dataesr/scientific-paragraphs-categorization huggingface dataset:
Candidate Eligibility is also satisfied. We note that, in MarQ tool the Candidate Eligibility monitor stops monitoring as soon as a transition to state (2) is made since there is no path to success from state  (2). Thus, only the first candidate that violates the property is reported. In order to report all such candidates, we had to add an artificial transition from state (2) to an accepting state that could never be taken. Then, monitoring after reaching state (2) remains possible. Moreover, the current implementation of MarQ does not support sets of tuples. Consequently, we could only collect the identities i in a set F instead of the tuples (i , q, a). Answer Authentication was violated only in Exam 1. We reported the violation to the e-exam's developers. The violation actually revealed a discrepancy between the initial specification and the current features of the e-exam software: a candidate can submit the same answer several times and this answer remains accepted.
On the other hand, off-line runtime verification of user-provided specifications over logs has been addressed in the context of several tools in the runtime verification community  [15] : Breach for Signal Temporal Logic. RiTHM and StePr for (variants of) Linear Temporal Logic, LogFire for rule-based systems, and Java-MOP for various specification formalisms provided as plugins. MarQ  [6]  is a tool for monitoring Quantified Event Automata  [4, 5]. Our choice of using QEA stems from two reasons. First, QEAs is one of the most expressive specification formalism to express monitors. The second reason stems from our interviews of the engineers who were collaborating with us and responsible for the development of the e-exam software at UJF. To validate our formalization of the protocol and the desired properties for e-exams, we presented the existing alternative specification languages.
QEAs turned out to be the specification language that was most accepted and understood by the engineers. Moreover, MarQ came top in the 1 st international competition on Runtime Verification (2014)  15  , showing that MarQ is one of the most efficient existing monitoring tools for both off-line and on-line monitoring. Note, off-line runtime verification was successfully applied to other case studies, e. g. , for monitoring financial transactions with LARVA  [16] , and monitoring IT logs with MonPoly  [17].
Acknowledgment. The authors would like to thank  Francois Géronimi  from  THEIA ,  Daniel Pagonis  from  TIMC-IMAG , and  Olivier Palombi  from  LJK  for providing us with a description of e-exam software system, for sharing with us the logs of some real french e-exams, and for validating and discussing the properties presented in this paper. The authors also thank  Giles Reger  for providing us with help on using MarQ. The authors also would like to thank the " Digital trust " Chair from the  University of Auvergne Foundation  for the support provided to conduct this research.
We chose to model a representative part of the terminal to observe the structure's behavior. The purpose of our research is to explain the structure's collapse and to see if there were deficiencies from the design phase. Also, our new fine-grained model using Ansys Software makes it possible to explain the progressive collapse of the structure, which was the main challenge of our study. Moreover, a sensitivity analysis was performed in order to study the importance of each of the variables taken into account in the model. modeling and problems encountered during the process. We will also talk briefly about the behavior of concrete (creep, shrinkage).
Modeling the terminal In a previous study, the terminal was modeled using ST1 software by a bar model that required 1558 nodes and 2320 bars. [1]. As for our modeling of the concerned section, we used Ansys software and a shell model that required 10,488 shell elements, 12,978 nodes and 814 bars for struts and ties  [2]. It has to be noted here that Ansys software allowed us to obtain a fine-grained model representing better the reality. The complexity of our structure has been considered: openings in the shell, connection between the arcs by fragile corners iron, shortened arcs right on the gateways placement, and finally the dissymmetry of the structure and the applied loads. This model using Ansys allowed us to properly take into consideration both wind and temperature loads. It also made it possible to represent the progressive collapse of the structure, which was the main breakthrough of our study.
Also, the obtained results using ST1 and Ansys software confirm the values measured on site. This allows us to validate our model. We note a remarkable difference for both short and long term deflections between predicted results and the ones measured on site. This proves that the simulation of the structure by the design office was not very accurate. Finally, on the long term, the measured deflection as well as the one given by Ansys exceeds the allowable value of 12. 40 cm.
