dataesr/scientific-paragraphs-categorization huggingface dataset:
g. roundabout), errors were also observed for objects sharing few or no similarities with the reference dataset (e. g. horse training area, large ditch). Such undesired behavior of the deep CNN models raised the question of negative training (i. e. providing the model with negative examples during training). While this was not implemented in the Mask R-CNN framework used in this study, it should be addressed in future works to improve prediction performances, for example using software frameworks that handle negative training for instance segmentation, such as Detectron2  (Wu et al. 2019). More generally, results showed that a particular attention should be paid to the selection of training examples. The sample selection strategy is still a challenging concern especially with the hidden and non-intuitive phenomena related to deep CNN.
Results showed an increase of the mAP@IoU0. 5 performance from 0. 64 to 0. 75. Assessing the overall generalization ability at a larger geographical scale (spatial generalization) and for more types of archaeological structures (typological generalization) would require further experiments. First, to assess spatial generalization, a pre-trained model could be used to identify topographical anomalies that have characteristics similar to those on the coast of Morbihan using the LiDAR dataset of relevant regions in the world. Second, to assess typological generalization, the model could be retrained to include new types of structures to increase the diversity of archaeological contexts assimilated by the deep CNN. These strategies would benefit from public benchmark dataset targeted to detect archaeological sites from remotely sensed data.
ONE-CLASS APPROACH AND POST-DETECTION CHARACTERIZATION: POTENTIAL FOR A NEW PARADIGM FOR (SEMI-) AUTOMATIC MAPPING IN ARCHAEOLOGY Most approaches in machine learning-based archaeological mapping use a pre-defined nomenclature (e. g. barrows, charcoal kilns, Celtic fields, burial mounds, mining pits) to consider local archaeological characteristics (e. g. site morphology, chrono-typological relation, spatial relationship). However, a standard and consensual typology appropriate for remotely sensed archaeological structures that span time and space remains a concern  (Tarolli et al. 2019). Moreover, classes are often distributed unequally (i. e. datasets of archaeological structures with a lack of samples for certain classes). We used a one-class rather than multi-class approach to address these two issues because we assumed that the deep CNN would have higher generalization abilities (i.
e. depend less on target type and variety) with a one-class approach. This was confirmed by the results obtained for the Er Castellic motte, whose structure type was not included in the training dataset. Although this artificially elevated terrain monument was the only example of its type in the study area, it was sufficiently similar to a tumulus for the model to detect it as an object of interest. These topographical and morphological similarities with certain tumulus were mentioned in an archaeological prospection report  (Brochard 1994 ) and reinforced our assumption. Indeed, from a LiDAR perspective, archaeological sites of different chronologies and typologies share patterns that the deep CNN can discover and extract. The characterization phase, based on the objectsegmented mask and data-driven description, provides information that can help to identify the nature of the archaeological structures.
specific sampling strategy was used to assess the model's stability (varying training/validation/test draws) and sensitivity to the number of training samples (varying training size). The initial dataset of 150 images was randomly split into 110, 20 and 20 images for training, validation and testing, respectively. This random split was performed 10 times to create 10 different experimental datasets (different draws). For each experimental dataset, the training dataset was divided into 11 sub-training datasets with 10-110 images, with an increment of 10. Given the number of experimental datasets and sub-training datasets, a total of 110 experimental configurations were available (see Appendix A. 1). Each experimental configuration was checked to ensure that no leaks occurred between validation, test and training datasets.
ACKNOWLEDGEMENTS The authors thank members of  DRAC Bretagne, Service régional de l' archéologie for providing access to the  LiDAR raw data , the archaeological documentation and reference dataset as well as for the field verifications. OSUR/OSUNA and GeoFIT are also acknowledged for the LiDAR acquisition. The Nantes Rennes LiDAR platform was funded by the  Region Bretagne  and the  Region Pays de la Loire  with  European Regional Development Fund (ERDF).
