dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability NextGen data are described at http://projects. ensembl. org/nextgen/. Ugandan cattle SNP data are available at ftp://ftp. ebi. ac. uk/pub/databases/nextgen/bos/ variants/chip_array/ in PLINK format (files UGBT. bovineSNP50. UMD3_1. 20140307. [ped/map]. gz) with the following data policy ftp://ftp. ebi. ac. uk/pub/databases/nextgen/documentation/ README_data_use_policy.
For both noise models, we benchmarked the method as a function of the number of data points, the level of noise in the data, and the number of replicas used. simulation time for each run was 120 ns. We determined the accuracy of our approach as a function of the number of data points, the level of noise in the data, and the number of replicas. We tested two different noise models: a Gaussian distribution with one uncertainty parameter per data point (Eq. 19) and the outliers model with one typical uncertainty per dataset (Eq. 20). In both cases, we used an uninformative Jeffrey's function σ σ = p( ) 1/ to model the uncertainty priors and we fixed the standard error of the mean σ SEM at. N 0 12/ To assess the sampling efficiency and ensemble accuracy provided by PBMetaD, we run a subset of the simulations of the benchmark using the pure metainference approach and coupling metainference with BEM  9, 16  , in the same way as MaxEnt replica-averaging was combined with BEM in RAM  9.
3A, C ), so that noisy points were downweighted in the model construction. As a consequence, a dataset containing outliers was equivalent to a smaller dataset in which errors were absent. Therefore, while at fixed number of data points the accuracy of the ensembles generated using experimental data with no errors was slightly higher, the derivative of the accuracy with respect to the number of data points was similar in presence and absence of errors. In both cases, we also found that the error in the reconstructed free energies along φ (Fig. 2B ) and ψ (Fig. 2E ) decreases with the number of replicas used, as observed in simpler heterogeneous model systems  2. The behavior with the number of replicas was also expected, as the accuracy in the calculation of average quantities increases with the dimension of the simulated ensemble.
The outliers model showed the same trend in accuracy as a function of data points as the Gaussian model (Fig. 2C, F ). When no systematic errors were present in the data, the outliers and Gaussian noise model resulted in ensembles of similar accuracy, especially when using 64 replicas and 36 data points, in which case the difference in accuracy between the two models was smaller than 0. 02 k B T. The distribution of the typical uncertainty of the dataset used in the outliers model (Fig. 3B ) was less broad than those of the individual uncertainties in the Gaussian model (Fig. 3A ), but it was peaked at the same value. In presence of systematic errors in the data, the outliers noise model generated ensembles slightly less accurate then those obtained with the Gaussian noise model. In the case of 64 replicas and 36 data points, the accuracy of the outliers noise model was worse than the Gaussian noise model of the order of 0.
06 k B T. This difference is due to the greater flexibility of the Gaussian noise model, where one uncertainty parameter is assigned to each data point, while in the outliers model uncertainties are marginalized using a unimodal distribution peaked around a typical (unknown) dataset uncertainty. However, the outliers model has the computational advantage of having a single uncertainty parameter to sample. The accuracy of a simpler Gaussian model with one uncertainty parameter for all data points, and thus the same number of parameters of the outliers model, was significantly worse in presence of systematic errors (Figs  S3  and S4 ). Finally, the typical uncertainty inferred using the outliers model (Fig. 3D ) was somewhere in between the uncertainties of the Gaussian model for points with and without systematic errors (Fig.
7). The latter is considered our reference (exact) free energy. Free energy obtained from reweighting a M&M simulation carried out using 64 replicas, all the 36 data points available (without addition of systematic errors), the Gaussian noise model with one uncertainty parameter for each data point (C) and the outliers noise model with one parameter per dataset (D). The visualization is truncated at 50 kJ/mol from the global minimum. For each case (prior, exact, Gaussian and outliers noise), we also reported the monodimensional free energies as a function of the dihedrals φ (E-H) and ψ (I-L), calculated directly from the bias potentials. The same analysis using a dataset with systematic errors is reported in Fig. S7. Finally, an important result presented in the original metainference paper 2 , and naturally extendable to M&M simulations, is that the accuracy of the reconstructed ensemble increases upon adding new experimental data, even when the quality of the prior information is poor.
