dataesr/scientific-paragraphs-categorization huggingface dataset:
Writing the application on BME will require to rewrite a part of PACHA only dependent on the TILEPro64. The platform-agnostic programming interface has no overhead because the layer is replaced by calls to the platform environment at the pre-compile time. However, an overhead can be brought by the parallel programming models. For this case study, we selected a pedestrian detection application, it is presented in the first part. With the help of this application, we evaluate the BME and Linux back-ends and then we compare the performance of the BME back-end to the classical parallel model methods on Linux, OpenMP and Pthread.
Evaluation of the BME back-end and Linux back-end on a TILEPro64. For these experimentations, the application is implemented with only one version of code. It is the backends that vary. The pre-processing part is implemented with the Pthread-like model and the classification part is implemented with the ARTM  [23]  model. Using the BME back-end, the maximum speed-up of the pre-processing is reached (Fig. 2. a), but the speedup is not maximal for the classification (Fig. 2. b). A lightweight bare metal library has been created to profile the BME version, drawing inspiration from the OProfile  [25]  implementation for TILEPro64. An existing bare metal version of Oprofile developed by A. Schmidt exists  [19]  but it does not support the TILEPro64. Our library consists in using the hardware counters and it counts the apparition of an event (cache miss, stalls, packet send).
So, the algorithm has to be rethought to run efficiently on the TILEPro64. With Linux Back-end on TILEPro64 or on a 8x8-core AMD Opteron (the speed up is 7x for 9 cores, 14x 20 cores, 19x 25 cores, 23x 56 cores), the speed up is more important because there is a huge overhead with the sequential version. For these versions, the contention appears starting from 25 cores. Despite these contentions, the gain using the bare metal programming environment is important. With the Tilera BME back-end, the application is 2x to 4x faster than Linux (Fig. 2 ). The implementation of the back-end with Linux may not be optimal, it is implemented especially to be close to the execution model of bare metal environment. So, we compare the implementation of the application with the BME back-end and the parallel programming models based on the common way of programming Linux: using OpenMP and Pthread libraries.
Comparison between the API in bare metal with common parallelism models Three versions of the application were implemented over Linux, one OpenMP, one Pthread and one optimized Pthread. This last one employs the Tilera optimisation library to set the affinity of the thread on a core. These versions are independent of our solution, however the parallelism is operated the same way. Fig. 4  shows that the OpenMP and non-optimized Pthread versions are the less efficient. With more than 8 threads, there is no more gain and even worse, the execution time by macro-bloc gets bigger. The bare metal version is in a mojority of cases the best solution. Concerning the pre-processing, it is 4x faster. Concerning the Classification, the performance gain is about 1,1x to 2x. However, at 35 cores, the optimized Pthread version is as efficient as the close-to-hardware version.
Conclusion Bare metal environments allow efficient access to dedicated hardware. To help developing an application on a bare metal environment, this paper presents PACHA. It contains a thin platform agnostic close-to-hardware programming interface and a multi-platform development environment. Benefits of PACHA for bare metal environment include unified access, debugging and profiling tools, a functional high speed simulator and unified parallel execution models. The development and the reuse of an application on bare metal environment and for multiple platforms become available at a low-cost. A case study on a TILEPro64 is presented with an application which detects pedestrians on a flux. Using PACHA with the bare metal environment rather than Linux with OpenMP or Pthread, the performance gain is about 1,8x to 4x.
We review the methods used to study the orbital structure and chaotic properties of various galactic models and to construct self-consistent equilibrium solutions by the Schwarzschild's orbit superposition technique. These methods are implemented in a new publicly available software tool, SMILE, which is intended to be a convenient and interactive instrument for studying a variety of 2D and 3D models, including arbitrary potentials represented by a basis-set expansion, a spherical-harmonic expansion with coefficients being smooth functions of radius (splines), or a set of fixed point masses. We also propose two new variants of Schwarzschild modelling, in which the density of each orbit is represented by the coefficients of the basis-set or spline spherical-harmonic expansion, and the orbit weights are assigned in such a way as to reproduce the coefficients of the underlying density model.
