dataesr/scientific-paragraphs-categorization huggingface dataset:
Method The primary data for this study derive from audio-recorded and transcribed counselling sessions. These sessions were recorded in a private language centre. Learners would come to the centre to work on a platform specifically designed for self-directed EFL learning. They would meet their counsellor for the first time before beginning to work on their English, and then twice a month to discuss their work, choices and progress. The sessions were all conducted in French, because the purpose was to think about their learning  (Holec, 1990) , not to practice speaking English. The data consist of two series of five interviews between adult counsellors and learners: the first series is composed of three interviews between a female 30-35 year-old counsellor (C1) and a male 45-50 year-old learner (A1), and two interviews between this male learner and another female 30-35 year-old counsellor (C2).
Aims and principles of analysis In order to obtain information that better represents an emotional relationship, the first step was to identify and classify similar pairs of turns. Ratings were used to create valence and activation scores for each pair of turns (learner at a moment in time t0, then counsellor at t1). The difference or similarity between the scores was used to define a relationship profile. More than 3,100 pairs of turns from the whole data set were classified according to the degree of parallelism between profiles. Similarity between profiles is represented by a correlation coefficient calculated from valence and activation. The structure of similarities between profiles was examined using hierarchical cluster analysis (Ward's method; see  Everit, 1977). The automatic classification procedure was applied to pairs of turns (learner at t0, then counsellor at t1).
83, p = <. 001). ), which means that every cluster reflects an emotional relationship which is clearly distinguishable from that of another cluster. Once the initial t0-t1 paired classification had been determined, t2 was taken into consideration. This way, average valence and activation levels were determined for each cluster, for learner at t0, counsellor at t1 and learner at t2 (see figures 2 and 3 below). The data presented in figures 2 and 3 suggest several differences between the learner and the counsellor. First, there are differences in levels: counsellors' emotional expressions (M= 4. 46) are on average slightly higher in valence than those of the learner (M=4. 22); the same can be said of emotional activation with a smaller difference between participants (valence: F(1, 3083) = 441.
Contour and trajectory analysis of the traumas 3D imaging provides an opportunity to present critical aspects of cranial blunt force trauma  [32] , such as shape analysis  [33] , and to investigate plausible injury trajectories. Cr-17 was CT scanned in the coronal plane using an industrial XYLON MU 2000-CT scanner at the Universidad de Burgos (Spain) with scanner energy of 180 kV and 4 mA. Slice thickness was collimated to 0. 5 mm, inter-slice spacing was 0. 2 mm, and the approximate field of view was 225 mm. A total of 1108 slices was obtained as a 1024 x 1024 matrix of 32 bit Float format, with a final pixel size of 0. 219 x 0. 219 mm. A virtual (3D CT) model of the cranium was generated from the resulting slices using the Mimics 16. 0 (Materialise N. V. ) software package. Both, fracture angle and cortical delamination were measured on the virtual reconstructions using Mimics 16.
This research was supported by the  Ministerio de Economía y Competitividad of the government of Spain  (Project Nos. CGL2012-38434-C03-01 ,  02 & 03 ). CT scanning was carried out in collaboration with the  Laboratorio de la Evolución Humana  at the  Universidad de Burgos (Spain)  with funding provided by the  Junta de Castilla y León  Project No. BU005A09. Fieldwork at the Atapuerca sites was funded by the  Junta de Castilla y León  and the  Fundación Atapuerca. N. S and A. P. P. have received postdoctoral and predoctoral respectively grants from the  Fundación Atapuerca. R. M. Q. has received financial support from  Binghamton University (SUNY).
NLU module: The NLU module is very simple as the service's utterances are totally known by the US and no parsing is involved. Each one of them is associated with a specific dialogue act. Intent Manager: The Intent Manager is somehow the brain of the US, as it determines its next intent given the general goal and the last NLU result. The general goal depends on the scenario at hand, which is in turn determined by two lists of events: the initial list (InitList) and the list of events to add to the agenda during the dialogue (ToAddList). The Intent Manager tries to add each event from the latter given the constraints imposed by the former. If the events of both lists cannot be kept, those with lower priorities are abandoned or deleted until a solution is reached. The service asks for the different slot values in a mixed initiative way.
