dataesr/scientific-paragraphs-categorization huggingface dataset:
The roads are well recognized using the spectral mean (Figure  6 , in red). The efficiency of the morphological classification results could depends on many factors; the level of adjustment of the used attributes, the concordance of the attribute to the objects of interest, the combination of several attributes. The attributes must be chosen and combined carefully regarding the objects of interest. In parallel of the morphological database, a spectral one was generated directly from the HySpex image, by extracting the major pure classes and their relative spectra, which are: roads, shadowed roads, vegetation, roofs, shadowed roofs, high emittance roofs. Each class was composed of at least 4 training spectra to perform the supervised spectral classification. The method is planned to incorporate an autonomous spectral database of laboratory and field campaigns urban objects.
Supervised classification To identify the build-up areas we have used two classical classification methods; Minimum Distance  (Richards and Jia, 1999)  and Spectral Angle Mapper (SAM)  (Kruse et al, 1993). The first classification is a supervised one based on the selection of Regions of interest (ROI) and combined with the minimum distance algorithm. In a second steps we wanted to take advantage of the high spectral resolution of the HySpex image, so we used a spectral classification based on the SAM algorithm to recognize the built-up areas regarding their spectral attributes. To do that, we have used the spectral database as an input of the SAM method. We have in a second step merged the results of the two classification methods, below, the final classification result (Figure  7 ). To recognize vegetation and roads, we have used the SAM method.
Classification by morpho-spectral database The last sequence of image processing concerns the classification and categorization of buildings according to their spectral, statistical and geometrical attributes (i. e. characters) registered in the morpho-spectral built-up spectral and object-oriented database. A hierarchical ascendant classification method combined with a spectral identification method was applied for calculating clusters and generating a typology of the shapes of buildings. The goal is to take advantage of both spectral and morphological attributes of urban objects (Figure  9 ). Figure  9. Classification of building types regarding their morphometric and spectral attributes.
Socio-economic urban classes' identification The example below (Figure  10 ), illustrates a typology of the attended morpho-spectral classification of urban objects. The morpho-spectral database will be very useful in the urban objects identification process, the objects will be identified and characterized regarding their spectral and morphometric attributes. In our example we have made a global identification of the urban materials by spectral classification (Figure  10 , in yellow), then we have extracted 2 sub-classes by morphological classification (Figure  10 , in magenta and blue), these sub-classes were recognized by textural attributes (texture mean) at bands 414 nm for the magenta class (i. e. complex and large forms) and 741 nm for the blue class (i. e compact forms). The urban classes map (Figure  10 ) was generated by superposing the 2 urban sub-classes to the global urban map.
Conclusion This paper present an original method based on the use of a morpho-spectral database to classify the objects in a dense urban context. The use of morphological approach to classify object is an interesting concept that take advantage of the objects geometry. The concept could be enhanced and optimized by introducing some spectral attributes in addition to the geometry; the objects could be identified and characterized more accurately. The use of ontological object characters (i. e. attributes) such as statistics and geometry for recognition, mapping, identification and categorization involves applications in the fields of urban climatology (e. g. Su et al, 2008) , geomorphology and hydrology (e. g. Dragut and Blaschke, 2006; Anders et al. , 2011) , and, at times, urban geography for the characterization of the attributes of buildings  (Carneiro et al, 2010).
Data Collected For each participant, data were derived from the MGEN database. Sociodemographic data retrieved included gender, date of birth, and region of residence. Regions were regrouped by European constituency boundaries  [18]. Reimbursement data included all reimbursement claims for medical consultations and examinations, and medication prescriptions occurring between 1 January and 31 December 2011. Data collected for each reimbursement claim for a consultation or an examination included the date of medical care delivery and the practitioner's medical specialty. Data collected for each reimbursement claim for medication, corresponding to each individual drug prescribed, included the commercial name of the drug, the presentation identification code (CIP), the relevant ATC (anatomical therapeutic chemical), and EphMRA (European Pharmaceutical Market Research Association) therapeutic classes, the medical speciality of the prescribing practitioner, the date the prescription was filled, and the number of boxes dispensed.
Moreover, a treatment could have been discontinued for intolerance or for perceived lack of efficiency. Some previous studies have shown that compliance to treatment is poor, especially for people suffering for depressive symptoms  [34, 35]. On the other hand, certain drugs that may have been prescribed or used do not appear in the MGEN database if they are either not eligible for reimbursement or given during hospitalization. In addition, claims data are generated for accounting purposes, which explains the presence of very few individual characteristics. Additional variables, such as educational level  [36] , would have been interesting to investigate. Finally, the Laroche criteria related to posology were not taken into consideration since no dosage information was available in the database.
