dataesr/scientific-paragraphs-categorization huggingface dataset:
3D model building and rendering. The final 3D reconstruction was processed to obtain a 3D model representing the cell bounds and the regions within the cell that may pertain to specific subcellular structures or organelles. The density per voxel of the 3D reconstruction was used as a metric for demarcating regions. The IMOD suite was employed for this and subsequent model building tasks  49. The program includes the 3dmod package, and facilitates semi-automatic model building, display, and image processing on a per-slice basis, to build a 3D model from the 3D cell reconstruction  49. This approach has been successfully applied to electron microscopy images  50. We relied on several manually chosen thresholds for semi-automatic segmentation. The cell bound was chosen using a threshold that excluded all voxels with density less than approximately 10% that of the maximum density in the reconstruction.
Acknowledgements This work is supported by the  DARPA   PULSE program  through a grant from  AMRDEC  and the  National Institutes of Health  (grant #  GM081409-01A1 ). P. J. B. thanks the supported by the  National Institutes of Health  (R01# AI064616 ). J. A. R. acknowledges the support of the  Howard Hughes Medical Institute Gilliam Fellowship  for graduate studies, the  UCLA MBI Whitcome Fellowship , and the A. P. Giannini Postdoctoral  fellowship. H. J. acknowledges the support of the  National Natural Science Foundation of China  ( 21390414 ).
S1A ). Because of the nucleus-to-nucleus variability of cH2AX-specific fluorescence, we compare histograms of cH2AX-specific fluorescence at the singlenucleus level from the entire cell population in triplicates rather than the averaged cH2AX signal (Fig  S1B ). The Pathfinder OSA instrument was programmed to visit each transfected cell spot on the siRNA microarray, perform an autofocus with DAPI, and automatically and uniformly acquire Cy3 images at a magnification of 409 with a final resolution of 0. 34 lm/pixel. Two-channel images (DAPI and Cy3) were stored as frames of 16-bit Tiff images in a built-in image database (Fig. 1b ). Each image contained approximately 60 HaCaT cells. Because each siRNA targeting a given kinase was spotted in triplicate on the microarrays, approximately 180 cells were analyzed per siRNA (Fig.
FGF signaling participates in H2AX phosphorylation Using the knowledge-based software Ingenuity, we identified the most prominent signaling pathways among our list of kinase ''hits. '' Interestingly, although we screened human HaCaT keratinocytes cells, B cell receptor (BCR) signaling was the first pathway extracted from the IPA database (p value = 10 -17 ) (Fig. S2 ). As H2AX is required for efficient immunoglobulin class switching in lymphocyte B  [34, 35] , this result was not unexpected and indicated that kinases belonging to the BCR signaling pathways in B cells also participate in H2AX phosphorylation in cells lacking B cell receptors, such as human keratinocytes. Several other pathways were also identified by the IPA software, including RAC signaling (p value = 10 -13 ) (Fig. ) and FGF signaling (p value = 10 -11 ) (Fig.
Acknowledgments The authors thank  Peggy Malte  `re and  Amelie Robert  for technical help. Irradiations were performed at  CEA-Fontenay-aux-Roses  and in the Anemome/Bio irradiator in the ARC-Nucleart facility at  CEA-Grenoble. This work was funded by grants from  Agence National de la Recherche (EPIGENOTOX , No. 07SEST007 ),  Electricitede France  (No. V3-20 ) and by  CEA. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The baseline features The quality estimation shared task organizers provided a baseline system including several interesting features. Among them, several are yet used in  (Raybaud et al. , 2011)  but we give below a brief review of the whole baseline features set 1  : • Source and target sentences lengths: there is a correlation between the sizes of source and target sentences. • Average source token length: this is the average number of letters of the words in the sentence. We guess that this feature can be useful because short words have more chance to be tool words. • Language model likelihood of source and target sentences: a source sentence with low likelihood is certainly far from training corpus statistics. There is a risk it is badly translated. A target sentence with low likelihood is not suitable in terms of target language.