NLG module: The NLG figures out the next sentence to utter given the current Intent Manager's output. A straightforward sentence is computed, for example, Add the event meeting Mary on July 6 th from 18:00 until 20:00. Verbosity Manager: The Verbosity Manager randomly expands the NLG output with some usual prefixes (like I would like to. ) and suffixes (like please, if possible. ). Also, a few sentences are replaced with off-domain words or repeated twice as it is the case in real dialogues  (Ghigi et al. , 2014). For questions concerning a specific slot, neither prefixes nor suffixes are added. Patience Manager: When the dialogue lasts too long, the US can get impatient and hang up. The US patience corresponds to a threshold on each task duration. It is randomly sampled around a mean of 180 seconds for the experiments.
Procedure Two sets of data were collected. To minimise the influence of previous and current teaching, Task 1 data was collected in the first 2-3 weeks of the university year, before explicit teaching on segmentals (in 1 st year) and at the start of a unit on syllable structure (in 2 nd year). Thirty first-year and 27 second-year students responded. In this task, students were asked to listen to two extracts via their electronic learning platform 2. It was assigned as homework but they were told it was part of a research project and would not be marked. One extract was a female presenter from the BBC programme FastTrack (11 seconds) and the second was the American comedian Jerry Seinfeld (15 seconds) 3. 2 Wikipedia defines this as "a range of integrated web based applications that provide teachers, learners, parents and others involved in education with information, tools and resources to support and enhance educational delivery and management" (2015).
e. more, less, faster/slower) as well as connotative verbs and nouns (i. e. force, vomit). Therefore, a second set of data was collected two months later. In Task 2, students were asked to answer two questions by writing on a piece of paper: What do you do when you want to sound more like an American? more like a British person? Thirty-three first-year and 23 second-year students responded. The fact that some had responded in Task 1 was assumed not to induce bias in their Task 2 replies, as they had received no feedback on the initial task. Initially the limits of the countable unit "a comment" were determined by spacing (written on separate lines or as bullet-points), capital letters or punctuation, with units separated by commas, full stops, colons or semi-colons. However, in certain cases the unit had to be semantically delimited.
Contrastive analysis predicts that French learners of English would be expected to comment on vowels, diphthongs, realisations of /t/, /r/, /l/ and /Ɂ/, and perhaps voice quality. Overall, in the two data collections of this study, realisations of /t/, /r/, and vowels were the most frequently commented upon. No one ever mentioned glottal stops or velarized /l/. It could be that these are not particularly salient features for these students or perhaps they did not have the words to refer to them. Very few comments involved voice quality. In Task 1 nasality was noticed by a first-year student: there's no sound which come from the nose. In Task 2, three participants commented on two qualities of an American accent: one needs a deeper voice and one needs to sound creaky or use a kind of creaky voice.
The effect of the prompts was obvious in the recategorization of evaluative comments, with three categories emerging uniquely for Task 1 replies (Shape, Ease of understanding, Similarities with French), and three others solely for Task 2 (Attitude, Physicality, Enunciation/Clarity, Music). Comments about Energy, Length and Speech rate were found in both datasets. Task 1 elicited far more comments about Energy and Length (48/69) than in Task 2 (24/173). Moreover, the nature of the question in Task 2 (What do you do to sound more …?) might explain the appearance of attitudinal comments (39). In Task 2, attitude was more commonly mentionedand with negative connotationsabout how to sound British whereas advice about Physicality (articulators, posture or props) was more commonly mentioned in relation to sounding more American.
