dataesr/scientific-paragraphs-categorization huggingface dataset:
Superstructure structural refinement The X-ray diffuse scattering data were complemented by full single-crystal X-ray data collection and structural refinement performed at 85 and 20 K. 3. 5. 1. The q 1 = (0, 1 2 , 1 2 ) superstructure refinement. X-ray data collection was performed at 85 K, slightly below T SP , because the q 1 = (0, 1 2 , 1 2 ) superstructure intensity was the strongest and the additional q 2 = ( 1 2 , 0, 0) contribution is still very weak. The data were refined by integrating the q 1 = (0, 1 2 , 1 2 ) superstructure and the structure was solved in the triclinic system, space group P1, now with four crystallographically independent o-Me 2 TTF molecules (A-D) and two NO 3 À anions (on atoms N1A and N1B), all in general positions. The lattice parameters (a, b 0 , c 0 ) of the triclinic cell are connected to the high-symmetry lattice parameters of the monoclinic phase: b 0 = b À c and c 0 = b + c (Fig.
We apply those methods to a dataset of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks. (Keywords: Phylogenetic Networks, Phylogenetic Comparative Methods, Transgressive Evolution, Heterosis, PhyloNetworks) The evolutionary history of species is known to shape the present-day distribution of observed characters  (Felsenstein 1985). Phylogenetic Comparative Methods  (PCMs)  have been developed to account for correlations induced by a shared history in the analysis of a quantitative dataset  (Pennell and Harmon 2013). They usually rely on two main ingredients: a time-calibrated phylogenetic tree, and a dynamical model of trait evolution, that should be chosen to capture the features of the trait evolution over time.
Simulation Study Setting. -We considered 4 network topologies, all based on the same symmetric backbone tree with unit height and 32 tips, to which we added several hybridization events (Fig. 3 ,  top). Those events were either taken very recent and numerous (h = 8 events each affecting 1 taxon) or quite ancient and scarce (h = 2 events each affecting 4 taxa). All networks had 8 tips with a hybrid ancestry. All the hybridization events had inheritance probability γ = 0. 3. We then simulated datasets on these networks with µ = 0, σ 2 = 1, and Pagel's λ transformation with λ in {0, 0. 25, 0. 5, 0. 75, 1}. Recall that λ = 0 corresponds to all tips being independent, and λ = 1 is the simple BM on the original network. Each simulation scenario was replicated 500 times. To study the scalability of the implementation, we then reproduced these analysis on networks with 32 to 256 tips, and 1 to 8 hybridization events, each affecting 8 tips.
We analysed each dataset assuming either a BM or a λ model of evolution. When λ = 1, we could study the effect of wrongly using the BM. All the analyses were conducted on a laptop computer, with four Intel Core i7-6600U, and a 2. 60GHz CPU speed. Results. -When the vanilla BM model is used for both the simulation and the inference, the two parameters µ and σ 2 are well estimated, with no bias, for all the network topologies tested (Fig. 3 , last two rows, dark grey boxes for true λ = 1). The estimation of µ is quite robust to the misspecification of the model, while σ 2 tends to be over-estimated (Fig. 3 , last two rows, dark grey boxes for true λ = 1). This is expected, as in this case, the BM model wrongly tries to impose a strong correlation phylogenetic structure on the data, and can only account for the observed diversity by raising the estimated variance, to accommodate both phylogenetic variance and independent intra-specific variation.
This average pairwise distance matrix was used to estimate node ages on each network (h = 0, 1, 3). The network pairwise distance between taxa i and j was taken as the weighted average distance between i and j on the trees displayed by the network, where the weight of a displayed tree is the product of the inheritance probabilities γ e for all edges e retained in the tree. We estimated node ages that minimized the ordinary least-squares mismatch between the genetic pairwise distances and the network pairwise distances. Traits. -With data presented in Cui et al. (  2013 ) and following their study on sword evolution, we revisited the hypotheses that females have a preference for males with longer swords, and that the common ancestor of the genus Xiphophorus likely had a sword. Rather than using the methods of parsimony character mapping and independent contrasts as in Cui et al.
