dataesr/scientific-paragraphs-categorization huggingface dataset:
In Figure  4 , we give an example of the backup scenario at sink level in case of failure of the node 3 in Figure  2. The packets transiting in the network have all the same structure, as in Figure  -5. The field ID_PCK represents the type of the packet which might be a signalisation, request or data packet. Sender field contains the address of the node which sends the packet and this one may be different from the source node of the packet which is specified in the third field. The size field contains the number of bytes of the payload, it is equal to zero except for data and request packets. Indeed, for the request packets, payload field is used to specify the destination of the packet (camera node concerned by the request). When a user asks through a web application for pictures related to a specific person, a request is addressed to the database to get all file names of pictures related to this person.
Network environment The sensors used in our platform are Arduinos Mega 2560 equipped with cameras. We used TTL Serial cameras and Wireless proto Shields with XBee S1 senders to handle ZigBee transmissions between camera nodes. It was possible for us to use a 32 bits long addresses for the nodes, however, due to the nature of the use case of our platform we opted for 16 bits addresses. The sink doesn't integrate a camera but has a Wireless WiFi Shield to connect to an access point and upload collected data into servers. This shield contains a MicroSD slot which is used by the sink to store a picture while the fragments composing it are being received. When a picture is fully received, the sink transmits its information to remote database and then uploads it into remote FTP server. We tried several scenarios for our network topology to retrieve pictures from nodes up to three hops far from the sink as in Figure  7.
Servers environment We have used three servers in the implemented platform, a MySQL database server V5. 7 to store information about end-users accounts, monitored persons as long as received pictures and sensors details. A FileZilla server V0. 9. 49 used to store photos of monitored persons and pictures sent by sensors. Finally, a JEE based web application running on an Apache Tomcat V8. 0 server. The web application communicates with FTP and database servers to retrieve pictures and information asked for by the end-user. Hereafter some screenshots of the developed web application to access data throw a web browser. Figure  -8  shows the identification form in the index web page which allows each end-user to access to elderly records for which he has the required access rights.
Airway Narrowing Studies Airway narrowing studies were performed within the same mouse on untreated slices and on slices treated with elastase (2. 5 µg/ml) or H 2 O 2 (200 µM). Dose response curves for MCh (10 -9 -10 -3 M) were recorded, after which the airways were dilated using the bitter taste receptor agonist chloroquine (10 -3 M, Sigma-Aldrich). A nylon mesh and metal washer were used to fixate the lung slice, as described previously  (Rosner et al. , 2014). Lung slice images were captured in time-lapse (1 frame per 2 s) using a microscope (Eclipse, TS100; Nikon). To quantify airway luminal area, image acquisition software (NIS-elements; Nikon) was used. Luminal area is expressed as percent basal.
Assessment of clinical and laboratory data The baseline demographic and serologic data were obtained from the patients; we aimed to find associated factors of the 4 groups before the antivirus treatment. Then the patient biochemical data were monitored every 3 months, including HBV DNA level, hepatis surface antigen (HBsAg), Hepatitis B e antigen (HBeAg), anti-HBe HBeAg, ALT, aspartate aminotransferase (AST), total bilirubin (TB), and creatinine (Cr) until the end of follow-up. HBV DNA was quantified by real-time PCR (Da a Genetics, lower limits of detection: 100IU/ml). HBsAg, anti-HBs, HBeAg, and anti-HBe were measured using commercially available chemiluminescence assay kits (Roche Diagnostic Systems).
Statistical analysis All data were entered and analyzed by using the SPSS software vision 20. 0 (SPSS 20. 0 Inc. , USA). Categorical data were presented as counts and percentages and were analyzed by using the chi-Square test or the Fisher exact test. Continuous quantitative data are showed as mean ± standard deviation (SD) and were analyzed by Student's t test. Cox regression analysis was performed in search of variables determining the virological response. Cumulative rates of complete viral suppression were analyzed by the Kaplan-Meier method. P<0. 05 was considered statistically significant. All statistical tests were twotailed and p-values less than 0. 05 were considered as statistically significant.
