dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion The code in Figure  2  is used to create plots as shown in Figure  3  (A-C ). Importantly, the R script allows processing of data from many experiments in a short time period. The output is a highresolution PDF file visualizing the levels of expression heterogeneity. Figure  3  illustrates three possible heterogeneity scenarios, namely homogeneous expression (A), 'highly noisy' heterogeneous expression, (B) and bistable heterogeneous expression (C). For statistical analyses and data processing in R  [13, 14, 15] , the data from ImageJ are saved in CSV format. The derived normalised fluorescence intensities are obtained using the formula: We recommend inclusion of controls for homogenous expression, as in Figure  3A , in all analyses to measure background levels of expression noise.
The structures of the host and guest molecules are shown in Scheme 1. The initial structures of both molecules were obtained at the B3LYP/6-31G(d) level of theory using the Gaussian 09  [50]  suite of programs. The starting geometry of the complex was obtained by placing the SAL molecule on the C 7 symmetry axis of BCD, with the line joining the C1 and C4 aromatic carbons coinciding with the symmetry axis. This initial structure was the starting point in Born-Oppenheimer MD (BOMD) calculations. The TeraChem  [51]  general purpose quantum chemistry software package was used. A 10 ps MD run with 1 fs time steps at 300 K was performed at the B3LYP/6-31G level of theory. An empirical dispersion correction was utilized. The trajectory obtained was recalculated at the molecular mechanics (MM) level of theory.
For the SAL molecule, defined using the CHARMM general force field for small molecules, the atom types were consistent with the previous CSA resolution, so the original parameter set was retained. All parameters are collected in Table  1. Three types of MM runs were performed using the NAMD package  [54]. In the first MM run, the charge distribution was frozen. The Mulliken charges obtained at the B3LYP/6-31G(d) level were used. In the second and third MM runs, charges were derived using Eq. 5 for each point on the trajectory. In the second MM run, the charges in each subsystem were constrained (see Eq. 4). In the third run, CT between subsystems was allowed, and only the global charge of the BCD/SAL complex was constrained. In other words, two first rows/columns of Eq. 4 were replaced with one row/column: (0, 1 N ).
Acknowledgments CSA and MM calculations were performed on a computer cluster purchased with financial support from the  European Regional Development Fund  in the framework of the  Polish Innovation Economy Operational Program  (contract no. POIG. 02. 01. 00-12-023/ 08 ). TeraChem calculations were performed at  Akademickie Centrum Komputerowe (ACK) CYFRONET. The authors acknowledge financial support from the  National Science Center  (project no. UMO-2011/ 01/B/ST4/00636 ).
Logics (DLs) are a family of languages used for the representation and for reasoning about the knowledge base of an application domain, in a structured and formal manner. To achieve this objectif, sevral provers have been implemented such as RACER and FACT++, but these provers themselves have not been certified. In order to insure the soundness of derivations in these DLs, it is necessary to verify formally the deductions applied by these reasoners. Formal Methods offer powerful tools for the specification and verification of proof procedures, among them methods of proving properties such as soundness, completeness and termination of a proof procedure. In this paper, we present the definition of a proof procedure for the Description Logic A L C , based on a tableau method. We then prove the soundness, completeness and termination of our reasoner with the proof assistant Isabelle.
Introduction In this paper, we present a definition of a prover for the description logic A L C  [18]  which is based on the method of semantic tableau  [5]. We ensure the validity of our method and its implementation by the proof of the properties of its soundness, its completeness and its termination. These proofs are performed using the Isabelle/HOL proof assistant. Description Logics (DL) are formalisms widely used in several areas such as the Semantic Web and ontology construction. Several description logics, eg, S H OI Q  [12]  and S H OI N  [3]  are more expressive extensions of A L C. S H OI N , which is considered as a formal model of Web Ontology Language OWL-DL, is used in several provers such as FaCT++  [19]  and RACER  [10]. Only a formal proof of the validity of the reasoning process applied to DLs can ensure the correctness of derivations of properties in these logics.
The second presents a formalization in the Coq proof assistant of some modal logics, and the third a formalization of a prover for LTL. Please note that DLs can be considered as specific modal logics. Closest to our work comes  [11] , which describes the formalization of A L C in the PVS proof assistant. Our formalization simplifies the termination argument, see Section 8, and allows to extract a prover directly executable in a " standard" programming language (Caml). This paper builds on and extends our previous work described in  [7] , which provides an abstract, non-executable proof procedure for A L C , that extension is presented in  [8]. This paper is organized as follows: In Sections 2, 3 and 4, we detail the description logics. In Section 5, we present the procedure of semantic tableau for A L C and the Sections 6 and 7, we describe the formalization of this method and present the proof of soundness and completeness properties in the Isabelle/HOL proof assistant.
