days, the statistical tests assessed the null hypothesis that the correlation coefficient is equal to 0, using two-sided t-tests. In the case of correlation between bulk histone acetylation and behavioral parameters (distance to the average or angle to the average), a similar approach was performed. When the differences between the inter-individual variability and the intra-individual variability of a behavioral parameter were assessed, the set of 30-s fragments of the trajectories of all the individuals were shuffled. Then, this random inter-individual and intra-individual variability was calculated (using the CV) and we compared these permutated values to the observed in the real dataset. Finally, by doing this process 1000 times, we were able to obtain a P value (the proportion of times in which the final values were higher in the case of permutation datasets) for testing the hypothesis that the inter-individual variability is equal to the intra-individual variability.
In the case of the analysis of changes in behavioral interindividual variability between two groups, we compared the difference between the generalized variance of these groups and the difference of two groups obtained after permuting the datasets. Specifically, we shuffled the individuals between the two groups, generating random groups of individuals. Then, we obtained the difference between the generalized variance in these two random groups and compared it to the real value. By doing this 1000 times, we obtained a P value for testing the hypothesis that the generalized variance of the two groups is the same. To test acetylated histone or methylated DNA differences between two groups, the levels of these groups were compared using two-sided t-tests. In the case of ChIP, reChIP, and gene expression analyses, the statistical tests to compare the difference between two conditions were conducted by calculating the representative parameter (fold change compared to a control, gene expression ratio between two groups), and P values were obtained using two-sided t-tests.
For motif finding, the MEME algorithm  [29]  was used to estimate an E-value of each motif, a parameter that quantifies the log-likelihood ratio of the motif depending on its size and the letter composition of the background. In the case of ChIP-seq analysis, a P value was extracted using the EdgeR algorithm, which calculates an exact test using the quantile-adjusted conditional maximum likelihood following a negative binomial model on the normalized counts of the samples. All the experiments (except ChIP-seq and RNA-seq) were done at least three times with different biological datasets, and P values were calculated using the three replicates. Figures related to behavior show a representative experiment of the triplicate, while molecular analyses show the average result with error bars representing the standard deviation.
Acknowledgements We thank  Rui Costa ,  Carlos Ribeiro ,  Antonia Groneberg , and other members of the  Champalimaud Neuroscience Programme  for discussions, and  Isidro Dompablo (CSIC)  and  Ana Catarina Certal (Champalimaud Foundation ) for technical assistance and animal care. CG2, hdac1 sa436, and WIK lines were kind gifts from  Sergei Revskoy ,  Elke Ober , and  Paola Bovolenta , respectively.
Availability of data and materials ChIP-seq and RNA-seq data are stored in the ArrayExpress repository (E-MTAB-5993 and E-MTAB-5992, respectively)  [49, 50]. RNA-seq data have also been included as part of the EBI expression atlas (http://www. ebi. ac. uk/gxa). The video trajectories of the fish experiments presented in the manuscript are available on Figshare  [51]. The ad hoc MATLAB functions for obtaining the behavioral parameters and to obtain and visualize generalized variance are available on GitHub  [52]  and the specific versions used for this manuscript are under Zenodo  [53] , distributed by MIT license. The tracking software is also freely available  [54].
Analytical framework Figure  1  summarizes the main analyses we carried to evaluate functional and phylogenetic constraints upon the emergence of MH plants. We first assembled a bipartite network representing which plants interact with which fungi. Next, we analyzed functional constraints using tools from ecological networks (Fig. 1b ). Functional constraints on MH cheaters (or MH-associated fungi) should be reflected by the specialization of MH cheaters (or MH-associated fungi) towards few fungal (or plant) partners. Strong constraints on both sides should drive reciprocal specialization and isolation of MH cheaters and MH-associated fungi into modules, thus decreasing nestedness. Finally, we analyzed phylogenetic constraints by analyzing the phylogenetic distributions of MH cheaters and MH-associated fungi (Fig.
