dataesr/scientific-paragraphs-categorization huggingface dataset:
The different resolutions of CCLM, 0. 44 • (∼ 50 km, 118 × 83 grid points, and 32 σlevels), 0. 22 • (∼ 25 km, 206×120 grid points, and 32 σ-levels), and 0. 08 • (∼ 9 km, 536× 295 grid points, and 40 σ-levels) that cover the Med-CORDEX domain  (Ruti et al. , 2012)  are employed. For both coupled and atmosphere-only simulations, the horizontal diffusion parameters in CCLM are tuned for better realization of medicanes. The 1-D NEMO-MED12 has a resolution of 1/12 • (∼ 9 km, 567 × 264 grid points, and 50 vertical levels), which remains the same for all the coupled runs. The atmospheric model uses ERA-Interim reanalysis data from the ECMWF for both the coupled and atmosphereonly runs. However, in the coupled runs, SST over the Mediterranean Sea is calculated by 1-D NEMO-MED12 and elsewhere prescribed and derived from the reanalysis data.
The selection criteria of these events were based on cloud structure, size, and lifetime of the cyclones  (Tous and Romero, 2013). Because of the limited availability of the MEDATLAS-II climatology , only the first 11 medicane events from 1980 to 1999 are included in this study. Since the last event occurred in 2003 (Table  1 ), MEDATLAS-II climatology is not available to initialize the ocean model. For each medicane event, both the coupled and atmosphereonly simulations with and without spectral nudging are conducted over a period of one month, starting approximately three weeks prior to formation of the medicane. The following datasets are used for validation: for wind speed, the NOAA "SeaWinds" dataset, available from 9 July 1987 to the present are used. The product contains global high-resolution ocean winds and wind stresses at 6 h intervals on a 0.
44 • and 0. 22 • ). The sea level pressure minimum appeared one hour earlier in CPL08 compared to the CCLM08 simulations, and the warm-core is more prominent in the CPL08 simulations. The medicane track length in CPL08 is 94 h, 92 h in CCLM08 and 72 h in MERRA reanalysis (Table  2 ). The medicane track in CPL08 is improved compared to CCLM08, and according to the MERRA reanalysis, with a more accurate position near the Sicily channel. Figure  3  shows the wind speed patterns in all the simulations (0. 44 • , 0. 22 • and 0. 08 • ) of the ME08 event and the NOAA dataset on 7 October at 18:00 UTC. The high wind speeds associated with the medicane are not observed in the CPL44 and CCLM44 simulations. Compared to this, the wind speeds in the CPL22 and CCLM22 simulations are slightly improved, but still do not match the high values of the NOAA dataset.
There is further improvement at 0. 08 • resolution; however, the maximum wind speed still does not match the maximum of 34 m s -1 seen in the NOAA dataset. The wind speed patterns in the coupled simulations (CPL08) are more intense compared to the atmosphere-only (CCLM08) simulations, and the structures are more delicate. According to  Cavicchia and von Storch (2012) , the high wind speeds recorded in the NOAA dataset may be due to Mistral winds, which are not well resolved in simulations. Most of the medicane features are well resolved at the higher resolution of 0. 08 • (Fig. 2 ). The correct times and locations of simulated medicane formations are not in good agreement with the satellite images and MERRA reanalysis data. This may be due to the small size of medicanes, boundary layer parameterizations, the role of moist microphysics, and deep convection  (Tous et al.
This is due to the sea level pressure being less deep and steep in CLP08, CCLM08, and the MERRA reanalysis data. The length of the medicane track in CPL08 is 42 h, 34 h in CCLM08, and 48 h in the MERRA reanalysis. The lowest minimum sea level pressure values and warm-core structures are similar in both the CPL08 and CCLM08 simulations (Table  2 ). However, the track length in CPL08 is longer in comparison to the CCLM08 simulations. The medicane tracks in CPL08 and CCLM08 are shifted toward the east compared to the MERRA reanalysis data. Compared to the atmosphere-only simulations, the medicane track and warm-core structure in the coupled simulations (CPL08) are in good agreement with the MERRA reanalysis data. Figure  9  shows the wind speed comparison in CPL08, CCLM08, and the NOAA dataset on 16 January 1995 at 12:00 UTC.