Simulation Setup Two types of parallel hybrid electric vehicles are tested in this work: (1) a light-duty parallel hybrid electric vehicle, whose electric motor is engaged in the second gear of its gearbox; (2) a virtual heavy-duty truck from  [15]  that is hybridized with real powertrain components. The main characteristics of the light-and heavy-duty parallel hybrid electric powertrains are summarized in Table  2. Apart from GRAB-ECO, both PMP and SHM, are tested in the platform of MATLAB 2015b on a DELL laptop of i7-4810MQ CPU @ 2. 80 GHz and 16 Gb RAM. The computation time is the execution time of each evaluation obtained with MATLAB function. When the minimal fuel consumption is under evaluation, it can be based on either mapped data or parametric models of powertrain components. Note that, the semi-analytical approach, SHM, depends on full parametric models.
Light-Duty Parallel HEV For the light-duty parallel HEV, the minimal fuel consumption is depicted in Figure  7. The minimal fuel consumption is evaluated separately over NEDC, FTP-72, and HYWFET. Concerning the minimal fuel consumption, mapped-databased PMP m and GRAB-ECO m , and the parametric-modelbased PMP p , SHM, and GRAB-ECO p presented similar minimal fuel consumption without significant discrepancy. The errors were about 1. 0% between PMP and GRAB-ECO both based on the same type of powertrain data. In detail, GRAB-ECO m had the least error of the minimal fuel consumption compared with PMP m over each investigated driving cycle, which was À1. 0%, 0. 4%, and 0. 9% over NEDC, FTP-72, and HYWFET, respectively. However, GRAB-ECO p presented higher error than GRAB-ECO m over the corresponding driving cycle, which were 1.
The energy difference stems from the axial kinetic energy of the particles and is removed when entering and leaving the shifting region due to the dipole force exerted by the laser. Our simulation is based on the SIMBUCA code  [19, 20] , which was specifically developed for the simulation of particles in a Penning trap. It uses a GPU to parallelize the calculation of the Coulomb interaction, allowing one to increase the total number of particles in comparison with a CPU-based simulation. The trajectories of the particles in the trap, including the cyclotron motion, are fully resolved using a fifth-order Dormand-Prince integrator. In order to include the interaction of the molecules with the light fields, we extended the code to include a simulation of the laser-induced electronic population transfers, which we based on rate equations and the Monte Carlo method.
4 mL/min for 5 min. After the aerosol phase, the tissue specimens were exposed to another 10 min of aerosolised oxaliplatin (exposure phase). Immunohistological analysis: For the evaluation of CD31 and Ki-67 staining, each frozen tumour sample was sectioned at a thickness of 5-µm using a cryostat (Leica Microsystems GmbH, Wetzlar, Germany). The sections were incubated with anti-CD31 antibody and anti-Ki-67 antibody (BD Biosciences) for 1 hour at room temperature and then with the second antibody. Images were acquired using a fluorescence microscope equipped with appropriate filters (Observer. Z1; Carl Zeiss Inc. ). Histo-Lab software (version 7. 0; Microvision Instruments, Evry, France) was used to quantify the results. Tumour tissue was then harvested and frozen for subsequent immunohistochemical analysis.
Image analysis. Images of the cells were taken every 10 s for 10 to 20 min with a Zeiss AxioCam Hrc camera controlled by the Axio Vision 4. 6 software (Carl Zeiss, Germany). The pulses were always delivered after at least 2 minutes of recording and 2 seconds before the next image. The excitation and emission wavelengths used for Fluo-4 were 496 nm and 515 nm respectively. The nuclear dye Hoechst 33342 (λex = 350 nm, λem = 461 nm) was used to track the cells during the videomicroscopy recording. There was no interference between Fluo-4 and Hoechst 33342 fluorescence because their emission wavelengths are separated enough. In this way, nuclei were recognized and the individual cells tracked using the Cell Profiler (version 2. 0) software (Broad Institute, Cambridge, USA), allowing the automatic measurement of the fluorescence intensity signal of each cell on every image.
Curves were plotted with a MATLAB program (version 7. 8. 0). All the observations were done at room temperature. The minimum opening time of the shutter for the fluorescent light was about 500 ms. To decrease the light energy applied on the cells, a 90% density Filter NE110B (Thorlabs, Maisons-Lafitte, France) was used. pcDNA-D1ER and endoplasmic reticulum imaging. pcDNA-D1ER was a gift from Amy Palmer & Roger Tsien (Addgene plasmid # 36325). This plasmid is coding for an endoplasmic reticulum (ER) marker fluorescing in green (535 nm)  69. Cells were transfected with pcDNA-D1ER using 8 electric pulses of 100 μs and 1500 V/cm or 1200 V/cm (for the haMSC and DC-3F cells respectively) delivered at a repetition frequency of 1 Hz by the Cliniporator TM (Igea, Carpi, Italy). We used 50 μg or 20 μg plasmid (for the haMSC and DC-3F cells respectively) in 100 μl, for 5.
