dataesr/scientific-paragraphs-categorization huggingface dataset:
[5]  introduce guidelines to be used in the building of software quality predictors in case of scarcity of data while D'Ambros et al. present a comparison between the different prediction approaches  [6]. Zhang   present in  [7]  a study for the specification of a universal defect predictor. Gamalielsson et al. [8]  define the health of an open source ecosystem as an important decision factor when considering the adoption of an OSS component. In  [9]  the trustworthiness of OSS projects are predicted through the study of the Elementary Code Assessment. RISCOSS can benefit from these studies since it can integrate such quality models and techniques in the risk analysis platform. Adhering to the position defended by Noll et al. [10] , RISCOSS calls for human-based qualitative analysis as a necessary component.
RISCOSS Main Functionalities The main objective of the RISCOSS platform is that of facing the problem of managing risks in a holistic way, supporting the data gathering from the environment of the adopting organisation and from the organisation itself, the analysis of this data for the purpose of OSS risks identification and impact analysis, and the presentation of the data for decision making  [3]. Moreover, the RISCOSS platform is designed to support two main operative modes. On the one side, it supports the analyst in performing an explorative analysis and assessment of the OSS ecosystem (in terms of communities and components), for example assessing the convenience of integrating an OSS component in the solution. On the other side, the platform implements a continuous assessment cycle that allows detecting the emerging risks related to OSS choices once, for example, an OSS component has been integrated inside a project.
Tool Architecture The section introduces the basic RISCOSS platform architecture (see Fig. 4 ). The central element is a content management tool, XWiki, which is the unifying element of the platform and covers three basic responsibilities: (i) Offering an interface to the user for dialoguing with the RISCOSS platform: as such, XWiki offers and organizes the required forms and dashboards, maintains documents, and supports, as a wiki tool, collaborative work as needed. (ii) Integrating other tools that perform functionalities required by the platform: XWiki is the umbrella that coordinates these tools, gathering the results of their computation to feed internal data structures, accessible from the tools in a blackboard architecture fashion, and allowing other tools to initialise their calculations.
(iii) Accessing the reusable knowledge of the platform, namely the model patterns, data, form templates created by experts. The platform defines families of tools that are integrated into XWiki by means of well-established interfaces:  Questionnaire tool. The tool gathers from decision-maker and experts of a given organisation the information related to the characteristics of the organisation and the initial scopes.  Goal and risk modelling tools. The questionnaires are used, among other things, to create models that represent the ecosystem of the organization, with organizational goals stated using i*  [16] , and risks models bound to them considering risks that are related to software quality, community behaviour or OSS licenses. This allows making explicit the consequences of risks in OSS ecosystem.
 Measuring and monitoring framework composed of several Risk Data Collectors. These tools measure risk indicators and feed XWiki with the obtained values, and also monitor, detect and communicate anomalous situations from the risk management point of view. To do so, this framework needs to: 1) obtain data from the selected data sources (such as license or code quality analysis tools or blogs, forums and mailing lists of the communities); 2) implement basic statistical analysis, for example to compute statistical distributions of data  [19] , analysis of OSS communities structure and behaviour  [20] ,  OSS Risk Dashboard. This element collects the output of the risk assessment process to visualise it in a single view summarising the main aspects and giving the p mean the a proce.
et al. Project Techniques Knowledge Models Tool support FLOSS Databases and analysis Model of data to describe the Tools to retrieve data from Metrics techniques to produce characteristics of the OSS repositories and OSS project reports different OSS projects produce statistics QualiPSO Integration of data A software maturity model A platform integrating tools from OSS repositories with three levels for projects to analyse the source code and statistical analysis categorization and the bug tracking systems QualOSS Checklist for data A quality model including Tools to store checklist data retrieving and characteristics, metrics and and perform analysis of data statistical analysis indicators ALERT Integration of data from Ontologies to support Components able to gather OSS repositories; statis- extraction and integration of data from OSS sources and tical analysis and recom- different data sources services for report visualiza- mendation techniques tion and recommendation OSSMETER Integration of data Model for OSS forge descrip- Execution of project metrics; from OSS repositories tion; models for the descrip- storage and analysis of the and statistical analysis tion of OSS quality attributes data and metrics MARKOS Integration of data Ontologies to support the Tools to perform code from OSS repositories; representation of concepts analysis and license conflict license analysis related to code and licenses analysis SQO-OSS Integration of data Model for OSS quality based Integration of metrics; from OSS repositories on source code and OSS analysis of the data through and data analysis community characteristics an array of algorithms U-QASAR Data gathering on the Models describing software Platform to obtain an objec- progress and quality of quality and contexts tive value of the software software development development process quality.
