dataesr/scientific-paragraphs-categorization huggingface dataset:
The underlying four principles of our representation framework As already discussed (see §4. 1), Typannot is based on three layers of information for each parameter described. Those layers allow translating the intrinsic dimensions of the SL sign, as it is perceived, into a systematic representation framework that can be grounded in two elementary perspectives: 1) the technical and typographical contexts of transcription is formed by the pragmatic and specific conditions in which the design process of a TranSys is conceived; 2) the modelization and analytical vocations inherent to the work of transcription implies a special reflection on the way data can be viewed, assembled and searched. We identified four principles that address the fundamental requirements for our TranSys: genericity, readability, modularity and scriptability.
For example, the fingers as perceived in a HS are made out of six generic features (3 rd featural layer): hand, finger, event, shape, angle, contact (see §5). This concept allows a constructivist approach to transcription that is built on the articulatory nature of gesture. Genericity thus can be understood as a level of reduction of information that allows a system to offer a symbolic and systematic space that can be used to model any production of a parameter for any SL. A TranSys requires not only a model that systematically characterizes each parameter but also a coherent internal organization. While the body parts have their own physical space and natural organization, transcription must follow the linear space of the writing form in which most data are input, i. e. a text. This approach requires a second radical shift of perspective to conceptualize a linear architecture in which the units of information can be consistently and logically assembled, i.
e. syntax. Generic notions also help organize information within this textual space; as we have defined categories of generic information (e. g. , fingers, shape, angle, events, etc. ) we are able to assign them an invariable position and organization in an exhaustive string of data representing the globality of a parameter. This syntax is essential to insure data integrity and Handling sign language handshapes annotationwith the typannot typefont CogniTextes, Volume 19 | 2019 compatibility when transcriptions are communicated beyond the individual annotator sphere, compared to each other or collaboratively edited. Thus, bricks of information are assembled in a so-called nomenclature that will be an essential criterion when it comes to reading, writing and, of course, searching. But how can Typannot guarantee that such a level of exhaustivity remain readable for the annotator (see §3 for compromises made by SignWriting or HamNoSys)? And how can readability be defined when talking about SLs?.
In order to meet the readability criterion, Typannot must take into account both the phonological and logographical format of information. This is a unique opportunity to challenge the modal and semiotic rupture that traditionally occurs between speech and writing (voco-acoustic vs. visuo-gestural). Indeed, writing does involve reduction and Handling sign language handshapes annotationwith the typannot typefont CogniTextes, Volume 19 | 2019 conventions but, in the case of SL, it can do so by following the fruitful relation between two extremes of writing forms: the "image" of reality as an analogical movement toward the referred idea (the signed body through a logographical perspective), and the modularity of a graphical decomposition of information (the parts and variations that compose that signed body through a phonological perspective).
To conclude, an SL TranSys needs to display the same information in two formats: 1) a generic form that visualizes the bits of information in a syntax that confers stability and regularity to the code architecture from the perspective of corporal and gestural models; 2) a composed form that integrates symbolic translations of the parts in an analogic space of representation (an image of the signed body). For that reason, we designed the two forms of representations and give Typannot users the ability to seamlessly and consistently view/read one or the other while retaining data integrity. Progress in typography technology allows us to design this dual perspective using the OpenType font format and functionalities (like the ligatures) that are nowadays widely implemented in all text editing environments.
Modularity Chinese writing system is an interesting example of massive glyphic combination. At the core of its structure is a small set of radicals (214) that have phono-semantic values. Those elementary components are assembled into more complex characters to represent words with related meanings or sounds. This principle of modularity can be adopted to translate the visual structure of the body and gesture in Typannot glyphic framework. The generic levels of description of a parameter can actually be visualized in a set of modules that share visual and spatial analogies with the SL sign (see example of HS in §5). During Typannot development, we searched for the best forms to represent SL features while assessing their ability to be assembled and remain legible. Those conventions cannot be formed arbitrarily.
Typannot modular system is the result of a back-and-forth between designers (carefully evaluating all the solutions they proposed) and future users (taking into account the way they perceive and understand the system), and it is still open. But designing the Typannot "modules" solves only half of the problem of representing all the combinations of parts and features for every single SL parameter. Designing the thousands (or millions) of possible combinations by hand is impossible, so it is mandatory to find a procedure to automate this work. In our glyphic framework, the design space is constant because it is a conventionalized projection of the body and its features. So we could write an algorithm to put each module at the right place and in the right order in order to solve the glyphic integration problem.
