dataesr/scientific-paragraphs-categorization huggingface dataset:
Each analysis is done separately for duplications, inversions and deletions. This means that when we are running the script for the duplication scan and we come across inverted loci having no duplication reads then these loci will not be reported in output of duplications call but will be reported in inversion calls. In contrast, if we find inverted reads where we have duplicated reads also, the loci will be stored with the information of read counts for duplications and inversions. The same principle applies for the scan of inversions. Variant parser. The purpose of this module is to extract only those loci where there is differentiation between the two groups analyzed (domestic and wild rabbits in the data analyzed in this study). The input is the result of Variant Caller raw calls obtained in the above step.
, 2014). For each group the average depth is extracted in the predicted regions and an M-value (log2 fold change) is calculated. This step gives CNVs that show significant allele frequency difference between two groups (absolute M-value >=0. 7). For visualization, log2 fold change is plotted for each candidate CNV in R. The breakpoints for inversions are shown using the positions of mate pair. We next annotate the candidates using information for genes, repeats, gaps or custom annotations. Finally, candidate SVs are scored as alpha, beta and gamma by using the following criteria for all SVs: Alpha: SV with absolute M-values greater than 0. 7 from MP and PE data and that fulfill the fraction check, where fraction check is at least 10% of deviant reads or signatures observed. Beta: Below 10% and above 1% of deviant reads support with or without M-values.
Together with this information, soft clips (search for right and left clipped positions at breakpoints) are used to detect breakpoints of deletions in the reference assembly. In order to know what part may have been deleted in the reference genome, we also make use of BLAT to map the unmapped pairs of singletons at breakpoints to the human genome assembly to infer whether a DNA fragment observed in human (and in other species) is homologous to those unmapped reads. The size and expected gene content within the part missing in the reference assembly is inferred. Datasets. Four rabbits (two domestics and two wild from the Oryctolagus cuniculus algirus subspecies) were re-sequenced at 3x coverage using Illumina mate pair (2x50bp) with an average insert of 4. 5kb. This dataset was used for paired-end mapping analysis.
Acknowledgements The work was supported by the  ERC  grant  BATESON  to LA, by  POPH-QREN  funds from the  European Social Fund and Portuguese MCTES  [postdoc grant to M. C ( SFRH/BPD/72343/2010 )], by  FEDER  funds through the  COMPETE program  and by Portuguese national funds through the  FCT -Fundação para a Ciência e a Tecnologia - ( PTDC/CVT/122943/2010 ), by an  EU   FP7 REGPOT  grant [ CIBIO-New-Gen ][286431], and by travel grants to M. C. ( COST Action   TD1101 ) and  Higher Education Commission in Pakistan  (support for S. S. ). Computer resources were supplied by  UPPNEX at Science for Life Laboratory.
xGEMM performs 2mnk floating-point operations over mk + kn + mn data. All the other BLAS-3 routines can be expressed in terms of xGEMM. Moreover, when different implementations of BLAS are compared, the first criteria used for this comparison is the performance of xGEMM. The profitable ratio between the computation and the memory references of the BLAS-3 routines has a strong impact on the design and automatic generation of linear algebra algorithms. For instance, in order to exploit the optimized BLAS implementations, the Linear Algebra PACKage (LAPACK) builds its blocked algorithms on top of the BLAS-3 operations. Furthermore, scientists either try to generate algorithms relying more on the BLAS-3 routines, in particular xGEMM, or try to rewrite their algorithms in order to benefit from the performance provided by the BLAS-3 routines  [3].
The idea of auto-tuning was extended to GPUs architectures applying different programming models such as CUDA and OpenCL. Apart from both code generation and heuristic search in conjunction with OpenCL, Matsumoto et. al. [17]  proposed to store data in memory not only in a standard row-/column-major order, but also in a block-major order. We revise these ideas and employ some of them in our implementations of exact xGEMM, which is described in Sect. 4. 1. Therefore, we combine together auto-tuning for standard non-deterministic xGEMM and machine-specific hand tuning for our reproducible approach.