However, our data did not indicate a lack of detections close to the transect line for any of the species analyzed. We therefore refrained from such an operation. The modeling of the detection function was conducted using the multi covariate distance sampling (MCDS;  [27; 28] ) engine of the "ddf"-function of the R-package "mrds"  [29]. Models were selected based on the Akaike's information criterion (AIC) and further evaluated using goodness of fit tests (q-q plots, Cramer-von Mises and Kolmogorov-Smirnov, and Chi-square tests;  [30] ). The density of striped dolphin and fin whale schools were then calculated per survey year, with Da ¼ n i 2wLP a ; ð1Þ giving the number of n sightings per km 2 in the combined area of both sides of a transect with a length L and within truncation width w, thereby accounting for changes in the detectability P a.
To facilitate comparison with other studies, we also calculated the sighting rate (SR), which is defined as ŜR i ¼ n i L ; ð2Þ the numbers of sightings per km transect. The SR does therefore not account for changes in the detection probability P a (like the strip transect approach). It further neglects the constant 2w in the denominator, as it is usually applied when sighting distances are unknown, thus potentially incorporating sightings over large distances. Sighting rates and densities of cetacean schools were calculated based on the pooled datasets per survey year, using the "dht"-function of the R-package "mrds"  [29]. Absolute dolphin and fin whale densities (Ind/km 2 ), as well as corresponding sighting rates (Ind/km) were also calculated, multiplying former school estimates (Eqs 1 and 2) with the average school size per survey year.
The underlying data on dive and surface intervals of striped dolphins was thereby obtained from an auxiliary boat survey. Considering the slightly higher speed at which our surveys were conducted, we recalculated the availability probability for striped dolphins. Besides the average duration of surface and dive intervals, we also applied the same lateral obstruction angles as in  [32]  (35°to the front and 40°t o the rear). In fact, their aerial surveys were also performed with a Cessna C 337 "Push Pull" aircraft with no bubble windows. Viewing conditions in the Cessna 208 ISR were considered to be quite similar. To our knowledge, availability bias has not yet been evaluated for fin whales in the Mediterranean Sea. However, the duration of surface and dive intervals of fin whales were assessed during a boat survey, conducted during June-August in 1995-1997 in the Ligurian Sea  [34] , which is located to the northeast of our study region.
Spatial distribution and co-occurrence In order to identify core areas of cetacean and ABFT occurrences in the GoL, we interpolated sighting locations per year on a square grid of 500x500 points (horizontal resolution: 6. 7 km), using a fixed kernel density estimation algorithm (KDE;  [40] ). More precisely, we applied a bivariate normal kernel, given by the "kde2d" function from the R-package "MASS"  [41]  To facilitate interpolation between sighting locations, we selected a bandwidth (search radius) of 0. 5 degrees, which is approximately four times the average inter-transect distance. In order to simplify comparison, kernel density estimates were rescaled to percentages. Based on annual estimates obtained in this manner, average spatial densities and their standard deviation during the survey period were calculated.
The aerial surveys were firstly funded by the  EU  project  STROMBOLI  ( 2000-2002 ), then by Ifremer (2003) and the French administration (convention IFREMER-DPMA, 2009 onwards). This author RKB was supported by a PhD grant from  France Filière Pêche  ( N°LM-2012-144 ) and  IFREMER  (fellowship contract).