2010)  or the structure of merger remnants  (Hoffman et al. 2010). We have developed a new publicly available software tool, SMILE 2 , for orbit analysis and Schwarzschild modelling of triaxial stellar systems, intended to address a wide variety of "generic" questions from a theorist's perspective. This paper presents an overview of various methods and algorithms (including some newly developed) used in the representation of potential, orbit classification, detection of chaos, and Schwarzschild modelling, that are implemented in SMILE. In the section 2 we introduce the basic definitions for the systems being modelled, and describe the main constituents and applications of the software. Section 3 presents the potentials that can be used in the modelling, including several flexible representations of an arbitrary density profile, section 4 is devoted to methods for analysis of orbit class and its chaotic properties, and section 5 describes the Schwarzschild orbit superposition method itself.
THE SCOPE OF THE SOFTWARE SMILE is designed in a modular way, allowing for different parts of the code to be used independently in other software (for instance, the potential represented as a basis-set expansion could be used as an additional smooth component in an external N -body simulation program (e. g. Lowing et al. 2011) , or the orbit analysis could be applied to a trajectory extracted from an N -body simulation). There are several principal constituents, which will be described in more detail in the following sections. The first is the potential-density pair, which is used to numerically integrate the orbits and represent the mass distribution in Schwarzschild model. There are several standard analytical mass models and three more general representations of an arbitrary density profile, described in section 3.
2), kinematic information (e. g. the velocity anisotropy as a function of radius), or the observational constraints in the form of surface density and velocity measurements with associated uncertainties. The latter case is not implemented in the present version of the software, but may be described within the same framework. The module for Schwarzschild modelling takes together the source and target components and finds the solution for orbit weights by solving an optimization problem (section 5. 3). The tasks that can be performed using SMILE include: • visualization and analysis of the properties of individual orbits; • exploration of the orbital structure of a given potential (one of well-studied analytical models, or an N -body snapshot with no a priori known structure); • construction of self-consistent Schwarzschild models for the given density profile, with adjustable properties (e.
In our implementation, we have a linear or quadratic optimization problem, defined by a set of linear equations (  12 ) and (  14 ) or (  15 ), optionally with additional quadratic penalty terms (18) and/or penalties for given orbit families (e. g. tubes, chaotic orbits, etc. ). This optimization problem is solved by one of the available solvers, using an unified interface. Presently, three options are implemented: the BPMPD solver  (Mészáros 1999) , the CVXOPT library 9  (Andersen et al. 2012) , or the GLPK package 10 (only for linear problems). The typical time required to handle a model with No = O(10 4 ), Nc = O(10 3 ) is within few minutes on a typical workstation, much less than the time needed to build the orbit library. A Schwarzschild model may be converted to an N -body model by sampling each orbit in the solution by a number of points proportional to its weight in the model.
All three models were solved with quadratic optimization routine and were required to keep velocity isotropy at all radii. The fraction of chaotic orbits was around 30%. Then a N = 10 5 N -body model was created from each Schwarzschild model and evolved for T = 100 time units with the direct, hardware-accelerated N -body integration code φGRAPEch  (Harfst et al. 2008) , which uses algorithmic regularization to improve the accuracy of integration of trajectories that come close to the black hole. The softening length was set to zero and the accuracy parameter of the Hermite integrator was set to η = 0. 01; total energy was conserved to a relative accuracy better than ∼ 10 -4. To check how close the models are to equilibrium, we used a number of indicators. First the density profiles of the N -body models were compared to that of the analytical mass profile used to create Schwarzschild model.
CONCLUSIONS We reviewed the methods for studying non-spherical galactic models, analyzing properties and regularity of orbits and the overall structure of phase space in a given potential, and constructing self-consistent equilibrium models by the Schwarzschild method. We developed a new software named SMILE, which is intended to be a convenient tool for performing orbit analysis and Schwarzschild modelling for a number of standard potential models, as well as for an arbitrary density profile (including a discrete N -body model) approximated by a flexible potential expansion. The new and improved methods implemented in SMILE include: • Two general-purpose potential expansions for arbitrary density models, in which the angular dependence of density and potential is decomposed into spherical harmonics and the radial dependence of the expansion coefficients is represented either as a sum of basis functions (using  Zhao (1996a)  basis set, which is a generalization of the commonly employed  Hernquist & Ostriker (1992)  set), or as a spline.
