dataesr/scientific-paragraphs-categorization huggingface dataset:
Single nucleotide polymorphisms (SNPs) were called in parallel from the genomes of the 30 isolates using Freebayes v1. 0. 0-19-gefg685d  (Garrison and Marth 2012). SNPs were filtered based on quality, coverage, and allelic frequencies (see Supplemental Material S3). Nuclear genetic distance between each pair of isolates was determined using an inhouse Perl script as the pairwise sequence divergence calculated over the entire set of filtered SNPs (n. 111,901 nuclear SNPs, Figure  S3 , see Methods S3 for details). Similarly, we computed genetic distance between pairs of mitochondrial haplotypes (n = 11 mitochondrial haplotypes, n = 145 SNPs, Figure  S4 ) found in the 16 isolates that grew normally on Hagem medium (see below). Finally, we defined four population subgroups corresponding to four large geographic areas (Table  S1 ) and analyzed the population structure of this species (see Supplemental Material S3 for details).
We implemented custom multiple-membership (Rode et al. 2017a) animal models  (Henderson 1950 ; Lynch and Walsh 1998) using the lme4 package  (Bates et al. 2015)  in R v3. 4. 4 (http://www. r-project. org/, R Development Core Team 2013). We used bivariate linear mixed models to partition genetic and environmental effects. Briefly, fixed effects comprised different intrinsic fitness effects for heterokaryons and homokaryons (strain type factor) and the genetic distance between parental homokaryons (set at zero for homokaryons, and at the genome-wide genetic distance between parental homokaryons for heterokaryons, genetic distance covariate). Random genetic effects where partitioned into genetic effects shared between a homokaryon and its heterokaryon offspring (i. e. , to estimate c nucl and c mit ) and homokaryon-specific or heterokaryon specific genetic effects (see File S1 for model details).
We simulated 100 independent datasets for each value of c nucl ranging from 0. 1 to 2. We analyzed each dataset using either a model where c nucl was free to vary or a model where it was set to 1. Power was estimated as the proportion of the 100 datasets in which the model with c nucl 6 ¼1 was better than the other model (i. e. , AICðmodel c nucl ¼ 1 Þ 2 AICcð model c nucl 6 ¼ 1 Þ. 2. We used an arbitrary threshold power of 80% for the analyses  (Johnson et al. 2015).
Acknowledgments We thank  Marie Rescan  for very helpful discussions on theoretical models regarding the evolution of haploiddiploid life cycles, and  Jean-Nicolas Jasmin  for suggestions regarding yeast literature. We are grateful to  Aneil Agrawal  and two anonymous reviewers whose comments helped improve previous versions of the manuscript. This work was supported by the  Swedish Foundation for Strategic Research , grant no. RBb08-0011  and  Carl Trygger Foundation  (P. -H. C. , Å. O. , M. B. D. ) as well as the  CeMEB LabEx/University of Montpellier , grant no. ANR-10-LABX-04-01  (N. O. R. ).
4mm diameter, 830. 0nm λ/2, Edmunds Optics) mounted in a motorized rotating stage (PR50CC, Newport) and set just above the entrance pupil of the microscope objective. The polarization extinction ratio of the fundamental laser beam was measured at the sample plane using the microscope slide thermal sensor and a high acceptance angle polarizer (ColorPol VIS-IR polarizer, Codixx). The extinction ratio was very close for all input polarizations with a value of 180:1 which means 4. 2° residual ellipticity. We developed a custom LabView program (National Instruments) to control and synchronize the 4 images acquisition sequence with the corresponding 4 input polarization states.
Sarcomere length and anisotropy parameter measurement Sarcomere length (SL) and anisotropy parameter ρ were calculated using custom Matlab software (Matlab R2010a, Mathworks). First, isotropic SHG image, equivalent to SHG image obtained under circular polarization excitation, was reconstructed from the sum of the 4 P-SHG images  [13, 20]. The regular arrangement of sarcomeres within myofibrils can be evidenced by the typical striated pattern observed in SHG imaging (Fig. 2(a) ). The process used to extract the sarcomere length consisted in: 1/ selecting a region of interest (ROI) containing ~20-30 sarcomeres within a given myofibril (Fig. 2 (a)), 2/ performing a 2D fast fourier transform (fft) of the ROI and searching for the position of the maximum to find the direction of the myofibril, 3/ rotating the ROI to align the myofibril horizontally, 4/ applying a vertical binning to reduce noise and generating a 1D intensity plot profile along the myofibril (Fig.