A. Use-case#1 : A new environment exploration 1) Dataset selection To the best of our knowledge, there is currently no dataset available on the web applicable to validate the proposed approach. Instead, most of the works are relying on a comprehensive ontology at a basis to describe all the knowledge for a given domain. Since ontology engineering is a time consuming task necessitating expertise to ensure knowledge modeling coherency, we have used DogOnt ontology  [20]  rev 3. 2. 11 describing 926 concepts and containing 9383 axioms. This ontology is general enough to be used in a wide range of domains. The dataset is then created by fragmenting the ontology into sub-ontologies defining and structuring all the knowledge necessary to fully Shop Home Friends home describe some devices. Then, from each sub-ontology, are generated a set of degraded sub-ontologies (see Table  1 ) containing a subset of the device complete knowledge.
B. Use-case#2 : Search for energy-efficient devices 1) Dataset selection For this use-case, we have developed simple heterogeneous ontologies describing the concepts for a Hi-fi system and an Android tablet along with a power consumption property (Fig. 9  and Fig. 10 ). The electric meter ontology defines SWRL rules allowing to classify the devices based on their power consumption. For instance, the following rule infers that devices with a power consumption property value in between 1W and 10W are classified in category "A": Device(?d), integer[>= 1 , <= 10](?c), has_power_consumption(?d, ?c) -> has_consumption_category(?d, "A").
Many other projects relies on ad-hoc ontologies specific to domain like smart offices  [24] , smart homes  [25] , ambient assisted living  [26] , sensors  [27] ,  [28]  , smart cities  [29] , etc… Some projects make use of heterogeneous ontologies. For example, in the context of ambient intelligent environments (AIEs), ATRACO project  [6]  is built around agents exchanging data between each other. This project is still based on an upper ontology but allows software agents to independently and locally describe and rely on their own ontology. While an ontology alignment engine is developed to cope with the semantic heterogeneity issue at run time, it still offers no perspective for the upper ontology to capitalize the contribution of agents' local ontologies over time. In  [22]  authors expose some challenges relative to SWoT domain.
At the end of operation, mediastinum (M end), sternum edges (SE end), subcutaneous tissue (SC end) and skin (C end) were sampled again according the step of surgical closure. At least, swabs were taken on surgical wound during dressing replacements, corresponding to skin and scar tissues sample (SST) (Supplementary Table  1 ). Primary human materials used in this study were collected on sterile cotton swabs as performed for the routine clinical diagnostic process without change in the surgical procedures or the nursing cares. Each patient included gave an oral informed consent. The study proposal was approved by the ethical committee of our institution: Comité de Protection des Personnes Sud Méditerranée IV, N • ID-RCB: 2011-A00078-33 (Ref CPP: 11 02 02SC), and the collection of human samples was declared in the database "Conservation d'éléments du corps humain" at the French Ministry of Higher Education and Research (n • DC-2014-2169).
5% agarose gel and sequenced on an ABI 3730xl sequencer (Cogenics, Beckman Coulter). Bands for which the sequencing process failed were analyzed by a new TTGE migration, as previously. The sequences were checked for ambigous positions and analyzed by comparison with Genbank (http://www. ncbi. nlm. nih. gov/) and RDPII databases (http://rdp. cme. msu. edu/) using Basic Local Alignement Search Tool (BLAST) and Seqmatch programs, respectively. The RDPII database was particularly used for comparison to type strains of species. A sequence was affiliated to an Operational Taxonomic Unit (OTU) on the basis of a 99-100% of sequence identity  (Drancourt et al. , 2000; Jacquot et al. , 2011). A sequence matching with the sequences of the type strain of only one validated species was affiliated to the corresponding taxonomic species.
After each wash the washing agent was removed by placing the beads on a magnet and carefully removing the supernatant. Upon washing the tube containing the magnetic beads was placed on top of a photographic film (MG-SR PLUS medical X-ray film from Konica Minolta, Åbyhøj, Denmark). After a three-day exposure time the film was developed and the signals quantified using the Image J software (http://imagej. nih. gov/ij/).