Physiological novelties are often studied at macro-evolutionary scales such that their micro-evolutionary origins remain poorly understood. Here, we test the hypothesis that key components of a complex trait can evolve in isolation and later be combined by gene flow. We use C 4 photosynthesis as a study system, a derived physiology that increases plant productivity in warm, dry conditions. The grass Alloteropsis semialata includes C 4 and non-C 4 genotypes, with some populations using laterally acquired C 4adaptive loci, providing an outstanding system to track the spread of novel adaptive mutations. Using genome data from C 4 and non-C 4 A. semialata individuals spanning the species' range, we infer and date past migrations of different parts of the genome. Our results show that photosynthetic types initially diverged in isolated populations, where key C 4 components were acquired.
Sampling, sequencing and genome sizing A low-coverage whole-genome sequencing approach (genome skimming) was used to reconstruct the genome history of Alloteropsis. This approach has become increasingly attractive for inferring population parameters (e. g. Buerkle & Gompert 2013; Fumagalli et al. 2013)  and for studying complex traits  (Li et al. 2011). It also allows de novo assembly of high copy number regions of the genome, such as organelle genomes  (Besnard et al. 2014; Dodsworth 2015) , and can be applied to samples of limited quality and quantity, such as herbarium or museum collections  (Besnard et al. 2014). Genome-skimming data for eleven Alloteropsis semialata individuals, and one of each of the congeneric Alloteropsis cimicina and Alloteropsis angusta, were retrieved from a previous study that used them to assemble chloroplast genomes (Table  S1 , Supporting information;  Lundgren et al.
The final data set consisted of sequence data for a total of 21 individuals, sequenced in six different runs (Table  S1 , Supporting information). The genome size was estimated for accessions for which live material was available by flow cytometry following the one-step protocol of Dole zel et al. (2007)  with minor modifications as described in  Clark et al. (2016). We selected Oryza sativa IR36 (2C = 1 pg; Bennett & Smith 1991) and the Ebihara buffer (Ebihara et al.
The filtered reads were mapped to the Setaria reference genome, using BOWTIE2 v. 2. 2. 3  (Langmead et al. 2009). Raw alignment files were cleaned using SAMTOOLS v. 1. 2 (Li et al. 2009 ) and PICARD TOOLS v. 1. 92 (http://pica rd. sourceforge. net/). PCR duplicates were removed, and only uniquely aligned reads in proper pairs were kept. This will remove most of the reads mapped to repetitive sequences, such as transposable elements, while retaining reads mapping to sequences that have been duplicated after the split of Alloteropsis and Setaria. The cleaned alignments were used to call single nucleotide polymorphic variants (SNPs) with SAMTOOLS v. 0. 1. 19 using the mpileup function followed by the vcfutil. pl script with default setting supplied with the program. The South African C 4 individual 'RSA3' was excluded during SNP calling to avoid any bias that might result from the presence of more than two alleles in this polyploid (see  Lundgren et al.
2015  and Table  S1 , Supporting information). Genotypes of each accession, including 'RSA3', at all called SNP positions were extracted from the alignments using the mpileup function in SAMTOOLS v. 0. 1. 19, supplying the program with the positions of the called SNPs, and in-house developed scripts for further processing (Appendix S1, Supporting information). The low-coverage data caused genotype probabilities to be low, which precluded effective filtering based on these probabilities. Therefore, fixed genotype calls were used. To evaluate the proportion of SNPs corresponding to exon sequences, annotations were extracted for the 25 727 coding regions of the Setaria genome with homologs in maize and rice genomes (from now on referred to as SZR homologs). The positions of the raw SNPs were intersected with the SZR homolog annotations in BEDTOOLS v.
