dataesr/scientific-paragraphs-categorization huggingface dataset:
To improve the phylogenic resolution among the major lineages, the matK gene and the ITS sequence datasets were combined into a single phylogenetic analysis where only well-supported nodes were considered (posterior probability (PP) ≥ 0. 5) (Fig. 1 ). Our analysis recovered a grade of five main lineages with a branching order that received robust support (PP ≥ 0. 92): (1) a basally branching lineage including A. americana, (2) an A. montevidensis lineage, (3) an A. evenia lineage corresponding to the Nod-independent clade  [15, 27] , (4) a new-identified lineage containing A. patula and (5) a lineage represented by an unresolved polytomy gathering the A. afraspera clade  [19]  and all the remaining taxa. Our work also provided in the main good species-level resolution and it showed that Aeschynomene subgenus Aeschynomene (as currently circumscribed) is polyphyletic, being interspersed on the phylogenetic tree with the lineage containing A.
5  a)  [21, 31, 32]. This is in sharp contrast with both A. americana and A. villosa that are well-collected, being widely found as weedy plants and sometimes used as component of pasture for cattle (Fig. 5   accessions for A. villosa, and spanning their known distribution was used (Additional file 16: Table  S4 ). A Genotyping-By-Sequencing (GBS) approach resulted in 6370 and 1488 high quality polymorphic SNP markers for A. americana and A. villosa accessions, respectively. These two SNP datasets subsequently served for a clustering analysis based on the multidimensional-scaling (MSD) method. The MSD analysis distinguished three major groups of accessions for both A. americana and A. villosa along coordinate axes 1 and 2 (Fig. 5 b ). When mapping the accessions globally, the three groups identified for A.
Phylogenetic analyses and traits mapping Sequences were aligned using MAFFT (--localpairmaxiterate 1000;  [52] ). Phylogenetic reconstructions were performed for each gene as well as for concatenated datasets under a Bayesian approach using Phylobayes 4. 1b  [53]  and the site-heterogeneous CAT +F81 + Γ4 evolution model. For each analysis, two independent chains were run for 10,000 Phylobayes cycles with a 50% burn-in. Ancestral states reconstruction was done through stochastic character mapping using the Phytools R package  [54]  running 10 simulations for each character.
GBS analysis A GBS library was constructed based on a protocol described  [56]. For each sample, a total of 150 ng of genomic DNA was digested using the two-enzyme system, PstI (rare cutter) and Mse (common cutter) (New England Biolabs, Hitchin, UK), by incubating at 37 °C for 2 h. The ligation reaction was performed using the T4 DNA ligase enzyme (New England Biolabs, Hitchin, UK) at 22 °C for 30 min and the ligase was inactivated at 65 °C for 30 min. Ligated samples were pooled and PCR-amplified using the Illumina Primer 1 (barcoded adapter with PstI overhang) and Illumina Primer 2 (common Y-adapter). The library was sequenced on an Illumina HiSeq 3000 (1 × 150 pb) (at the Get-PlaGe platform in Toulouse, France). The raw sequence data were processed in the same way as in the study described in  [57].
SNP calling from the raw Illumina reads was performed using the custom python pipeline VcfHunter (available at https://github. com/South-GreenPlatform/VcfHunter/) (Guillaume Martin, CIRAD, France). For all samples, these sequence tags were aligned to the A. evenia 1. 0 reference genome (JF Arrighi, unpublished data). The SNP results from all the samples were converted into one large file in VCF format and the polymorphism data were subsequently analyzed using the web-based application SNiPlay3  [58]. First, the SNP data were treated separately for each species and filtered out to remove SNP with more than 10% missing data as well as those with a minor allele frequency (MAF) 0. 01 using molecular cytogenetic facilities of the AGAP laboratory (http://umr-agap. cirad. fr/en/plateformes/plateau-de-cytogenetique-moleculaire).