The metalanguage with which teachers address these subjects should be carefully chosen and could be based on students' written comments. Simple tasks could provide the raw material for awareness-raising activities, and the former could be verbal, as in this study, or aural, visual, gestural. Once teachers have identified features which their learners find salient, numerous suggestions for classroom activities can be found in articles, books, and Web-based resources. One such free resource is Huckvale's RTPitch software (  2009 ), which merits greater use. Teachers might also want to consider non-imitative approaches such as Silent Way techniques, for example as updated by  Young and Messum (2011). If these same teachers then carried out action research, valuable contributions could be made to research into the effectiveness of pronunciation instruction (i.
While the typical scale of heterogeneity of painting material is in the range of a few microns, the extremely bright and collimated synchrotron source offers an appropriate high spatial resolution (a few μm down to some tens of nm) as well as very low detection limits (a few ppb for XRF analyses). Moreover, the energy tunability of the source allows spectroscopic analyses to be carried out in a wide energy domain, ranging from the infrared to hard X-ray beams. In recent years, the combined access on a multi-modal platform to both X-ray and vibrational micro spectroscopies has become a main asset in the characterization of painting samples. However, such combination can be improved through dedicated efforts on instruments, data analyses software packages and on sample preparation. Regarding the last point, painting fragments are historically and usually prepared and analysed as polished thick sections.
In this case, the simple correlation between the beam absorption and the thickness of the sample is established, which facilitates semi quantitative analysis of the results. Thus concentration of a particular molecular bonding is proportional to the integral of its absorption peak. Moreover, even if the increasing use of portable in-situ FTIR equipments in the field of Cultural Heritage (CH) is pushing for the development of databases in diffuse reflectance mode, the most famous databases were acquired in transmission mode (e. g. IRUG, the Infrared and Raman Users Group (http://www. irug. org/), the IBeA, Ikerkuntza eta Berrikuntza Analitikoa  [33] ). In this case, spectra can be directly compared to reference spectra from databases. The recent combination of bright SR source with FPA detectors, at the Synchrotron Radiation Center (SRC, Stoughton, USA), requires also thin-sections preparation as the analyses are carried out in transmission mode.
New technical imaging capabilities available based on thin-section transmission analyses Another advantage of acquiring XANES spectra in transmission is the possibility to go towards 2D XANES. Generally, speciation images are obtained by acquiring n XRF maps at n peculiar energies giving the maximum contrast between the different speciations of the probed element, in scanning mode. This approach is efficient if the different species show a pronounced shift of the edge position with the oxidation state since no spectral data are acquired. Moreover, the field of view is limited by the dwell time. In this context the use of ultra fast detectors such as MAIA allow n to be significantly increased (>100) and to decrease pixel acquisition time providing a better identification of the various species  [45].
Special sample holders allow transferring the sample from one microscope to another and are compatible as well with the visible light microscope (Zeiss). μXRF, XANES spectra and maps are analysed using the PyMca and TXM-Wizard software package  [64]. The ID21 FTIR end-station at ESRF is primarily composed of a commercial FTIR spectro-microscope from Thermo with a Nicolet Nexus infrared bench associated with an infrared Continuμm microscope  [65]. The infrared beam is emitted from the short straight section (focusing electron lenses) upstream of a bending magnet. An extraction mirror, flat un-cooled aluminium mirror with a horizontal slot, lets the energetic part of the synchrotron light go through for absorption 2. 5 m further down. The edge radiation is collected, collimated and transferred to the spectrometer and microscope using a set of 12 mirrors.
In the microscope, two × 32 Schwarzschild objectives are used in a confocal mode and an aperture defines the spot size illuminating the sample. In the mid-IR domain, the signal is detected using a liquid N2 cooled single element 50 μm MCT detector. In this configuration, the typical beam size is diffraction limited in the range of 3 to 10 μm. For mid-IR, measurements can be carried out in transmission, reflection, transreflectance or ATR mode as well. μFTIR spectra and maps are analysed using the PyMca and OMNIC software package.