10 5 cells in a cuvette of 1mm distance between the electrodes. D1ER is retained in the ER lumen through its C-terminal KDEL (lysine, aspartic acid, glutamic acid, leucine) sequence  70. A confocal microscope Leica TCS SPE with an objective HC PL APO CS2 63x, 1. 30 NA oil and the LAS AF software version 3. 3 (Leica, Germany) was used to visualize the endoplasmic reticulum, labelled by the ER marker. Excitation of the marker was done at 480 nm and emission captured at 535 nm. Cells nuclei were marked with Hoechst 33342 (excitation and emission at 405 and 486 nm respectively). The images were taken without electronic zooming for the haMSC and using a × 2 zoom for the DC-3F cells. 23 slices were taken each time to produce stacks of ER images with a Z distance of 0. 8 µm between them. The area of ER has been identified and reconstructed on the basis of the green fluorescence by using a custom semi-automated MATLAB TM routine, obtained by a modification of the procedure of Joensuu et al.
Acknowledgements This work has been supported by  CNRS ,  Gustave Roussy ,  Université Paris-Sud , and the  ITMO Cancer and INSERM  in the frame of the Plan Cancer 2015-2019 (project  PC201517 ). The research was also conducted in the scope of the  EBAM European Associated Laboratory (LEA)  and of the  COST action BM 1309 EMF-MED. This project received support within the framework of the  Joint IIT-Sapienza LAB on Life-NanoScience Project  ( 81/ 13 16-04-2013 ). This work has benefited from the facilities and expertise of the Imaging and  Cytometry Platform  ( Sophie Salome ),  UMS   3655 CNRS/US 23   INSERM. Gustave Roussy Cancer Campus Villejuif, France. We thank  Dr. Bassim Al-Sakere  for providing the biological samples for haMSC isolation. Authors also want to thank  Paola Camilleri  and  Mattia Corradini  for their contribution to the setup of the ER images extraction procedure,  Antoine Azan  and  Tomas Garcia-Sanchez  for their help in establishing Matlab program, and  Thierry Ragot  and  Francesca Apollonio  for scientific discussions.
Methods Samples. (2004-2005-2006-2010). Families included in the EMIGARDE cohort were drawn from a larger pool (N = 809) of families with a child born between June 2003 and April 2004 and having taken part in a prenatal-perinatal study conducted in four Montreal maternity hospitals (for details, see  33  ). A subgroup of fathers and mothers of these children completed the ANPS at the 2 last data collection points, in 2006 (T1, N = 520) and 2010 (T2, N = 569). The exclusion of individuals with questionnaires having more than 10% of missing items (N = 11 at T1 and N = 1 at T2) resulted in a final sample of 509 participants (222 men, and 287 women) at T1 and 568 participants (249 men, and 319 women) at T2. At T1, the mean age of the men and women was 38. 4 ± 6. 3 and 35. 2 ± 5. 0 years respectively (overall mean age: 36.
5 ± 5. 8 years). Most participants had an intermediate to high level of education: university degree (56. 4%), high school grade (24. 7%), partial college education (8. 6%), and partial secondary education (7. 9%); 2. 4% had no secondary education. Concerning the ethnicity, the origin of most participants was Canadian (65%) or French (8%), and the remaining participants had other ethnical origins (27%; mainly British and Italian). The study received approval from Sainte-Justine Hospital research centre and McGill Institutional Review Boards. Participants gave their written informed consent at each data collection, and all procedures were carried out in accordance with the relevant guidelines and regulations. Participants in Sample 2 came from the DETENDOEMO survey, conducted in France in 2008.