2. 19. 1 using default settings  (Quinlan & Hall 2010). SNPs with coverage above 2. 5 times the genomewide coverage (Table  S2 , Supporting information) were converted to unknown genotype calls. Furthermore, genotypes with more than two allele calls were also converted to missing data, and finally, positions with more than 50% missing data/unknown genotypes were discarded. The remaining 170 629 positions were used to infer a phylogenetic tree, using PhyML  (Guindon et al. 2010 ) and a GTR substitution model (the best fit model as determined by hierarchical likelihood ratio tests), after coding heterozygous sites with IUPAC codes. Support was evaluated with 100-bootstrap pseudoreplicates. The low-coverage data likely cause some alleles to be missed, leading to an overestimate of homozygosity.
Genetic structure and test for secondary gene flow Preliminary cluster analyses with a focus on A. semialata showed that a more stringent filtering of the SNPs improved convergence of the analyses. Only positions with <10% missing data (2607 SNPs) within this species were consequently kept for analyses of its population structure, using the STRUCTURE software  (Pritchard et al. 2000). Ten independent analyses were run for each number of population components (K) from one to ten, under the admixture model. The adequate run length and burn-in periods were determined through preliminary analyses, which indicated that a burn-in period of 300 000 generations followed by 200 000 iterations provided stable estimates for all K values. The optimal K values were determined using the method of  Evanno et al.
2010; Durand et al. 2011) , was used to test for secondary gene flow on a genomewide level in cases suggested by phylogenetic and clustering analyses (see Results). The low coverage likely leads to an overestimate of homozygous sites, but no bias is expected towards ABBA or BABA sites, leaving estimations of distorted gene flow unaffected. For each test, a four-taxon phylogeny was selected, consisting of an outgroup and three tips among which secondary gene flow is suspected. Reads mapping to the 170 629 SNPs were recovered from the filtered alignment files using BEDTOOLS v. 2. 19. 1 by intersecting the alignment files with positional information of the SNPs using default settings. The recovered reads were evaluated using the -doAbbababa option in the ANGSD program version 0. 911  (Korneliussen et al.
Using molecular dating, the divergence times of the laterally acquired genes were compared to those of vertically inherited homologs belonging to the same set of accessions. Reads were first mapped on gene segments of the ppc and pck genes from different accessions of Alloteropsis (grass co-orthogols ppc-1P3 and pck-1P1;  Christin et al. 2012 Christin et al. , 2015)). These segments have been previously sequenced and analysed in a number of other C 3 and C 4 grasses  (Christin et al. 2012). The availability of this rich reference data set allows mapping to closely related accessions of Alloteropsis, which improves the alignment success compared to the whole-genome approach described above, and increases the confidence in the assignment. The gene segments cover exons 8-10 for ppc and exons 3-10 for pck, including introns, and represent approximately 46% (1492 bp) and 63% (1487 bp), respectively, of the full-length coding sequences.
In-house Perl scripts (Appendix S3, Supporting information) were used to unambiguously assign reads to genes of these data sets, following the phylogenetic annotation method of  Christin et al. (2015). In summary, this approach consists of: (i) building a reference data set of sequences with known identity for closely related gene lineages, (ii) using blast searches to identify all sequences homologous to any of these reference sequences in the query data set (the filtered reads in this case), (iii) independently aligning each homologous sequence to the reference data set and inferring a phylogenetic tree and (iv) establishing the identity of each of the query sequences based on the phylogenetic group in which it is nested. Assignment of reads to the gene lineages was verified by visual inspection of the phylogenetic trees and the alignments.
These PCR were conducted only on samples for which good quality DNA was available. Indeed, DNA isolated from herbarium samples is highly degraded, precluding reliable PCR screening. To verify the gene models assembled from genome skimming for ppc and pck, the PCR amplified and Sanger sequenced fragments of the vertically inherited and laterally acquired genes were added to the genes assembled from short-read data. The data sets were aligned using MUSCLE v3. 8. 31  (Edgar 2004 ) with default parameters, and the alignments were manually refined. Maximum-likelihood phylogenetic trees were inferred using PhyML, under a GTR+G model, and with 100bootstrap pseudoreplicates. Molecular dating was performed on the same alignments using BEAST as described above for chloroplast markers, but with a coalescent prior.