Conclusions and Future Work xGEMM is the core of the BLAS library and all the other BLAS-3 routines are virtually built on top of it. Furthermore, the development and automatic generation of linear algebra algorithms are driven by the goal of achieving best performance on various architectures. One step towards this goal is made by using blocked versions of algorithms that are capable to obtain much higher performance compared to non-blocked algorithmic variants. This is achieved thanks to the usage of BLAS-3 routines, in particular xGEMM. Understanding such importance of the matrix multiplication routine, we targeted xGEMM and for the first time delivered a multi-level reproducible approach along with implementations. Even though the performance, which corresponds to roughly 5 % of the efficiency, can be argued (we think that a 10 times overhead at most for reproducible compute-bound algorithms is reasonable), the output of ExGEMM is consistently bit-wise reproducible and accurate, in terms of rounding-to-nearest, independently of threads scheduling, instruction set, and data partitioning.
Acknowledgement. This work undertaken (partially) in the framework of CALSIM-LAB is supported by the public grant  ANR-11-LABX-0037-01  overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-11-IDEX-0004-02 ). This work was also (partially) supported by the  FastRelax project  through the  ANR  public grant (reference:  ANR-14-CE25-0018-01 ).
Ground-based microwave radiometers (MWRs) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations, a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upwardlooking microwave sensors.
, 1998)  that takes the corresponding uncertainties into account to retrieve temperature and humidity profiles in the first few kilometers from the ground, where MWRs provide the maximum information content. In addition, the Jacobians (i. e. , partial derivatives with respect to the state vector) of the radiative transfer model are required to minimize the distances of the atmospheric state from both the first guess and the observations in a variational data assimilation process. These Jacobians represent the sensitivities of observations to the atmospheric thermodynamical state. The fast RT model RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder (TOVS)) is widely used to simulate radiance from space-borne passive sensors. RT-TOV has already been used for many years by many na-tional meteorological services for assimilating downwardlooking observations from visible, infrared, and microwave radiometers, spectrometers, and interferometers  (Hocking et al.
, 2015, and references therein)  aboard satellite platforms. The FORTRAN-90 code originally developed at ECMWF in the early 90s  (Eyre, 1991)  was intended for TOVS direct radiance assimilation within three-and four-dimensional variational analysis schemes (3DVAR, 4DVAR). Subsequently the original code has gone through several developments (e. g. ,  Saunders et al. , 1999; Matricardi et al. , 2001) , more recently within the EUMETSAT NWPSAF, of which RTTOV v11. 3 is the latest version available. Since its first implementation and throughout its current version, RTTOV has been developed and exploited for satellite observation perspective only. The model allows rapid simulations of radiance for a suite of passive sensors given the atmospheric state vector, i. e. , profiles of temperature, gas concentration, cloud liquid water content, and surface properties.
The only one variable gas needed for RTTOV v11 in the microwave band is water vapor. An important feature of RTTOV is that, in addition to the forward (or direct) radiative transfer, it also computes the Jacobians, i. e. , the gradient of the radiance with respect to the state vector at the location in state space specified by the input state vector values. The Jacobians are calculated in tangent linear (TL), adjoint (AD), and K-modules of RTTOV. There are other fast RT models used by the NWP community for satellite data assimilation, like the Community Radiative Transfer Model (CRTM -  Ding et al. , 2011). However, to our knowledge no fast RT model is currently available to simulate ground-based radiometric observations. In this work, version 11. 2 of RTTOV has been modified to handle ground-based microwave radiometer observations.
For the regression, d ij are calculated using a line-by-line (LBL) model for a set of atmospheric profiles. LBL RT models provide accurate calculation of the atmospheric transmittances and radiance, given the atmospheric profile of gas concentrations, and predefined spectral frequency grid. The LBL optical depths must cover the full spectral range of all the radiometer channels of interest and provide a sufficient resolution to represent the transmittances in the channel spectral bands accurately. The LBL model described by  Rosenkranz (1998; R98 herewith)  has been used for gas absorption to calculate the clear-sky transmittances needed in the RTTOV-gb regression coefficients' computation. Here we use 83 profiles from an NW-PSAF profile dataset interpolated on 101 pressure levels, already used for training RTTOV.
