dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, the main challenges for programming data-intensive applications on exascale computing systems come from the potential scalability and resilience of mechanisms and operations made available to developers for accessing and managing data. Indeed, processing very large data volumes requires operations and new algorithms able to scale in loading, storing, and processing massive amounts of data that generally must be partitioned in very small data grains on which analysis is done by thousands to millions of simple parallel operations. Evolutionary models have been recently proposed that extend or adapt traditional parallel programming models like MPI, OpenMP, MapReduce (e. g. , Pig Latin) to limit the communication overhead (in the case of message-passing models) or to limit the synchronization control (in the case of shared-models languages)  [2].
• Selective Reliability Programming (SRP): SRP provides the programmer with the ability to selectively declare the reliability of specific data and compute regions. The User Level Failure Mitigation (ULFM) interface has been proposed to provide faulttolerant semantics in MPI. In  [43] , the authors present their experiences on using ULFM in a case study to exploit the advantages and difficulties of this interface to program fault-tolerant MPI applications. They found that ULFM is suitable for specific types of applications, but it provides few benefits for general MPI applications. The issue of fault-tolerant MPI is also considered in  [44]. Due to the fact that the system kills all the remaining processes and restarts the application from the last saved checkpoint when an MPI process is lost, it is expected that this approach will not work for future extreme scale systems.
Life cycle of codes HPC world is comprised of a few widely used codes that serve as base library and a majority of ad-hoc codes often mainly designed and programmed by non-computer scientists. As an example for the first category, in the scientific computing domain, which aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering, Scalapack  [51]  is a largely used library of high-performance linear algebra routines for parallel distributed memory machines. It is a base of large-scale scientific codes and can run on nearly every classical supercomputers. It encompasses BLAS (Basic Linear Algebra Subprograms) and pBLAS (parallel BLAS) libraries. This package is comprised of old C and Fortran codes and was first released in 1979 from NASA  [52]  for BLAS and 1996 for Scalapack  [51].
Finally, a way to provide higher abstraction from design time to execution time that will be investigated is to extend MPI standards to support this abstraction and to provide higher scalability support. The work presented in this article has been partially supported by EU under the COST program Action IC1305, "Network for Sustainable Ultrascale Computing (NESUS)". This paper is distributed under the terms of the Creative Commons Attribution-Non Commercial 3. 0 License which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is properly cited.
Discussion: The authors of SteerTV presented a reformulation that allows the fast computation of dense fields. Even if the stick voting was not a concern if compared to the computation of derived fields that require numerical integration, the stick voting was still a time-consuming task when votes are cast densely. It is remarkable that the implementation efforts are highly reduced now that no care on orientation or rotations is to be taken and considering that the implementation of (  35 )-(  38 ) is straightforward. We provide a MATLAB/Octave implementation as supplementary material to this paper with the goal of providing a way to quickly evaluate the behaviour of tensor voting in an oriented 2D input, one of its most common applications  [37]. This reformulation is so much faster that it suppresses the need for computing the stick field as needed during the feature extraction stage to improve execution time (as explained in Section 2.
SteerTV improves on the asymptotic complexity of the algorithm regarding stick voting, enhancing execution time even further to the exclusive aim of computing tensor voting for this kind of field. In addition, SteerTV constitutes a distinguishable improvement in implementation efforts since its computation can be expressed as a set of filters and elementary operations. Programming languages or libraries with support for easy matrix operations, like MATLAB or OpenCV, allow SteerTV to be implemented easily. Concerning the actual execution times, ETV, STV and SteerTV feature comparisons against OTV in their original publications.
Firstly, for SteerTV, we suggest adopting the methodology of evaluation of STV and ETV  [13]  to assess angular errors against the results of applying the original formulation on a given dataset. This experiment has not yet been carried out for this reformulation and it would be important in order to evaluate the behaviour of the modified decay functions. Some of the works (STV and SteerTV) build their reformulation upon replacing arc length with Euclidean distance. We suggest experimentally evaluating the impact of the usage of this measure, both in execution time and quality. If quality increases considerably when using the original distance measure, but execution time is a concern, we also suggest considering the use of approximations of inverse trigonometric functions (see for instance  [73] ).
pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small-and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration. 1.