Sensitivity analysis In previous works  [1, 7] , a reliability-based assessment of the structure was made in terms of deflection  [8, 9]. A mechanical-reliability coupling was performed between the design software and Phimeca. Results gave us a reliability index b of 1. 824. Since an airport is considered to have a very serious financial impact, and since it endangers a large number of  persons, the obtained value is smaller than the allowable values for this kind of structures and we can fairly say that the structure presented a deficiency from the beginning according to the previous reliability study. In the following, we have studied the sensitivities s i of each of the variables  [10, 11]. The sensitivity measures reflect the importance of each random variable drawn from the direction cosines.
e. , high stress tolerance, stress effects with high patchiness, and/or low stress intensity; see Additional file 3). To explore further the possible applications of W max , we performed the same analysis on two other datasets. First, we used an independent dataset (not generated with the PHENOPSIS platform) to explore the genetic variability in photosynthetic performance in six accessions of Arabidopsis from contrasted geographic locations. The plants displayed little variation during plant development in mean F v /F m values (Figure  6A ). However, we observed an increase in photosynthetic heterogeneity S and W max during plant development (see Additional file 4: Figure  S3 ). We calculated the increase in W max during development as the slope of the relationship between W max and plant age.
Phenotyping platform PHENOPSIS The PHENOPSIS platform  [33]  includes three automatons set up in growth chambers strictly controlled for environmental conditions. In each chamber, an automated arm is equipped with a balance (XB620C; Precisa, Dietikon, Zurich, Swiss) and a complete system of irrigation (A1645-6; Electro finish, Saint-Egrève, France) to accurately weigh and irrigate up to 504 Arabidopsis thaliana plants in individual pots. The arm is also equipped with multiple devices for non-destructive acquisition of plant phenotypic data such as Charge Coupled Device (CCD) cameras or fluorescence sensors  [1]. Light is provided by a bank of cool-white fluorescent tubes (36 W T8 Grolux, 120 cm; Sylvania) and quartz metal halide lamps (HPI-T Plus 400 W 645 E40; Philips). Light intensity is measured continuously at plant height, using a light sensor over the waveband of 400-700 nm (SKP215; Campbell Scientific, Logan, USA).
Air temperature and relative humidity are measured every 20 s (HMP45C-L; Campbell Scientific). All measurements of temperature, light intensity and relative humidity are averaged and stored every 600 s in a data-logger (CR10X; Campbell Scientific) with data-logger support software (Loggernet V4; Campbell Scientific). The climatic regulation of the growth-chambers is controlled by Loggernet software allowing the control of the desired environment by employing an air drier or a water sprayer to modify air humidity, an air-cooler or a heater to modify air temperature.
High resolution of chlorophyll fluorescence imaging Acquisition of chlorophyll fluorescence images ChlF measurements were performed using Imaging-PAM chlorophyll fluorometer and ImagingWin software application (ver. 2-45d, Heinz Walz GmbH) connected with PHENOPSIS automaton. ImagingWin software is driven by Optima PLC (ver. 2-14, build v323, by Optimalog SARL; Saint-Cyr-sur-Loire, France) that allows the automatic movement of Imaging-PAM implemented on the arm of robot (Figure  1A ) and the ChlF measurement of each pot with identical settings. The measuring system consists of a 2/3″ Gigabit Ethernet IMAG K6-CCD camera (Manta, G-145B, ASG Allied Vision Technologies GmbH, Stadtroda, Germany), 1392 × 1040 pixel primary resolution with enhanced sensitivity by 4-pixel-binning resulting in 640 × 480 pixel images, coupled to an objective lens (F1.
The image acquisition takes 30 s per plant, and different files are generated such as. PIM files (which are only usable by ImagingWin software) and. CSV files, which contain requested parameters such as F 0 , F m and F v /F m averaged on a region of interest. Two images of ChlF acquisition are also generated: *. JPEG files which give a representation of the image acquired in false colour (from black pixel values (0) through red, yellow, green, blue to purple (ending at 1)) and *. TIFF files with contain different stacks of fluorescence parameters (see below).