Many initiatives are made in this direction, for example the recent creation of an international observatory of social impacts of AI and digital technologies (https://observatoire-ia. ulaval. ca). AI technologies could use the user's digital phenotype on the phone's health data captured continuously (e. g. , number of steps) or occasionally. The concept of digital phenotyping appears to be very efficient to implement data for these systems. Introduced by Jain et al. (22) , it is based on the idea of collecting in real time human behavior data (the momentary ecological assessment or EMA) and markers of their functioning in order to characterize the "digital signature of pathology. " The emotions, the energy level, or the presence of symptoms with their perceived intensity (ruminations, hallucinations, and suicidal ideation) can be analyzed.
This collection of subjective data is clearly crucial in medicine and in psychiatry in particular. The notion of contextualization is central in order to personalize follow-up and better understand the appearance of symptoms. Since all these data exceeds the psychiatrist's real-time analysis capabilities, many of the difficulties encountered in consultation (forgetfulness, recall bias, loss of valuable context elements) could be overcome in the data obtained through AI technologies. They could exploit this digital signature by confronting it with important databases that group those of other patients to draw predictive information from them. Digital phenotype also gives useful first points of contact in the detection of a crisis that can be used, if the patient has given his or her consent, to inform the treating health in situ professionals.
This provides the opportunity to set up an emergency consultation to deal more effectively with the difficulty when it arises. Overall, AI technologies offer CDSS tools that are interesting in clinical evaluation by creating relevant algorithms for diagnosis, decision support, relapse prediction, and neuroprediction. Including the patient's consent, data extraction, and anonymization, this could contribute to a certain therapeutic innovation by creating an ample database: phenotypes (typical symptom profiles, relevant indicators) and patterns (monitored treatments, epidemiological data). For instance, in France, "Health Data Hub" is the initiative following these goals. This combination of tools could be useful and could facilitate PAD completion as peers and caregivers already do  (18).
"WHAT"-In Terms of Access to Data Sources Data sources are on different graduated levels, from public available sources (Google search, civil status, etc. ) to semi-public (social networks, medical data) and finally private (mailbox, web-browser history, etc. ). As especially in psychiatry, useful information is often of a private nature, and the right to privacy should be protected. For PADs, particular attention should be paid to the way in which data are used: explanations to patients of the issues and rights, patients must give their free and informed consent to the use that can be made of their data, what data they wish to have added to a knowledge database or not, and how they can exercise the right to withdraw. Also, it would be appropriate for the professional and the patient to agree on the symptoms to be followed, the key indicators of relapse.
This kind of microdirectives could be extracted from large databases, but personalized too: from an incremental point of view, these microdirectives could be enhanced with the others patient's experiences after an algorithmic treatment with AI, so new directives benefit from past directives. This feedback permits the selection of a new panel of possible and efficient directives. The use of AI will create a supplementary shift, between the real preference and the inferred preference, independently of the time factor. Furthermore, ADs express conscious or public preference, whereas AI could access the unconscious or private preference, raising the ethical issue of which preference is the most beneficial for the patient (Figure  1 ). The incrementation process and feedback offer a large field of directives for the constitution of the ADs that could act as "validated alarms" for the different actors implicated in ADs.
as defined in the FPHC. At present, systems, such as those mentioned above, are capable of making reliable predictions based on algorithms and a large database, respecting legislation on confidentiality and the use of health data do not yet exist. In this context, we can propose several recommendations: 5. 1. Support: AI Must Remain a Decision Support System, and Seen as a Complement to the Decision, a Partner of the Parties of the PAD Contract.
ACKNOWLEDGMENTS This work was supported by the  French National Institute for Health and Medical Research (INSERM) , the  University Hospital of Strasbourg  ( HUS N • 6366 ), and the  FRM (Medical Research Foundation  ( FRM DPP20151033957 ). We would like to thank  Julien Krieg  for his help in finalizing the programming of the scale, the  International Consortium on Hallucination Research , whose meetings inspired this scale, the  SATT Conectus  for registering the software under the initial name of Audiographe ( FR. 001. 460017. 000. S. P. 2019. 000. 10000 ), and  Tamim Daoudi  for his help when the scale was only an idea, and for putting the first author in touch with TH.