The mean sea level pressure is slightly deeper in CPL08 compared to CCLM08 (Table  2 ) and the warm-core values are approximately the same in both simulations. However, the MERRA reanalysis data shows more intense values of mean sea level pressure (Table 2) and warm-core. The medicane tracks in CLP08 and CCLM08 are shifted towards the south compared to the MERRA reanalysis data. Figure  11  shows the wind speed comparison of CPL08, CCLM08, and the NOAA dataset on 27 January at 00:00 UTC. The wind speed patterns in CPL08 are stronger and finer compared to the CCLM08 simulations, and in good agreement with the NOAA dataset.
Back Close Full CPL22 and CCLM22 ranges from 0 to 4. 7 hPa. However, the lengths of the medicane track in CPL08 are longer than CCLM08, except ME02, where both have the same track length, and ME10 where CPL08 shows a shorter (1 h) track length compared to CCLM08. The difference between the lowest minimum sea level pressure values in most of the 0. 08 • simulations ranges from 0 to 1. 6 hPa, except ME03, where the difference is slightly higher (3. 7 hPa). The results show that warm-core structures are also more intense in the CPL08 simulations compared to CCLM08. The wind speed is strongly underestimated in all cases of the 0. 22 • simulations. However, the wind speed is significantly improved in the 0. 08 • simulations. Compared to CCLM08, the wind speed in CPL08 is more intense and in good agreement with the NOAA dataset.
Acknowledgements. The authors acknowledge funding from the  Hessian Initiative for the Development of Scientific and Economic Excellence (LOEWE)  through the  Biodiversity and Climate Research Centre (BiK-F), Frankfurt  am Main. The authors thank the  Center for Scientific Computing (CSC)  of the  Goethe University Frankfurt am Main  for providing computational facilities, especially LOEWE-CSC. We acknowledge  Cindy Lebeaupin Brossier ,  Jonathan Beuvier ,  Thomas Arzouse ,  Samuel Somot  and  Philippe Drobinski  for their help for the NEMO-MED12 model and the atmospheric coupling and  Eric Maisonnave  for his support. We also acknowledge the  French GIS  and GMMC which have supported the  NEMO -MED12 model development. N. Akhtar  and  B. Ahrens  acknowledge the support from the  German Federal Ministry of Education and Research (BMBF)  under grant  MiKliP :  DECREG/01LP1118B.
and 0. 08 • (about 50, 25, and 9 km, respectively) and an ocean grid-spacing of 1/12 • to simulate medicanes. The results show that the model's performance depends strongly on the atmospheric grid resolution. The large-scale disturbance is well simulated in all three resolutions. Medicane signals are not observed in the 0. 44 • simulations. In the 0. 22 • simulations, the mean sea level pressure and warm-core are captured, and are more intense and finer in the 0. 08 • simulations. The wind speed is strongly underestimated in the 0. 22 • simulations compared to the NOAA dataset. Most of the medicane features are well resolved at high-resolution (0. 08 • ) in both coupled and atmosphereonly simulations. Discussion Paper | Discussion Paper | Discussion Paper |.
Some of the functions are shared with the smart butler. To make the virtual nurse not only look realistic but also have natural and human-like behaviors, the following three key characteristics of modeling realistic agents  [24]  are emphasized: (1) autonomy: agents can function on their own; (2) interactivity: agents can interact naturally with other agents, human players, and the environment; (3) personality: agents can exhibit human traits and characteristics. Working towards these challenges, we propose a brain-inspired agent architecture (using the fusion ART  [25]  neural network for knowledge storage, retrieval, and adaptation) to integrate autonomy, natural language interaction, and human-like personality. The system architecture of the virtual nurse is presented in Fig. 5. The intention or response of the elder is interpreted from the input (either text or speech) by the Speech Recognition and Natural Language Understanding (NLU) module.
For example: agent i suggests the elder for certain healthier dietary and agent j supports agent i by stating that the elder has not eaten that for a long time (see Fig. 8 ). We implement a collaboration proxy to continuously collect the standardized information updates from all the intelligent agents, detect any belief inconsistency, possible conflict, and potential for cooperation, and send information to the related agents to resolve the situation (if necessary). In other words, the proxy manages the collaboration among all the agents by ensuring the consistency of their aggregated mental states  [28]. The pseudo code of the coordination proxy is given in Algorithm 1. The proxy is designed to be scalable, which means it can be directly applied to a group of agents (other than the smart butler and the virtual nurse).