The LORIA features In a previous work  (Raybaud et al. , 2011) , we tested several confidence measures. The Quality Measure Task campaign constitutes a good opportunity for us to compare our approach to others. We give below a brief review of our features (we cite again features which are yet presented in baseline features because sometimes, we use a variant of them): • lengths: three features are generated, lengths of source and target sentences (already presented in baseline features), and ratio of target over source length • n-gram based features  (Duchateau et al. , 2002) : each word in the source and target sentences is given its 5-gram probability. Then, the sentence-level score is the average of the scores across all words in the sentence. There are 4 features: one for each language (source and target) and one for each direction (left-to-right and right-to-left 5-gram).
Results We used the data provided by the shared task on Quality Estimation 2 , without additional corpus. This data is composed of a parallel English-Spanish training corpus. This corpus is made of the concatenation of europarl-v5 and news-commentary10 corpora (from WMT-2010), followed by tokenization, cleaning (sentences with more than 80 tokens removed) and truecasing. It has been used for baseline models provided in the baseline package by the shared task organizers. We used the same training corpus to train additional language models (forward and backward 5-gram with kneyser-ney discounting, obtained with the SRILM toolkit) and triggers required for our features. For feature extrac-2 http://dl. dropbox. com/u/6447503/resources. tbz tion, we used the files provided by the organizers: 1832 source english sentences, their translations by the baseline translation system, and the score given by humans to these translations.
g. the number of correlations between a LORIA feature and another LORIA feature) pairs, number of BASELINE-BASELINE pairs, number of LORIA-BASELINE pairs. We remark that only 6% of the pairs correlates (column 0. 7, line +/-) and that the correlations are mostly between LORIA features. This last point is not surprising because there are more LORIA features than BASELINE ones. There are very few correlations between LORIA and BASELINE features. We studied precisely the correlated pairs. There is a strong (more than 0. 9) positive correlation between n-gram and backoff based features and their averaged feature versions. Sometimes, there is also a strong correlation between 'forward' and 'backward' features. Source and target sentences lengths linearly correlate (0. 98). This is the same case for source and target language model likelihoods.
Second, the more OOV in the sentence, the fewer known unigrams. Last, we present the set of features discarded by our feature selection algorithm. We give only this description for the LORIA+BASELINE set, with linear kernel. The algorithm discards 18 LORIA features out of 49 (37%) and 3 BASELINE out of 17 (18%). The features discarded from LORIA are mostly averaged features based on n-gram and backoff. This is consistent with the fact that these features are strongly correlated with n-gram and backoff features. We remark that very few BASELINE features are discarded: lengths of source and target language because these features are yet included in LORIA features, and "average number of translations per source word in the sentence" maybe because the LORIA feature giving the average IBM1 probabilities is more precise.
It is notable that the numerical results were produced using the commercial finite-element software Comsol Multiphysics and that the infinite strip is created computationally using (elastic) perfectly matched layers; these are standard numerical approaches. There is, however, an issue with a large number of spurious modes detected that lie in-between the light-lines. We have carefully tracked the decaying RB modes and have left gaps in the third and fifth RB modes in figure  2b  where the accuracy was diminished by the proximity of spurious modes. However, as we shall see, the asymptotic method developed herein will allow us to extract all the salient information regarding the behaviour of RB waves despite the numerical difficulties associated with the full computation.
The T-values represent the stiffness of the homogenized material, which replaces the array of voids on the long-scale. The implementation uses a bespoke algorithm developed for the commercial finite-element package Comsol Multiphysics in order to solve the required eigenvalue problems for U (0) (ξ ) and U (1) (ξ ) and, hence, obtain the T-values shown in table 1. It is worth mentioning that other appropriate methods do exist, which could be used to investigate the problem at hand. In particular, an alternative, semi-analytical method, would be to use multipole expansions about the centre of the circular voids in a similar approach to that used in scattering problems for elasticity (e. g. [25, 26] ). The use of multipole expansions may also lead to further simplification of the expressions for the T-values.