(  2013 ), we tested the effect of hybridization on the ancestral state reconstructions and the correlation between both traits using networks with zero, one or three hybridization events, using phyloNetworklm. For each network, the topology and branch lengths were assumed to be perfectly estimated, and fixed. We also tested for phylogenetic signal in both traits on all networks using Pagel's λ, as well as for transgressive evolution, using the F statistics defined above. For the phylogenetic regression, more than half of the species were excluded because they lack information on female preference. Along with the datasets used, two executables IJulia notebooks (. ipynb) files are provided in the online supplementary material (Dryad data repository doi:10. 5061/dryad. 60t0f), allowing the interested reader to reproduce all the analyses described here.
Impact of the Network The results from the fish dataset analysis using a tree (h = 0) or a network (h = 1 or h = 3) show that taking the hybridization events into account has a small impact on the ancestral state reconstruction and on the estimation of parameters, both for the regression analysis and for the test for phylogenetic signal. This finding was corroborated by simulations: when we ignored hybridization events, using a tree while the true underlying model was a network, we found that the estimation of parameters µ and σ 2 was only slightly affected (data not shown). These results may indicate that major previous Figure  7 : Results of the analysis on the fish dataset. Top left: negative pseudo log-likelihood score of the estimated networks with various numbers of hybridizations. Top right: scatter plot of sword index and female preference.
Acknowledgments We thank  Frank Burbrink  and all participants of the spotlight session on the impact of gene flow and reticulation in phylogenetics, at the 2017 Evolution meeting. PB thanks  Mahendra Mariadassou  and  Stéphane Robin  for enlightening comments on an early version of this work, and  Tristan Mary-Huard  for useful discussions about the linear mixed model. The authors thank  Mohammad Khabbazian  for insights on the topological sorting algorithm, and  Guilherme Rosa  for discussions on the pedigree model. This manuscript also highly benefited from feedback by  Frank Burbrink ,  Brian O'Meara  and two anonymous reviewers.
Context. The NASA Kepler space telescope has detected solar-like oscillations in several hundreds of single stars, thereby providing a way to determine precise stellar parameters using asteroseismology. Aims. In this work, we aim to derive the fundamental parameters of a close triple star system, HD 188753, for which asteroseismic and astrometric observations allow independent measurements of stellar masses. Methods. We used six months of Kepler photometry available for HD 188753 to detect the oscillation envelopes of the two brightest stars. For each star, we extracted the individual mode frequencies by fitting the power spectrum using a maximum likelihood estimation approach. We then derived initial guesses of the stellar masses and ages based on two seismic parameters and on a characteristic frequency ratio, and modelled the two components independently with the stellar evolution code CESTAM.
Introduction Stellar physics has experienced a revolution in recent years with the success of the CoRoT  (Baglin et al. 2006)  and Kepler space missions  (Gilliland et al. 2010a). Thanks to the high-precision photometric data collected by CoRoT and Kepler, asteroseismology has matured into a powerful tool for the characterisation of stars. The Kepler space telescope yielded unprecedented data allowing the detection of solar-like oscillations in more than 500 stars  (Chaplin et al. 2011 ) and the extraction of mode frequencies for a large number of targets  (Appourchaux et al. 2012b; Davies et al. 2016; Lund et al. 2017). From the available sets of mode frequencies, several authors performed detailed modelling to infer the mass, radius, and age of stars  (Metcalfe et al. 2014; Silva Aguirre et al.
Most stars are members of binary or multiple stellar systems. Among all the targets observed by Kepler, there should be many systems showing solar-like oscillations in both components, that is, seismic binaries. However, using population synthesis models,  Miglio et al. (2014)  predicted that only a small number of seismic binaries are expected to be detectable in the Kepler database. Indeed, the detection of the fainter star requires a magnitude difference between both components typically smaller than approximately one. Until now, only four systems identified as seismic binaries using Kepler photometry have been reported in the literature: 16 Cyg A and B (KIC 12069424 and KIC 12069449;  Metcalfe et al. 2012 Metcalfe et al. , 2015;; Davies et al. 2015) , HD 176071  (KIC 9139151 and KIC 9139163; Appourchaux et al.