Conclusion In this paper we have presented a definition of a reasoner validated for the description logic A L C based on the method of semantic tableaux. This formalization in Isabelle and development is based on several modules: • Specifying the syntax and semantics of A L C , • Coding of Abox and the formalization of the transformation rules of semantic tableaux, • The proof of the soundness and completeness of semantic tableaux, • The proof of termination requires the definition of a measure for each Abox. We have shown that this measure decreases for each application of a rule. • The implementation of Abox, tableaux and rules transformation • Defining a strategy of proof for A L C • Finally, the extraction of an executable, certified reasoner in the Caml language. We will consider several extensions of this work, among which are: • Extensions of this work to more expressive logics used in the semantic Web, such as S H OI Q and S H OI N.
Participants Data was collected by an on-line survey which was e-mailed to participants. These were recruited from previous consumer data bases considering gender, age and education level. Consumers lived in 3 cities in Argentina (Buenos Aires, Mendoza and Cordoba) and in 3 cities in France (Angers, Lille and Lyon). Only those who answered positively to apple consuming were taken into account obtaining a middle-class adult population segmented by gender and age as detailed in Table  1. In this way, a total of 311 answers were obtained in each country.
Comment analysis The analysis of open-ended questions required a particular treatment, since some consumers wrote only words while others gave long sentences explaining what they liked and what they disliked. Therefore, comments were transformed into precise modalities using the process presented by  Symoneaux et al. (2011). In the present manuscript, the dataset was presented in a MSExcel file having for each consumer: a) the different open-ended questions and b) all the initial information provided by each consumer in separate rows. For postcoding transcoders had to: verify typing and/or spelling mistakes; remove connectors, auxiliary terms and adverbs; regroup terms when necessary  (Rostaing, Ziegelbaum, Boutin, & Rogeaux, 1998). At this stage of the postcoding a total of six transcoders participated in France and Argentina, completing the whole process in around 40 working hours.
Four native Spanish speakers worked together on the Argentinean dataset and two French native speakers on the French one and a bilingual (French-Spanish) speaker was present to harmonize transcoding rules between both countries. In order to standardize the treatment of subtleties the postcoding proposed in Table  2  was used for each language. Once the re-transcription of the 311 consumers in France and in Argentina for the seven questions was done, all words were translated to English by a French and an Argentinean transcoder (the first one French-English speaker and the second one Spanish-French-English speaker; both familiar with the culture of both countries). A data set of 459 different terms in English used in Argentina and/or in France, and 577 if all subtleties (a little, too, not, etc; Table  2 ) were considered, was obtained.
Analysis of Variance (ANOVA) Analysis of variance (ANOVA) was conducted with the data on the Likert scale for question 9, to assess significant differences in the product conception between the two countries. In addition, to evaluate if the number of words used by consumers was related to the question, country, cultivar knowledge and/or consumption level, two-ways variance analyses with interaction were carried out for each question on number of citations per consumer with two factors: country and apple consumption frequency. Statbox software (Version 6. 6, Grimmersoft, Issy les Moulineaux, France) was used.
Global Chi-square and Chi-square per cell In order to observe differences among the words used in Argentina and in France, contingency tables were obtained crossing each question with country, apple consumption frequency and known varieties. Then to test the differences between each factor, global Chi-square and Chi-square per cell were used. The present approach had been previously validated by  Symoneaux et al (2011)  who analyzed open-ended questions by crossing products and consumers' comments in the contingency table. After global Chi-square used for testing the independence between rows and columns of the contingency table, the Chisquare per cell indicates for each cell if the observed values were significantly higher, lower or equal to the theoretical values. In the present work, Chi-square per cell analysis was done with a specific Excel Macro specially developed for users who have no access to statistical softwares.
Multiple Factorial Analysis Complementary to contingency tables analyses and Chi-square tests, a Multiple Factor Analysis for Contingency Table (MFACT) was performed in order to visualize: 1) the way consumers answered the descriptive apple questions (Q05_1 to Q07_2) and 2) the use of the descriptive terms in each country. MFACT is a principal axes analysis  (Bécue-Bertau & Pages, 2004)  allowing to compare the structure of several contingency tables using an extension of the correspondence analysis. The two datasets compared in the present work were the two contingency tables (one per country) with the words generated by consumers in rows, the six questions (Q05_1 to Q07_2) in columns and the number of consumers using each modality for each given question in each cell. This analysis was computed with R language (R Development Core Team, 2011) and FactoMineR  (Husson, Bocquet, & Pagès, 2004)  using the function MFA precising in the code that data sets are contingency tables.
