dataesr/scientific-paragraphs-categorization huggingface dataset:
001). A similar correlation was observed from promoters in a 100-Kb window around the enhancers (data not shown). (E) GREAT was used to assign a biological meaning to cell-specific MLV clusters. The analysis showed that MLV is able to target genomic regions involved in cell-specific functions. (F) Top enriched TF motifs in cell-specific MLV clusters. HOMER was used to predict TFBS in cell-specific genomic regions targeted by MLV. The frequency of target (background) sequences enriched in TF motifs and p-values are indicated. enhancers. The analysis of a larger dataset of integrations (> 1 million) in HSPC  37  showed that the same genomic regions and genes related to HSPC cell identity were highly targeted by MLV (data not shown). Chromosome conformation capture combined with immunofluorescence experiments will be performed to investigate whether MLV is recruited to transcription factories, where enhancers are juxtaposed to promoters of co-expressed genes  59  , and eRNAs expressed in response to RNA PolII transcription.
(C) Expression levels of CAGE promoters surrounding HSPC-, EPP-and MPP-specific enhancers (± 5 kb interval) in HSPC, EPP, MPP, embryonic stem cells (ES) and keratinocytes (KER). As control, expression levels of total HSPC, EPP and MPP CAGE promoters were analyzed. A t-test was used to determine significant differences in the expression values associated to CAGE promoters in the different cell types (*P < 0. 05). The median expression levels of genes close to weak/inactive enhancers was similar among the different cell populations (data not shown). Comparable results were obtained by analyzing the expression levels of CAGE promoters in a 100-Kb window (data not shown).
Acknowledgements We thank  Maria Carmela Latella  and  Francesco Gandolfi  for technical assistance. This work was supported by grants from the  European Research Council  (ERC-2010-AdG, GT-SKIN), the  Telethon Foundation  ( TIGET Core Grant ) and the  Italian Ministry of Education, Universities and Research (FIRB-Futuro in Ricerca   2010-RBFR10OS4G ,  FIRB-Futuro in Ricerca 2012-RBFR126B8I_003  and  EPIGEN Epigenomics Flagship Project ).
Methods Cell culture and purification. We obtained human cord blood from healthy donors. Informed consent was obtained from all subjects. All experiments were performed in accordance with the Declaration of Helsinki (TIGET01 protocol). San Raffaele Scientific Institute Ethical Committee approved this study. Culture conditions and purification are described in Supplementary Methods. Gene expression profiling and microarray analysis. We determined the transcriptional profiles of multipotent and lineage-committed progenitors using Affymetrix HG-U133 Plus 2. 0 GeneChip arrays (3 biological replicates for each population) (Affymetrix, Santa Clara, CA). We performed quality controls in R using the Bioconductor AffyQControl package. All arrays in the dataset have good and reproducible quality metrics according to standard guidelines.
To convert CEL file fluorescence signals to log2 expression values, we used the robust multi-array average procedure RMA of the Bioconductor affy package and the HG-U133 Plus 2. 0 custom Chip Definition Files (CDF) based on GeneAnnot  60. We used Principal Component Analysis (PCA) coded by the prcomp function of the R stats package to verify the reproducibility of the 3 biological replicates. We used DNA-Chip Analyzer (dChip) software  61, 62  to identify differentially expressed genes with fold-change > 2, p-value < 0. 05, and False Discovery Rate < 5%. In dChip, the False Discovery Rate (FDR) has been estimated permuting the sample labels randomly and computing the number of genes that satisfied the comparison criteria when applied to the randomly composed groups, i. e. , quantifying the number of false positives (see http://www.
Additional Information Accession codes: The datasets supporting the results of this article are available in the Gene Expression Omnibus repository under the accession number GSE70677. This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/.
We have developed a crowdsourcing web application for image quality control employed by the Dark Energy Survey. Dubbed the "DES exposure checker", it renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes. Users can also generate custom labels and thus help identify previously unknown problem classes. User reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. We report on the implementation of the application and our experience with its over 100 users, the majority of which are professional or prospective astronomers but not data management experts. We discuss aspects of user training and engagement, and demonstrate how problem reports have been pivotal to rapidly correct artifacts which would likely have been too subtle or infrequent to be recognized otherwise.
, 2015) comprises 62 science CCDs (2048×4096 pixels) and 12 focus and guiding CCDs (2048×2048 pixels), covering a roughly hexagonal footprint of 3 deg 2. Each region of the survey footprint will be observed 10 times in each band over the course of five years. The total number of exposures is thus expected to be approximately 10 5 , with a data volume in science images of order 100 TB. An overview of the data processing and management pipeline (DESDM) is given by Mohr et al. (2012) and Desai et al. (2012). The application, dubbed the "DES exposure checker", is geared for a professional user base of several hundred scientists and seeks to identify flaws in the DES images that will be used for most science analyses. Problems discovered with this application can then be fixed in hardware or in subsequent data processing runs.