In general for the entire region, the determination of mantle seismic anisotropy patterns helps to reconstruct the current and past plate motions and dynamics in three dimensions; the broad coverage with the uniform AASN will allow a more comprehensive analysis with respect to previous studies often focused on smaller areas  (Margheriti et al. 2003; Plomerová et al. 2006; Kummerow et al. 2006; Fry et al. 2010; Barruol et al. 2011; Salimbeni et al. 2013; Qorbani et al. 2016; Subašić et al. 2017). These and other Alpine debates raise the question of data resolution and coverage and call for an integrated geophysical-geological approach. Are some of the published models' inconsistencies due to sparse sampling and to (overlooked) artefacts? Can they be overcome by optimizing seismic network aperture and station coverage? The purely passive experiments planned by AlpArray involve a more densely spaced network than previous experiments.
1 ). The societal relevance of the AlpArray project clearly lies in the opportunity to improve seismic hazard maps (e. g. ,  Stucchi et al. 2012; Grünthal et al. 2013). This is especially needed for areas that are among the most densely populated in Europe: while the Alps sensu stricto host "only" 14 million people in ca. 0. 2 million km 2 , the greater Alpine area (as defined in Sect. 4. 1) is the home of about 115 million people  (CIESIN and CIAT 2005)  and a significant economic value over ca. 0. 9 million km 2. The homogeneous data provided by AlpArray will augment the reliability of earthquake hazard assessments. The Alps are one of the (if not "the") best-studied active mountain ranges in the world. They have exceptional geophysical and geological databases which are excellent requisites for advancing our general knowledge and understanding of orogenic processes on all scales.
The scientific controversies and questions above make it evident that the time is ripe for a new generation of cross-disciplinary geophysical-geodynamical initiatives. These will shed new light on the current state and long-term evolution of the deep structure of the Alps at crustal and mantle depths. The AlpArray Seismic Network is designed as the main field component of the AlpArray programme and will hopefully provide high-resolution, large-aperture seismological data for unprecedented views of orogenesis.
USArray is certainly an example to follow for AlpArray; however, the political and funding conditions are neither comparable nor reachable in Europe, which necessarily have led to a very different organization (see below). Note that while the Transportable Array can also be used to study sources, it is generally employed as a large-aperture network. IberArray (http://ibera rray. ictja. csic. es) is a part of the TopoIberia programme and focused on and around the Iberian Peninsula. More than 50 seismometers have been deployed at a time in different phases moving northward from Morocco to the Pyrenees between 2007 and 2014. The over 200 sites have an average spacing of 60 km. The data became publicly available 3 years after the end of the experiment (Institute Earth Sciences "Jaume Almera" CSIC (ICTJA Spain) 2007).
50% increase from 234 stations in summer 2011 to 352 in summer 2017. This growth rate is beyond the typical evolution of mature permanent networks. The AlpArray initiative coincided with the development of the European Integrated Data Archive (EIDA), which encouraged open access to broadband seismic data sets from permanent networks across Europe. Nevertheless, the AlpArray initiative created additional momentum to encourage the opening of data sets and the construction of new stations in the Alpine area, thus creating a legacy beyond the lifetime of the ongoing temporary experiment. The design target for the sites of the AASN temporary network was to obtain homogeneously spaced coverage with the minimum number of stations, while exploiting the existing permanent stations as well as possible.
alpar ray. ethz. ch/ expor t/sites /alpar ray/. galle ries/dwn-exper iment s/AlpAr ray_Techn icalS trate gy. pdf). Prior to the overall deployment a station naming convention was also adopted following the Standard for the Exchange of Earthquake Data (SEED) (IRIS 2012). A unique station name is composed of 5 alphanumeric characters: a leading "A" (for AlpArray), followed by three digits from a range of numbers distributed for each country, ending with an "A" for the initial site and changed to subsequent letters (B, C, etc. ) in case of major (> 10 m) site changes. In this way the deploying groups could work in parallel and independently. The ranges of station names for the ten countries of temporary station deployment as well as the OBS component are shown in Table  1. AlpArray temporary stations belong to the Z3 network, a code reserved with the International Federation of Digital Seismograph Networks (FDSN).