Event selection This analysis is based on pp data collected with the CMS detector at a center-of-mass energy of 7 TeV in 2011. Events selected with displaced-vertex dimuon triggers are considered, corresponding to an integrated luminosity of 5. 1 fb -1. The analysis is driven by the J/ψ meson reconstruction. The dimuon triggers apply topological and kinematic selections on dimuon candidates: cos α > 0. 9, where α is the pointing angle, in the transverse plane, between the dimuon momentum and the direction from the mean pp collision position (beam spot) to the dimuon vertex; L xy /σ xy > 3, where L xy is the transverse distance between the beam spot and the dimuon vertex, and σ xy is the corresponding uncertainty. In addition, the two muons must have opposite charges, dimuon p T > 6. 9 GeV, satisfy an invariant mass requirement 2.
9 < m(µ + µ -) < 3. 3 GeV, and have a mutual distance of closest approach in the transverse plane of less than 0. 5 cm. Trigger selection requirements on the χ 2 probability of the dimuon kinematic fit (P VTX ) and muon transverse momentum p T (µ) were made more restrictive as the luminosity increased and ranged from P VTX > 0. 5% to 15% and up to p T (µ) > 4 GeV, respectively. A requirement on the muon pseudorapidity, |η(µ)| < 2. 2, is also applied. Monte Carlo (MC) simulations are employed to design the offline selection, assess the reconstruction efficiency, and study systematic effects. The B + c signal events are simulated using a dedicated generator (BCVEGPY)  [14, 15]  interfaced with the PYTHIA simulation program (version 6. 424, Z2 tune  [16, 17] ), which hadronizes the whole event.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  9 MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
These simplified descriptions are obtained through an abstraction process consisting in extracting and formalizing knowledge embedded in the literature in order to make it available through information resources. Facing the richness of the literature, in this paper, we concentrate our effort on the generalization technique for relational databases. This paper presents some of our simplified descriptions and the process which helped us getting them. The remainder is organized as follows. Section 2 presents the generalization technique and a brief comparison of nine generalization algorithms. Section 3 details our abstraction process and its different outputs. It also describes an experimentation conducted to validate our results. Section 4 concludes the paper by describing some use cases illustrating how our contributions can be used and by sketching future research.
Preliminaries Privacy is one of the major concerns when publishing or sharing data. It refers to different forms of disclosure regarding the type of published or shared content. Identity disclosure, for instance, can occur when publishing or sharing personnel data. In our research, we focus on microdata (atomic data elements describing the individual objects) contained in relational databases. Each tuple has a value (microdata) for each relational attribute. The latter can be an explicit identifier, a quasi-identifier, a sensitive attribute or a non-sensitive one. An explicit identifier (EI) directly identifies an individual (e. g. social security number). A quasi-identifier (QI) is an attribute set which, when linked to external information, enables the re-identification of individuals whose identifiers were removed ({sex, zip code, and birthdate} is a well-known quasi-identifier in many data sets).
This ontology will exhibit conflicts between techniques and then will contribute to a definition of combined anonymization scenarios for a whole database. Finally, we believe that the availability of such knowledge should facilitate the adoption of new anonymization techniques and minimize the loss of competencies that arise when an employee leaves the company. There are several avenues for future work. First, we want to perform the same effort of abstraction for other algorithms based on swapping technique for example. We aim to check if the process also leads to a useful abstraction. Second, we want to reach a higher level in the taxonomy by abstracting the different techniques (generalization, shuffling, swapping, etc. ). Finally, we also expect to develop an ontology of techniques and algorithms.
(2014) , specifically the first 50 instances which involve up to 59 stations. This set of experiments have been performed on another computer with an Intel i7 CPU running at 3. 60 Ghz and 8 GB of RAM. The software parameters are identical to the previous set of experiments. The demand values for this set of instances were not balanced (i. e. did not sum up to 0), so we have balanced the demand values by setting the demand of the depot to d 0 = -i∈V \{0} d i. Finally, we have determined the initial level of inventory at the stations as max i∈V |d i |. The details of our results are provided in Tables  5 6 7 8. The performances of our algorithms are slightly better for this data set, and our observations for the previous data set are confirmed. As a final note, we observe that the longer runs for both variants are for the instances for which CPLEX fails to find a high quality solution in the early stages of the branch-and-cut algorithm.