Funding statement. D. J. C. gratefully acknowledges financial support from the  EPSRC  in the form of a  Doctoral Prize Fellowship  and grant  EP/J009636/1. R. V. C. thanks the  EPSRC  for their support through research grants  EP/I018948/1 ,  EP/L024926/1 ,  EP/J009636/1  and  Mathematics Platform  grant  EP/I019111/1. S. G. is thankful for an  ERC starting grant (ANAMORPHISM)  which facilitates the collaboration with  Imperial College London.
Publicly available spectroscopy One of the two studied candidate clusters (D3-43) was also covered by the KECK/DEIMOS DEEP2 spectroscopic survey  (Newman et al. 2013). This provided 336 redshifts in a 5 arcmin radius circle around the cluster centre, in addition to the GEMINI/GMOS redshifts. Ten of these GEMINI/GMOS redshifts were also measured by the KECK/DEIMOS DEEP2 survey, and this allowed us to assess our own redshift measurements, as shown in Fig. A. 1. We do not detect any noticeable difference. Even the two galaxies with the most different redshift values in the two surveys do not exhibit a red- The other candidate cluster (D3-6) has only three additional objects with known spectroscopic redshifts available through the NED and Simbad databases  (Howell et al. 2005 , Hsieh et al. 2005 , Walker et al.
Publicly available X-ray data Both fields D3-6 and D3-43-S3 were observed by the Chandra X-ray telescope. Even if the collecting area of Chandra is not favourable to the characterisation of such distant structures, the exposure times were long enough to at least give an estimate of the X-ray luminosities of these structures. There are four pointings at the D3-43 region with the ACIS-I detector (obs id 5845, 5846, 56214, 6215, PI K. Nandra, taken in 2005). Following the "Science Threads" from the Chandra X-ray Center (CXC), using ciao 4. 6 1 , we have reprocessed these observations and merged them together producing a single broad-1 asc. harvard. edu/ciao/ band (0. 5-7. 0 keV) exposure-map corrected surface brightness image with a pixel scale of 1. 968 arcsec (14/h 70 kpc assuming z = 0. 739).
Weak lensing characterisation of D3-43-S3 To check if we can investigate the mass distribution around D3-43-S3 in an independent way, we took advantage of the CFHTLS D3 images in the i ′ band for which a special release is available with a seeing of 0. 64 arcsec. Even with such a good groundbased seeing, it is still a difficult task to try to detect modest mass concentrations at z∼0. 74 , but we have no other possibility with the data available to tell if D3-43-S3 has a significant mass or not. We cut a subimage centred on our structure with a field of view of 6. 3'x6. 3' (corresponding to 2. 74 × 2. 74 Mpc 2 ). This is roughly the size of VLT/FORS2 images, which are well suited for cluster weak lensing studies at this redshift (e. g. Clowe et al. 2006). We measure object positions using SExtractor  (Bertin & Arnouts 1996)  and shapes using the latest imcat software tools  (Kaiser 2011).
Conclusions With data obtained with GEMINI/GMOS and data taken from the literature and NED, we spectroscopically confirmed two structures initially detected with the AMACFI photometric redshift based cluster finder in the CFHTLS D3 field. The first structure, D3-43-S3, can be decomposed into two substructures, each with a velocity dispersion of ∼350 and ∼600 km s -1 , which could be in the process of collapsing into a large structure more or less along the line of sight. Since no Xray emission is detected, the interaction is probably not strong yet, explaining the fact that we (barely) detect it in weak lensing (where we are probably seeing the addition of the two structure masses along the line of sight) but not in X-rays. The D3-6 structure is found to be a single structure at an average redshift z = 0.