Detector Like most other diffraction image processing packages, pyFAI allows the definition of two-dimensional detectors with a constant pixel size (in metres), but this approach reaches its limits with several detector types, such as multi-module and fibre optic taper coupled detectors. Large-area pixel detectors are often composed of smaller modules (e. g. Pilatus from Dectris, Maxipix from ESRF etc. ). By construction, such detectors exhibit gaps between modules along with pixels of various sizes within a single module, and hence they require specific data masks. Optically coupled detectors need also to be corrected for small spatial displacements, often called geometric distortion. This is why detectors need more complex descriptions than just the pixel size. To avoid complicated and error-prone sets of parameters, detector classes have been introduced.
Pixels of an area detector are saved as a four-dimensional data set: i. e. a two-dimensional array of vertices pointing to every corner of each pixel, generating an array of shape (N y , N x , N c , 3), where N x and N y are the dimensions of the detector, N c is the number of corners of each pixel, usually four, and the last entry contains the coordinates of the vertex itself. This kind of definition, while relying on large description files, can address some of the most complex detector layouts: (i) hexagonal pixels (N c ¼ 6, e. g. Pixirad detectors) (ii) curved/bent imaging plates (e. g. Rigaku) (iii) pixel detectors with tiled modules (e. g. some Xpad detectors from ImXpad) (iv) semi-cylindrical pixel detectors (e. g. Pilatus12M from Dectris).
Histogram versus lookup table. The naive way to integrate data (also called 'direct integration') is to treat an image pixel by pixel, very much like a histogram. This is a scatter operation, which is hard to parallelize but cheap as to memory occupation. Using a scatter to gather transformation, the azimuthal integration for a given geometry can be stored into a lookup table (LUT) and applied like a sparse-matrixtimes-dense-vector multiplication (sometimes called 'backwards integration'). Whilst being much more memory Methods available within pyFAI for azimuthal integration, along with their speed and memory footprint. Measurements were performed on a 3 GHz quad-core computer using a 2048 Â 2048 pixel image. The pyFAI calibration window: manual peak picking and ring assignment can be performed though it.
The data correspond to a lanthanum hexaboride (LaB 6 ) calibrant on the Cristal beamline at Synchrotron Soleil taken at 18. 57 keV on an Xpad S540 flat pixel detector tilted vertically by about 15. This detector presents large (vertical) gaps between modules, explaining the incomplete arcs of rings a, b and d. Extracted control points are marked with dots, one colour per group (assigned to a letter), and the fitted iso-2 contours are overlaid as dashed lines (red, orange and yellow coloured). The iso-2 contour plot is not smooth because of gaps in the detector, explaining the incompleteness of some rings. consuming, this implementation is effective in terms of parallelization and speed. The compressed row storage (CSR) matrix representation is now used instead of the LUT and generates a smaller memory footprint.
Amorphous background removal The pyFAI azimuthal integrator features a separate method for separating automatically a background featuring an azimuthal symmetry (amorphous scattering or powder ring) from the Bragg peaks. Based on what was described by  Kieffer & Wright (2013) , two-dimensional azimuthal integration is performed on the input image. The output two-dimensional image is filtered along the azimuthal axis using a percentile (often the median) filter to reconstruct the powder diffraction curve without the sharp Bragg spots. The number of points in the azimuthal and radial directions as well as the percentile value can be adjusted, but the default values are in general reasonably good. The reconstructed two-dimensional image corresponds to the amorphous/powder/isotropic component of the input image and the subtraction of this image from the raw data contains only the signal coming from large crystals.
Fig. 5  (left hand side) presents a close-up of protein single-crystal data recorded on a Pilatus3-2M detector (image taken at the ID23-2 beamline of the ESRF). A diffuse amorphous halo is clearly visible. After using the automatic amorphous background removal, which takes into account the mask needed for such pixel detectors, only Bragg peaks remain (right hand side of the image). 5. 3. 1. Application to serial crystallography. In serial crystallography experiments, tiny crystals in their solvent are moved into the X-ray beam (using a jet or moving a sample holder) and scattering data are acquired continuously, using a fast detector (from dozens of Hz to kHz). These experiments produce a huge quantity of data while only a small fraction of the frames contain some diffraction signal. pyFAI has been integrated into the processing software NanoPeakCell, which provides a graphical interface for frame selection in serial crystallography.