Introduction This paper addresses Démonette, a new lexical resource for French that offers derivational morpho-semantic information and whose content, design and development prospects make it a novel tool that fills a gap among lexical databases. In its first version, Démonette contains 31,204 verbs, adjectives and nouns. It is a network, organized as a graph, in which vertices and edges carry morphological and semantic features; it is designed based on a cumulative conceptualizion of the constructed meaning of derived lexemes and combines the semantic typing of the lexemes with the use of abstract definitions (i. e. , generalized descriptions that allow us to group words with similar meanings). One principle underlying the construction of this resource is that the meaning of a derived word is a combination of partial semantic properties introduced by each of the derivational relations in which the word is involved.
g. , 5) and concrete cross-definitions (e. g. , 4 and 8); semantic types, abstract oriented definitions (e. g. , 3), abstract opposite definitions (e. g. , 6) and abstract cross-definitions (e. g. , 7 and 9). The motivations behind the choice of semantic types on the one hand and opposite and cross-definitions on the other hand will be explained in Sections 4. 2 and 4. 3. First, we will explain how relevant features are imported from DériF and Morphonette (Sections 3. 2 and 3. 3). Then, in Section 3. 4, we will provide an overview of the scripts that have been implemented to generate Démonette's original information, i. e. , the contents of the white boxes in Figure  3. It is worth noting that morpho-semantic definitions in Démonette describe relations between the parts of word meanings that are used or constructed via morphology.
Experimental Framework All experiments to assess the performances of model (  4 )-(  6 ) are carried out on an Intel Core TM i7-870 PC (2. 93 GHz, 8MB cache, 8GB memory) running Linux 2. 6. 34 (single thread). In all our experiments the CPU time limit is set to 1 hour. For solving the column generation MIP subproblems we use the branch-and-cut algorithm implemented in CPLEX 12. 2, with single thread and default parameter settings. In order to obtain a generic multiple pricing strategy, each time a new incumbent solution having negative reduced cost is found during the optimization of a MIP subproblem, the corresponding column is inserted into the master. Whenever a MIP subproblem is found to be unbounded, its integrality conditions are relaxed, and an extreme ray is generated and added to the master by solving the resulting LP problem to optimality.
Sets of Decompositions Different decompositions can be obtained using the hypergraphs described in Sect. 3. 1 with different sets of input parameters. For instance, completely different decompositions can be derived by changing the number k of blocks, the number of dummy vertices, and the weights of the hyperedges given as input parameters to the partitioning algorithm. We do not optimally solve the NP-hard minimum weight balanced k-partition problem. Instead, we use the heuristic multilevel hypergraph partitioning algorithm in  [18] , of which the package hMETIS  [19]  is an implementation. In particular, the hMETIS heuristics follow a multilevel recursive bisection paradigm, working in three phases: coarsening, partitioning, and uncoarsening. Coarsening aims at constructing a sequence of successively smaller hypergraphs by contracting hyperedges with a so-called "FirstChoice" scheme; then, balanced partitioning is performed on the contracted hypergraph by bisection; finally, during uncoarsening, the contracted hypergraph is expanded by successive projections, following backward the coarsening steps, and running after each projection a local refinement procedure (FM), that is based on tabu search.
One can see that a decomposition with the respective best proxy measure (marked by a ×) most of the time leads to a very good choice among all decompositions we tried with respect to the root node integrality gap closed, also compared to the bound obtained by CPLEX after the cutting plane process at the root node (marked by a ). In Table  3  we detail, for each instance in the dataset, our results for both the "DWR auto" and "DWR best" methods, compared to the results obtained by CPLEX when stopped after the cutting plane process at the root node as well as the time CPLEX needs to solve the original problem to optimality. Listed are: the instance name, the value of an optimal solution or best solution computed by CPLEX in 1 hour (opt*), the value of the LP relaxation (LP) and the corresponding duality gap (LP gap).
Normalization is then performed with respect to the fastest algorithm. For each value π on the horizontal axis, we report on the vertical axis the fraction of the dataset for which the corresponding algorithm is at most 1 π times slower than the fastest algorithm. The value on the vertical axis, corresponding to π = 1, indicates the fraction of the instances in which the considered method is the fastest among the two considered. As far as "DWR auto" is concerned, it outperforms CPLEX in around 8% of the instances, while "DWR best" in around 19%. From the figure, we can also see that in around 50% of the instances "DWR auto" is at most two orders of magnitude slower (see also discussion of Table  3  in Sect. 6. 2), while "DWR best" only one order of magnitude. In Fig. 15(b) , we display the second performance profile based on the bounds of the three methods.