Acknowledgements. We would like to dedicate this paper to  Alain Mazure , deceased in 2013, and who was part of this work in its early stages. We gratefully acknowledge financial support from the  Centre National d'Etudes Spatiales during many years. This work was also supported by the  Brazilian agencies FAPESP  and  CNPq  and benefited from the  CAPES-COFECUB  agreement number  711/11. We thank the referee for interesting comments. Based on observations obtained at the  Gemini Observatory , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under a cooperative agreement with the NSF on behalf of the Gemini partnership: the  National Science Foundation (United States) , the  Science and Technology Facilities Council  ( United Kingdom ), the  National Research Council (Canada) ,  CONICYT (Chile) , the  Australian Research Council (Australia) ,  Ministério da Ciência, Tecnologia e Inovacão (Brazil)  and  Ministerio de Ciencia, Tecnología e Inovación Productiva (Argentina).
This research has made use of the VizieR catalogue access tool at  CDS, Strasbourg, France. This research has also made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. Finally , this paper is based on observations obtained with  MegaPrime/MegaCam , a joint project of  CFHT  and  CEA/IRFU , at the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at Terapix available at the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS.
In the context of our project COAST (for Computational Astrophysics), a program of massively parallel numerical simulations in astrophysics involving astrophysicists and software engineers, we have developed visualization tools to analyse the massive amount of data produced in these simulations. We present in this paper the SDvision code capabilities with examples of visualization of cosmology and astrophysical simulations realized with hydrodynamics codes, and more results in other domains of physics, like plasma or particles physics. Recently, the SDvision 3D visualization software has been improved to cope with the analysis of astronomical surveys catalogues, databases of multiple data products including redshifts, peculiar velocities, reconstructed density and velocity fields. On the basis of the various visualization techniques offered by the SDvision software, that rely on multicore computing and OpenGL hardware acceleration, we have created maps displaying the structure of the Local Universe where the most prominent features such as voids, clusters of galaxies, filaments and walls, are identified and named.
INTRODUCTION Initially developed for the visualization of the huge amount of data coming from numerical simulation results in astrophysics, we improved our visualization tools for processing other types of huge datasets. The interface was realized in the framework of the COAST (for COmputational ASTrophysics) project in our institute; COAST (COAST n. d. )  (Thooris & al. 2009 )  (Audit & al. 2006 ) is a program of massively parallel numerical simulations in astrophysics involving astrophysicists and software engineers. Magneto-Hydrodynamics simulation codes are developed and optimized for the latest generation of mainframes. The goal is the understanding of the structuring of the Universe from large-scale cosmological structures down to the formation of galaxies and stars. Visualizing the massive amount of data produced in these simulations is a big issue: visualization tools have been developed to analyze these results; we present in this paper the interface capabilities with examples of visualization of simulations results for cosmology and galaxy formation, interstellar medium and magneto-hydrodynamics of stars realized with local simulation codes.
We also present visualizations in other domains of physics like fusion plasma or accelerators. Finally, we recently developed our interface to establish a cosmography of the Local Universe, based on multiple data products including catalogues of redshifts, peculiar velocities, reconstructed density and velocity fields. On the basis of the various visualization techniques offered by our software, that rely on multicore computing and OpenGL hardware acceleration, we have created maps displaying the structure of the Local Universe where the most prominent features such as voids, clusters of galaxies, filaments and walls, are identified and named.
THE SDVISION INTERFACE The interactive, immersive, three-dimensional visualization of complex large scientific datasets is a real challenge. Due to the complexity, the geometry or the size of the calculations, the simulations codes are using different numerical techniques, regular Cartesian meshes or structures such as Adaptive Mesh Refinement, spherical coordinates or multi-meshes embedded in the geometry. The post-treatment software, and in particular the visualizing software tool, must fulfil all these requirements, so a visualization code has been developed inside the COAST team: the SDvision code  (Pomarède & al. 2008a )  (Pomarède et al. 2009 ), which will be described below. The SDvision graphical interface is implemented as an interactive widget as displayed in (fig. 1&2 ) in its running state.