MaarjAM database and interaction matrix MaarjAM database is a web-based database (http://maarjam. botany. ut. ee; accessed in June 2019 after a very recent update) of publicly available sequences of Glomeromycotina fungi, with information on the host plants, geographical location and biomes for the recorded interactions  (Öpik et al. , 2010 (Öpik et al. , , Davison et al. , 2015)). We used an approach with a compiled network, where all locally described physical mycelial interactions between species are merged and studied at larger scales (as in  Joffard et al. , 2018; Braga et al. , 2018). Although such a compiled network can be sensitive to several biases (see Discussion), it is a unique opportunity to study MH cheating emergence in a large evolutionary and ecological picture (e. g. Werner et al.
The filtered dataset yielded a binary interaction matrix of 490 plant species (hereafter 'plants'), 351 VT (hereafter 'fungi'), and 26,350 interactions, resulting from the compilations of 112 publications from worldwide ecosystems (Supporting Information Fig. S6 ; Supporting Information Table  S1 ). In order to estimate the sampling fraction of Glomeromycotina fungi in our dataset, we performed rarefaction curves of the number of fungal VTs as a function of the sampling fraction (on the observed number of interactions or on the number of sampled plant species) and we estimated the total number of units using the 'specpool' function ('vegan' R-package, based on Chao index;  Oksanen et al. , 2019).
Phylogenetic reconstructions We aligned consensus sequences of the 351 fungi with the software MUSCLE  (Edgar, 2004) , and ran a Bayesian analysis using BEAST2 to reconstruct the fungal phylogeny  (Bouckaert et al. 2014 , see Supplementary methods). We obtained the phylogenetic relationships between the 490 host plants by pruning the time-calibrated supertree from  Zanne et al. (2013)  using Phylomatic (http://phylodiversity. net/phylomatic/). We also used the Open Tree of Life website (http://opentreeoflife. org) and the 'rotl' package  (Michonneau et al. 2016 ) of R (R Core Team, 2019) for grafting 41 plant taxa missing in the pruned supertree (as polytomies at the lowest taxonomy level possible, see Supplementary methods). We set tree root calibrations at 505 million years (Myr) for the fungi  (Davison et al.
Nature of the interaction We assigned to each plant its 'nature of the interaction' with fungi according to its carbon nutrition mode: autotroph (AT, n=434, 88. 6%), entirely mycoheterotroph (EMH, n=41, 8. 4%), or initially mycoheterotroph (IMH, n=15, 3. 1%). According to an on-line database (http://mhp. myspecies. info/category/myco-heterotrophic-plants/) and individual publications  (Boullard, 1979; Winther & Friedman, 2008; Field et al. 2015)   (Jacquemyn & Merckx, 2019).
Network nestedness and specialization of cheaters In order to assess functional constraints, we tested the effect of MH cheating on network structure. First, we measured nestedness in: (i) the overall network (490 plants, 351 fungi, and 26,350 interactions), (ii) the network restricted to AT plants only  (434, 344, and 26,087)  and (iii) the network formed by EMH and IMH plants only  (56, 71, and 263) , using the function 'NODF2' in the R package 'bipartite'  (Dormann et al. 2008). Positive NODF values (nestedness metric based on overlap and decreasing fill; see List of abbreviations in Supplementary material) indicate nested networks. We tested the significance of nestedness values by using two types of null models (N= 100 for each type): both maintain the connectance (proportion of observed interactions) of the network, but the first model ('r2dtable' from 'stats' R package -null model 3) also maintains the marginal sums (the sums of each row and each column) while the less stringent second model ('vaznull' from 'bipartite' R package -null model 2) produces slightly different marginal sums (interactions are randomized with species marginal sums as weights, and each species must have at least one interaction).
We calculated the Z-score, which is the standard deviation between the observed value and the mean of the distribution of null-model networks (Z-scores greater than 1. 96 validates a significant nestedness with an alpha risk of 5%). Second, to further evaluate the specialization of MH cheaters, we computed several network indices for each plant. The degree (k) is the number of partners with which a given plant or fungus interacts in the bipartite network. The degree is high (resp. low) when the species is generalist (resp. specialist). The partner specialization (Psp) is the mean degree (k) averaged for all the fungal partners for a given plant species  (Taudiere et al. , 2015)  Third, we used projected networks to detect cases of extreme reciprocal specializations leading to independent modules.