Related work Currently, the pyFAI library runs either as a standalone application or embedded in other software on several beamlines at the ESRF to perform azimuthal/radial integration online: (a) inside the LImA image acquisition library, running on the computer controlling the camera; (b) in one dedicated data analysis server like EDNA  (Incardona et al. , 2009)  in the case of the BioSaxs beamline, BM29  (Pernot et al. , 2013) , or the Dahu server at the TRUSAXS beamline, ID02. Other institutes have independently integrated pyFAI into their processing pipelines: NanoPeakCell (developed at IBS by N. Coquelle), PySAXS (developed at CEA by O. Tache), Dpdak [developed at the Petra III synchrotron by G. Benecke et al. (2014) ] and Dioptas (developed at the APS synchrotron by C. Prescher). Most of these software packages offer a GUI to facilitate the data processing for a specific type of experiment.
B2. Automatic distance and centre calibration The calibration procedure has been automated for the macromolecular crystallography beamlines (MX) at the ESRF. The sample-to-detector distance and the beam centre need to be input in the header of the collected images in order to process them automatically. As the area detector is on a moving stage, the distance and centre position change at every data collection. Via the MX-calibrate tool of pyFAI, it is possible to calibrate the geometries of a set of images recorded at various distances using the Debye-Scherrer rings of a reference compound. After subsequently performing the peak picking using blob detection (as described in x3. 2. 3) and the least-squares refinement of the geometry on every input frame, the program returns distances and beam centre positions as a function of the detector motor position, without manual intervention.
C3. Top level Python interface pyFAI provides at the top level the AzimuthalIntegrator object, which can be created from a PONI-file and used to integrate data. Pre-processing options (dark, flat, mask, solid angle and polarization correction) can be passed to the inte-grate1d or integrate2d methods. These methods also 'know' how to save processed data on disk. This is probably the most flexible level of use of pyFAI, and the best tested one. Moreover, nothing from the underlying levels is hidden.
III. C. 1. Grid generation For the wing alone, the size of the computational domain is [-2. 4 m; 7. 2 m]×[0 m; 6. 0 m]×[-6. 0 m; 6. 0 m] in X, Y and Z directions. Y = 0 corresponds to the symmetry plane of the wing. The origin of the computational domain is located at the trailing edge of the wing in the symmetry plane. The computational domain is shown in Figure  3. The mesh is generated by the unstructured automatic mesh generator Hexpress. This software generates meshes containing only hexahedral cells. The peniche is not taken into account in this mesh. A second mesh is created with the anisotropic automatic grid refinement procedure. The criterion used is the pressure Hessian tensor with a minimum size of 8×10 -4 m. Concerning the boundary conditions, a uniform velocity V ∞ is prescribed at the inlet plane.
III. D. ReFRESCO ReFRESCO is a viscous-flow CFD code that solves multiphase (unsteady) incompressible flows with the RANS equations, complemented with turbulence closure models, cavitation models and volume-fraction transport equations for different phases, see Vaz et al. 20  The equations are discretized using a finite-volume approach with cell-centered collocated variables and in strong-conservation form. A pressure-correction equation based on the SIMPLE algorithm is used to ensure mass conservation as discussed by Klaij and Vuik. 21 Time integration is performed implicitly with first or second-order backward schemes. At each implicit time step, the non-linear system of velocity and pressure is linearized with Picard's method and either a segregated or coupled approach is used. In the latter, the coupled linear system is solved with a matrix-free Krylov subspace method using a SIMPLE-type preconditioner.
The code is parallelised using MPI and sub-domain decomposition, and runs on Linux workstations and HPC clusters. ReFRESCO is currently being developed, verified and validated at MARIN in the Netherlands in collaboration with IST (Lisbon, Portugal), USP-TPN (University of São Paulo, Brazil), Delft University of Technology, the University of Groningen, the University of Southampton, the University of Twente and recently Chalmers University of Technology. Automatic wall functions are available. However, for all cases presented in this study the y + 2 values are below the threshold value for the automatic wall functions of 5, such that the equations are integrated down to the wall.