∇ x = ∇ x y • ∇ y = K(x 0 ) T • ∇ y (14) δx (x 0 ) = The TL code is derived directly from the forward model because it represents the analytic derivative of the radiance (forward model outputs) with respect to the atmospheric state vector x. The AD code is derived from the TL code. Finally, the K code is obtained from the AD code distributing the AD level derivatives through the number of channels. Before running TL, AD, and K models, the direct model needs to be run because many of the intermediate variables calculated by the direct model are needed by the TL, AD, and K-modules.
Performance of RTTOV-gb The performance of RTTOV-gb has been tested in four different ways, reported in the following subsections: validation against the LBL RT model used as reference for the training and against another independent reference LBL RT model (3. 1); a comparison of TB simulated with RTTOV-gb from a radiosonde profile dataset with nearly co-located MWR measurements (3. 2); a comparison of Jacobians calculated with the RTTOV-gb K-module and the brute force method, and also with Jacobians computed with an analytical model (3. 3); the exploitation of RTTOV-gb as a forward model operator within a one-dimensional variational scheme (3. 4).
For this particular case, TB differences between the two models are within 0. 1 K for all channels. For the whole 83-profile dataset, Fig. 3  shows statistics (bias, rms, and maximum value) of the LBL R98 minus RTTOV-gb TB difference at four elevation angles (90, 30, 19, and 10 • ). The best training configuration determined above (elevation angles from 90 to 16 • ) is used. At 90 • elevation, bias and rms are respectively less than 0. 030 and 0. 060 K for K-band (22-31 GHz), while they are 0. 003 and 0. 025 K for the V-band opaque channels (54-58 GHz). For these channels the maximum difference does not exceed 0. 15 K. The agreement is slightly worse at transparent V-band channels (51-54 GHz), with bias, rms, and maximum difference respectively within 0. 03, 0. 2, and 0. 6 K. The larger discrepancies at transparent V-band channels are probably due to the combined influence of temperature and water vapor, which likely decreases the correlation of layer opacity with the two thermodynamical variables.
, 2011 (Cimini et al. , , 2015)). Statistics, reported in Table  3  in terms of bias and rms, are similar to those obtained for HATPRO channels, at both K-and V-band. Note that LBL R98 is the model used to train the regression scheme. In order to perform a completely independent test, we compare RTTOV-gb with an independent reference radiative transfer model, the Atmospheric Radiative Transfer Simulator (ARTS,  Buehler et al. , 2005; Eriks-son et al. , 2011; Eriksson and Buehler, 2015) , and a completely different profile dataset. In this test, HATPRO observations are simulated using RTTOV-gb and ARTS from a set of 1327 thermodynamic profiles from the AROME analysis over Bordeaux from April to October 2014. AROME is the French convective-scale NWP model with a 2. 5 km horizontal grid mesh developed by Météo France  (Seity et al.
, 2010). Both clear-and cloudy-sky conditions are considered. This dataset, which is limited in space, time, and thus in atmospheric conditions, was chosen to demonstrate the performance of RTTOV-gb in typical deployment environment. Since the goal of this analysis is to test the fast RT modeling (RTTOV-gb) with respect to accurate LBL calculation, all other settings being equal, ARTS settings for absorption model have been selected to adopt the same absorption model as RTTOV-gb as much as possible: R98 for oxygen and water vapor absorption, and the model described in  Liebe et al. (1993)  for cloud liquid water (referred as MPM93 within ARTS). Note that MPM93 is the only option for liquid water absorption available in ARTS. Conversely RTTOV-gb is consistent with the original RTTOV, which adopts a combination of  Liebe et al.
3-0. 5 K, not shown). This may be caused by small differences in the implementation of the R98 gas absorption and/or the radiative transfer code. This issue is currently under investigation, though its understanding goes beyond the scope of this paper. Comparing Figs. 4  and 5 , we notice slightly larger differences (by 0. 1-0. 2 K) in the RTTOVgb vs. ARTS than in the RTTOV-gb vs. R98 tests. We attribute this to the fact that RTTOV-gb is totally independent of ARTS and moreover to the specific profile dataset, which likely introduces biases with respect to the RTTOV-gb training climatology. Note that TB differences for all the channels are of the same order of magnitude as those found between ARTS and the original nadir-looking RTTOV  (Buehler et al. , 2006). This demonstrates comparable capabilities between RTTOV-gb and the original version of RTTOV.