As a performance index we consider, for each instance and for each method, the ratio between the bound obtained by such a method and the best bound obtained using either "DWR auto," "DWR best," or CPLEX. Normalization is then performed with respect to the index of the best algorithm among the three considered. The picture reports the normalized performance index on the horizontal axis; for each value π on the horizontal axis, we report on the vertical axis the fraction of the dataset for which the corresponding algorithm closes at least a fraction π of the best achieved gap, on the considered set of 39 instances. We always assume that the LP relaxation value is available, thus the minimum closed gap for each instance and each algorithm is set to 0. For instance, if we look for a method which is able to close at least about 20% of the best achieved gap, then CPLEX is the best choice.
The five authors were partially supported by the programme " Ingénierie Numérique & Sécurité " of the  French National Agency of Research (ANR) , project " MALTHY" , number  ANR-13-INSE-0003. Xavier Allamigeon  was partially supported by the  same programme, project  " CAFEIN" , number  ANR-12-INSE-0007. Ricardo D. Katz  was partially supported by  CONICET  Grant  PIP 112-201101-01026. Xavier Allamigeon ,  Ricardo D. Katz  and  Stéphane Gaubert  were partially supported by the  PGMO program  of  EDF  and  Fondation Mathématique Jacques Hadamard. Uli Fahrenberg  and  Axel Legay  were partially supported by the  EU   IP  project  DANSE  and by the regional  CREATE Estase initiative.
5. Of the remaining genes, 14% of the Mt3. 5 genes have been deprecated to an "unsupported" status and 4% are absent from the Mt4. 0 predictions. Conclusions: Mt4. 0 and its associated resources, such as genome browsers, BLAST-able datasets and gene information pages, can be found on the JCVI Medicago web site (http://www. jcvi. org/medicago). The assembly and annotation has been deposited in GenBank (BioProject: PRJNA10791). The heavily curated chromosomal sequences and associated gene models of Medicago will serve as a better reference for legume biology and comparative genomics.
Background Legumes contribute a significant portion of protein and oil intake in human and animal diets. An agronomically significant feature of the legume plants is their ability to fix atmospheric nitrogen as a result of symbiosis with rhizobial bacteria. Among the legumes, Medicago truncatula naturally stands out as a model legume organism, with several unique characteristics: compact genome size (estimated ~465 Mb according to Plant C-values database [http://data. kew. org/cvalues/] [1] ), rapid life cycle, accessible genetics tools including transposon tagging and easy transformation, as well as a rich collection of mutants and ecotypes. Research on Medicago has focused on symbiotic nitrogen fixation  [2]  as well as a reference for cross-legume comparisons. A high-quality M. truncatula reference genome and gene models provide a solid foundation for plant physiologists and legume biologists, therefore, deserve continuous improvement.
Medicago re-annotation overview Our Medicago re-annotation strategy is a hybrid approach that attempts to combine a set of transcriptional and translational evidence. Mt3. 5 legacy gene models, predictions from Augustus and FGENESH, and EST, 454 and RNA-seq expression data were combined using both EVidence Modeler (EVM) and MAKER with minor differences (Table  2 ). Precedence was given to the EVM predictions that were then supplemented with MAKER models that did not have a counterpart in the EVM dataset. Approximately 1,500 models for small secreted peptides predicted from custom HMMs  [20]  and community annotated genes were also added to generate the final Mt4. 0 gene set. A schematic outline of the Medicago re-annotation pipeline is available in Figure  1B.
Training sets For evaluation and optimization of our gene predictions, we developed manually curated training sets either using models with full-length transcript support (EST or RNA-seq) on chromosome 1 or genes directly transferred from Mt3. 5 to Mt4. 0 chromosome 5, which is the best-assembled chromosome in both Mt3. 5 and Mt4. 0 (Table  3  and Figure  3 ). We classified transcripts as 'full-length' using TargetIdentifier  [21]. We used full-length transcripts (fl-ESTs and fl-Rnnotators) on chr1. We used 'F' class genes on chr5 which were considered the highest confidence class in Mt3. 5  [3]. Both sets were manually verified by manual inspection using a genome browser, JBrowse  [22].