Acknowledgements This work was funded by a  Royal Society University   Research Fellowship   URF120119 , a  NERC grant   NE/M00208X/1 , an  ERC  grant  ERC-2014-STG-638333  to PAC and a ' Ciência sem Fronteiras ' CNPq scholarship 201873/2014-1 to MB. GB is supported by the  'Laboratoire d'Excellence (LABEX) ' entitled  TULIP  ( ANR-10-LABX-0041 ;  ANR-11-IDEX-0002-02 ) and received support from the  PhyloAlps  project. The authors thank the  Royal Botanic Gardens, Kew , the  Botanic Garden Meise , and the  National Museums of Kenya, Nairobi , which provided the samples used in this study. Olivier Bouchez  from the  Genopole  in Toulouse helped with the Illumina sequencing.
Let us consider an example from the cloud computing domain. The company offers a service to rent computers on a cloud with different possible software configurations using Linux-based distributions. In the back-end, instead of providing physical machines, the company provides virtual machines with some software package installed on them. Thus, the configuration of machines can be generated on demand according to the needs of the users. In order to improve the speed of creation of a new machine, there are pre-configured machines ready to launch. In this example, the possible configurations offered to the users define the problem space. The set of available Linux packages implementing the features is the core assets. The pre-configured machines can be seen as another set of assets (limited but available immediately).
Analysis of such a huge search space is a non-trivial task. Some interesting analyses that could performed in this scenario of a Virtual Machine Product Line (VMPL) are as follows: 1. Check if at least one of the pre-configured machines covers the needs of a new user configuration: In VMPL, there is always a need to check the existence of any virtual machine as per the given user specification. For example, the specification F={Virtual Machine, UserInter f ace, Console, GU I, GNOME} should be first analyzed to check the existence of any implementation that implements F. The implementation C={LinuxCore, IUser, IConsole, Terminal, IGU I, GNOMEApp, IServer, TomcatApp} (equivalent to preconfigured virtual machine 2 in Figure  3 ) provides all the features in the specification F, it means that there exists a pre-configured machine which covers the user specification F.
2. Check if at least one of the pre-configured machines realizes (exactly) the needs of a new user configuration: Multiple implementations may cover a given user specification F. We can analyze the VMPL to find the realized implementation for the user specification. For example, the implementation C={LinuxCore, IUser, IConsole, Terminal, IGU I, GNOMEApp} (equivalent to preconfigured virtual machine 3 in Figure  3 ) exactly provides all the feature in the specification F. 3. Check if there are dead packages: Actual VMPLs contain a huge number of components for Linux systems. The components that are not present in any of the products are termed as dead elements in the product line. In the given VMPL, none of the components is dead.
SPLAnE SPLAnE (Software Product Line Analysis Engine) is designed and developed to analyze the traceability between the features and implementation assets. Nowadays, there is the large set of tools that enable the reasoning over feature models. However, none of them is capable of reasoning over the feature model and a set of implementations as described throughout this paper. For the sake of reusability and because it has been proven to be easily extensible  [38, 39] , we chose to use the FaMa framework  [29]  as the base for SPLAnE. The FaMa framework provides a basic architecture for building FM analysis tools while defining interfaces and standard implementation for existing FM operations in the literature such as Valid Model or Void Product Model. On the one hand, SPLAnE benefits from being a FaMa extension in different ways.
Later, we executed the analysis operations with SPLAnE reasoner -CirQit and the FaMa reasoner -Sat4j. Figure  10  shows the results for QSAT vs SAT based reasoning for few analysis operations on real models taken from the SPLOT repository. QSAT defeat SAT encoded formulae for every analysis operations. The execution of all models can be found at www. cse. iitb. ac. in\~splane as well as the scripts used to generate this data. The first noticeable results are that SPLAnE overtakes all executions of all operations when comparing to the standard FaMa version (which is using Sat4j as a solver). Moreover, we see improvements of more than 70% -note the log scalewhen talking about the soundness operation. Therefore, after trying to refute the null hypothesis H 0 ( for experiment 5. 2. 5) with no luck, we have to accept the alternative hypothesis H 1 which states that SPLAnE is faster and scalable than previously standard SAT-based techniques.