2010)  through the Kepler Asteroseismic Science Operations Center (KASOC) database 2. The SC time series for HD 188753 (KIC 6469154) is divided into quarters of three months each, referred to as Q13 and Q14. Custom Aperture File (CAF) observations are handled differently from standard Kepler observations. A specific Kepler catalogue ID 3 greater 1 https://keplergo. arc. nasa. gov/ 2 http://kasoc. phys. au. dk/ 3 Kepler IDs are 100 004 033 and 100 004 071 for Q13 and Q14 respectively. a) Magnitude difference between the visual components. ( b) Systemic velocity and parallax derived in Sect. 3. 2. ( c) Parallax and corresponding distance derived in Sect. 6. 2. than 100 000 000 is then assigned to each quarter. This work is based on SC data in Data Release 25 (DR25) 4  , which were processed with the SOC Pipeline 9.
Method and model In this section, we present the methodology employed to determine the orbital parameters of the triple star system HD 188753. For this, we performed a combined treatment of more than a century of archival astrometry (AM) along with the radial-velocity (RV) measurements found in the literature. Recently,  Appourchaux et al. (2015)  derived the orbit of the seismic binary HD 177412 (HIP 93511) by applying a Bayesian analysis to the astrometric measurements of the system. We adapted this Bayesian approach in order to include the radialvelocity measurements for HD 188753. We defined the global likelihood of the data given the orbital parameters as: L = L AM L RV , (1) where L AM and L RV are the likelihoods of the AM and RV data respectively, computed from: ln L AM = - 1 2 N AM i=1               x mod i -x obs i σ x,i       2 +       y mod i -y obs i σ y,i       2         , (2) ln L RV = - 1 2 N RV i=1       V mod i -V obs i σ V,i       2.
Orbit of the AB system We applied the methodology described above in order to derive the best-fit orbital solution of the visual pair, hereafter referred to as the AB system. However, the radial-velocity measurements need to be reduced from the 154-day modulation induced by the close pair, hereafter referred to as the Bab sub-system. We then fitted this 154-day modulation as described in Sect. 3. 3 (see also Appendix D) to obtain the long-period orbital motion of the AB system. Figure  2  shows the best-fit solution of the astrometric orbit, plotted with all micrometric and interferometric data listed in Appendix A. The position of the primary component is marked by a plus symbol at the origin of the axes. O -C residuals of the orbital solution are indicated by the solid lines connecting each observation to its predicted position along the orbit.
Input physics In order to determine precise stellar parameters for HD 188753, we performed a detailed modelling of each seismic component using the stellar evolution code CESTAM (Code d' Évolution Stellaire, avec Transport, Adaptatif et Modulaire;  Morel & Lebreton 2008; Marques et al. 2013 ). For our model calculations, we adopted the 2005 version of the OPAL equation of state  (Rogers & Nayfonov 2002)  and the OPAL opacities  (Iglesias & Rogers 1996)  complemented by those of  Alexander & Ferguson (1994)  for low temperatures. The opacity tables are given for the new solar mixture derived by  Asplund et al. (2009) , which corresponds to (Z/X) ⊙ = 0. 0181. The microscopic diffusion of helium and heavy elements, including gravitational settling, thermal and concentration diffusion, but no radiative levitation, was taken into account following the prescription of  Michaud & Proffitt (1993).