Acknowledgments This study was supported by  ARFITEC  (Project  ARF-10-13 ,  2011-2012 ). The authors would like to thank  Beatriz Coste  ( UBA ),  Alejandra Picallo  ( UBA ),  Fernando Pedri  ( UCC ),  Laura Cánovas  ( UNCU ),  Adriana Perez  ( UCA ),  Anne Dubray  ( ISARA ) for their assistance on data acquisition.
The aim of each cruise was to assess the stock of small pelagic fishes using acoustic methods and continuous observations along regularly spaced (12 nautical miles) inshoreoffshore transects (Fig. 2 ). Acoustic sampling was performed with scientific split-beam echo sounders operating at 38 kHz and calibrated following standard techniques  [39]. Data were recorded at a constant speed of 8 knots. The minimum sampling depth varied between 10 and 20 m depending on the area. The size of the Elementary Distance Sampling Unit (EDSU) was one nautical mile (nmi, 1. 852 km). Pelagic trawls were performed to identify target species. A complete description of the anchovy biomass estimation procedures based on acoustic PELMED surveys is available at the IFREMERs institutional repository  [40]. Acoustic PELMED surveys data for anchovy from 2003 to 2010 are provided in a compressed file (zip file) in supporting information S1.
Process Overview and Scheduling Virtual eggs were released within the specified period in the virtual environment according to the spawning strategy defined in two types of simulation experiments. In the first simulation (hereafter, RandomDist experiment), 10,000 virtual eggs were released randomly in the horizontal domain but restricted to the first 15 m of the water column  [44]. In the second experiment (hereafter, RealDist experiment), we established the peak of spawning, location and number of virtual eggs using the information from egg abundance samples collected during the MPOCAT surveys in June 2007/08. We only selected data corresponding to early egg stages from the surveys to establish the initial scaled-down abundance and distribution of the 10,000 particles released in this experiment.
The virtual eggs were released randomly in the first 15 m of the water column. The overall simulation schedule is summarized in Fig. 4 , and each step is described in detail in the Input data and Submodels sections. At the beginning of each time step, the environmental conditions (see Submodels section) were used as input for the SEIBM, using fields from the hydrodynamic model. Thereafter, information concerning the anchovy agents was updated, modifying the data for age, size (using a growth procedure, Submodels section) and the number of individuals using the out of the domain procedure.
Observations. The model output is primarily presented as charts of the distribution and abundance of larvae at a length of 14 mm. The number of recruits in a particular geographical area is a function of the number of accumulated particles, which is summed for comparisons between areas. Sensitivity analyses are performed on subsets of parameters (i. e. , larval length and nursery area reached) and input data (release area, date and type of experiment: RandomDist or RealDist) to explore their influence on the resulting patterns.
Sensitivity Analysis The objective of the sensitivity analysis performed in this study was to estimate the relative importance of various model components and to identify key processes. The sensitivity analysis was performed using a multi-factor analysis of variance from the general linear model (GLM) module of R v. 2. 13. 1  [74]. We focused on the processes affecting the late larval recruitment index. The independent factors selected for the analysis were the type of experiment (RandomDist or RealDist), release date and release zone. Additionally, we selected the orthodromic distance (i. e. , the shortest distance between any two points on the surface of a sphere) as an indicator of the differences in horizontal transport between the experiments. The orthodromic distance (hereafter termed distance) indicates dispersion  [9].
Relationship between Daily Egg Production and Total Adult Biomass In this section, we clarify how the daily egg production (number of stage I eggs per square meter of sea surface) is estimated from the total adult biomass obtained with acoustic surveys and from fecundity data. In addition to this document, a fully commented R script, that has been used to produce the outputs presented here, is available in a compressed file (zip file) in supporting information S1. First, the number of fish is calculated based on the anchovy biomass and average weight, as calculated from data from the PELMED surveys N~B W malefemale ð2Þ where N, B and W malefemale are the estimated number of fish, the estimated spawning biomass and the estimated average weight of adult anchovy at each station, respectively.
Acknowledgements We are grateful to  P. Garreau  and  A. Nicolle  for their support with MARS-3D hydrodynamic model outputs, as well as to  P. Verley  for his assistance in ICHTHYOP code. We are grateful to  S. de Juan  for discussions and suggestions that greatly improved previous versions of this manuscript. Finally, the authors gratefully acknowledge the assistance of the captain and crew of the  R/V Garcı ´a del Cid  and  R V LEurope  for their help during the cruises.
This work was supported by project  PERSEUS  ( FP7-287600 ). Egg samples were obtained from the  MPOCAT-DEPM  surveys financed by  Generalitat de Catalunya. A. Ospina-A ´lvarez benefited from a PhD grant of the  JAE program  ( Spanish Science and Technology Council, CSIC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
As such, the underlying image processing algorithms should face rather complex environments. Moreover, hand gestures only are not sufficient to face the requirements of such applications. Unlike hand gestures, hand postures describe the hand shape and not its movement, and can be very different from each other. It is extremely difficult to recognize all possible configurations of the hand starting from its projection on a 2-D image. Indeed, some parts of the hand can be hidden. There exist some reference databases of specific hand postures, such as the Triesch database  [11] , available on the Web  [12]. However, this database has a number of limitations: the number of images is low, the viewing angle and the size and the orientation of the hand are always the same, the images are in gray level and contain only the hand, and the background is rather simple.