Our algorithm runs under Robofont, a font production software using Python coding language. It allows the automatic generation of all possible combinations inside a single parameter. At present, we have already generated all possible combinations for HS. The same procedure will be used for all the other parameters represented by Typannot. Last but not least, we need to consider how the user will input this new type of graphical representation of SLs. Although a writing system inherently points toward the act of writing, should we still consider it through the perspective of the pen and the hand that controls it? Or maybe this traditional modality is not as relevant here?.
Coding Handshapes with Typannot system Typannot transcribes at low (phonological) level every existing SL (142, according to  Simons et al. 2018). As already mentioned, Typannot peculiarities are: a) it takes into consideration the parametric, the part and the featural layer of sign information (see §4. 1); b) it is based on four underlying principles (genericity, readability, modularity and scriptability (see §4. 3 for a detailed explanation); c) it is built on three levels of representation (a graphemic formula, a set of generic characters, and a typographic font made of composed forms). Handling sign language handshapes annotationwith the typannot typefont.
e. event, shape, etc. ; see Table  2) 9. The generic characters are the translation of the selected features (i. e. , 21 for HS) into graphical forms; to compose a complete HS, these characters are then arranged in a linear way following strict syntactic rules (see Table  1 ). At present, to ensure portability and data queries in every software and operating system, each representation system needs to be recognizable by the Unicode Standard: to be sure to comply with all the Unicode Consortium requirements, Typannot has been developed from the very beginning to abide to the guidelines. Furthermore, thanks to its genericity and modularity, Typannot requires the formal recognition of just the generic characters (i. e. , only 21 slots for HS) while, for example, SW needs 261 slots for HS. 40 To ensure easy readability and scriptability and the integration of several pieces of information into a single glyph (see §4.
The formula line gives feedback to the user and can be displayed as text (as in Figure  7 ) or can be switched to generic characters. When the combination matches the HS to be transcribed, the glyph is ready to be sent to different software. Typannot's main purpose is to transcribe SL on the ELAN annotation tool as any other typeface, but the keyboard can also be used with any other text software (Word, text edit, Google doc, etc. ). The gestural interface, using motion capture (MoCap, based on Leap Motion device -  Avola et al. 2014) , is under development. It will enable users to transcribe glyphs by positioning their hand, in the searched HS, above a Leap Motion sensor that will automatically capture its shape. When the right HS has been achieved, the corresponding glyph can be sent, in a single click, to any annotation or text-editing software.
Typannot usage on SL linguistics : preliminary results The use of Typannot for the HS (complete typeface) and for the LOC parameter (only the graphemic formula) reveals some preliminary results concerning the presence of praxic gestures (the way we handle objects) in LSF (French Sign Language) and, therefore, the way praxis influences the form of symbolic gestures. According to  Napier (1956) , we differentiate grips between: 1) precision grip posture, characterized by an opposition of the thumb (T Opp ) with at least one finger, inducing an extension of the wrist; 2) power grip posture, characterized by a non-opposition of the thumb (T NOpp ) with other fingers and no specific extension of the hand. (Note that, if a precision grip is used to seize light objects, an extension of the wrist is possible -this is not the case with power grips).
This assumption is applied to an excerpt of LSF, of 1'38" duration, extracted from the LS-Colin corpus  (Braffort et al. 2001) , transcribed with Typannot HS complete typeface for HS and the graphemic formula for LOC. The latter codes the relative location of each segment (hand, forearm, arm) according to an intrinsic frame of reference  (Levinson 1996 , Boutet 2010). Handling sign language handshapes annotationwith the typannot typefont CogniTextes, Volume 19 | 2019 Despite the fact that, among HSs gathered in the analysis, some have nothing to do with a grip and have nevertheless a T Opp or a T NOpp , these preliminary results show that the a priori association between the opposition of the thumb and a relative location of the wrist caused by praxic situations of differentiated grips does have an impact on symbolic gestures as signs.
At present, HSs are complete, we have developed the graphemic formula for LOC and movement, and we are well advanced in the generic characters for facial expression. For those parameters, we also have first versions of their generic characters, but further tests are needed to confirm their design. Once the design of a typeface is completed, it will be integrated into the Typannot Keyboard. The work on the generic and the semi-composed interfaces will be simple; for the gestural interface, it will be necessary to find the correct MoCap device (already done for movement and LOC parameter) and to transform this data into generic characters. To complete the use of the Typannot system for transcription, we have already conceived an ELAN template that allows linguists to transcribe with all Typannot typefaces (only using the generic formula for those parameters that are not yet fully developed).