Acknowledgements: I am grateful to  Cs. Mészáros  for providing me with his linear/quadratic programming solver BPMPD, to  David Merritt  for drawing my attention to non-parametric density estimators which resulted in implementation the Spline potential expansion as an alternative to basis-set, to  Lia Athanassoula  for fruitful discussions on the chaos-related topics, and to the referee,  Daniel Pfenniger , for important comments concerning the new variants of Schwarzschild models based on expansion methods. This work was partially supported by  Russian Ministry of science and education  (grants No. 2009-1. 1-126-056and P1336 ), by  NASA  under award No. NNX10AF84G , by  NSF  under award No. AST1211602 , and by  CNRS  which made possible the visit to  Laboratoire d'Astrophysique de Marseille.
VFTs were first identified as bacterial periplasmic-binding proteins involved in the transport of small molecules, such as amino acids, sugars or ions and they constitute the binding pocket of different receptor types such as class C G-protein coupled receptors  [13]. The VFT domain of VKR is related to that of the ANF receptor in protein family databases (pfam 01094)  [12]. VKR kinases can be activated following the binding of amino-acids to the extracellular VFT domain  [12, 14] , and this opens interesting perspectives on a novel mechanism for RTK activation as well as on possible specific and new functions of these receptors in cellular signalling. In this paper, we present an up-dated version of the VKR family, resulting from an exhaustive research of VKR orthologs in the published genomes of a variety of organisms belonging to major phyla in Metazoa.
A large distribution of vkr genes in eumetazoan genomes Previous studies have shown that VKRs constitute a distinct RTK family. These receptors were described in 15 different protostomes, including insects and the platyhelminth S. mansoni. A vkr gene was also found in the deuterostome Strongylocentrotus purpuratus  [12]. The aim of this study was to extend the VKR family to a large number of phyla in order to evaluate the place of these receptors in the animal kingdom and to contribute to a better understanding of their evolution. Using BLAST approaches, we searched for vkr genes in multiple genomic databases (see Methods). Results showed that vkr genes were present in at least 36 species from six distinct phyla (Cnidaria, Echinodermata, Platyhelminthes, Mollusca, Annelida and Arthropoda, see Table  1 ).
The protein architecture of each putatively encoded VKR was analysed using the SMART online software http://smart. embl-heidelberg. de/ in order to verify that newly discovered genes effectively encoded struc-turally conserved proteins, composed of an extracellular VFT module and an intracellular TK domain linked together by a single transmembrane α-helix. In Platyhelminthes, two putative vkr genes were detected in both Clonorchis sinensis and S. mansoni  [14]  parasitic trematodes. By contrast, only one vkr was found in the genome of the three parasitic cestodes, Echinococcus multilocularis, Echinococcus granulosus and Hymenolepis microstoma. No unequivocal VKR sequence could be found in the genome of the planarian Schmidtea mediterranea, but a sequence encoding a polypeptide composed of a TK domain and a truncated VFT module (GenBank: AAWT01078636.
1) was identified, suggesting that a vkr gene might still exist in Turbellaria. However, deeper investigations are needed to extend reliably the existence of VKR to planarian species and to the whole of the Platyhelminth phylum. Several new vkr genes were also found in diverse insect genomes. We identified 15 new vkr genes in 13 species belonging to the Hymenoptera and Lepidoptera orders. In Hymenoptera, on top of the two genes previously characterized in the honeybee Apis mellifera and in the parasitic wasp Nasonia vitripennis  [12] , we were able to identify 11 new vkr genes in the genomes of eight Formicidae and three Apidae species. In Lepidoptera, we could show that both the silkworm Bombyx mori and the monarch butterfly Danaus plexippus possess two vkr genes located in tandem on a single scaffold (GenBank accession numbers DF090406.
Thus, in these studies, we have confirmed the distribution of VKR in arthropods (in insects particularly), platyhelminths and echinoderms and found new VKR orthologs in three additional phyla, Annelida (Capitella teleta), Mollusca (Lottia gigantea) and Cnidaria (N. vectensis) (Figure  2 ). Additionally, putative VKR sequences were found in EST databases of different orders of Arthropoda (Cochliomyia hominivorax (gb| FG295125. 1), Rhipicephalus microplus (gb|FG302900. 1), Coptotermes formosanus (GI:345171826) and Crassostrea gigas (GI:313329111)), in the mollusc Biomphalaria glabrata (Contig1132. 1, Biomphalaria glabrata Genome Initiative, biology. unm. edu/biomphalaria-genome/index. html), in the annelid Helobdella robusta (gb|EY370614. 1) and in the echinoderm Paracentrotus lividus ( emb|AM524433.