The Reynolds-Averaged approach is generally adopted for practical engineering calculations and uses models such as, the k-ε and its variants, the k-w and its variants, and the RSM model. To conduct all the numerical simulations on the high Reynolds turbulent flows in a rectangular open channel, we used the ANSYS FLUENT CFD software. The objective of our study is to model the flow in a rectangular open channel with the presence of obstacles placed across the experimental channel.
c) The formation of a liquid stream which bypasses the obstacle and then starts to move much more rapidly in the section opposite the obstacle (speeds of the order of a few tens of cm/s). The very successful results using the ANSYS FLUENT CFD software on the horizontal and vertical profiles of the velocities of the three turbulence models, the RSM, the k-ε and the k-w, were verified and validated experimentally. Our work achieved different results on the same vertical plane: z=200 mm (Figs. 6 and 10), z=400 mm (Figs. • In the area downstream of the obstacle: o The RSM and the k-w models give higher values (especially for the horizontal velocities); o The experimental results have a tendency to be more or less stable in the x direction; the three models, however, provide an increase in the horizontal velocity and a decrease in the vertical velocity values.
Nematode abundance and biomass On board, the faunal samples were pre-sieved through 1000-, 250-and 20-μm mesh nets. Samples were preserved in seawater-buffered formalin (10 %). In the lab, nematodes were counted under a stereomicroscope. The nematode biomass was calculated from the biovolume, which was estimated from all specimens per replicate using the Andrassy formula (V=L × W 2 × 0. 063×10 -5 , with body length, L, and width, W, expressed in μm;  Andrassy 1956 ). The carbon contents were identified as 40 % of the dry weight  (Feller and Warwick 1988). Nematode measurements were taken using an AxiozoomV16 stereomicroscope (400× magnification) using the software Zen 2012 (blue edition).
In order to facilitate comparison among samples, the expected number of nematode species for a theoretical random sample of 100 individuals, ES (100), was calculated. All indices were calculated using the PRIMER6 software (Plymouth Marine Laboratory, UK; Clarke 1993). The trophic diversity of the nematodes was determined by analysing trophic groups, as reported by  Wieser (1953). The nematodes were divided into four groups, as follows: (i) no buccal cavity or a fine tubular one, as selective (bacterial) feeders (1A); (ii) large, but unarmed, buccal cavity, as non- , and g is the relative contribution (in terms of number of specimens) of each trophic group to the total number of individuals, and n is the number of trophic groups  (Heip et al. 1985). For n=4, Θ ranges from 0. 25 (highest trophic diversity; i,.
Statistical analyses Premised on the fact that these experiments are characterised by a lack of replicates, statistical analyses in this study are limited to a principal component analysis (PCA) and a nonparametric multivariate multiple regression analysis. The Bray-Curtis similarity matrix (with data square-root transformed) was used for the PCA, which allowed visualization of between-site and between-substrata variations in the nematode assemblages after 9 months (PRIMER6 software, Plymouth Marine Laboratory, UK; Clarke 1993). We evaluated the relationship after 9 months between nematode abundance, biomass, diversity (as nematode species composition, NSR, ITD and MI) and environmental variables (temperature and fluid inputs reported as high, medium, low, absent) and type of substrata (slate, wood and bone) conducting a non-parametric multivariate multiple regression analysis (DistLM: distance-based linear model) using the PERMANOVA + add-on package for PRIMER6 software  (McArdle and Anderson 2001; Anderson et al.
Acknowledgments We thank the captains and crews of the  R/V Pourquoi Pas ? for their steadfast collaboration in the success of the MoMARSAT cruises. We are also grateful to the Victor6000 ROV pilots for their patience and constant support. We also warmly thank the LEP technical team for its valuable help both at sea and in the lab. DZ was supported by the  BLaboratoire d'Excellence^LabexMER  ( ANR-10-LABX-19 ), co-funded by a grant from the  French government  under the program BInvestissements d'Avenir^by a grant from the  Regional Council of Brittany  (SAD programme), and by the project " Deep-sea hYdrothermal Vent nematodes as potential source of new Antibiotics " (DYVA) funded by  Fondation Total  and  Ifremer. This  project is part of the  EMSO-Açores research program  that benefited from an  ANR  research grant ( ANR LuckyScales ).
Patients and methods 34 patients were selected on clinical grounds by members of the French reference center for autoinflammatory diseases (CeReMAI). 9 of them were considered positive control samples as they had previously known variants (by Sanger sequencing) and were run in parallel for methodological validation. A custom panel of 108kb was designed to capture the genomic regions of interest using the Illumina Nextera Rapid Capture enrichment DNA preparation kit. MicroV2 chip were then loaded with 12 samples on a MiSeq equipment for multiparallel sequencing. Annotation and filtering were performed with Seqnext software (JSI), and annotation was performed with Alamut Visual (Interactive Biosoftware).
The occurrence of freezing-tolerant phenotypes in panagrolaimids other than P. davidi has not been investigated previously. In the present study we experimentally determined the anhydrobiotic and freezing tolerance phenotypes of 24 Panagrolaimus strains from tropical, temperate, continental and polar habitats and analysed their phylogenetic relationships. We found that several other Panagrolaimus isolates, in addition to P. davidi from Antartica, are able to survive freezing when fully hydrated and that tissue extracts from these freezing-tolerant nematodes can inhibit the growth of ice crystals. These isolates include P. superbus from Surtsey, Iceland, along with several anhydrobiotic strains from temperate regions. Using this dataset we addressed several questions concerning the biology and evolution of freezing and desiccation tolerance in panagrolaimid nematodes: 1.