Projected area of the rosette (RA proj ) was also determined from plant selection. Different plants in the same pot can be independently measured. Whole-rosette mean F v /F m extracted with the macro "PHENOPSIS-Fluo" was highly correlated with the mean of the rosette extracted from the commercial software ImagingWin (R 2 = 0. 98; Additional file 7: Figure  S5 ). The "PHENOPSIS-Fluo" macro is available on the PHENOPSIS website (http://bioweb. supagro. inra. fr/phenopsis/MacroImageJ. php). Under SWD conditions, growth dynamics of surviving plants were modelled as a sigmoid curve fitted following RA proj = a / [1 + exp-[(d-a/2)/b]] where a is the maximum area, and d is the number of days after L4 stage. Under WW conditions, an exponential curve was fitted such as RA proj = exp (ab × d).
Statistical analyses and modelling procedures All analyses were performed using R 3. 1  [49]. Comparisons of mean trait values between treatments were performed with Kruskal-Wallis non-parametric tests. Survival percentage was calculated as the proportion of surviving plants at the end of experiment compared to initial number of plants. Plant survival was analysed by Chi 2 tests. Non-parametric significance test for unimodality, Hartigan's dip test (R package 'dip test'  [35] [36] [37] ) was used to identity multimodal distribution, with p-value < 0. 01 as the significance threshold for departure from unimodality. Then, the REBMIX algorithm for finite mixture models (  [38] ; R package 'rebmix') as used to characterize each mode i of the bimodal distributions of F v /F m values (i. e. , mean μ i , standard deviation σ i and weight ρ i ), using the Bayesian Information Criteria (BIC).
We developed a modelling procedure of the temporal dynamics of the means, standard deviations and proportions (weights) of the mixture distributions. We then simulated the variation of these parameters and, subsequently, the variation of S and W max (Additional file 3). All meteorological and phenotypic data, ChlF files and images, R scripts are available in the PHENOPSIS web site (http://bioweb. supagro. inra. fr/phenopsis/) and database  [50].
Acknowledgements We are very grateful to  G. Rolland ,  A. Bediee ,  F. Bouvery  and  C. Balsera  for help during the experiments. We thank  F. Fiorani  for giving us advice on the design of the ChlF system. We thank  D. Fourré ,  M. Desigaux ,  O. Meyerhoff  and  J. Kolbowski  for helpful technical supports. We thank  B. Touraine  and  F. Varoquaux  for constructive remarks on plant-microbes interactions and for providing us the rhizobacteria ( STM196 ). We thank  B. Genty  and  S. Bieker  for helpful comments and suggestions on this paper. We thank  D. Weigel  for his contribution and support at different stages of the study. We thank  A. Christophe  for the helpful discussions during the modelling procedure. We also thank two anonymous reviewers for helpful comments on this paper. JB was funded by  French Ministry of Higher Education and Research  and was supported by the  Institutional Strategy of the University of Tuebingen (Deutsche Forschungsgemeinschaft ,  ZUK 63 ).
FV was funded by a post-doctoral grant from the  Max Planck Society. This work was made possible by a series of grants supporting the development of the plant phenotyping platform: the  European Plant Phenotyping Network  funded by the  FP7 Research Infrastructures Program  of the  European Union  (EPPN, grant agreement no. 284443 ) and the  EIT Climate-KIC  project  AgWaterBreed.
The virtual world We carried out an experiment in a virtual world specially designed to test our hypothesis. The 3D view of a room in the virtual world is shown in Fig. 2. The virtual world was designed using the GIVE platform  (Koller et al. , 2008). The GIVE platform includes a set of tools for designing virtual houses that can contain different virtual objects with distinguishing properties (such as color and shape). We chose a virtual environment because we find it to be more immersive for language acquisition than flashcards or images: participants could see and interact with the objects from a variety of angles and viewpoints, and examine them more closely by moving closer to the objects. The GIVE platform and the design of the experiment were such that each time a button was pressed, we logged the referring expression given, the corresponding referent, and whether the participant pressed the correct button.
Results In order to have a more complete view of the participants' experience, we completed the data logged during the experiment with results that were obtained via questionnaires given to the participants after the experiment was completed. We designated the first type of metrics as objective metrics (participant performance and behavior) and the second type as subjective metrics (participants' opinion of the experiment) and we will present the results of both below. We describe the objective metrics in Section 3. 1, and the subjective metrics in Section 3. 2.