Marcilio et al. presented the experimental results to prove the SAT-based approach to analyze a variability models like feature model is easy  [51]. Authors have used feature models with maximum of 10000 feature. To increase the hardness, feature models where added with 10%, 20% and 30% of cross-tree constraints (CTC). They found that realistic feature models are not difficult for SAT solvers. Steven et al. present the heuristic to generate a feature model from an existing system using reverse engineering  [52]. They used three real system as Linux, eCos and FreeBSD. The Linux has a variability model represented by Kconfig Language and eCos has Component Definition Language, where as FreeBSD has a list of features. The approach was able to successfully generate the feature model from Linux, eCos and FreeBSD systems.
Conclusion In this paper, we stress the need to jointly analyze the specification and the implementation of SPLs. Thus, we have started from a formal definition of the notion of traceability and a set theoretical based framework. We imported existing analyses and propose new analyses such as superfluousness, explicitness, redundant, union, intersection, valid model, void product model, complete traceability, etc. The analysis problems have been translated into Quantified Boolean Formula and solved efficiently using a QBF solver. The approach is supported by a software tool called SPLAnE and integrated with the existing FaMa framework. We conducted a detailed experimentation with SPLAnE on i) Large debian models ii) Randomly generated models and iii) SPLOT models. We executed all analysis operations with 5 solvers i.
This led to the installation of such networks, first on Montserrat Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2015 -380, 2016  Manuscript under review for journal Atmos. Meas. Tech. Published: 18 January 2016 c Author(s) 2016. CC-BY 3. 0 License. dilution effects is direct, and this will be investigated in more detail in a future paper. Finally, as we show in the case studies below, the SO2 absorption itself has a large broadband component that is removed in the DOAS approach, but which contains useful information that aids in the intensity fitting procedure. In the following sections we briefly review the DOAS approach, and describe a hybrid retrieval which is currently in use on Etna and Stromboli scanning networks. We then explain the iFit methodology in detail. We present case studies from clear sky spectra, calibration cell spectra and traverse measurements on Mt.
The question we pose is: if we are performing the calculations of transmittance in terms of intensity, why not perform the fit in terms of intensity as well? Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2015 -380, 2016  Manuscript under review for journal Atmos. Meas. Tech. Published: January 2016 c Author(s) 2016. CC-BY 3. 0 License. Direct fitting of measured intensities is, in fact, frequently used for analysis of UV spectra. The Global Ozone Monitoring Experiment (GOME) is analysed with such a method  (Van Roozendael et al. , 2012). In their analysis they find significant advantages for the direct fitting approach over DOAS, which neglects the wavelength dependency of the photon path length, producing misfits in ozone retrievals. We chose the direct fitting approach of iFit for three main reasons.
Test Cases In the following sections we present a series of test cases, where the iFit retrieval is applied to spectra collected in a range of situations, from clear sky spectra, to SO2 calibration cell spectra and finally volcanic plume traverse data. The purpose of these tests is to demonstrate the detection limit and error performance of the iFit system using the basic fit, flat spectrum removal and solar spectrum residual removal. By using the calibration cell spectra we determine the accuracy and precision of iFit. Finally, we use the traverse data test case to demonstrate the efficacy of the retrieval for volcanic plume SO2 and BrO retrievals.
USB2000 spectrometers exhibit temperature-dependent wavelength shifts, probably due to thermal expansion effects in the chassis. This helps to explain why in a classic DOAS analysis of traverse data collected using a clear sky spectrum the fit may degrade during the course of the traverse, if a non-temperature stabilised spectrometer is used. The radiation spectrum will be shifted relative to the flat spectrum, producing an increasingly significant misfit. This, together with better dark spectrum stability, is one of the main reasons why temperature stabilisation is a good idea, if the flat spectrum is not known. However, it is extremely simple to measure the flat spectrum, and once it is done can probably be used for extended periods of time before the CCD performance changes significantly.