Gene identifier assignments Most of the gene identifiers (Medtr) have been preserved between Mt3. 5 and Mt4. 0. New identifiers have been instantiated to replace the gene identifiers previously found on the unanchored contigs. We have assigned gene identifiers based on the following three rules: Rule 1: All Medtr genes that can be moved over either in whole or in part will retain the same identifier; Rule 2: All contig genes (with identifiers like contig999_1) received new Medtr identifiers; Rule 3: New gene predictions in regions of the genome not present in Mt3. 5 were assigned Medtr identifiers consistent with their chromosomal location. When multiple old identifiers mapped to the same locus on Mt4. 0 (e. g. when a new gene was a fused model), we used the EMBOSS 'needle' program  [24]  to select the legacy identifier with highest identity and coverage to carry over.
Functional annotation Functional assignments were based on a weighted keyword (WK) approach (Hoover et al. in preparation). Briefly, each predicted protein was searched against a collection of databases (Priam, Uniref100, PFAM/TIGRFAM, CAZY, CDD) and motif finders (TMHMM, InterPro). Meaningful keywords were extracted from the definition lines of sets of best matches from each database. A set of heuristic rules were then used to score each candidate definition line and the highest scoring line was assigned to the query protein. A second iteration of the WK scoring algorithm was used to standardize functional assignments across members of paralogous gene families wherever possible. As a result of the protein naming pipeline, 37,561 genes (74%) contain informative protein names, while the remaining 13,367 genes are labeled as "hypothetical protein".
Repeat analysis and transposon classification A multi-evidence approach was used to distinguish between canonical genes and transposon derived gene models. Gene predictions were classified as transposonsrelated based upon one or more of the following criteria: 1) intersection with computationally predicted repeats; 2) membership in a paralogous gene family composed predominantly of gene predictions that received a functional annotation of transposon from our naming pipeline; 3) matches to an in-house transposon protein database; 4)  possession of an appropriate InterPro domain. This integrated repeat analysis pipeline improved classification of loci as either genes or transposable elements.
UTRs and isoforms Splice isoforms and UTRs were instantiated by running PASA  [25]  on Sanger/454 EST and RNA-seq data. Publicly available RNA-seq data (Additional file 1: Table  S1 ) were assembled using a combination of de novo and genome-guided Trinity  [26]. Transcript diversity was captured by assembling reads on a per tissue type and per time point basis pooling the biological replicates within each sample/treatment. PASA was run twice to assemble the transcriptome data; first on EST data followed by UTR and isoform updates and then on the RNA-seq transcript assemblies. Within each gene locus, assemblies were filtered to remove any transcripts with low read depth, using RSEM  [27]. Within the set of isoforms that were instantiated, we observed two different types of variation: within the UTR regions which did not affect the encoded protein sequence and within the coding regions which encoded variant proteins.
Since RNA-seq reads from a wide variety of different tissue types (root, nodule, seedpod, blade and flower) were used in this high-throughput step of instantiating isoforms, without large-scale manual curation it is hard to verify the authenticity of all the computed variants. For example, within certain gene loci, the only variation observed was in the UTR regions, many of which showed only minute differences in the UTR start/stop positions. To filter the excessive number of isoforms possibly due to read-mapping artifacts, we filtered through these isoforms using the following method: for every locus, identify sets of isoforms sharing the same coding region using the Gene Structure Annotation Comparison (GSAC) tool and retain only the longest transcript within each such set. A total of 6,377 gene loci (13% of all loci) contain more than one isoform.
The Mt4. 0 release The sequence data are released as a set of Mt4. 0 pseudomolecules comprising FASTA files and their alignments to the genetic and optical maps. Annotations of genes, TEs, tRNAs are available as GFF files and as CDS and protein sequences in FASTA format. We also generated a "chain" file (coordinate mapping between Mt3. 5 and Mt4. 0) that can be used in conjunction with the UCSC liftOver tool, in order to quickly map any genomic features or annotations from the Mt3. 5 assembly to the Mt4. 0 assembly. The release files are available at the JCVI Medicago website (http://www. jcvi. org/medicago). The same set of Mt4. 0 assembly and associated gene models are also available in GenBank under accession APNO00000000. The optical maps for all 8 chromosomes are available in the Genbank nucleotide database under accessions MAP_000013 to MAP_000020.