Additionally, we can see in the VFT alignment several conserved residues and motifs (highlighted in red, Figure  5 ), such as C 35 and W 128 which are present in all domains and the motif (G 247 Y(V/I)WFLPxWL 256 ) which is present in most VKRs. Finally to investigate the potential importance of these conserved residue positions for the VFT properties, we performed a comparative modeling of the VFT domain of VKR using the ModWeb server of the ModBase databases. A VFT model of AmVKR was generated with the human Glutamate Receptor 5 (mGluR5) as homologous template (PDB: 3lmkA) (Figure  6 ). Results confirmed the position inside of the ligand pocket formed between the two lobes of the VFT, of the residues potentially involved in amino-acid recognition. The model also indicates that many of the other conserved residues are constituents of alpha-helices or beta-sheets, and thus take part very likely in the tertiary structure of the domain.
Vkr genes were found preferentially in the genomes of protostomes and particularly in insect genomes, but the large number of insect VKR sequences could likely result from the relative abundance of insect sequences in genomic databases. However, it was very interesting to observe that the finding of one vkr in all species of a given genus is not a general rule. For example, in the Drosophila genus, D. melanogaster and some others exceptionally do not possess a vkr gene  [12]. Also, it is surprising that no vkr exists in other ecdysozoa like nematodes, and in C. elegans particularly. In this work, we have analysed the exon/intron structure of all vkr genes and shown that their organization is widely heterogeneous across the different phyla. However, all vkr genes share intron positions common to the ancestral gene Nvevkr, and this suggests that they might have been derived from this common ancestor, then subjected to more or less marked reorganization along evolution.
Genome database searches Putative vkr sequences were searched using tBLASTn on genomic sequences available in the following databases: GenBank (http://www. ncbi. nlm. nih. gov/blast/Blast. cgi), FlyBase (http://flybase. org/), VectorBase (http://www. vectorbase. org), Wellcome Trust Sanger institute databases (http://www. sanger. ac. uk/resources/databases/) and the JGI genome portal (http://genome. jgi-psf. org/). Additionally, we also searched for vkr sequences in Hydra magnipapillata (hydrazome), Schmidtea mediterranea (SmedDB) and Schistosoma japonicum(schistoDB) genome databases. Selected genomic sequences were analysed by GenScan (http://genes. mit. edu/GENSCAN. html) and Augustus (http://bioinf. uni-greifswald. de/augustus/) gene prediction servers, and putative VKR proteins were then determined.
1), D. plexippus (DplIR, EHJ65074. 1), D. virilis (DvEgfr, ABD64816. 1),E. multilocularis (EmEGFR, CAD56486. 1), H. saltator (HsIR, EFN83767. 1; HsEGFR, EFN75184. 1) Hydra vulgaris (HvIR, Q25197. 1), Ixodes scapularis (IsIR, XP_002416224. 1), Lymnaea stagnalis (LsEGFR, ABQ10634. 1), Metaseiulus occidentalis (MoIR, XP_003739590. 1), N. vitripennis (NvROR, XP_001601308. 2; NvEGFR, XP_001602830. 2), P. humanus corporis (PhcIR, XP_002430961. 1), S. mansoni (SmIR1, GenBank: AAN39120. 1; SmIR2, GenBank: AAV65745. 2 and SER, GenBank: AAA29866. 1), S. purpuratus (SpIR, XP_784376. 3; SpErbB4, XP_791361. 3 and ROR_Sp, XP_003729469. 1) and T. castaneum (TcIR, EFA11583. 1). For VKR abbreviations and accession numbers, see Table1.
We present the stellar atmospheric parameters (effective temperature, surface gravity, overall metallicity), radial velocities, individual abundances and distances determined for 425 561 stars, which constitute the fourth public data release of the RAdial Velocity Experiment (RAVE). The stellar atmospheric parameters are computed using a new pipeline, based on the algorithms of MATISSE and DEGAS. The spectral degeneracies and the 2MASS photometric information are now better taken into consideration, improving the parameter determination compared to the previous RAVE data releases. The individual abundances for six elements (magnesium, aluminium, silicon, titanium, iron and nickel) are also given, based on a special-purpose pipeline which is also improved compared to that available for the RAVE DR3 and Chemical DR1 data releases.