Principal Component Analysis Principal component analysis was performed on the proportional desiccation and freezing data (five desiccation treatments and two freezing treatments-S2 Table ), using the princomp function in R. The multivariate normality of the untransformed data was first confirmed (P<0. 01) using the mshapiro. test function (Shapiro-Wilk test) in the R software package mvnormtest. Prior to PCA the proportional data were scaled by subtracting the mean from the standard deviation of each sample.
2/blunt vector (Fermentas, Thermo Scientific, Waltham, Ma, USA) and transformed into E. coli TOP10 cells (Invitrogen), according to the manufacturers' instructions. The plasmids were purified from E. coli cultures using a QIAprep Spin Miniprep kit (Qiagen) and the purified plasmids were sequenced by LGC Genomics (Berlin, Germany). For each nematode strain the inserts from at least two recombinant plasmids were sequenced in the forward and reverse directions. The GenBank accession numbers of these D3 sequences are presented in S3 Table. Multiple sequence alignments were created using ClustalW2  [41]  as implemented in Mega 5. 1  [42]. Phylogenetic trees were constructed using maximum likelihood  [43]  and Bayesian methods  [44]. The best-fit evolutionary model was determined using the Find Best-Fit Substitution Model (ML) in MEGA 5.
We estimated λ using the plgs function in the caper package  [53]. The caper package calculates confidence intervals of the ML estimate and it incorporates log likelihood ratio tests of the significance of the estimated value of λ under the hypothesis λ = 0 and under the hypothesis λ = 1, with df = 1. (d) Correlation analyses. The relationship between the survival of the cold acclimated nematodes and those that survived desiccation (preconditioning: 98% RH, 96 h) was investigated using Spearman's non-parametric correlation test using the GraphPad Prism statistical package. To consider the impact of phylogeny on trait correlations within the PCA groups 3 and 4 from temperate and polar regions we generated an rDNA D3 phylogeny for these PCA Groups using a ClustalW2 multiple sequence alignment  [41]  and Bayesian phylogeny reconstruction methods  [43] , as described above.
The phylogenetic relationships of these strains are presented in Fig. 1 (a) and S3 Fig. The effect of preconditioning the nematodes at 98% RH for 0, 24 or 72h on their subsequent anhydrobiotic survival, Table  2. Estimation of phylogenetic signal in Panagrolaimius freezing-tolerance and anhydrobiosis phenotypes, based on fit to a Brownian motion model of trait evolution estimated using Pagel's λ metric  [43]  and the caper package  [48]. When 24 unacclimated strains of Panagrolaimus were frozen at a cooling rate of 3. 02°C min -1 and stored at -80°C for 24 h prior to thawing, their freezing-tolerance phenotypes appear to form a continuum [Fig. 1(b)] , with survival values ranging from 0% to 56%. Eight strains are excellent survivors of freezing, with 39% or higher of the unacclimated nematodes surviving.
Principal Component Analysis of Combined Anhydrobiotic and Freezing-Tolerance Datasets Reveals Four Phenotypic Groups Principal component analysis (PCA) showed that two components explained 82. 2% of the variability of the combined desiccation and freezing-tolerance phenotypes (S2 Table ). These two components discriminated between four Groups (Fig. 2 ; S3 Fig. ). The three strains in PCA Group 1 are desiccation sensitive and are freezing sensitive when unacclimated. Group 2 comprises Panagrolaimus sp. AS01 from Ireland, along with nine Panagrolaimus isolates with JU designations (from regions as different and distant as, for example, Southern India and Cape Verde in the Atlantic ocean). These nematodes have excellent "slow-dehydration" phenotypes, with maximal desiccation tolerance being achieved following preconditioning at 98% RH for 72-96 h.
6(a) ] is congruent with the evolutionary history of the ice growth-inhibition phenotype postulated above. Cold acclimation improves the freezing tolerance of the majority of Panagrolaimus strains, and it has a marked effect on the survival of some strains from the earlystemming lineages that are freezing sensitive when unacclimated. Reconstructions of the ancestral character state of the freezing-tolerant phenotypes of cold-acclimated nematodes reveal a complex evolutionary pattern [Fig. 6(b) ], suggesting that inducible proto-cryobiotic phenotypes may have evolved as independent lineage-specific events in several of the earlydiverging Panagrolaimus species. In addition, this latter dataset provides further support (p = 0. 77) for the hypothesis that the ancestor of the davidi, superbus and JU Cape Verde clades was freezing tolerant.