Stent: geometrical modeling and mesh We adopt a stent model resembling the design of the Medinol/ Boston Scientific NIR™ stent, which is nowadays a classical coronary stent design. The geometry of the adopted stent model is based on the information reported by Sweeney et al. [39]. The generation of such a model is defined by a series of steps, detailed in the following. We firstly create a planar computer aided design representation of the stent, using the software Rhinoceros v. 4. 0 (McNeel & associates, Seattle, WA, USA). The unfolded geometry of the device can be constructed by mirroring and replicating a primitive geometry (see Fig. 1a ). The FE mesh is obtained by meshing the primitive geometry (see Fig. 1b ) and similarly by mirroring and replicating it, as shown in Fig. 1c. The software used for this operation is Abaqus v.
6. 11 (Simulia, Dassault Systems, Providence, RI, USA). The final mesh of the stent is presented in Fig. 1d. The whole stent model has an outer diameter of 0:876 mm and a length of 8:049 mm. As depicted in Fig. 1a , both strut thickness and width are approximately 0:10 mm. We remark that the characteristic dimensions of the stent geometry are comparable to the dimensions (ranging from 0:05 to 0:15 mm) of the specimens  [22, 21, 27] , on which the fatigue criteria are calibrated. The mesh is defined by 89,760 nodes and 57,600 three-dimensional brick elements (C3D8) available in the element library of the software Abaqus v. 6. 11, used as FEA solver. Taking into account the grain size given in  [22, 21, 27] , the actual mesh has about 2 elements per grain. To estimate possible errors due to large spatial gradients, we also adopt quadratic brick elements (C3D20R) for the same stent mesh.
Stent: constitutive modeling The stent is assumed to be made of a 316L stainless steel. Since stent lifetime is affected by the initial deformation due to the crimp and deployment processes and by cyclic loading, the adopted material model has to simulate both monotonic and cyclic behavior of 316L stainless steel  [39]. Therefore, we adopt a J 2 elastoplastic constitutive model with non-linear isotropic and kinematic hardening (with one kinematic center). The constitutive model is already implemented within the material library of the FEA software Abaqus v. 6. 11 and labeled by the keyword *Plastic, hardening = COMBINED, number backstresses = 1. The same model has been used in  [47]  for the calibration of the fatigue criteria on experiments  [27, 22, 21] ; for further details, see  [47]. Here we just note that the constitutive model has been calibrated on experimental data related to both monotonic and cyclic behavior of 316L stainless steel.
Vessel: geometrical modeling, mesh, constitutive modeling The coronary artery is modeled as an idealized vessel represented by a thick-walled pipe with an initial inner diameter of 2 mm, wall thickness 0:5 mm and length 14 mm. The mesh model consists in 19,552 nodes and 14,508 hybrid three-dimensional brick elements (C3D8H) available in the Abaqus v. 6. 11 element library. The mechanical response of the artery wall is modeled using a polynomial hyperelastic model, suitable for incompressible isotropic materials and available in the software material library as *Hyperelastic; see  [52]. The adopted material parameters stem from the work by Lally et al. [53, 54] , who performed FEA of coronary stenting to investigate the impact of stent design on vessel tissue solicitation.
The DV parameter b N is then derived; Table  3  lists the DV parameters for all the specimens. Then, since the stabilized cycle of the stent results under plastic shakedown after bending cyclic loading (see Section 2. 5), we adopt the criterion based on the dissipated energy per cycle, defined as follows  [48, 49] : W p ¼ cN b f ð2Þ where W p is the dissipated energy per cycle, integrated in each point of the structure over the complete stabilized cycle, as follows: W p ¼ Z cycle r : _ e p dt ð3Þ Parameter b and c have been calibrated by Auricchio et al. [47]  and are equal to À0:4687 and 431:4 MPa, respectively. Both the criteria are computed as post-processing in all the Gauss integration points of the stent model. Particularly, the post-process of the DV criterion is performed with the Matlab implementation of the optimization code SDPT3  [65].