8. A previous research project of the GestualScript team, called Photocalligraphy, investigated the relation between writing and language in SL using long exposure photos to catch in image the trace left by gestures. From an extensive corpus of hand "traces", we studied the graphical strategies and relationships the signer had with the visual trace inscription. 9. Note that the greatest difference between the work done for the HS and that for the other parameters (movement, LOC, facial expression, just to mention the ones that Typannot team is developing at present) is that, since no exhaustive list of features exists for non-HS parameters, it does require the development of an ad hoc list using an anatomical or kinesiological approach (see §4. 1). 10. The AUTOCOMPLETE field displays signs that are close to the current input.
ABSTRACTS The typographic system Typannot, presented in this paper, allows the analyst to transcribe Sign Language (SL) signs. The structuring of this system is described in this presentation. It encodes three layers of information : parameters, parts and features. In order to visualize them at a typographic level, we adopted four design principles : genericity, readability, modularity and scriptability. Together, they guide us in the representation and integration of the three layers of information. The system can transcribe an SL sign with accuracy and display the transcription in a flexible way by means of two types of representation forms : a generic form and a composed form. These forms facilitate transcription and have the potential to help other practices (for example : lexicography or writing SL).
Fondation Le Corbusier and A-BIME have collaborated to collect, analyse and organize preliminary research data for the restoration of the Le Corbusier's apartment-studio in Paris using a methodology based in a 3D model. This work, developed by A-BIME since 2014, has been financed by Fondation Le Corbusier thanks to a 'Keeping it modern Grant", giving by the Getty Foundation. The aim is to have faster and easier access to various data of the building by using a clear and documented digital graphic document. For each step, the archives provided by the architect and the companies have been studied to understand and know the nature and quality of materials, technical arrangements and their on-site implementation. A study on technical installations and isolation as be provided as well by investigation in the apartment.
3D model construction The model has been built with the software Revit with all the information collected before. Each element has been construed thanks to the archive's information and the onsite investigations. The informed elements have then been placed in the 3D model thanks to a point cloud of the apartment delivering the exact position were each element must be. However, loss of data is possible during the exportation from. rvt file to. ifc file. That's why, during the building of the digital MockUp in Revit, it is very important to make precise correspondence between the categories used in Revit and the classes IFC in order to limit this lost. The software has the tools needed to define this correspondence. Some searches are made to erase the loss of data. Thanks to the ifc format, it is possible to put on the internet the digital MockUp which is a graphical representation of data.
Cultural mediation By linking the MockUp and the database in a web project, it will be possible for people to interact with the MockUp : different data of an element of the building will be accessible online, such as photos, archive about the selected element, etc… The different states of the building through time (for instance Le Corbusier made some change in his apartment during his life) Figure  16. By clicking on the bench, information about it appear. The second stage of the cultural mediation project is to create a collaborative platform between the MockUp and visitors: For instance, if someone has an information about on element of the building which does not appear on the MockUp, he will be able to add the information. It will allow to have the best knowledge possible of the building.
Constraints on the size of inter-word fluxes We have seen that the sum of the lengths of the dependencies is equal to the sum of the sizes of the interword fluxes. Since there are as many dependencies as there are inter-word positions in a sentence (n-1 for a sentence of n words), this means that the average length of the dependencies is equal to the average size of the inter-word fluxes. For the entire UD database (version 2. 4, 146 treebanks), this value is equal to 2. 73. But the equality of the average values does not mean that the values of these two variables, dependency length and flux size, are distributed in the same way. We give the two distributions in Figure  2.
The disjoint dependencies correspond to nested constructions. For example, in our example, the dependency between risks and climate and the dependency between mitigate and alleviating are disjoint and therefore the unit [risks,mitigate] is fully embedded in the unit  [climate,alleviating]. 2  The number of disjoint dependencies in a flux is very constrained as shown by  Kahane et al. (2017) : 99. 62% of the fluxes in the UD database have less than 3 disjoint dependencies. This suggests that bouquet structures are less constrained than disjoint structures. This is quite predictable if we consider that there are con-straints on working memory and that dependencies in a bouquet share more information than disjoint dependencies. Note that non-projectivity can also be detected from a structured flux, if we take into account the order in which the vertices of the dependencies of the same flux are located.
Data analyses For each participant and emotion, the percentage of correct responses was calculated, separately for sentences and vowels, and for correctly categorized trials, the average ratings of intensity were calculated, again separately for sentences and vowels. In light of recent criticism of classical frequentist analysis  (Lee & Wagenmakers, 2014; Wagenmakers et al. , 2018) , a Bayesian approach was used to analyze the data. Bayesian analyses allow to perform model comparison and to select the best model (with the best evidence), given the data. We analyzed the data with the software JASP  (Wagenmakers et al. , 2017) , with a 2×5 Bayesian mixed repeated-measures analysis of variance (ANOVA) on percentages of correct responses and intensity ratings with group (amusic or control) as a between-participants factor, and emotion (joy, sadness, fear, anger or neutral) as a within-participant factor (note that for intensity rating, the emotion factor had only four levels as neutral stimuli were not rated for intensity).