It benefits from hardware acceleration through its interface to the OpenGL libraries, including GLSL shaders. SDvision has been developed in the framework of IDL Object Graphics  (IDL n. d. ). IDL, the Interactive Data Language, is a firmly-established software for data analysis, visualization and cross platform application development. IDL provides a set of tools for developing object-oriented applications. A class library of graphics objects allows to create applications that provide equivalent graphics functionality regardless of the computer platforms. Other powerful visualization codes exist and are widely used in the astrophysics community, for instance VISIT  (VISIT n. d. )  or PARAVIEW  (PARAVIEW n. d. ). We developed our own tool from scratch using IDL framework, for historical reasons (IDL is the dominant platform for analysis and visualization in the astrophysics community) and as a consequence many home format reading and data handling modules were readily available; also, IDL provides mathematical and scientific libraries which help both simulations visualization and analysis.
Large-scale simulations are conducted on high-performance mainframes with potentially thousands of processors associated with a non-trivial domain decomposition. Parallelism is needed for the processing and the visualization of large data sets; some elements of parallelism are provided in IDL, for example we benefit from a multiple-CPU implementation of the IDLgrVolume class to render volume by ray-casting. The development of SDvision was particularly focused on the visualization of grid data produced by finite volumes hydrodynamics codes; the particles clouds are treated as mere 3D scatter plots, typically in astrophysics for dark matter, or for visualization of macro-particles in an accelerator (as shown in section 4). The SDvision software package, intended primarily for the visualization of massive cosmological simulations, has been extended to provide an interactive visual representation of different classes of redshift surveys.
SIMULATIONS IN ASTROPHYSICS The RAMSES code (RAMSES n. d. )  (Teyssier & al. 2002)  is designed as a N-body and hydrodynamical code based on the Adaptive Mesh Refinement (AMR) technique  (Pomarède & al. 2008b )  (Labadens & al. 2011 )  (Labadens & al. 2012). Hybrid simulations are performed using the RAMSES code to study cosmological large scale structures and galaxy formations (fig. 3 ). RAMSES code simulations in cosmology need thousands of processors running in parallel during several weeks and producing tens of Terabytes of data. The RAMSES code, relying on the Adaptive Mesh Refinement technique, is used to perform massively parallel simulations at multiple scales. The interactive, immersive, three-dimensional visualization of such complex simulations is a real challenge. The analysis of results from complex MHD and N-body AMR-Octree code for cosmological simulations implies two steps of processing as we need Cartesian grids as input for multithreading processing.
The highest levels of the AMR resolution are reached by successive and synchronous spatial and resolution zooms, using an interactive definition of the sub-volume in which the AMR extraction is performed. New algorithms are studied for direct reconstruction of images from the AMR-Octree structures, to avoid using intermediate Cartesian grids. The SDvision tool provides a visualization of the scenes though either an OpenGL, hardware-based rendering or through a pure software computation. Several rendering techniques are available, including ray-casting and isosurface reconstruction, to explore the simulated volumes at various resolution levels and construct temporal sequences. These techniques are illustrated in the context of different classes of simulations. RAMSES was used most recently in the studies of galaxies formations (Bournaud 2010)  (Chapon & al.
2010). A first example of high-resolution simulation of a galaxy disk is shown in fig. 4. The image represents the density of the baryon gas in a galaxy disk. Fig. 5  shows a simulation of galaxies collision. Using the PLUTO code (PLUTO n. d. ), a freelydistributed software for the numerical solution of partial differential equations, astrophysicists can simulate turbulences inside a protoplanetary disk; after interfacing the code with SDvision, we obtain nice views of the simulation (fig. 7 ). The anelastic spherical harmonic code ASH  (Brun & al. 2004 ) (STARS2 n. d. ) solves the threedimensional anelastic equations of motion in a rotating spherical geometry using a pseudospectral semi-implicit approach. The code is used in our institute to study the Magneto-Hydrodynamics of the Sun. SDvision provides also nice images and videos, after some work on the coordinates (fig.