Presentation Like many citizen science projects, we adopted a web-based approach to reach as many users (affiliated with the survey) as possible and to allow them to inspect images wherever and whenever they want. We also sought to eliminate installation hassles, which means that all features needed to be readily available without the need for extra software. The web application is built with a SQLite database 3  and PHP scripting 4  on the server side, with most of the functionality happening on the client side. The HTML page design derives from the Bootstrap framework 5  , and essentially all interactive elements are written in JavaScript, utilizing the jQuery library. 6 o allow maximum flexibility in handling the images, we decided not to convert them into static images (e. g. , in PNG format), but to load FITS files directly into the browser.
For displaying the images in our viewer, we employ the pure JavaScript implementation WebFITS 7   (Kapadia & Smith, 2013). This approach allows the users to alter properties of the visualization, such as image stretch, zoom, or the inclusion of overlay information, on the client side, without the need to reload additional data. Even when we deal with FITS files of only one of the 62 DECam CCDs, the full image comprises 4096×2048 pixels, which would exceed the size of virtually all commercial computer monitors and visually overwhelm the user. We therefore decided to compromise between providing the most authentic data unit, namely the entire unaltered image, and the most enjoyable user experience, which generally means reducing file sizes and load times. We decided to downsample the FITS image by a factor of 4 on the server side, reducing the image to 1024×512 pixels which, with compression, is about 450 kB in size.
These findings all point to an engaged user base, and our usage statistics support such an interpretation. By now, users have submitted more than 39,000 reports. Many of the reported flaws were initially not expected, so we created the umbrella class "Other", for which user-defined labels are required. By rank-ordering those labels, we could identify the most common types of flaws and extend our classification scheme to currently 26 distinct classes. The tutorial, which summarizes the classes and the current knowledge of their causes, has become an often-visited visual catalog of DES data flaws, and has clear educational value for survey participants. We furthermore provide an API to query anonymized problem reports. This database has been instrumental to identify the origin of several problem classes and to generate specific test cases for algorithms that mitigate or entirely eliminate the impact on subsequent science analyses.
Acknowledgments We thank high school student  Tawny Sit , who, while working at BNL, helped to identify and categorize problem cases that did not yet have a previously dedicated category. We are grateful for the extraordinary contributions of our CTIO colleagues and the  DECam Construction, Commissioning and Science Verification  teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management group. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovacão , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
The DES data management system is supported by the  National Science Foundation  under Grant Number  AST-1138766. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2012-39559 ,  ESP-2013-48274 ,  FPA2013-47986 , and  Centro de Excelencia Severo Ochoa   SEV-2012-0234. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP7-2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478. PM is supported by the  U. S. Department of Energy  under Contract No. DE-FG02-91ER40690. ES is supported by  DOE  grant  DE-AC02-98CH10886. This work was supported in part by the  National Science Foundation  under Grant No. PHYS-1066293  and the hospitality of the  Aspen Center for Physics.
lsce. ipsl. fr) (Dulac et al. 2012) , an international cooperative program investigating Mediterranean regional chemistry-climate interactions, organized in conjunction with the Aerosol Direct Radiative Forcing on the Mediterranean Climate (ADRIMED) project a special observing period (SOP-1a) with airborne measurements in summer 2013 in the western and central Mediterranean basin. The main campaign's objective was to gather an innovative database of the aerosol physical, chemical, optical properties in order to 1) estimate precisely 1D local direct radiative forcing and constrain 3D regional climate models, and 2) investigate how the modifications of the radiative budget (especially at the sea-surface) due to aerosols affect the sea-surface evaporation fluxes and the Mediterranean hydrological cycle during the dry season.
Acknowledgements This study was performed in the framework of workpackage 4 on aerosol-radiation-climate interactions of the coordinated program ChArMEx (the  Chemistry-Aerosol Mediterranean Experiment ; http://charmex. lsce. ipsl. fr). EARLINET measurements were supported by the  ACTRIS (Aerosols, Clouds , and  Trace Gases Research Infrastructure Network) Research Infrastructure  Project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement n. 654169 and previously under grant agreement n. 262254 in the 7th  Framework Programme  (FP7/2007-2013). Measurements in Barcelona were also supported by the  Spanish Ministry of Economy and Competitivity  (projects  TEC2012-34575  and  TEC2015-63832-P ) and of  Science and Innovation  (project  UNPC10-4E-442 ) and  EFRD (European Fund for Regional Development)  and by the  Department of Economy and Knowledge of the Catalan autonomous government  (grant  2014 SGR 583 ).