DNaseI-seq. DNase I digestion  26  and high-throughput sequencing were performed on intact nuclei from DP thymocytes. In brief, nuclei were extracted using NP-40 in an isotonic buffer. The NP-40 detergent was removed and the nuclei were incubated for 3 min at 37 °C with limiting concentrations of the DNA endonuclease, DNase I (Sigma-Aldrich) supplemented with Ca 2 þ and Mg 2 þ. The digestion was stopped with EDTA and the samples were treated with proteinase K. The small double-hit fragments (o500 bp) were recovered by high-resolution agarose gel electrophoresis followed by gel purification. DNase I hypersensitive sites were sequenced on a 5500 SOLiD System. ChIP-seq and DNaseI-seq data processing. ChIP-seq data sets for Pol II, MNase, the transcription factors (TFs) ETS1, GATA3, Ikaros, RUNX1 and CTCF in DP thymocytes were downloaded from Gene Expression Omnibus (GEO) database (Supplementary Table  2 ).
ChIP-seq for all TFs and DNaseI-seq samples were mapped to the mouse genome (assembly NCBI37/mm9) using BFast tool (v0. 7. 0)  27  and only reads that have a unique best scoring alignment were used. ChIP-seq for Pol II, H3K4me1, H3K4me2 and H3K4me3 in P5424 cell line was mapped to the mouse genome (assembly NCBI37/mm9) using the Illumina's integrated Eland software. To exclude redundancies from PCR amplification, we counted only unique fragments. Fragment size was computed using the Hypergeometric Optimization of Motif EnRichment (HOMER) tool (v6. 1)  28  (Supplementary Table  1 ). Read-count intensity profiles (wiggle files) were constructed by elongating each mapped read to the estimated fragment size, and counting the elongated read overlaps within a window of 50 nucleotides after normalization of the profile by the number of mapped reads.
Peak-calling was performed using the HOMER tool with default settings (FDR: 0. 001; local and input fold enrichment: 4. 0). Processed data for Pol II, MNase and epigenetic modifications H3K4me1, H3K4me3, H3K27ac and H3K27me3 in DP thymocytes were downloaded from GEO  25, 29, 30. ChIP-seq data were visualized using the Integrated Genome Browser (http://bioviz. org/igb/). Identification of CRMs. CRMs were extracted by merging peaks of TFs ETS1, GATA3, HEB, Ikaros, RUNX1 and TCF1 that overlap DNaseI peaks. These resulted in a set of 7,542 CRMs that were retained to design the capture microarray (Supplementary Data 1; the average size of the CRMs was 188 bp). As a control, we included 69 CTCF peaks (Supplementary Data 2). Generation of genomic library for CapStarr-seq. Genomic DNA from C57bl6 mouse thymus was sheared by sonication (Diagenode pico), and DNA fragments between 330 and 430 bp were size-selected on a 1.
Finally, libraries were sequenced on ION 318 Chip v2 (Life Technologies) using the Ion PGM Sequencing 200 kit (Life Technologies), according to the manufacturer's instructions. CapStarr-seq data processing. Sequencing data of CapStarr-seq samples were mapped to the mouse genome using the Ion PGM TM pipeline (Supplementary Table  1 ). Wiggle files were generated as described in section 4. To quantify the coverage of each CRM, we intersected the elongated mapped reads (that is, fragments) with the CRMs using BedTools (v2. 17. 0)  31  and calculated FPKM values (Fragment Per Kilobase per Million mapped reads). After processing the two P5424 biological replicates separately to assess reproducibility (R 2 ¼ 0. 90), we pooled both replicates and re-computed FPKM values for all subsequent analyses.