Comparison with real observations Another way to evaluate RTTOV-gb is to compare TB simulated from radiosonde profiles with TB measured by a nearly co-located microwave radiometer. This comparison provides an end-to-end evaluation of the model, though radiosonde drift, MWR calibration, finite beamwidth, discretization, and instrumental noise all contribute to the total uncertainty. Nevertheless, observations minus background model (O -B) differences are the primary input for direct radiance assimilation into an NWP model, and thus need to be investigated and understood. For this analysis, we exploit a dataset of 365 radiosonde profiles collected over Bordeaux from April to October 2014, together with the nearly simultaneous TB observed by a ground-based microwave radiometer (HAT-  PRO) operated at the radiosonde launching site.
The dataset was first reduced to clear-sky conditions. To be conservative, clear-sky conditions have been selected using three-fold screening, based on (i) ceilomenter cloud base height (CBH), (ii) sky infrared temperature (T IR ), and (iii) 20 min standard deviation of liquid water path (σ LWP ) from HATPRO. Thus, periods with CBH below maximum range (8000 m), T IR > -30 • C, or σ LWP > 10 -2 kg m -3 were rejected. Moreover, cases with integrated water vapor differences between microwave radiometer and radiosonde profiles larger than 1 mm have been discarded in order to reduce instrumental uncertainties involved in the comparison. After this screening, only 23 profiles remained for the analysis. Bias, standard deviation, and rms differences between TB observed by the microwave radiometer and simulated with both RTTOV-gb and ARTS are shown in Fig.
1D-Var application Finally, RTTOV-gb has been tested as a forward model within a one-dimensional variational (1D-Var) scheme. For this purpose, the 1D-Var software package provided by the NWPSAF  (Weston, 2014)  has been adapted in the framework of the COST Action TOPROF to exploit RTTOV-gb. Among other modifications, the 1D-Var tool has been modified to al-low the assimilation of observations at different elevation angles for the same instrument. The 1D-Var approach searches the atmospheric state x that minimizes both the distance to the background x b and the observation y. The cost function J needs to be minimized, modifying the different variables defined in the control vector x  (Cimini et al. , 2010) : J = 1 2 y -H (x) T R -1 y -H (x) + 1 2 [x -x b ] T B -1 [x -x b ]. (16) Here B represents the background-error covariance matrix and R the observation error covariance matrix.
A comparison between temperature and humidity retrievals obtained with 1D-Var and the corresponding unperturbed and background profiles for two retrieval examples are shown in Fig. 11. As expected, the 1D-Var retrievals are closer to the truth than the background profiles. In this case 1-D-Var provides an improvement with respect to the background in the first 2 km for temperature and in the first 4 km for humidity, which is encouraging for future data assimilation experiments. A comprehensive evaluation of RTTOV-gb plus 1D-Var for data assimilation using real MWR observations will be the subject of future work. Here, we just underline that the main advantage of RTTOV-gb with respect to LBL models is the considerably lower computation time. Of course the priority of LBL models is more accuracy than speed, though settings may be tuned to improve the computation performances.
Summary Version 11. 2 of the fast radiative transfer model RTTOV, developed for space-borne sensors, has been successfully modified to simulate ground-based microwave radiometer observations. In addition to the direct module, which allows ground-based MWR observations to be simulated, the TL-, AD-, and K-modules of RTTOV have been modified in order to provide temperature, humidity, and cloud liquid water Jacobians for the ground-based perspective. We introduced the ground-based version of RTTOV, called RTTOVgb, and demonstrated its potential for fast MWR TB simulations from thermodynamic profiles. RTTOV-gb has been validated against accurate, but less time-efficient, reference lineby-line models and real MWR observations. Results demonstrate its applicability as a forward model within a variational scheme for fast and safe MWR data assimilation into NWP models.
As expected, RTTOV-gb is demonstrated to be faster than the lineby-line models such as ARTS for both the direct and the Jacobians calculation. Ultimately, this analysis confirms that RTTOV-gb is able to correctly simulate ground-based MWR radiance and to reproduce reasonable temperature, humidity, and cloud liquid water Jacobians. In conclusion, RTTOV-gb is well suited for serving as a forward model in a variational data assimilation scheme for a direct, safe, and fast NWP data assimilation of real MWR radiance observations. As from the user perspective, RTTOV-gb works exactly the same as RTTOV; its implementation and maintenance shall require minimal technical overheads at those NWP centers already using RTTOV. This shall facilitate the road towards the data assimilation of ground-based MWRs worldwide.