EEG recordings Low-cost EEG recordings were collected using Emotiv EPOC+. The sampling rate was 128 Hz, and signals were bandpass-filtered between 0. 5 Hz (high pass) and 40 Hz (low pass). This system has 14 electrodes placed in locations consistent with the 10-20 montage. Impedance was kept according to EPOC calibration at the threshold between YELLOW and GREEN. The presentation of the stimuli and the recording ran on the same computer. Recordings were released from the same python program using our own functions. Events were also marked using those functions. Briefly, to establish the communication between the display/data acquisition computer and the Emotiv system we used the research edition SDK (Standard Development Kit) provided by Emotiv, which allows raw data access in real time. Because the SDK was written originally for C++, we developed a binding for Python to use it with PsychoPy.
The pipeline consisted of receiving the electrode signals, adding referential marks about experiments' stimuli and responses and, finally, saving it for later analysis. The code of the experiments and the communication between the EMOTIV and the computer are available at https://github. com/mathigatti/Emotiv-Experiments. High-quality (HQ) EEG recordings were obtained using a Biosemi 128-channel Active Two system (Amsterdam, NLD). The sampling rate was set to 512 Hz, and signals were bandpassfiltered between 0. 5 Hz (high pass) and 40 Hz (low pass). The EOG signals were collected with four electrodes that were located on the outer canthi of both eyes (HEOR and HEOL) and above and below the left eye (VEOL and VEOG). The reference electrodes were placed in the right and left ear lobes and mastoids.
EEG preprocessing and data analyses As explained in the previous section, both HQ and LC EEG systems ended up with a sampling rate of 128Hz and 14 electrodes after an initial preprocessing. The following steps of the pre-analysis were implemented in parallel for both systems and both tasks. Analyses were performed using EEGLAB (version 13. 5. 4b) [30]  in MATLAB (version R2013a). The EEG activity was re-referenced to the average reference. Removal of noisy intervals by visual inspection of the data was followed by Independent Components Analysis (ICA) using the InfoMax algorithm that was implemented by the EEGLAB group  [30]  to identify blink and saccade components in the continuous EEG recordings and to remove them from the data. Further analyses were done specifically for each task, but in parallel for both systems.
Grand Average data from both task conditions for frontal ROIs (F3/F4) were permuted by applying 1000 permutation draws, using a non-parametric bootstrap method (statcond. m function from MATLAB). There were significant differences between Go and NoGo conditions at the N2 time-window in both EEG systems over the frontal ROI (p<0. 01). Closer inspection of the waveforms revealed that the above effects were driven by larger amplitudes of the N2 component in the NoGo condition relative to the Go condition. These findings indicated that we replicated the traditional NoGo > Go effect for N2.
These findings converge with a prior study  [25]  that reported that spontaneous activity was characterized robustly by long-range temporal correlations that decayed as a power-law function. Interestingly, these measures were also consistent between EEG systems, thus creating the possibility to implement more complex analyses on data with these low-cost systems. Finally, these findings are the result of joint efforts from us and others that are aimed at transferring laboratory methodologies to different contexts, such as schools or workplaces, thus the possibility of extending their inclusion to studies with greater ecological value. Nevertheless, in the present study, the evaluation was based on data that were acquired from a small sample of adults who performed a particular cognitive task in a laboratory setting.