Decoding LZC-Compressed Files in Parallel The most popular text compressors are based on Lempel-Ziv string complexity  [1]. Zip compressors apply the Lempel-Ziv sliding window method  [2] ,  [12] ,  [13] , while other applications use the LZW (Lempel, Ziv and Welch) compressor  [3] ,  [5] , also called LZC  [6]  when implemented as the command line "compress" does on Unix and Linux platforms. LZC compression is less effective but faster than the zipping applications. Zip compressors are based on a string factorization process, where each factor is independent from the others since it extends by one character the longest match with a substring to its left in the input string. On the other hand, the LZC compressor is based on a factorization, where each factor is the extension by one character of the longest match with one of the previous factors.
This computational difference implies that while the zipping applications have theoretical parallelizations, LZC compression is hard to parallelize. On the other hand, parallel decompression is possible for both approaches. Zipping and unzipping files in parallel requires a logarithmic number of global computation steps even if concurrent reading is allowed. On the other hand, the LZC encoder/decoder pair presents an asymmetry since the decoder has a very efficient parallelization requiring much less than ten iterations on the CREW PRAM. As mentioned above, this makes LZC more attractive than Zip in those cases (which are the most common in practice) where compression is performed very rarely while the frequent reading of raw data needs fast decompression. Finally, we show that this advantage is lost if the LZC decoder is implemented on the EREW PRAM.
Requiring our simplified top-philic dark matter model to result in a dark matter relic density consistent with the most recent Planck measurements  [30]  implies strong constraints on the viable regions of the parameter space. As an illustration, we consider the region of the parameter space in which m t m X m Y , where we expect the dominant annihilation mechanism of dark matter to be process (III) and to give rise to a pair of mediators. In this region, the thermally averaged annihilation cross section approximately reads σv rel ann ∼ g 4 X m 2 X ∼ 10 -9 GeV -2 , (3. 1) so that it is clear that imposing that the relic density predictions agree with Planck data leads to a stringent constraint on the ratio g 2 X /m X. The argument is more involved in parameter space regions where the total mediator width Γ Y plays a role, as the relevant quantity involved in the relic density calculation is in general not σv rel ann but dx σv rel ann (x) where x ≡ m X /T and σv rel ann is a non trivial function of x.
We moreover allow the mediator couplings to be as large as 2π and do not impose any constraint on the mediator width over mass ratio. We summarise the relevant 8 TeV LHC constraints used in this study in table  4  and give details on the t t + / E T and monojet searches that have been recast in the MadAnalysis 5 framework in appendix C. Dark matter production in association with a top-quark pair (t t + / E T ) has been explored by both the ATLAS  [58]  and CMS  [59]  collaborations within the 8 TeV LHC dataset, and limits have been derived in particular in the effective field theory approach  [60, 61]. Such analyses could however be used to derive constraints in other theoretical contexts, and we choose to recast the CMS search to constrain the parameters of the simplified top-philic dark matter model under consideration.
C Recasting of LHC searches within the MadAnalysis 5 framework In this appendix, we detail the implementation, within the MadAnalysis 5 framework  [20] [21] [22] , of the two dark matter searches that we have investigated in this work. More precisely, this consists of the CMS-B2G-14-004 analysis  [59]  that probes final states comprised of a top-antitop system produced in association with a pair of invisible dark matter particles (see Section C. 1) and the CMS-EXO-12-048 analysis  [51]  related to the production of a pair of dark matter particles together with a hard jet (see Section C. 2). Both recasting codes have been validated within the version 1. 3 of MadAnalysis 5, although the monojet search reimplementation is also compatible with the version 1. 2 of the program. The simulation of the detector response is performed with the standard Delphes 3 package that we have run from the MadAnalysis 5 platform.
Acknowledgments We would like to thank the LHC dark matter forum for motivation to pursue this project and for useful discussions during the April 2016 Amsterdam workshop. We are also grateful to  Lana Beck  for providing her four top analysis code for cross checks of our results. This work has been supported by the research unit New physics at the LHC of the German research foundation  DFG , the  Helmholtz Alliance for Astroparticle Physics  and the  German Federal Ministry of Education and Research BMBF. CA is supported by the  ATTRACT -Brains  back to Brussels grant from  Innoviris. MB is supported by the  MOVE-IN Louvain Cofund  grant. EC, BF and KM are supported by the  Theory-LHC-France initiative of the CNRS  ( INP/IN2P3 ). EV is supported by the  European Union  as part of the  FP7 Marie Curie Initial Training Network MCnetITN  ( PITN-GA-2012-315877 ).