We also analyzed the data of sentences and vowels together with group (amusic or control) as between-participants factor, and emotion (joy, sadness, fear, anger or neutral, the latter only for percentage of correct responses) and material (sentences or vowels) as within-participant factors. We reported Bayes Factor (BF) as a relative measure of evidence. To interpret the strength of evidence, we considered a BF under three as weak evidence, a BF between three and 10 as positive evidence, a BF between 10 and 100 as strong evidence and a BF higher than 100 as a decisive evidence (according to  (Lee & Wagenmakers, 2014). BF 10 indicates the evidence of H1 compared to H0, BF inclusion indicates the evidence of one effect over all models. As no post-hoc tests with correction for multiple comparison have as yet been developed for Bayesian statistics  (Wagenmakers et al.
, 2017) , we used t-tests with Bonferroni correction for multiple comparisons. In a second step, based on the percentage of correct responses of the emotion categorization task for the vowel material, we performed a multifactorial analysis (MFA) using the package FactoMineR  (Lê et al. , 2008)  in R to analyze the underlying data structure (emotional space). We could not perform a MFA for sentence material due to high performance (ceiling effect). For each participant (18 amusics and 18 controls), we built a matrix with the responses for each stimulus in each of the five-emotion categories (20 in total), equivalent to a contingency table. Each participant and each stimulus were projected in the multidimensional space recovered by the analysis. R2 (correlation factor) is retrieved for each participant for each dimension and then compared between groups with t-tests.
Acknowledgments This work was supported by a grant from the  Research National Agency  ( ANR-11-BSH2-001-01  to  B. Tillmann  and  A. Caclin. This work was conducted in the framework of the  LabEx CeLyA (''Centre Lyonnais d'Acoustique ",  ANR-10-LABX-0060 ) and of the  LabEx Cortex (''Construction, Function  and  Cognitive Function and Rehabilitation of the Cortex ",  ANR-11-LABX-0042 ) of  Université de Lyon , within the  program ''Investissements d'avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This work was realized as part of a  Marie Curie fellowship  ( International Incoming Fellowship ) of  A. Bhatara. We would like to thank  Céline Tourlonnias  for pretests with the sentence material. We also thank  Lison Fanuel  for her help with the Bayesian Analyses.
The comparative data can be consulted in table  S4. Bone surfaces of all faunal remains were inspected macroscopically and microscopically with a stereomicroscope (OPTHEC, 120 Hz model), using magnifications from ×15 to ×45. Cut marks and their relationship with specific butchering activities were identified on the basis of the criteria of Domínguez-Rodrigo et al. (28)  and Romandini et al. (15). In addition, 3D reconstructions of the marks were carried out following the methodological protocol established by  Courtenay et al. (48). This approach digitalizes each trace using the HIROX KH-8700 3D Digital Microscope with an MXG-5000REZ triple objective revolving lens. First, cross sections of each mark were produced using the midrange lens at a ×600 magnification. A fixed high-intensity light-emitting diode light source was placed above each sample, combining the use of coaxial and ring illumination.
3D digital reconstructions were produced using a combination of quick auto focus and depth synthesis functions that are provided by the HIROX's system, generating a 3D display of each mark where measurements could be taken and cross-section profiles could be extracted. To construct each digital image, between 110 and 130 photos were taken for each profile. The capturing and assessment of the morphology of each mark's profile were carried out using a total of three cross sections, taken at 30, 50, and 70% of the total length of each mark. As described by Maté González et al. (30) , this range along the groove is suggested to be the most representative for cut mark morphological analysis. These profiles were then exported to the free tpsDig2 (v. 2. 1. 7) software where the allocation of seven homologous landmarks was carried out following the geometric morphometric models described by Maté González et al.
(30). The resulting files produced through landmark allocation were then edited and imported into the free software R [www. r-project. org; (49) ], where a full Procrustes fit was performed using the Geomorph library  (50). This package can be used to prepare the sample for multivariate statistical analysis and is commonly referred to as a generalized Procrustes analysis (GPA). Through GPA, each individual is standardized through a series of superimposition procedures involving the translation, rotation, and scaling of each shape. Any differences in structure can thus be studied through patterns of variation and covariation, which can then be statistically assessed  (51). The library Shapes (52) was then used to calculate and plot the mean shape of each cross section. Additional measurements concerning the depth and opening angle of each of the profiles were later taken.