To this end, a investigation of the error correcting performance of FPMAP decoders at high code rates and a detailed discussion of a iteration unrolled, fully pipelined turbo decoder is missing from literature. This work presents the first turbo decoder achieving a throughput of more than 100 Gb/s in 28 nm technology using an Unrolled XMAP (UXMAP) architecture (Section IV). The remainder of this paper is structured as follows: After giving an overview on state-of-the-art parallel turbo decoder hardware architectures in Section II, we compare the error correcting performance for the FPMAP and UXMAP architecture for different code rates in Section III and show how the error correcting performance for high code rates can be improved by employing carefully designed interleavers. In Section V, we give post place & route results for UXMAP and FPMAP decoders with frame size K = 128 and compare them with other high throughput implementations of different turbo decoder architectures.
D. Fully Pipelined Iteration Unrolled MAP Architecture Further pipelining of the decoding by unrolling the individual HI of the turbo decoding leads to the fully pipelined Iteration Unrolled XMAP (UXMAP) decoder architecture (Fig. 1 (d) ), which processes complete code blocks in a pipeline. Note, that, in contrast to an early VLSI implementation of a turbo decoder, which also mentioned the concept of iteration unrolling  [30] , the UXMAP architecture is fully pipelined. Assuming a completely filled pipeline, this allows for the output of a complete decoded frame per clock cycle resulting in a very high throughput, which is only limited by the achievable clock frequency and frame size. The idea for this architecture was first presented in  [19]  but no detailed description, performance numbers or place & route results were given.
III. DECODING AT DIFFERENT CODE RATES Applying different decoding architectures, the two considered decoders UXMAP and FPMAP do not achieve the same performance with the same set of simulation parameters. Any fair comparison should be performed at comparable performance levels. Fig. 2  shows the Frame Error Rate (FER) of UXMAP and FPMAP decoders for a LTE turbo code with frame size K = 128 and a quantization of 6 bits for the channel LLRs. When decoding for 4 iterations (UXMAP) and 40 iterations (FPMAP) respectively, both turbo decoders show similar performance for lower code rates. For the high code rates 8/9 and 18/19, however, the FER performance of the FPMAP decreases and the number of required iterations increases drastically (up to 80) which can be attributed to the scheduling of the decoding operations within the FPMAP algorithm.
Indeed, for FPMAP, forward and backward state metrics exchange only a limited amount of information within one component decoder, largely penalizing the reliability of exchanged extrinsic information based on parity channel metrics. Consequently, for high code rates, the surviving (after puncturing) parity information from one trellis section needs several iteration steps to propagate to far away sections in the trellis. For the UXMAP decoder, this information influences a larger section of the trellis in each HI, because the processing is in essence that of a serial MAP processing. This effect is still observed for larger frame sizes as seen also in Fig. 2 , where the FER performance of a FPMAP with K = 6144 is added for comparison. Because of the larger frame size, only 17 FPMAP iterations achieve comparable FER performance to 4 UXMAP iterations at rate 1/3.
The FER performance of UXMAP and FPMAP for a turbo code with PPC interleaver and frame size K = 128 is shown in Fig. 3. In comparison to Fig. 2 , both decoders show improvements for all investigated code rates. The largest gain in performance is observable for rate 8/9, where there is an improvement of more than 1. 5 dB at a FER of 10 -3 for both decoders. However, the FPMAP decoder still requires 80 iterations to match the performance of the UXMAP decoder for rate 8/9.
Only the FPMAP implementation from  [15]  is estimated to achieve a throughput of roughly 40 Gb/s when scaled to 28 nm technology. Nevertheless, a reduction of iterations leads to a considerable FER performance penalty for the FPMAP at high code rates (see Fig. 2 ). With respect to area efficiency, our UXMAP implementation achieves a very good 4. 34 Gb/s/mm 2 , a value that is only matched by the full custom design from  [37]  when scaling it to 28 nm. Note, that the area efficiency in terms of Gb/s/mm 2 will be slightly higher for all reference implementations if the difference in FER performance due to the larger frame size is accounted for by performing less decoding iterations. On the other hand, and in contrast to the UXMAP architecture which supports a streaming approach due to its pipelined structure, additional area for buffering will be required for PMAP, XMAP and FPMAP.