This feature is desirable for iterative solvers, such as GMRES, which do not require an explicit matrix formulation but can handle matrix-vector products to solve large sparse linear systems. Moreover, all the components in elsA are computed analytically by hand-differentiating the code. To obtain the matrix-vector products, modifications to the existing elsA source code were necessary. The opt-module in elsA, dedicated to aerodynamic shape optimization, implements a fixed-point iterative scheme for the aerodynamic sensitivity problem. An extensive overview of the gradient computation capabilities is covered in Blondeau et al. 5  The preconditioned fixed-point iterative scheme for the direct method gradient computation can be formulated as V ∆t I + ∂ R a ∂w Implicit Operator dw dp (n+1) - dw dp (n) Correction = - ∂R a ∂x dx dp - ∂R a ∂w dw dp (n) Right -Hand Side (4) where a pseudo-time term ∆t in the implicit operator is introduced to stabilize the convergence behaviour by increasing the diagonal dominance of ill-conditioned systems.
V are the cell volumes and dx/dp is a geometric gradient term that can be determined by the coupling module in Subsection C. It is apparent that the unknown vector, dw/dp, converges when the correction, and hence the right-hand side (RHS), approaches zero. The modification sequence in the elsA/Opt module can be narrowed down to three steps: 1. First, the computation of (∂R a /∂x) × (dx/dp) is dropped in the RHS of (  4 ). This operation is only required once per gradient computation. Components 3 and 4, which are required for the aeroelastic sensitivity analysis, are obtained in separate routines. 2. The RHS at this point is zero. As it is a restart procedure, the term dw/dp is initialized to an external vector of our choice. The RHS construction routine is then called which results in the product of the exact flux Jacobian matrix ∂R a /∂w by the prescribed vector.
The main additional computation time that emanates from the GMRES algorithm is the formulation of the Kylov basis by the Arnoldi iteration which is very small compared to the matrix-vector products. Hence, by implementing non-intrusive modifications to the current source code of elsA we have shown the benefits of implementing a matrix-free GMRES solver. Moreover, the LU-SSOR preconditioner does not require the explicit storage of the flux Jacobian as opposed to ILU-preconditioned GMRES solvers. 18 This implies a significant advantage for large-scale CFD problems. 8 However, it should be mentioned that the factorization error of LU-SSOR is larger than ILU. An ILU-preconditioner is therefore anticipated to yield higher converge rates than LU-SSOR.
B. Structural Solver The structural solver in this work is the finite element analysis program Nastran. 19 The main task of Nastran is to assemble and output the structural stiffness matrix, K, to be used externally for the aeroelastic analysis and the sensitivity analysis. If the deformations are of moderate size the structure can be assumed to exhibit a linear-elastic behaviour. Hence, the resulting stiffness matrix need only be generated once for the initial finite element model and stored in memory. In the event of large structural deformations the assumption of linear elasticity no longer holds and the stiffness matrix need to be continuously updated. In this paper only linear elasticity is considered. The discrete structural equations can then be expressed as R s (u) = Ku -f s = 0 (7) where u are the structural degrees of freedom and f s are any externally applied loads.
The gradient term in Equation (  9 ) is the right-hand side for the sensitivity analysis in Section IV and is of size n f dof × n p , where n f dof is the number of unconstrained structural degrees of freedom and n p is the number of structural design variables. The stiffness matrix and the pseudo-loads can be readily accessed by adding Direct Matrix Abstraction Program (DMAP)  21  alters in the case control section of the bulk data files. The stiffness matrix requires a SOL 101 solution sequence whereas the pseudo-loads require a SOL 200 static analysis solution sequence. The DMAP alters can then export the matrices during the execution of Nastran. The exported op4 files are converted to convenient sparse matrix triplet format using external fortran scripts to be used in the aeroelastic analysis and sensitivity analysis.
This is in fact results in Newton's method for solving the static aeroelastic problem where the tangent operator is the aeroelastic Jacobian. The diagonal blocks are inherently single-disciplinary and are therefore determined individually by their respective computational solvers. The off-diagonal blocks include coupling matrices that require information from both computational solvers as well as the coupling module. The Schur complement of the upper-diagonal block in the tangent operator is: S =   ∂R s ∂u - ∂R s ∂γ ∂ R a ∂γ -1 ∂ R a ∂u   (15) The four blocks required to construct the LoFi Shur complement are studied in detail: 1. ∂R s /∂u is nothing but the reduced stiffness matrix K which is already provided as an output by the structural solver, as described in Subsection B. 2. ∂R s /∂γ describes how the structural residual change with respect to vortex strength perturbations.