The extracted DNA was resuspended in 30 L TE buffer and stored at -20 ∘ C. PCR amplification for 16S rRNA gene from the extracted DNA samples was done using the primer pair PA-PB (PA: 5  -CAGAGTTTGATCCTGGCT-3  ; PB: 5  -AGGAGGTGATCCAGCCGCA-3  ) as described previously  [14]. Amplified PCR products were purified and sequenced by Sangon Biotech (Shanghai). Identification of phylogenetic neighbours and calculation of pairwise 16S rRNA gene sequence similarities were achieved using the EzTaxon server (http://www. eztaxon. org/) [15]  and BLAST analysis (http://blast. ncbi. nlm. nih. gov/Blast. cgi). The alignment of the sequences was done using CLUSTALW  [16]. The phylogenetic tree was constructed using the aligned sequences by the neighbour-joining method  [17]  using Kimura 2-parameter distances  [18]  in the MEGA 6 software  [19].
Acknowledgments The authors are grateful to  China Postdoctoral Science Foundation  (Project no. 2016M602566 ),  Visiting Scholar Grant  of  State Key Laboratory of Biocontrol, Sun Yat-Sen University  (Project no. SKLBC14F02 ),  Vietnam Ministry of Education and Training  (Project no. B2014-01-79 ), and  Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme  ( 2014 ) for financial support for this study.
I. INTRODUCTION Arabic is the first language of more than 200 million individuals across the world and the fifth most spoken one. Surprisingly little work has been done on the building of Arabic semantic resources. Semantics is one of the major components in natural language processing and plays a very important role in improving the performance of information retrieval systems. So there is good motivation to work on such resources. Lexical semantic relations are associations between meanings of words. For example the nouns "school" (when denoting a building) and "schoolhouse" have a synonymic relation  [1]. Semantic resources are lexical databases containing words and semantic associations between them. Arabic semantic resources are scarce; one can cite Arabic WordNet  [2]  or improved Structured and Progressive Electronic Dictionary for the Arabic language (iSPEDAL)  [3].
The aim of this paper is to survey and classify a comprehensive set of methods for building and/or enriching Arabic semantic resources. Each method uses its own methodology and algorithm, usually on relational databases. We show that all existing methods are founded on five types of resources: dictionaries, translation resources, WordNets, ontologies and morphological information, and that this fact makes way for new methods on Arabic corpora resources. This paper is organized as follows. Section II will review the basic resources used in building Arabic semantic resources, Section III will present a survey of the existing methods and give a detailed description of the procedure for each method. The last section concludes with the classifications of these methods according to their resources and a discussion.
A. WordNet WordNet is a lexical database representing concepts and their relations, which was developed by linguists and psychologists in 1985 at the Laboratory of Cognitive Science of the University of Princeton  [4]. It provides a large repository of English lexical items, and is available online. WordNet was designed to establish relations between the four main parts of speech (POS): noun, verb, adjective and adverb  [5]. As similar databases have been devised for other languages (German, French and other European languages, Arabic, among others) "WordNet" is now a generic noun and the original WordNet is here called Princeton WordNet (PWN). The atomic component of any WordNet is the concept or synset. It is represented as a list of synonymous word forms denoting one sense or a particular purpose; a word can appear in different synsets in case of polysemy, as in the following examples: • synset 1 : {car, auto, automobile, machine, motorcar}, • synset 2 : {car, railcar, railway car, railroad car}.
Synsets are related to each other by semantic relations, such as antonymy, hyponomy, and meronymy. 1) BalkanNet: BalkanNet  [6]  is a multilingual lexical database relating individual WordNets for the Balkanic languages (Bulgarian, Greek, Romanian, Turkish and Serbian), with the objective of creating a large-scale linguistic resource. It implemented for the first time a new feature, the Inter-Lingual-Index (ILI), to ensure linking of conceptual equivalencies across WordNets in the development of an internetworked WordNet Management so that each partner retains full responsibility and independence of his local WordNet while simultaneously being able to view other WordNets and check their compatibility. 2) EuroWordNet: EuroWordNet  [7]  is a multilingual database with individual WordNets for several European languages (Dutch, Italian, Spanish, German, French, Czech and Estonian).
Each WordNet is structured in the same way as PWN, with synsets and their relations. It represents a unique language-internal system of lexicalizations. In addition, individual WordNets are linked by the Inter-Lingual-Index to PWN. Via this index, languages are interconnected so it is possible to go from words in one language to similar words in any other language. The index also gives access to a shared top-ontology (Section II-B) with 63 semantic distinctions. This ontology provides a common semantic framework for all languages, while language specific properties are maintained in individual WordNets. 3) Arabic WordNet (AWN): Arabic WordNet is a lexical database that was created manually in 2006 by  [2] ,  [8]  In 2015, to improve the coverage and usability of AWN,  [10]  defined its extended version, AWN:V2.