Model optimisation For each seismic component of HD 188753, we constructed a grid of stellar evolutionary models using CESTAM with the input physics described above. The goodness of fit was evaluated for all of the grid models from the merit function χ 2 given by: χ 2 = (x mod -x obs ) T C -1 (x mod -x obs ), (14) where x obs denotes the observational constraints considered, that is, the frequency ratios defined in Eqs. (  10 ) and (  11 ), where x mod denotes the theoretical values predicted by the model and T denotes the transposed matrix. Here, C refers to the covariance matrix of the observational constraints, which is not diagonal due to the strong correlations between the frequency ratios. The theoretical oscillation frequencies used for the calculation of the frequency ratios were computed with the Aarhus adiabatic oscillation package (ADIPLS; Christensen-Dalsgaard 2008).
We then estimated the initial [Z/X] 0 ratio as being in the range 0. 01-0. 05, which was shifted to a higher value to compensate for diffusion. We adopted α MLT ∈ [1. 6, 2] following the solar calibration of  Trampedach et al. (2014)  that corresponds to α MLT,⊙ = 1. 76 ± 0. 04. Secondly, in order to determine the final parameters of the two stars, we employed a Levenberg-Marquardt minimisation method using the Optimal Stellar Models (OSM) 7  software developed by R. Samadi. For star A, we selected the 50 grid models with the lowest χ 2 values and used them as a starting point for the final optimisation. We then performed a set of local minimisations adopting the age of the star, the mass, the initial helium abundance, the initial (Z/X) 0 ratio and the mixinglength parameter as free model parameters.
Acknowledgements. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the  NASA Science Mission directorate. The authors gratefully acknowledge the  Kepler Science Operations Center (SOC)  for reprocessing the short-cadence data that enabled the detection of the secondary seismic component. This research has made use of  NASA's Astrophysics Data System Bibliographic Services , the  Washington Double Star Catalog  maintained at the  U. S. Naval Observatory , the SIMBAD database, operated at  CDS, Strasbourg, France  and the VizieR catalogue access tool,  CDS, Strasbourg, France. The original description of the VizieR service was published in A&AS 143, 23. Finally, we also thank the anonymous referee for comments that helped to improve this paper.
Acknowledgments We thank  Richard Damania ,  Jorn Scharlemann , and  Julia Baker  for comments. A special thank you to  Matthew Gleeson  for help creating figure 1. This article originated at a 2016 workshop, entitled the " Interdisciplinary Conservation Network " and was supported by the  Pew Charitable Trusts  through a  Pew Marine Fellowship  to EJMG, the  University of Oxford , and the  ARC Centre of Excellence for Environmental Decision-Making. WNSA is supported by the  Commonwealth Scholarship Commission  in the United Kingdom, and the  University of Oxford  with a PhD scholarship no. NZCR-2015-174. JWB is supported through a  Marie Skłodowska-Curie Fellowship  no. 655497  and acknowledges the  Danish National Research Foundation  for funding the  Center for Macroecology, Evolution and Climate  grant no.
The legal system does not constitute a typical database. The Polish legal system is an example of the continental law system. It consists of norms-rules of conduct, which should be derived from the legal text. This means that the Polish legal system does not consist of provisions (editorial units of legal text), the potential identification of which will constitute no difficulty for the computer program using the advancements in the field of AI, but of legal norms included in the provisions of normative acts, to be derived by the process of interpretation. While studying the admissibility of automation of the civil procedures using AI tools, it is necessary to analyze, whether the relevant system would be able to establish which legal norm should govern the issue to be decided on, which norm is currently binding in the legal system, and its meaning  (Lang et al.
1986 ). An artificial intelligence system would have to be equipped with tools making it possible to interpret the law according to linguistic, systemic and functional interpretation, in order to determine the meaning of the norm, to identify its linguistic context, place it in the legal system and determine the implicit intentions of the legislator (which often means going beyond strictly legal criteria, moving to moral, political or economic assessments; Leszczyn ´ski 2003). It is worth noting that just search for the relevant provision of law would constitute a good task for the tools of artificial intelligence, and automation of this particular stage of law application would certainly contribute to improving the speed and quality of the proceedings. Artificial intelligence techniques have currently the possibility to perform fast and accurate databases searches, much better than humans can.