To filter out CRMs not captured by the array, all CRMs with a FPKM lower than 1 in the input sample were excluded, thereby removing 390 CRMs. A total of 7,152 CRMs were then retained for further analyses (genomic coordinates are provided in Supplementary Data 1). The enrichment of CapStarr-seq samples over input was computed by calculating the ratio of FPKM. The FC was used to determine the enhancer activity of each CRM. CRMs with a FC lower than 1. 5 were labelled 'inactive', CRMs with a FC between 1. 5 and 3 were labelled 'weak' and CRMs with a FC equal or higher than 3 were labelled 'strong' (Supplementary Data 1). We identified 4,440 inactive, 2,279 weak and 433 strong CRMs in the P5424 pooled sample, and 4,407 inactive, 2,559 weak and 186 strong CRMs in the 3T3 sample. Of these 1,205 (33%) weak and 75 (24%) strong CRMs were common to the two cell types.
CapStarr-seq data were visualized using the UCSC genome browser (https:// genome. ucsc. edu/). FACS analysis. A total of 5 Â 10 6 P5424 cells were transfected, as described above, with the empty STARR-Seq screening vector or the STARR-Seq screening vector containing the library. Twenty-four hours post electroporation, GFP expression was assessed on a FACSCalibur (BD Biosciences). For CD4/CD8 staining, 1 Â 10 6 P5424 cells were incubated for 10 min with 5ml of mouse CD4-FITC antibody (Miltenyi, Ref No 130091608) and with 5 ml of Mouse CD8a-PE (Milteny, Ref No 130102595) in 50mL of PBS/0,5% BSA/2 mM EDTA. After washing, CD4 and CD8 expression was analysed on a FACSCalibur (BD Biosciensces). Gene association. CRM-associated genes were identified using the online tool GREAT (Genomic Regions Enrichment of Annotations Tool, v2.
A Mann-Whitney U-test was then performed between each pair of CRM sets. Gene expression. Gene expression data of ab T cells were downloaded from the Immunological Genome Project website (www. immgen. org). A quantile normalization was then applied on gene expression of stages ETP (Early Thymic Progenitor), DN2 (Double Negative stage 2), DN3, DN4, ISP (Immature Single Positive), DPbl (Double Positive blast), DPsm (Double Positive small) and single-positive CD4 þ (4SP) and CD8 þ (8SP) thymocytes. Gene expression in P5424 cells was analysed in duplicate using the SurePrint G3 Mouse GE 8x60K microarray platform (Agilent). Statistical significance of differences between inactive, weak and strong CRM sets was calculated using a Student t-test. Conservation analysis. The genomic evolutionary rate profiling (GERP) scores were used to assess the level of evolutionary constraint of each nucleotide within and around CRMs.
GERP scores, downloaded from the UCSC table browser tool (mm9 assembly), are based on an alignment of 22 mammals to the Mus musculus genome (mm9) and have a maximum level of 4. 14. The average profile ( þ / À 1 kb from the CRM centre) was generated by smoothing GERP scores with a window of 50 bp. Statistical significance of differences between inactive, weak and strong CRM sets was calculated by taking into account the average GERP scores in each CRM and applying a Mann-Whitney U-test. Identification of super-enhancers. Super-enhancers in DP thymocytes were identified using H3K27ac peaks and ROSE tool (http://bitbucket. org/young_ computation/rose)  35  with the sequence alignment files of H3K27ac and Input samples, the default stitching distance of peaks (12. 5 kb) and an exclusion region of þ / À 2.
We identified 375 HAL (Supplementary Data 4). Enrichments of HAL or remaining loci in gene ontology terms for biological process were calculated using DAVID tool  36  with default settings (count threshold: 2; EASE threshold: 0. 1; multiple testing correction by the Benjamini procedure) and the set of genes associated with CRMs as a background model. Reporter assay. A total of 1 Â 10 6 P5424 cells were transiently transfected in sixwell plates with 200 ng of Renilla vector along with 1 mg of pGL3-promoter vector (Promega) or pGL3-promoter vectors containing the specified genomic regions cloned downstream the Luciferase gene (Supplementary Data 3). The transfection was performed with NEON 100 ml transfection kit (Life technologies) according to the manufacturer's protocol. Twenty-four hours after transfection, cells were washed once with 1 Â PBS and the luciferase assay (Dual-Luciferase Reporter Assay System, Promega) was performed according to the manufacturer's protocols.