We would like to thank the two anonymous reviewers for their relevant comments and suggestions, which have helped us to improve the final version of the paper. This work has been partially funded by a public grant overseen by the  French National Research Agency (ANR -Agence nationale de la recherche)  as part of the program " Investissements d'avenir"  ( ANR-10-LABX-0083 ); and by the research project  FFI2017-82547-P  ( Spanish Ministry of Economics, Industry and Competitivity ), awarded to the  Center of Linguistic Theory of the Universitat Autònoma de Barcelona. It also contributes to the  IDEX ("Initiative d'excellence") Université de Paris  - ANR-18-IDEX-0001. It was, furthermore, funded by the  German Research Foundation (DFG -Deutsche Forschungsgemeinschaft) :  SFB 833  " The Construction of Meaning ",  Tübingen -Project ID 75650358 ; and  SFB 1253 "Prominence in Language", Cologne.
Acknowledgments This work was funded by the  ANR  ( ANR-19-MIN2-0002-01 ), the  AEI (MICIU/AEI/REF. :  PCI2019-103779 ) and author's institutions in the framework of the ERA-MIN2  AUREOLE  project, as well as by Project  SBPLY/17/180501/000273 ,  Consejería de Educación, Regional Government of Castilla-La Mancha, Spain.
Condition MP-Neg In order to state the relevant predictions pertaining to Condition MP-Neg in both languages, we need to proceed more cautiously given the non-trivial role of negation as a complex interpretational component realized simultaneously at different linguistic levels, including syntax, semantics as well as pragmatics. With regard to the latter, there are diverging views in the literature about the impact of negation in computing pragmatic inferences, and relevant empirical effects of negation in pragmatic contexts are often difficult to evaluate. For instance, in the realm of scalar implicatures, those are often regarded as suspended in downward monotone environments including negation, whereas other works regard scalar implicatures in these environments as present but reversed  (Levinson 2000; Chierchia 2004 ; among others).
All participants had a very low age of acquisition (AoA) and they systematically used both languages on a daily basis. The decision about language (non-)dominance for each participant was based on an analysis of extensive questionnaire data filled in by parents. 37 bilingual participants were assigned to a dominant-Italian group and received a Slovenian c-test, whereas 23 bilingual participants were assigned to the dominant-Slovenian group and received an Italian c-test. 57 monolingual Italian participants and 60 monolingual Slovenian participants completed a c-test in their respective native language. Each participant was asked to complete a c-test after a short break following the main experiment. There was no time limit on this task, which took about 15 minutes to complete.
Statistical analyses For inferential statistical analyses of the main conditions in this study, we used mixed effects logistic regression models in R version 4. 0. 2, using the glmer function from the lme4 package  (Baayen et al 2008; Jaeger 2008; Bates et al. 2014; R Core Team 2020). Felicitousness of the chosen alternative in answering the target question was coded as a binary-valued dependent 9 An anonymous reviewer expresses a concern that using the same types of experimental items related to the <know, believe> pair in the training session might have biased the participants toward the intended response in the main task. We acknowledge this as a potential limitation of our design, but we should also note that it is difficult to find other pairs of predicates both in Slovenian and Italian that differ in precisely the same way as members of the <know, believe> pair, that is, in the presuppositional component only.
In each analysis, the best fitting model was chosen by comparing pairs of models with different degree of complexity using the likelihood ratio test. The simplest model considered included only by-subject and by-item random intercepts. More complex models included by-subject and by-item random intercepts and slopes varying across subjects and items. For each pair of models, the results of the likelihood ratio test were used to evaluate whether inclusion of additional random-effects parameters provided a better fit to the data, so that more complex models were excluded only if the p-value for the significance of the difference between the two models was above 0. 2 (cf. Matuschek et al. 2017). Posthoc planned comparisons were performed as Tukey's estimations using the multcomp package in R. Graphical representations of effects were plotted using the effects package (Fox & Hong 2010).