Concerning the strategies: top-down consists of the definition of the highest part of the conceptual structure of the criminal domain which is modeled as ontology modules (upper and core). In this strategy, reusing existent ontologies, that capture similar or complementary knowledge (foundational and core-legal ontologies such as UFO  (Guizzardi 2005) , LKIF-Core  (Hoekstra et al. 2007; Hoekstra 2000)  can help in building a well-founded ontology. Meanwhile, the bottom-up strategy consists of extracting the legal concepts and relations among them from textual resources (the Lebanese criminal code) by using Ontology Learning and NLP techniques and then modeling this knowledge as domain and domain-specific modules. Eventually, the resulting ontologies are combined to form the complete architecture of the criminal domain ontology.
2012). Therefore, UFO permits the building of an ontology reusing some generic concepts such as Category, Kind, Subkind, Relator, Role, Role_Mixin and Event, where the ontologist does not need to rebuild these concepts. In order to make possible the activity of conceptual modeling via UFO, a conceptual modeling language, named OntoUML  (Guerson et al. 2015) , is used. OntoUML uses the ontological constraints of UFO as modeling primitives and is specified above the UML 2. 0 meta-model. 20 ore ontology module The COM consists of concepts and relations that are common across the domains of law and can provide the basis for specialization into domain and domain-specific concepts. The same perspective is applied, as for the upper module, for reusing partially the legal core ontology, LKIF-Core, 21  to build this module (see Fig.
Acknowledgements The participants of the doctoral consortium would like to thank  Guido Governatori, Leon van der Torre ,  Bart Verheij ,  Enrico Francesconi , and  Giovanni Sartor  for volunteering as mentors and  Trevor Bench-Capon  for his constructive comments and editorial efforts while developing their research reports. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymous mutations computed either from exome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci.
A major challenge in ABC, as compared to explicit likelihood methods, is the selection of summary statistics, which involves a trade-off between loss of information and reduction of dimensionality. Several methods have been suggested to select the most appropriate statistics for a given dataset and a set of models  (Wegmann, Leuenberger & Excoffier, 2009; Nunes & Balding, 2010; Aeschbacher, Beaumont & Futschik, 2012) ; but most of these summarize the SFS (but see, e. g. ,  Boitard et al. , 2016)  leading to a loss of information. Recently, several ABC studies have used the SFS directly to reconstruct the history of single populations  (Boitard et al. , 2016) , or multiple populations  (Xue & Hickerson, 2015; Smith et al. , 2017). However, the number of statistics, that is, Figure 1 Models of speciation.
, 2014 , 2016). Here, we analyzed coding sequence datasets from this well-known pair of sister species in Europe, and systematically reconstructed its speciation history by ABC, using different sampling strategies. By different sampling strategies, we mean that we varied (i) the number of individuals sampled (two, four or eight), (ii) the number of SNPs (which were obtained by different sequencing techniques, "exome capture" vs. "rna-seq"), and (iii) the number of bins in the jSFS (binning the spectrum into four, seven or 23 classes). We then evaluated the influence of these choices on model selection, using eleven distinct scenarios of speciation. Our results show that the influence on inferences of the number of individuals and loci sampled is surprisingly limited, while the different ways of binning the jSFS strongly affect the results.
MATERIALS AND METHODS Sampling, sequencing, mapping, and calling Two datasets were analysed for demographic inferences. They both comprise measures of molecular polymorphism and divergence in coding sequences, obtained for the pair M. edulis and M. galloprovincialis. Although the adopted sequencing techniques were different among datasets ("exome capture" vs. "rna-seq"), the surveyed populations were similar. The usage of coding sequences to infer divergence histories of closely-related species (e. g. , see  Roux et al. , 2013 Roux et al. , , 2014 Roux et al. , , 2016;; McCoy et al. , 2014; Li et al. , 2017; Qi et al. , 2017)  is justified for several reasons: (i) synonymous mutations are less affected by direct selection than other categories of mutation, and selection affects chromosomal regions larger than genes themselves, including non-coding regions (e.