Acknowledgments: We are thankful to technicians  Noriko Y. Hansen  and  Lone Højgaard Nielsen  for technical assistance during this project and to  Mirjam Groger ,  Rella Manego Zoleko ,  Selidji T Agnandji ,  Lilian Endame ,  Johanna Kim  for contributing with sample collection. This work was supported by the  Danish Research Councils  ( 11-116325/FTP ), the  Lundbeck Foundation  ( R95-A10275 ),  Arvid Nilsson 's Foundation, The  Augustinus Foundation ,  Marie & M. B. Richters Foundation ,  Aage and Johanne Louis-Hansens Foudation, Aase and Ejnar Danielsens Foundation ,  Familien Erichsens Mindefond , and  Familien Hede Nielsens Fond.
For the measurement of the AP effective refractory period (AP ERP), one atrial extra-stimulus was delivered after 7 paced atrial stimuli at a cycle length of 400 ms from 390 ms until the AP refractory pathway or the atrial effective refractory period with 10-msec decrements. The disappearance of the pre-excitation pattern was indicated upon reaching the AP ERP. When a fast AF conducted over AP was induced with this method, the protocol was halted; in the absence of induction of tachycardia conducted over AP at a rate higher than 240 bpm, isoproterenol (0. 02e1 mg min À1 ) was infused to increase sinus rate to at least 130 bpm after which the pacing protocol was repeated. Pre-excitation was characterized by the following data: AP location was determined with the 12-lead ECG recorded in maximal pre-excitation according to classical data [13e24].
Statistical analysis Data were expressed as means ± standard deviation (SD) or proportions, as appropriate. Categorical variables were compared using the Chi-square test and continuous variables with the unpaired t test. Univariable logistic regression was used with the following dependent variables, age, gender, AP location, history of poorly-tolerated tachycardia and hemodynamically poorly-tolerated tachycardia occurrence during follow-up, malignant form at electrophysiological study. Variables associated with the considered outcome with a p value <0. 10 in univariable analysis were entered in the multivariable models. A p-value <0. 05 was considered statistically significant. All statistical analyses were performed using the SPSS package for Windows (version 20, SPSS Inc, Chicago, Illinois).
Results 1 General data on the location of AP in total population: AS AP location was identified in 94 patients aged 6e57 years. Right lateral (RL) AP was noted in 51 patients. Remaining patients had a different location, right or left posteroseptal (PS) location in 459 patients, left lateral (LL) location in 359 patients and or nodo-ventricular (NV) AP in 22 patients. In 8 patients, 2 locations were identified; one of them had right PS location associated with AS AP. This patient was classified with AS locations. Three patients had associated PS AP and right lateral AP; they were classified with right lateral location. Three patients had a LL and PS AP and another patient had a LL AP and a NV AP; they were classified as LL Patients with AS AP or RL AP were younger than patients with another AP location (25 ± 13, 25.
For example, Arya  [19]  reported that conduction properties of accessory pathways (APs) are independent of location and conduction mode (except in patients with multiple, Mahaim, and slowly conducting APs). Patients with right-sided APs i n d i a n p a c i n g a n d e l e c t r o p h y s i o l o g y j o u r n a l 1 5 ( 2 0 1 5 ) 2 2 7 e2 3 5 showed higher rates of atrial fibrillation and longer arrhythmia cycle length due to slower anterograde conduction over the atrioventricular node during atrioventricular reentrant tachycardia. Some of our data were similar to data of de Chillou and al  [20]  in 1992. The authors reported that location of the accessory AV pathway was associated with specific electrophysiologic characteristics. As in our study later age at onset of symptoms in the left free wall versus other AP locations and later age at the time of EPS in the left free wall AP location were reported.