This version can be downloaded from the Open Multilingual Wordnet 1. It includes 37,342 lemmas, 2,650 irregular plurals and 14,683 roots. It also includes 10,169 synsets of which 253 are nondiacritized. The database scheme has been changed and the data cleaned. AWN:V2 is presented in two formats: Lexical Markup Framework and Lemon Format. The first format is used for standardizing lexical representations of language resources, while the second one is used for linking lexical resources and ontologies. Despite the efforts made to improve AWN, it remains limited compared to other WordNets.
The Suggested Upper Merged Ontology (SUMO), owned by IEEE, is described in its portal  2  as "the largest publicly available formal ontology today [. ] written in SUO-KIF language". SUO-KIF is a simplified derivative of Knowledge Interchange Format, a language equivalent to first-order logic. A translation to OWL (the semantic web language) is also available. The set has 20,000 terms and 60,000 axioms. There is a complete mapping (by hand) of SUMO to the different versions of PWN (up to version 2. 1), including MILO (middle level ontologies)  [14, p. 73 ]. This mapping of SUMO concepts to all of the PWN synsets  [15]  has been very useful; SUMO concepts were also mapped by hand to AWN synsets; thus SUMO plays the role of intermediary between these two WordNets  [2] ,  [8]. SUMO is used to maximize the semantic consistency of hyperonymy links.
• Classical Arabic dictionary A classical Arabic dictionary is a paper-based monolingual dictionary. The keys are words in Arabic and the descriptions are also in Arabic. Arabic has a very rich variety of monolingual dictionaries, such as the famous Lisan-Al-Arab or Muajam Makayse Al Luga  [16]. • Structured electronic Arabic dictionary A structured electronic Arabic dictionary is a monolingual resource. This kind of dictionary contains Arabic words ordered according to a specific logic. Two notable examples: • iSPEDAL (improved Structured and Progressive Electronic Dictionary for the Arabic Language) relational database  [3]  has been designed to be easy to use, with its appropriate query language. For each word it provides links to root, affixes and possible models (patterns). Morphological derivation has much more influence on semantics in Arabic than for instance in English.
In addition, there are semantic links between words (definition, spelling, meaning, synonyms, antonyms, usage patterns. ). iSPEDAL has been produced from digitized monolingual paper dictionaries, and contains a large number of unused words that need to be verified. • LOGOS 3  is a relational database of Arabic verbs with 944 fully conjugated ones. • Multilingual translation resources There are quite a number of bilingual translation resources, from standard paper dictionaries, to New Mexico State University (NMSU) and Al-Mawrid Arabic-English databases  [17]. Aligned translation corpora are of course of importance here. Though not truly a translation resource, Wikipedia, a project of universal encyclopedia, is a multilingual resource with links that join equivalent resources in different languages.
Arabic words linked to the same concept were candidates to enter a synset in AWN. The candidates were to be validated by lexicographers using a methodology with eight heuristic procedures that had been devised while building Spanish WordNet (part of EuroWordNet). This methodology assigned a score to each association between a word and a PWN synset based on some Arabic English bilingual lexicon; AWN being hand-made, no threshold was set and all associations were provided to the lexicographer for verification. Thus, when editing an Arabic synset, the lexicographer begins with a suggested association, rather than an empty synset with only the English data to go by. Some suggestions were correct or very similar to correct ones. Others were incorrect but served to trigger an Arabic word that might otherwise have been missed.
B. Semi-automatic Extension of Arabic WordNet using Lexical and Morphological Rules The same team obtained better results with another method  [9]  which derived all possible forms from Arabic words in AWN (thus producing many non-existent words), tested them with existing databases, looked for their translation in English, linked them to PWN and then back to AWN, to be manually validated as in Section III-A. More precisely, their procedure can be described into two phases. The first phase includes: • Collect a set of validated basic verb forms from AWN (in Arabic, most noun and adjective forms are derived from verb forms). • Apply to this set all Arabic derivational morphological patterns and generate all possible stems. • Attach affixes to each produced stem according to Arabic morphological rules and generate all possible words.