This polarity system expresses the saturation state of partial syntactic structures and their ability to combine. The present paper reports on the construction of a syntactic resource for the French language and shows that it is possible to build a wide coverage IG grammar of French which encodes fine-grained linguistic knowledge. In this grammar (called Frigram), the syntax of French sentences is described by dependency structures which contain the usual surface syntactic dependencies but also by additional dependency relations which contain the information needed to produce a deeper syntactic analysis. These additional relations are: infinitival subjects, participial subjects and pronoun antecedents that are syntactically predictable. The main challenge is to guarantee and maintain the consistency of the grammar while aiming for large coverage.
the principles of the grammar frigram The formalism of IG is very general and does not impose any linguistic choice apart from the phrase structure tree representation of the syntax of sentences. When specifying a grammar, however, a number of linguistic decisions must be taken such as, for instance, how the notion of a constituent head should be defined and whether VP constituents should be used or not. In Frigram parse trees, each node is a constituent linked to a leaf (either an anchor or an empty node) of its subtree called its head. These choices are reflected in the EPTDs of the grammar through principles. In this section, we present the principles used in the design of Frigram. These principles are not specific to the French language, however, and can be used or easily adapted to other languages.
Frigram, like other grammars with a large coverage, is a large resource and maintaining its consistency is a challenge. The principles facilitate the checking of this consistency. Principles are also heavily used in the conversion from constituent based parse trees to dependency trees discussed in the next section. The first decision remains the feature domain: the choice of the set of feature names and of the possible feature values for each feature name. In Frigram, cat is a resource sensitive feature used to encode the category of a constituent. Principle 1 (cat). In an EPTD, every node has a cat feature. This feature is used to make a partition on the set of description nodes into two sets: concrete and abstract nodes. Definition 1. A node with a positive or saturated cat feature is called a concrete node.
XMG provides a language to define a grammar as a set of classes. A class can be defined directly but also from other classes by means of two composition operations: conjunction (represented as diamond nodes labeled AND in Figure  9 ) and disjunction (represented as diamond nodes labeled OR). Each class is structured according to several dimensions. Frigram uses two dimensions: the first one is the syntactic dimension, where objects are EPTD templates, and the second one is the dimension of the interface with the lexicon, where objects are feature structures. Defining the conjunction of two classes requires a specification of how the components are combined for each dimension: for the syntactic dimension, PTD union is performed; for the dimension of the interface with the lexicon, it is realized as unification between feature structures.
Comparing a parsing result with a gold standard corpus requires conversion from one format to another and this conversion may induce biases. In the case of Frigram, to evaluate the accuracy and coverage is even more problematic for two reasons. First, when building  [ 308 ]  Frigram, we decide to focus on a set of linguistic phenomena and cover them in an exhaustive way. At the same time, we also decide not to take into account (at least in this version of the grammar) several kinds of phenomena. We think that a fully lexicalized grammar is not the most efficient way of modeling some phenomena that break the basic structure of natural language sentences; for instance, dislocation, non-constituents coordination, parenthetical clauses, parenthesis, or direct reported speech are not taken into account.
The labeled attachment score (LAS) is the proportion of dependency links that are correctly predicted by the grammar, whereas the unlabeled attachment score (UAS) is the proportion of dependency links that have the correct head disregarding the link label. We used Sequoia as the gold standard. On the successfully parsed sentences of Sequoia, the LAS is 81. 6% and the UAS is 84. 0%. Again, many errors are linked to PPattachments not correctly ranked by our rules. Recently, partially supervised learning techniques were used to improve the performances of FRMG  (Villemonte De La Clergerie 2013) ; with this hybridization technique, the LAS score of FRMG on Sequoia is 85. 21%. All results on freely available data can be found on the Frigram web page. 12 Raw corpora are useful to check for the robustness of a parsing system but they have some limitations concerning the coverage of the grammar.