VI. CONCLUSION In this work, we presented an implementation of a fully pipelined iteration unrolled turbo decoder. For a fair comparison, we re-implemented the VLSI implementation of a FPMAP architecture for the same frame size. With a throughput of 102. 4 Gb/s and an area consumption of 23. 61 mm 2 , our rate flexible UXMAP clearly outperforms the re-implemented FPMAP as well as previously reported architectures in terms of throughput and area efficiency. We show, that the UXMAP architecture is superior to the FPMAP architecture for high code rates and demonstrate that FER performance can be further improved by employing PPC turbo codes. Future work will focus on re-introducing flexibility with respect to frame sizes to the architecture.
The first data release from Gaia observations (GDR1) was produced in September 2016  (Lindegren et al. 2016 ) after 14 months of nominal operations. The Gaia data treatment is conceptually self-calibrated. This means that instrumental parameters are determined from the observations in parallel of science parameters. This is done through iterations of the data processing, leading each time to better results on both science and instrumental parameters. Gaia-DR1 was made available during the first step whereas the data processing, in its initial configuration, has not been totally completed, disabling the classification of the observed sources (as quasars, stars, galaxies). Consequently, among the 1141 million sources detected by Gaia, a huge number of quasars have been observed but their exact number is as yet unknown.
Identification of quasars in the Gaia DR1 catalogue Due to the lack of classification of objects in the Gaia DR1, no quasar could be newly discovered or confirmed with respect to  a preliminary list of candidates as that given by, i. e. ,  Secrest et al. (2015)  following colour-colour selection diagrams from a huge infrared survey. For this reason, the cross-identification between a quasars compiled catalogue as the LQAC-4 and the multi-object DR1 catalogue is particularly long-awaited for several reasons. The most fundamental being that it enables us to qualify as quasars a large sample of objects among the whole DR1 population which consists of more than one billion objects. Second, for the cross-identified quasars, the DR1 data furnishes a priori very accurate and precise celestial coordinates, at the millisecond of arc (mas) level or even one or two orders less, a considerable improvement with respect to the accuracy of the main contributor, the SDSS, typically of the order of 0.
Acknowledgements. We thank  C. Babusiaux  ( Observatoire de Paris/GEPI ) for useful advice on the use of the Gaia DR1 catalogue,  S. Derriere  ( CDS, Strasbourg ) for his help when using the VO tools,  T. Carlucci  ( Observatoire de Paris/SYRTE ) for the maintenance of the LQAC data servers and  S. Lambert & J. -Y. Richard  ( Observatoire de Paris/SYRTE ) for useful advice on the statistical approach used in these studies. A. H. Andrei thanks the  Observatoire de Paris/SYRTE  and the  CNPq/Brazil  for grants received to join the project. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
We present an update of the Mathematica package SARAH to calculate unitarity constraints in BSM models. The new functions can perform an analytical and numerical calculation of the two-particle scattering matrix of (uncoloured) scalars. We do not make use of the simplifying assumption of a very large scattering energy, but include all contributions which could become important at small energies above the weak scale. This allows us to constrain trilinear scalar couplings. However, it can also modify (weakening or strengthening) the constraints on quartic couplings, which we show via the example of a singlet extended Standard Model.
Implementation in SARAH We have extended the Mathematica package by the results and procedures summarised in the previous sections; in particular, the restrictions/assumptions that we apply are described in section 2. 3. The user has two possibilities to use the new functionality: (i) during the Mathematica session analytical expressions for specific scattering processes or the full scattering matrix are available; (ii) the necessary routines for a numerical calculation of the unitarity constraints are included in the SPheno output. Below, we give some details how to work with both methods.
¦ ¥ where we only have shown the first two rows. In order to see the basis in which the matrix is given, one can check § ¤ In  [1]  scatteringPairs ¦ ¥ which reads in our case § ¤ While it might be helpful to obtain analytical expressions for some specific channels, in practice a numerical calculation is often more useful. However, Mathematica is not the preferred environment for exhaustive, numerical calculations. Therefore, it was natural to extend the existing SPheno output of SARAH by the new function. So far, SARAH is already producing Fortran source code which can be compiled with SPheno. This provides the possibility to calculate many things for a given model very quickly, e. g. two-loop RGEs, one-and two-loop masses  [28] [29] [30] , flavour and precision constraints  [31] , two-and three-body decays at tree-level, loop corrections to two-body decays  [32] , and so on.