Regarding realtime communication of the stations, about 75% of the land stations could be equipped with an online communication device (Table  2 ). Among the permanent sites all except two stations are online. Full station details with photographs and further notes are kept up to date on the European Station Book hosted by the Orfeus Data Centre (http://www. orfeu s-eu. org/openc ms/stati onboo k/index. html). The completion of the AASN was achieved by a joint French-German OBS deployment cruise campaign in June 2017. The 30 stations deployed in the Ligurian Sea were offline by nature. Following a joint German-French OBS cruise in February 2018, 27 stations have been successfully recovered, 1 has been released from the ocean-bottom but not yet found, 1 could not be released but includes an automatic trigger set for October 2018.
Data Examples, Access and Network Perspectives To illustrate the power of the AASN, two examples of waveforms across the entire network are shown in Fig. 8 , from a teleseismic and a local earthquake. In the teleseismic case, the coherency of waveforms across the array allows us to see body waves with multiple legs easily. In addition, two waveform animations from a teleseismic and a regional event are provided as Online Resource 5 and 6 to visualize the passage of waves through the AASN. Over 18 GB of data is collected each day by the AASN, and the final size of the data archives is expected to be on the order of 15 TB. The seismological waveforms of the AASN are distributed through the European Integrated Data Archive (EIDA, http://145. 23. 252. 222/eida/webdc 3/), where the data from the 36 institutions that collect seismological data are archived across seven different nodes in five countries.
Data from the temporary sites are available under the Z3 network code, while the virtual network "_ALPARRAY" comprises all stations of the AASN, permanent and temporary. The AASN data are distributed with password protection. Data access is immediate for the Core Group of the AASN members. Registered seismological observatories with monitoring and alerting duties can use the Z3 data with real-time access and may report phase picks in their catalogues; however, waveforms shall not be published. The use of data for research by AlpArray participants requires a priori submission and approval of the research topic by the AASN Core Group. The waveform data will be freely shared among the entire AlpArray Working Group at most 1 year after AASN operation has ended (currently planned for 1 January 2020).
The waveform data will be freely available to the public 3 years after AASN operation has ended (currently planned for 1 January 2022). The quality and completeness of AASN data have been, are and will be periodically reviewed and will be the scope of a forthcoming manuscript. For the earliest set of site noise characterization in different parts of the AASN, we refer to  Fuchs et al. (2016) ,  Molinari et al. (2016) ,  Govoni et al. (2017)  and  Vecsey et al. (2017). The AASN can be cited by referring to this paper and the temporary component (Z3) by the AlpArray Seismic Network (2015) seismic network DOI. The AASN demonstrates the current capability of the community to integrate mobile data within infrastructures traditionally built for permanent network archives -which has been an ORFEUS focus for many years and has been funded through the EU projects NERIES, NERA and now EPOS-IP and SERA.
More secure and professional management of the archives allows better curation and dissemination of these data sets and benefits the wider scientific community. AASN data will be used for numerous seismological applications, which will be subject to forthcoming publications. The depth targets of these will range from shallow (e. g. , sedimentary basins, landslides) to deep (e. g. , mantle transition zone) depth. The methodologies expected to be applied include body and surface wave tomography,.
(a) Swiss National Science Foundation (Grants SINERGIA CRSII2_1544341 and OROG3NY PP00P2_157627); (b) National Research, Development and Innovation Fund, Hungary (Grant NKFI K124241); (c) Hungarian Academy of Sciences (Grants EU-04/2014, EU-07/2015); (d) Czech Academy of Sciences (Grant M100121201); (e) Operational Programme Research, Development and Education (Project CzechGeo/EPOS-Sci, CZ. 02. 1. 01/0. 0/0. 0/16_013/0001800); (f) CzechGeo/EPOS (large research infrastructure, Grants LM2010008 and LM2015079); (g) Austrian Science Fund FWF (Project Numbers 26391 and 30707); (h) Deutsche Forschungsgemeinschaft DFG (Grants SPP2017 and MerMet 14-94); (i) INGV for committing internal funding; (j) Agence Nationale de la Recherche, France (contract ANR-15-CE31-0015); (k) RESIF National Research Infrastructure (Investissements d'Avenir, France, ANR-AA-EQPX-0040); (l) French Environment and Energy Management Agency (ADEME) for funding the EGS-Alsace project; (m) Labex OSUG@2020 programme (Investissements d'Avenir, France, ANR10 LABX56); (n) Croatian Science Foundation (Grant HRZZ IP-2014-09-9666).