To better understand the three-way interaction model and specific loci of the effects in the interaction terms, we constructed smaller two-way models, while keeping the third factor constant, and focusing on interactions that are likely to be of interest in the context of our present research questions. The results were the following. A significant interaction between GROUP and CONDITION was found across the LANGUAGE factor (χ 2 (2) = 45. 30, p < 0. 0001). An examination of the respective model revealed that for the bilingual group, the odds ratio of giving a correct answer to Condition MP increase by factor 1. 67 (84 %) compared to the monolingual group across the two languages. Post hoc pairwise Tukey comparisons indicated that this increase is significant (Estimate = -1. 21, z = -3. 881, p = 0.
Acknowledgements We thank  Anne Cheylus ,  Guglielmo Cinque ,  Viviane Déprez ,  Ludivine Dupuy ,  Jean-Baptiste van der Henst ,  Jacques Jayez ,  Manca Mušič ,  Giustina Selvelli ,  Maria Vender ,  Alessandra Zappoli  and three anonymous Glossa reviewers for valuable assistance, comments and discussion. We also thank the staff and children from the monolingual and bilingual schools and school centers in the Primorska region of Slovenia and the province of  Friuli-Venezia-Giulia in Italy  who collaborated in this study. Portions of this research were presented at the  Universities of Plovdiv and Venice. We thank the audiences for comments and feedback.
Among the 28 individuals of this Gr. 337, we conducted isotope analyses for 6 teeth and 22 bones belonging to 23 different individuals. For sulfur isotopes, 16/22 of the human bone data and 3/6 human tooth data are from this study, and the rest was published in Colleter et al. [74]. For the Gr. 322 and 337, 7/10 Sr isotope analyses were already published in Jaouen et al. [26]. The 3/10 additional Sr isotope data are from this study. All oxygen and carbon enamel isotope data (10/10) are from this study. Eight mg of the remaining collagen extracted for C and N isotope analyses was sent to Isoanalytical for S isotope analyses by EA-IRMS. Dental enamel was first mechanically cleaned prior to sampling for Sr, O and C isotope analyses. The strontium was extracted using a modified protocol from Deniel and Pin  [76]  in the clean lab facility of the Max Planck Institute for Evolutionary Anthropology.
All the isotopic values for individual 20185 are compatible with the local range, and triple isotope assignments show strong probability of childhood origin in Brittany. Nevertheless, the isotopic values are also compatible with international regions: Northern Spanish coast, Aquitaine coast, Cornwall and Wales. However, since the isotope values for nitrogen, carbon and sulfur are extremely similar between teeth and bones, it is likely that the individual did not move during his life and was local. Mass grave Gr. 337 Among the 28 individuals of the Gr. 337, we conducted isotope analyses for 6 teeth and 22 bones belonging to 23 different individuals. For sulfur isotopes, 16/22 of the human bone data and 3/6 human tooth data are from this study, and the rest was published in Colleter et al. [74].
All δ 18 O and δ 13 C of tooth enamel are from this study. Finally, 3/6 87 Sr/ 86 Sr human isotope data are from this study, the three other individuals were analyzed in Jaouen et al. [26]. The δ 34 S values in both teeth and bones are lower than the local range. A Nemenyi test following a Kruskal Wallis test shows that the individuals from the Gr. 337 have δ 34 S values significantly different from the individuals buried in the church who were likely locals (group I; p = 0. 0002). Only three of the 21 individuals analyzed from bones (14%) of the mass Gr. 337 have isotopic values compatible with the local range (20781, 20789 and 20768) (Fig. 4 ). One of these individuals (20781) had a tooth preserved and was analyzed for S, O and Sr isotopes. This individual is not compatible with the local range but is rather compatible with western France (Poitou, Charentes area) but also England, the Netherlands and Asturia.