To  This analysis was not performed on MH Petrosaviaceae, represented by only one species and too divergent to define a reliable AT sister clade. We tested differences between groups of distances using Mann-Whitney U tests. We also performed a principal coordinates analysis (PCoA) from all the UniFrac dissimilarities of sets of fungal partners, and tested the effect of the nature of the interaction on the two principal coordinates, using Kruskal-Wallis tests. Finally, to examine the extent to which the nature of the interaction predicts fungal partners, we used permutational analysis of variance (PerMANOVA, 'adonis' function in R package 'vegan'), with 10,000 permutations.
Results We analyzed the global arbuscular mycorrhizal network filtered from the MaarjAM database (490 plants, 351 fungi, and 26,350 interactions) combined with the reconstructed phylogenetic trees of both partners (Fig. 2 ). We estimated a total number of 373 +/-9 fungi (Chao index), which indicates that the 351 fungi in the dataset included most of the arbuscular mycorrhizal fungal diversity (94% +/-2%; Supporting Information Fig. S1 ).
acquisition of MH-susceptible fungi depends on the MH family. In MH families such as Burmanniaceae, fungal partners are closely-related to the fungal partners of AT relatives, suggesting that the MH-associated fungi are derived from the fungal partners of cooperative AT ancestors. In other MH families, such as Gentianaceae or Corsiaceae, fungal partners are more closely-related to fungal partners of other MH families than to AT relatives, suggesting that the MH-associated fungi were secondarily acquired rather than derived from the partners of AT ancestors. A few MH lineages lack closest AT relatives in our analysis (e. g. MH Gentianaceae should be compared to AT Gentianaceae, which are not represented in the MaarjAM database), which may bias our analyses towards supporting secondary transfer from other MH plants rather than acquisition from AT ancestors.
Acknowledgments The authors thank members of the INEVEF team at ISYEB and BIODIV team at IBENS for helpful comments on the article. They also thank  C. Fontaine  for helpful discussions on network analyses. This work was supported by a doctoral fellowship from the  École Normale Supérieure de Paris  attributed to BPL and the  École Doctorale FIRE -Programme Bettencourt. MÖ was supported by the  European Regional Development Fund  ( Centre of Excellence EcolChange ) and the  Estonian Research Council  ( IUT20-28 ). HM acknowledges support from the  European Research council  (grant CoG-PANDA).
We propose to do so by defining a set of common basic components that can be assembled together to compose different morphologies. In section 2, we present an overview of the existing social robots design landscape. In section 3, we define categories of basic components shared by those robots. In section 4, we present the 3D animation software Blender and an overview of its features. In section 5, we describe how our system map Blender features to animate different components categories. Finally, in section 6, we present 2 case studies of robots animated thanks to our system.
4 ; Supplementary Table  3  and Supplementary Data 3). We assigned X-Y genes to evolutionary strata based on nucleotide divergence of synonymous sites (K s ) between equine gametologs and their location in the X chromosome, following Skaletsky et al. 2003  5  (Fig. 4 , Supplementary Tables  3  and 5 ). For each gene, we also estimated the divergence time of homologs to approximate their acquisition in the Y chromosome. The oldest horse stratum corresponded to the therian Stratum 1  12, 13  and contained four genes (SRY, RBMY, HSFY, CUL4BY) with a mean divergence date of 170. 7 MYA and mean K s values of 1. 18 (Fig. 4 ; Supplementary Table  5  and Supplementary Data 13). The second horse stratum was the same as Stratum 2/3 13 of other eutherians and contained 19 genes (Fig. 4b ) with mean K s of 0.
Even though expansion of lineage-specific testis transcripts is a characteristic feature of all studied mammalian Y chromosomes  5, 10, 13, 14, 26  , ETSTY7 distribution in equids is unique with no analogy in other eutherians. Perhaps the most intriguing discovery was ETSTY7 sequence similarity with the equine intestinal parasite Parascaris equorum (PEQ; genome assembly GenBank LM462759), better referred to Parascaris spp, because of difficulties delineating parasite species  30. The region of homology included part of ETSTY7 intron 2/3 (containing a LTR element) and exon 3, with 84-96% sequence similarity to multiple contigs in Parascaris genome assembly. To rule out the potential that these shared sequences were the result of the host horse sequences contaminating the parasite assembly, we extracted DNA from multiple individual adult worms, dissected adult tissues, L4 larvae and eggs (Supplementary Table  6 ) and designed primers from horsespecific, Parascaris-specific, and horse-parasite shared sequences (Supplementary Data 11).