Generating the Fortran code The properties of the new spectrum generator based on SPheno are defined within SARAH by using the input file SPheno. m. SPheno. m contains for instance the information about the free input parameters expected from the user, the boundary conditions at different scales, choices for involved scales and several other settings. With SARAH 4. 13. 0 the following settings are supported: SPheno. m § ¤ 1 A d d T r e e L e v e l U n i t a r i t y L i m i t s=True ;.
Summary We have presented an extension of the Mathematica package SARAH to calculate unitarity constraints in BSM models. It is now possible to obtain predictions for the maximal element of the scattering matrix in a wide range of models without making use of the large s approximation. We have provided generic expressions for the calculations, along with pedagogical derivations, and clarified some technical issues concerning additional gauge bosons and the choice of gauge. We have briefly shown the importance of these improved constraints in the example of the real singlet extended SM. More detailed discussions of the effects of the new constraints in doublet and triplet extensions will be given elsewhere  [26, 27]. kb =(z b , sin θ b , 0) kc =(zc, sin θc cos φc, sin θc sin φc), (A. 6) where z b ≡ cos θ b , zc ≡ cos θc.
Acknowledgements We thank  Marco Sekulla  for helpful discussions. FS is supported by  ERC Recognition  Award  ERC-RA-0008  of the  Helmholtz Association. MDG acknowledges support from the  Agence Nationale de Recherche  grant  ANR-15-CE31-0002  "HiggsAutomator", and the  Labex "Institut Lagrange de Paris " ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ). We would like to thank  Sophie Williamson  and  Manuel Krauss  for helpful discussions and collaboration on related topics.
In this paper, we consider a parallel finite volume algorithm for modeling complex processes in porous media that include multiphase flow and geochemical interactions. Coupled flow and reactive transport phenomena often occur in a wide range of subsurface systems such as hydrocarbon reservoir production, groundwater management, carbon dioxide sequestration, nuclear waste repository or geothermal energy production. This work aims to develop and implement a parallel code coupling approach for non-isothermal multiphase multicomponent flow and reactive transport simulation in the framework of the parallel open-source platform DuMu X. Modeling such problems leads to a highly nonlinear coupled system of degenerate partial differential equations to algebraic or ordinary differential equations requiring special numerical treatment.
We propose a sequential fully implicit scheme solving firstly a multiphase compositional flow problem and then a Direct Substitution Approach (DSA) is used to solve the reactive transport problem. Both subsystems are discretized by a fully implicit cell-centred finite volume scheme and then an efficient sequential coupling has been implemented in DuMu X. We focus on the stability and robustness of the coupling process and the numerical benefits of the DSA approach. Parallelization is carried out using the DUNE parallel library package based on MPI providing high parallel efficiency and allowing simulations with several tens of millions of degrees of freedom to be carried out, ideal for large-scale field applications involving multicomponent chemistry. As we deal with complex codes, we have tested and demonstrated the correctness of the implemented software by benchmarking, including the MoMaS reactive transport benchmark, and comparison to existing simulations in the literature.
Implementation The physical models described above are built in DuMu X  [58, 59]  framework, which handles general input/output, memory management, grid generation, parallelism, etc. The code is an object-oriented software written in C++ and equipped with efficient solvers and massively parallel computation capability. The first sub-problem consists in solving a convectiondiffusion system modeling a two-phase compositional flow. To tackle this system, we have used a module implemented in DuMu X called 2pnc (two-phase, n-component), where n denotes the number of components present in both phases. The approach is fully implicit. The spatial discretization is performed by the cell-centred finite volume approach described in Section (3. 2) by equations (  22 )-  (24). The nonlinear system is solved by a Newton method and a preconditioned BiConjugate Gradient STABilized (BiCGSTAB) method is used to solve the linear system.