In this work we consider an application where our industrial partner wishes to have a rather elevated number of postures, which can yield later to various instructions for a HMI: this dictionary of postures is meant for automatic distributors such as drink vending machines. For this reason, we have built our own database (also partially used in  [3] ). It contains 11 postures, which are displayed in Figure  1 , with the corresponding posture index above. As will be shown in the results section, the background in these images is rather complex so that if our method works well with our database, it will certainly work with Triesch database. These postures have been chosen to be easily performed by any person. They differ from the sign language which aims at easing lip reading. To afford a large dictionary, and thereby numerous functionalities of the human-machine interface, the dictionary must include a number of postures which is large enough.
This implies that similar postures may be present in the dictionary. Some postures permit to test the discrimination performances of the proposed and comparative methods: they are visually very close, such as postures 4 and 5, as well as postures 8 and 9. The images of the database were obtained as follows: an expert user shoots a movie containing the 11 postures. Then the frames of the video are split to get the images in the RGB color space. These images compound the learning set. Other users which are not the expert user shoot a movie containing the 11 postures. The same process as for the learning database is adopted to get a set of images; these images compound the test set. To generate the learning database and the test set, a simple setup involving a camera is used.
Overview of classical detection methods A rule of thumb about contour characterization methods such as Fourier descriptors  [10, 13]  is that they require a binary image I, possibly noise-free. The same constraint holds in the frame of our work. To perform hand contour detection, some classical preprocessing methods have been applied in previous works  [3, 4, 10, 13] : the YC b C r mapping and the selection of the C r component emphasize the hand surface with respect to the background. Another mapping could also be used: in  [14] , HSV mapping is combined with a hue setting to distinguish the hand from other body parts. The results presented are convincing but this method exhibits a limitation: the user must wear a red glove. To our knowledge, no study based on mappings from a color space to another such as YC b C r or HSV provides good detection results on databases where the object of interest may exhibit any color.
Numerical complexity of the proposed methods To express the computational complexity of the proposed method, let us assume that if f denotes the actual computational complexity depending on the parameters of the methods, and if g is another function of these parameters, we can use the notation f = O(g), if and only if there exist a positive constant a such that for all sufficiently large values of the parameters, the numerical complexity f is at most a multiplied by g in absolute value. Therefore, we will use this notation by considering the case where the computational load is the highest. We will consider successively the methods applied both off-line and on-line, only off-line, and then only on-line. • The following steps are applied both off-line to build the reference database, and on-line to identify a hand posture.
Results on hand posture recognition and discussion In the database, the images have a size of either 320 × 240 or 640 × 480 depending on the camera used at the acquisition step. The machine used has a 2-core processor running at 3. 2 GHz, using Matlab. This result section falls into two subsections: firstly, we present the results of hand contour segmentation with optical flow and adequate preprocessing; secondly, we exemplify the discriminative capabilities of the signature Z and present the statistical results of hand posture recognition obtained with PCA and Mahalanobis distance.
To reduce the computational load and reach a delay between two subsequent frames which is as low as possible, we set the best empirically chosen parameters: we reduced the number of moving points taken into account by optical flow to 1,000, which permitted to reach a time delay of 1 10 -1 s between two frames. • Secondly, while creating our learning database, we reject the frames which yield such characteristics: if one axis of the fitting ellipse (see Subsection 3. 3) is larger than the image size, or if the large axis is larger than three times the small axis. We consider that in these cases, the moving points detected by optical flow do not permit to delimitate a reliable region of interest around the hand. The drawback/limitation for a user point of view is that the hand posture recognition may take a bit longer for the recognition result, until the luminosity does not vary too much, or until his hand moves slowly enough.
, Q (see Equation  1 ) a value of P = 24 levels is large enough to get an exclusive signature for each posture and small enough to get a reasonable computational load. A value Q = 120 yields a good compromise between computational cost and discriminative capability. While performing the recognition of any test image, the regularization term in Equation 3 is fixed to 10 -4 max( ) where max(•) stands for maximum value. Firstly, we illustrate the discriminative capability of the method when one test image is considered. Secondly, we provide a statistical study involving all images of the test database. For each of the 11 classes h, we have used on average M h = 250 images in the learning database and 150 images in the test database. In the test database, for each class, one image out of four contains a hand from either a colored person, or wearing a glove, either black, red, yellow, or blue.