Acknowledgements. This work has been stimulated through the  COST Action ES1303  (TOPROF), supported by  COST (European Cooperation in Science and Technology). Part of the work was supported by the  EU   H2020  project  GAIA-CLIM (Ares ( 2014 ) 3708963/Project 640276 ). The authors would like to acknowledge the NWPSAF and  Met Office , in particular  Peter Rayer , for providing support with RTTOV coding, and  Météo France , for providing AROME analyses and measurements performed in the Bordeaux campaigns. Edited by:  K. Gierens Reviewed  by: two anonymous referees.
The best-fit values of the five parameters are shown in Table  4. The matter-core fraction is quite different in the two new PYTHIA6 tunes. This is due to the fact that this parameter is very sensitive to the behaviour of the PDF at small x. Predictions obtained with PYTHIA6 Tune Z2 * lep , CUETP6S1-CTEQ6L1 and CUETP6S1-HERAPDF1. 5LO are compared in Appendix B to the UE data. The new PYTHIA6 tunes significantly improve the description of the UE data relative to PYTHIA6 Tune Z2 * lep at all considered energies, due to the better choice of parameters governing the MPI energy dependence.
(  5 ) and  (6). For this study, we produce MADGRAPH parton-level events with a W boson and up to four partons in the final state. The cross section is calculated using the CTEQ6L1 PDF with a matching scale for ME and parton shower (PS) jets set to 20 GeV. (In Section 4, we show that the CMS UE tunes can be interfaced to higher-order ME generators without additional tuning of MPI parameters). Figure  6  shows the CMS data  [36]  for the observables ∆S and ∆ rel p T measured in W+dijet production, compared to predictions from MAD-GRAPH interfaced to PYTHIA8 Tune 4C, to Tune 4C with no MPI, to the partial CDPSTP8S1-Wj, as well as to the full CDPSTP8S2-Wj (CDPST stands for "CMS DPS tune"). Table  6  gives the best-fit parameters and the resulting σ eff values at √ s = 7 TeV. The uncertainties quoted for σ eff are computed from the uncertainties of the fitted parameters given by the eigentunes.
Double-parton scattering in four-jet production Starting from the parameters of PYTHIA8 Tune 4C, we construct two different four-jet DPS tunes. As in the W+dijet channel, in the partial tune just the exponential-dependence parameter, expPow, while in the full tune all four parameters of Table  7  are varied. We obtain a good fit to the four-jet data without including higher-order ME contributions. However, we also obtain a good fit when higher-order (real) ME terms are generated with MADGRAPH. In Fig. 7  and 8  the correlation observables ∆S and ∆ rel p T in four-jet production  [37]  are compared to predictions obtained with PYTHIA8 Tune 4C, Tune 4C without MPI, CDPSTP8S1-4j, CDPSTP8S2-4j, and MADGRAPH interfaced to CDPSTP8S2-4j. Table  7  gives the best-fit parameters and the resulting σ eff values.
The data are compared to MADGRAPH (MG), interfaced to PYTHIA8 using CUETP8S1-CTEQ6L1 and CUETP8M1, and to POWHEG (PH), interfaced to PYTHIA8 using CUETP8S1-HERAPDF1. 5LO and CUETP8M1. The bottom panels of each plot show the ratios of these predictions to the data, and the green bands around unity represent the total experimental uncertainty. [50]  for the energy flow dE/dη, in MB pp collisions. The data are compared to PYTHIA6 using CUETP6S1-CTEQ6L1, and to PYTHIA8 using CUETP8S1-CTEQ6L1, CUETP8S1-HERAPDF1. 5LO, and CUETP8M1. The bottom panels of each plot show the ratios of these predictions to the data, and the green bands around unity represent the total experimental uncertainty. [50]  for the charged-particle distribution dN ch /dη, in inclusive inelastic (top left), NSD-enhanced (top right), and SD-enhanced (bottom) pp collisions.