These numerical methods are implemented through Heat, Air, and Moisture (HAM) software, becoming an ideal method for carrying out the hygrothermal assessments, and nowadays there are several available tools for it  [22]. Furthermore, various standards and handbooks in this matter serve as support and guides to establish the hygrothermal evaluation criteria of buildings' envelopes  [23] [24] [25] [26]. Previous research studies carried out in this field have generally focused on the analysis of severe winter climates, with a medium or high precipitation rate and brick assembled masonry walls  [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]. Nevertheless, there are also other cases that have not been investigated yet. For instance, in some cases where the winter climate is not so severe, usually less moisture risks are presupposed, although in fact, due to their particularities, they present the same risk of problems associated with the hygrothermal behavior of the walls and its components.
Numerical Model The research carried out is composed of three different parts. The first consists of the simulation process through which the hygrothermal records of the evaluated walls are obtained. Once the results are collated, they are analyzed to detect the inappropriate and appropriate solutions for their implementation in ITICS systems. The second step carries out the post-processing of the records obtained from a group of selected cases, i. e. , those considered the most appropriate. For the hygrothermal simulations, WUFI Pro v. 6. 2 and WUFI 2D v. 4. 1 from the Fraunhofer IBP has been used. They are both widely used software programs in this kind of study and are also verified according to the EN 15026  [23]. As analyzed later, the first is used to carry out the set of general simulations and thus be able to obtain some first conclusions.
Materials, Properties, Climate, and Surface Transfer Conditions The external conditions taken into consideration for the simulation, as well as the internal conditions, can be found in Figure  6. Considered internal boundary conditions will be in accordance with Section 4. 2 of the WTA 6-2  [25]. In this model, the indoor air temperature is in function of the outside temperature, as well as the indoor air humidity (see Figure  7 ). As the present study is based on a design phase, it has taken into account the case of Medium Moisture Load, increasing its coefficient by 5% in order to offer a safety margin. In addition, the external conditions have been obtained from the Meteonorm database  [87]. As pointed out above, the climate, although characterized by moderate temperatures, is notable for its high humidity throughout the year (Figure  6A ) as well as its high rate of precipitation (Figure  6C ).
Data collection and post-processing MSCT images were obtained using a picture archiving and communication system (PACS, McKesson Medical Imaging Group, Richmond, BC, Canada) used by our institution. Examinations were performed on a Sensation 16 Scanner (General electric medial system, Erlangen, Germany) with 16 × 1. 5 mm collimation. The image matrix was 512 × 512 pixels, axial slices' thickness was 0. 6 mm and voxel size was 15 μm. The CT scans were saved as Digital Imaging and COmmunications in Medicine (DICOM) files. Post-processing was performed using Horos™ Medical Imaging software v. 3. 3 64-bit and consisted in the application of a bone filter to improve spatial resolution.
Morphological 3D analysis Calculations and statistical analyses were carried out using R 3. 0. 2 software version 3. 6. 1 © [33], the shapes (v1. 2. 5; Dryden, 2019)  [34] , geomorph (v3. 2. 1; Adams, Collyer & Kaliontzopoulou, 2020)  [35]  packages, as well as the mshape, procGPA, procD. lm, procdist and CVA functions. The data and R code that support the findings of this study are openly available in Open Science Framework at http://doi. org/10. 17605/OSF. IO/KB3HG  [36]. Repeatability. Geometric morphometric methods rely on the accurate identification and placement of landmarks on biological specimens. A landmark presenting a strong homology is a landmark that can repeatedly be identified with precision and that can provide information about shape changes along any of the three axes. In order to validate the accuracy and reproducibility of the landmark placement method used in this study, the assessment of inter and intra-observer agreement is desirable  [37] , so that any researcher can repeat the presented protocol.
FUNDING AB was supported by a fellowship from the  Université de Paris. VC was supported by the  Agence Nationale de la Recherche  grants  ANR-16-CE37-0012-01  ( ANR JCJ ) and  ANR-19-CE37-0014-01  ( ANR PRC ). VC and PJ were supported by a department-wide grant from the  Agence Nationale de la Recherche  ( ANR-17-EURE-0017 ) and by  ANR-10-IDEX-0001-02   PSL (program Investissements d'Avenir ).