Related Work The most relevant to our research is the work of  Pennacchiotti and Wirth (2009) , which introduces the notion of "frame relatedness" and proposes different types of measures to asses it. These measures are grouped in three categories: i) based on the hypothesis that frames are related if their lexical units are semantically related; ii) corpus-based measures, which suggest that related frames tend to occur in the same or similar contexts (e. g. , measured by pointwise mutual information or distributional semantic models); iii) hierarchy-based measures, which leverage the FrameNet hierarchy, assuming that frames are likely related if they are close in the network structure of FrameNet. The results of their experimental tests show high correlation between some of these measures and a dataset of human judgments of semantic similarity.
Evaluation by Text Similarity To our knowledge, there is no manually annotated dataset of frame instances and their relatedness. In order to circumvent this shortcoming, we propose an indirect methodology for the evaluation of the frame instance relatedness measures we introduced in Section 3. The key idea of our evaluation approach is to measure the relatedness of frame instances extracted from pairs of short texts, for which a gold standard pairwise similarity score is given. We parse the text with a knowledge extraction system to extract all the frame instances. We then measure the semantic relatedness of the extracted frame instances and compare the outcome with a judgment of pairwise semantic similarity given on the original sentences. The aim of this experiment is to show that our measure of frame instance relatedness correlates with the semantic relatedness of the text that evokes the frame.
Data The dataset we selected to carry out this experiment is provided by the shared task on Semantic Text Similarity (STS) held at SemEval 2017 (task 1, track 5 English-English)  (Cer et al. , 2017). The set is composed of 250 pairs of short English sentences, manually annotated with a numerical score from 1 to 5 indicating their degree of semantic relatedness. Examples of sentence pairs from the gold standard set, along with their human judgments of semantic similarity, are shown in Table  1. Table  1 : Examples of the sentence pairs in the Se-mEval 2017 STS dataset, with numbers indicating their semantic similarity on a scale from 1 to 5.
Knowledge Extraction To compute the relatedness score of pairs of frame instances, we need to extract them from the natural language text. For this purpose, we use KNEWS (Knowledge Extraction With Semantics), a fully automated pipeline of NLP tools for machine reading  (Basile et al. , 2016). The input of KNEWS is an arbitrary English text, and its output is a set of RDF triples encoding the frames extracted from the text by Semafor  (Das et al. , 2014). KNEWS integrates the Word Sense Disambiguation tool Babelfy  (Moro et al. , 2014)  to extract concept and entities from the input sentences, and maps them to the frame roles provided by Semafor, creating frame instances where the frame types are from FrameNet 1. 5 and the frame roles are filled with concepts from Ba-belNet. An example of the extraction of frame instances from natural language performed by KNEWS is shown in Figure  1.
In the example, three frame instances are extracted from the sentence "two men sit on a bench", with frame types People, Cardinal_numbers and Being_located. The frame elements are completed with BabelNet synset identifiers, e. g. , the Theme of Being_located is bn:00001533n (man, adult male, male: An adult person who is male (as opposed to a woman) 1 ) and the Location of the same frame instance is bn:00009850n (bench: A long seat for more than one person 2 ). We ran KNEWS on the 500 sentences from the STS dataset and extracted 1,650 frame instances of 178 different frame types. Each frame instance has on average 1. 2 frame elements, for a total of 2,107 roles filled by 457 different types of concepts.
Discussion It must be stressed that the aim of the experiment presented in this section is not to achieve state of the art performance on the STS task, for which better algorithms based on word similarity and other techniques have been proposed. In fact, many tasks that rely on sentence level semantics can be solved without the need of extracting frame instances. Rather, we show that our method to compute a relatedness score between frame instances works in practice, despite the inevitable shortcomings of the frame extraction process, i. e. , wrong and/or missing classifications of frames, roles and concepts. The STS dataset has a strong bias towards people-centric frames. In fact, the most frequent frame type in our collection is People (345 occurrences in 1,650 frame instances), and the most frequent concept is bn:00001533n (man, adult male, male, 226 occurrences in 2,107 frame elements).