Acknowledgements This work is partially funded by: the  Cariplo Foundation  through the Project No. 2009. 2822 ; ERC Starting Grant  through the Project  ISOBIO: Isogeometric Methods for Biomechanics  (No. 259229 );  Ministero dell'Istruzione, dell'Università e della Ricerca  through the Project No. 2010BFXRHS ; the  Italian-French University (UIF-UIF)  through the ' Bando Galileo   2013-2014 ' Grant No. 148-30174TJ. Moreover,  Regione Lombardia  and  CINECA Consortium through a LISA Initiative (Laboratory for Interdisciplinary Advanced Simulation ) 2013 grant are gratefully acknowledged.
Data cleaning and descriptive statistics Prior to addressing our research questions, we first examined all of our predictor and dependent variables for skewness, kurtosis, and outliers. There were a large number of highly skewed variables, especially in the ARI emotions for various agents. Many of the negative emotions, such as Disgust, Frustration, or Anger, were not elicited by any of the agents and were removed from subsequent analyses (which does not mean learners never experienced these emotions, as seen in other studies  [7] , only that they did not identify the agents as the cause of those emotions). We set the threshold for identifying significant skew or kurtosis at z = +/-3. 35. statistics for the personality scales, the trait emotion scales, and the agent-directed emotions are reported in Tables  2  and 3  (skewed variables that were not included in further analyses are italicized).
Acknowledgements. The research presented in this paper has been supported by a doctoral and postdoctoral fellowship from the  Fonds Québécois de recherche -Société et culture (FQRSC)  and a  Joseph-Armand Bombardier Canada Graduate Scholarship for Doctoral research  from the  Social Sciences and Humanities Research Council (SSHRC)  of  Canada  awarded to the first author. This research has also been supported by funding awarded to the sixth author from the  National Science Foundation  (IIS  1008282 ),  SSHRC , and the  Canada Research Chairs program.
Sequencing and assembly of the oak transcriptome Transcriptomes are a valuable genomic resource for studies in non-model organisms for which genome sequences are not available, because they are smaller and less complex than genomes. The de novo assembly of transcriptome sequence data from a single sequencing platform has become a routine task, and a handful of de novo transcriptome assemblers have been developed  [20] , but combining the outputs from multiple sequencing platforms remains challenging  [21]  and involves the use of suitable assembler software for different types of datasets (short/long; single/paired-end reads). In this study, we used a combination of Sanger, Roche-454 and Illumina technologies and bioinformatic tools to generate a catalog of oak transcripts from RNA obtained from different tissues, developmental stages and in response to biotic and abiotic stresses (Additional file 1).
Long-read pre-assembly The sequencing of 29 and 36 cDNA libraries with Sanger and 454 technologies (sets #1, #2A and #4 in Figure  1 ) resulted in 94,174 and 2,790,004 trimmed reads respectively. The distribution of trimmed ESTs is shown in Additional file 3. A total of 6,571 putative FL-cDNA clones (set #3) sequenced with the Illumina/Solexa GA-II X (PGTB, Plateforme Génome transcriptome de Bordeaux), yielded  17, 196 ,106 paired end reads. Then, de novo assembly with Velvet and TGICL software yielded 4,359 contigs. By combining Sanger, Roche-454 and reconstructed FL-cDNA data, we obtained 2,888,537 long sequences used to construct a long-read pre-assembly with MIRA. We finally obtained 44,272 contigs, with a mean sequence length of 937 bp (standard deviation: 521 bp; N50: 1,118 bp, defined as the largest entity E such that at least half of the total size of the entities is contained in entities larger than E; see the black curve in Additional file 4).
The number of oak contigs displaying similarity in terms of deduced amino-acid sequences with the content of at least one of the databases studied  (90, 786)  was much greater than the number of genes present in oak (about 30,000, as estimated from BAC-end sequences,  [5] ). This overestimation may be due to contig redundancy, contig fragmentation (contig breaks in low-coverage regions), unassembled alleles, particularly for highly polymorphic diploid species of this type, with a mean of one SNP or Indel every 25-30 bp  [28] , splicing variants, sequencing errors, or sequence read misattribution between closely related paralogs due to the presence of recently duplicated genes. We found that the 18,587 Prunus persica A Characteristics of the long-reads, short-reads and meta assemblies (OCV3).