Adjusting for Multiple Testing The trial was a priori powered for multiple time points at a predefined 0. 05 significance level, using PDI-21 conviction as the primary outcome. Therefore, this level of significance was kept when the primary outcome results were interpreted. However, to minimize the chance of spurious results pertaining to the secondary outcomes, we adjusted them for multiple testing by Bonferroni's correction (α ≤ 0. 05/16 = 0. 0031). All the statistical analyses were performed with the Statistical Package for the Social Sciences (SPSS version 18. 0, IBM, Chicago, IL, USA).
Acknowledgments We thank the participants and the game leaders, as well as  Joanne Corpataux ,  Milena Kriz ,  Dr. Giacomini ,  Claude Hayoz  ( Fondation HorizonSud ),  Dr. Georges Klein ,  Phillippe Laffond ,  Prof. Eric Bonvin ,  Prof. Sonia Dollfus ,  Prof. Thierry D' Amato ,  Prof. Jean-Michel Llorca ,  Dr. George Berthon ,  Dr. Céline Plassereaud ,  Dr. Véronique Roure ,  Natacha Boureykoff ,  Marie-Josée Durak ,  Dr. Maria Teresa Caldera ,  Dr. Roberta Bossoletti , and  Dario Caffaro. Funding:  Swiss National Science Foundation  Grant  32003B-121038.
A Nikon 100x (NA 0. 9) objective was used. The spectral analysis and imaging processing was performed using the WITecProject software (version 2. 04, WITec GmbH, Germany). Peak positions were determined using the "Mulipeak Fitting 2" routine of IGOR Pro using a Gauss shape for the fitting (version 6. 11, WaveMetrics, Inc. USA). To identify calcite, we used calcite peak T c at 155 cm -1 (translation mode) and L c at 282 cm -1 (librational mode) and the two internal modes (in-plane band ν 4 at 711 cm -1 and symmetric stretch ν 1 at 1085 cm -1 ). Scanning Electron Microscopy. For SEM, several freshly broken, unetched radial shell fragments were prepared. A staining assay was conducted by putting one of the fragments in a sealed container next to an OsO 4 solution (4% vol. in H 2 O) for 1h. This compound is commonly used to stain organic components in TEM preparations.
Acknowledgments This research received support from  CNRS  through the interdisciplinary action  AIR-Archaeometry  and from the  SYNTHESIS  Project http://www. synthesis. info/which is financed by  European Community Research Infrastructure Action  under  FP7 Integrating Activities Programme  (we especially thank  J. Spratt ,  A. Ball  and  L. Howard  from the mineralogy departement of  NHM, London  for their support). We would like to thank the  Institut National des Sciences de l'Archéologie et du Patrimoine (dir. A. Akerraz) , the  Mission archéologique El Harhoura-Témara , funded by the  Commission consultative des recherches archéologiques à l'étranger of Ministère des Affaires Etrangères et Européennes of France  and the  Ministry of Culture of Morocco. We also would like to thank the  French Agence Nationale de la Recherche  and the  ANR-09-PEXT-004   MOHMIE  project (dir.
This research received support from  CNRS  through the interdisciplinary action  AIR-Archaeometry  and from the  SYNTHESIS  Project http://www. synthesis. info/which is financed by  European Community Research Infrastructure Action  under  FP7 Integrating Activities Programme  (the authors especially thank  J. Spratt ,  A. Ball  and  L. Howard  from the mineralogy department of  NHM, London  for their support). The authors would like to thank the  Institut National des Sciences de l'Archéologie et du Patrimoine  (for the funding provided in the field work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Since Digital Humanities researchers and developers are regularly creating somehow industrial applications concerning international business, it is time for those communities to be aware and make the most of legacy constraints and opportunities. For instance, let us consider the Computer Music state of the art, and particularly the Music Information Retrieval community and the wonderful algorithms it produces around authorship attribution and style recognition: even if some music style or authorship is finally attributed to some persons, this attribution may not result from a set of computable data somewhere reportable, the information being typically learned (in the sense of Machine Learning, more or less supervised) from dislocated data throughout the big data or the global database, and disseminated in the global programming system.