Evaluation on Gold Standard Frame Instances The evaluation conducted in the first experiment has the advantage of being fully automated. However, measuring frame instance relatedness indirectly through text similarity entails that two distinct effects are measured at once: 1) the relatedness of the frame instances extracted from the text, and 2) the accuracy of the frame instance extraction process. In this section we propose a revised methodology for the evaluation of the frame instance relatedness measure that focuses only on measuring the effect (1), canceling the interference of (2). In short, we manually correct the frame instances extracted with KNEWS from the STS sentence pairs and re-run the evaluation process as described in Section 4. As by-products, we create a gold standard dataset of frame instances aligned with the text that evokes them 3 , and we provide an evaluation of the performance of the KNEWS knowledge extraction system.
Manual correction We corrected each frame instance individually. For the frame types, they were either confirmed or marked as wrong. In the latter case, the frame instance is discarded from the data set without further process. This was also the procedure applied when an entity was not filling any role for a particular frame instance, due to a parsing mistake. If the frame type was confirmed by the annotator, then the role and sense labels were checked and possibly corrected by replacing them with the correct ones from FrameNet and BabelNet respectively. We split the STS dataset (250 sentence pairs) in three parts and assigned each of them to an annotator. A subset of 37 frame instances extracted from 10 sentences was annotated by all three annotators in order to compute a measure of inter-coder reliability, resulting in a Fleiss' Kappa of 0.
81 on the annotation of frame types, 0. 76 for roles, and 0. 90 for concepts. Note that the annotation of roles and concepts is only considered when frame types are not discarded by the annotators as wrong. Once the annotation was finished, we compared the obtained dataset with the one we produced with KNEWS (Section 4. 2). The accuracy at the frame instance level (rate of frame instances that were not corrected at all) is 77. 1%. More in detail, 79. 5% of the frame types were found correct. Among the frame instances with correct frame types, 95. 9% of the roles and 82. 5% of the concepts were correct. During the manual inspection, we confirmed that Semafor (like most semantic parsers) is biased towards the most dominant frame for ambiguous forms. The final gold standard set comprises 1,261 frame instances and 1,579 frame elements.
). We start by observing that defining a quantitative distance metric between homogeneous instances allows us to apply a clustering algorithm. The result of such clustering is a partition of the original set into subsets that can be either overlapping (soft clustering) or nonoverlapping (hard clustering). Moreover, clusters have a definite shape, with one of the elements being the most central one (called the clustroid), and the others being more or less far from the center. We perform a hard clustering of the frame instances collected from the STS dataset and used for the experiment in Section 4. 3, and formulate three hypotheses: i) elements close to the center of their respective clusters are the best candidates to represent prototypical frame instances; ii) elements near the border of their respective clusters are less likely to represent prototypical frame instances, and therefore can be filtered out; iii) the size of each cluster influences the prototypicality degree of the elements in its central region, with larger clusters containing more prototypical frame instances near its center.
X-ray observations and database of the G&C The description of the practical requirements and of the observing strategies which prevailed in the definition of the XXL X-ray sample are fully described in Paper I. The final selected areas were (1) the North region: the XMM-LSS field, with 10 ks observations over 25 deg 2 in the CFHTLS-W1 Field (2h23 -05d00) with 11 deg 2 previously covered with XMM exposures of 10-20 ks  (Pierre et al. 2004 ) plus the XMM-Subaru Deep Survey  (Ueda et al. 2008 ) and (2) the South region: the BCS/XMM field with the same 10 ks exposure time as the north, covering another area of 25 deg 2 (23h00 -55d00). The flux limit for 10ks observations is 4 × 10 -15 and 2 × 10 -14 erg cm -2 sec -1 in the soft ([0. 5-2] keV) and hard ([2-10] keV) bands, respectively. The data processing of X-ray observations and the sample selection are described in detail in  Pacaud et al.