Until the release of the first substantial catalogue of Gaia (estimated to be available in early 2017), the already current main large spectroscopic surveys of the Milky Way are the Sloan Extension for Galactic Understanding and Exploration (SEGUE), the RAdial Velocity Experiment (RAVE), the APO Galactic Evolution Experiment (APOGEE), the LAMOST Experiment for Galactic Experiment and understanding (LEGUE), the GALactic Archaeology with HERMES (GALAH) and the Gaia-ESO Survey (GES). RAVE 2  began observations in 2003, and since then has released three data releases (noted DR hereafter):  DR1 in 2006 , DR2 in 2008 and DR3 in 2011   (Steinmetz et al. 2006; Zwitter et al. 2008; Siebert et al. 2011b). Furthermore, three catalogues with spectrophotometrically derived distances were published  (Breddels et al.
4 We note that the effective resolution of RAVE can in reality vary from 6500 < R < 8500, the changes being a function of both time and position on the CCD. Nevertheless, simple tests, degrading the S 4 N library (see Sect. 4. 5) to R ∼ 6500, 7500 and 8500 and then analysis as if the spectra were at R ∼ 7500, show that the effect on the parameter estimation was of the second order. We hence did not take into account these resolution changes for DR4. However, future data releases will implement these second order effects. We recall that the synthetic library has been computed using the 1D, LTE and hydrostatic equilibrium MARCS model atmospheres  (Gustafsson et al. 2008) , in combination with the Turbospectrum code  (Alvarez & Plez 1998). The atomic line list has been calibrated on high SNR and high resolution spectra of the Sun and Arcturus  (Brault & Neckel 1987; Hinkle et al.
25 dex for all the metallicities ranging from [-5. 0; +1. 0] dex. These new spectra, whose atmospheric models did not exist in the MARCS database, have been linearly interpolated from the existing synthetic spectra. One of the noticeable differences between previous data releases and DR4 is that, in this work, only three parameters are independent in the grid: the effective temperature: T eff , the logarithm of the surface gravity: log g, and the overall metallicity: [M/H]. This restriction decreases the number of the secondary minima of the distance function, hence increasing the accuracy of the parameter derivation. It should be noted though that the microturbulent velocity (ξ) is not constant within the grid, but is changed in lock-step with the gravity of the stars. Dwarfs (log g≥ 3. 5 dex) have a microturbulent velocity of ξ = 1 km s -1 , whereas giants have ξ = 2 km s -1.
75 ⇒ T eff < 4500 K • 0. 4 < (J -K s ) < 0. 75 ⇒ 3750 <T eff < 6000 K • (J -K s ) < 0. 4 ⇒ T eff > 5250 K. The few stars for which no 2MASS photometry was available (less than 2%) form a fourth category for which there is no prior on the solution space. The above mentioned effective temperature ranges have been determined by requesting a colour-magnitude diagram of the Galaxy from the web interface 6  of the Padova database with the 2MASS photometric system. Then, for the above three colour ranges we inferred the full range of effective temperatures of the simulated stars, and increased these limits by ±500 K. We note that the effective temperature bins have been made deliberately large, because neither the photometric errors nor the extinction have been taken into account when separating the spectra into colour bins.
The calibration data sets of observed spectra First, the RAVE database has been explored to find spectra of stars which had atmospheric parameter determinations available from high-resolution spectroscopy. For that purpose, we made extensive use of the heterogeneous PASTEL catalogue 7 to identify such targets, retrieving roughly 400 star candidates. Following Soubiran et al. (  2010 ), we considered only the reference values coming from  Fuhrmann (1998a Fuhrmann ( ,b, 2004 Fuhrmann ( , 2008)) ;  Gratton et al. (1996 Gratton et al. ( , 2003)) ;  Hekker & Meléndez (2007) ;  Luck & Heiter (2006 , 2007) ;  McWilliam (1990) ;  Mishenina & Kovtyukh (2001) ;  Mishenina et al. (2004 Mishenina et al. ( , 2006 Mishenina et al. ( , 2008)) ;  Ramírez et al. (2007) ;  Valenti & Fischer (2005). These studies, when considered by author, all include a large number of stars (at minimum 222 stars), and are all analysed in a homogeneous way.