Transmission Electron Microscopy Arabidopsis wild-type and triple mutant flowers at stage 13 of floral development were fixed in 3% (v/v) glutaraldehyde in 0. 1 M phosphate buffer overnight at 4°C, postfixed in osmium tetroxide for 2 h at room temperature (22°C-25°C), and poststained with uranyl acetate overnight at 4°C. After dehydration and embedding in Spurr's resin, ultrathin sections (80-90 nm) were obtained on a Reichert 2040 microtome and mounted on copper grids. Ultrathin sections were observed with a Tecnai G2 Spirit TWIN transmission electron microscope at 120 kV. Sequence data from this article can be found in the Arabidopsis Genome Initiative or GenBank/EMBL databases under the following accession numbers: CER2, At4g24510; CER2-LIKE1, At4g13840; CER2-LIKE2, At3g23840; CER1, At1g02205; FAR2, At3g11980; Actin2, At1g49240; eIF4A-1, At3g13920, GAPC (for glyceraldehyde-3-phosphate dehydrogenase C subunit 1), At3g04120; KCS1, At1g01120; KCS5/CER60, At1g25450; KCS6/CER6, At1g68530; KCS9, At2g16280; KCS10/FDH, At2g26250; and KCS20, At5g43760.
ACKNOWLEDGMENTS We thank the  Salk Institute for Genomic Analysis Laboratory  and the  Arabidopsis Biological Resource Center  for providing the sequence-indexed Arabidopsis T-DNA insertion lines,  Lysiane Brocard  ( Bordeaux Imaging Center, University of Bordeaux ) for help with transmission electron microscopy analysis, the  BioImaging Facility  ( University of British Columbia ) for assistance with confocal microscopy and SEM, and the  Metabolome facility of Bordeaux  for assistance with GC-MS analysis (http://www. cgfb. u-bordeaux2. fr/en/metabolome).
Tomograms composed of 500 projections with a resolution of 10. 87 mm were captured every 0. 4 s over a 360°rotation at 150 r. p. m. Tomograms composed of 1,000 projections with a resolution of 10. 87 mm were captured every 0. 8 s over a 360°rotation at 75 r. p. m. To achieve the required tomogram frequencies, the images were stored on the PCO Dimax camera which, with 36 GB of on-board memory for fast intermediate storage, could store 17 s of recordings under these conditions. A post event triggering capability was implemented, whereby the detector recorded continuously until triggered, at which point it would store the previous 17 s of high-speed tomograms. During high-speed tomography, the sample was continuously rotated through 360°. Data processing. The transmission images were reconstructed in 3D using standard reconstruction methods  45  and saved as 32 bit float files.
The reconstructed tomograms were processed using Avizo Fire 7 (FEI VSG, France), where the 3D images were displayed as stacks of 2D image slices. An edge preserving non-local means filter  46  was applied to the images, to remove noise, while preserving fine features and edge definition. Material phases were defined by grey-scale value using Avizo's segmentation editor and selectively displayed. The radiography movies were developed using MATLAB R2013a. Flat field correction was applied to the raw radiographs to remove artefacts and the grey scale image contrast was enhanced using MATLAB's 'adapthisteq' function for improved feature recognition. Images were converted to 16-bit unsigned integers before compiling into. avi movies. The real-time thermal imaging movies and mean temperature versus time graphs were developed using FLIR's Altair software.
Acknowledgements P. R. S. acknowledges funding from the  Royal Academy of Engineering. D. P. F. acknowledges funding from the  EPSRC and National Physical Laboratory. We are grateful to the  STFC  for travel funding through the  Global Challenge Network in Batteries and Electrochemical Energy Devices. These experiments were performed on the ID15A beamline at the  ESRF, Grenoble, France. We are grateful to  Marco Di Michiel  and  Mario Scheel  at  ESRF  for providing assistance in using beam-line ID15A.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FASO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
The camera performs a perspective projection relative to its optical center, consisting of two transformations: the first transformation projects a 3D point M (x, y, z, 1) T into p(u, v, 1) T (in a homogeneous coordinates system) in the image plane Ic, and it is written as follows: w p = [K|0]I 4x4 M (1) w    u v 1    =    -k u f x s u 0 |0 0 k v f y v 0 |0 0 0 1 |0         1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1           x y z 1      (2) where w is a scale factor representing the depth of M (x, y, z, 1) T relative to the camera. K is the matrix of intrinsic parameters, assumed to be known, since the camera is calibrated using the MATLAB toolbox of  [34]. The vertical and horizontal dimensions of a pixel of the optical photosensitive sensor are denoted d x and d y , so f x = f /d x and f y = f /d y.