dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The present study brings a new perspective to the grammaticalization of early learner varieties in L2 acquisition, in particular with respect to the French determiner system, by examining the productions of two beginning learners of L2 French and comparing them to the production data of French-speaking children. This approach clearly has its limits in that this type of comparison requires that the L1 and L2 acquisition data be in the same form, free conversations in this case. To move beyond these limits, analysis of the productions of these same learners, Berta and Zahra, in different discourse types is possible. Available as part of the ESF corpus, these data could confirm certain phenomena described in this article. Additionally, it is important to note that controlling the lexical repertoire and the category of nouns produced in free production tasks is difficult and adds to the challenges of describing a developing nominal determination system.
Materials and Methods Four case studies in Mediterranean France have been undertaken, all avant-garde manifestations of a reaction to the Modern Movement: additive architecture, designed with the environment. The case studies were redrawn using Archicad software, based on cadaster plans, architects' plans, and aerial pictures. This information was completed with on-site fieldwork. The drawings and 3D models were used for morphology analysis and parameter quantification. Comparison to research reference values allowed environmental assessment. To give a "complete" environmental assessment, there were parameters related to all four climatic aspects: wind, temperature, humidity, and solar radiation  (Adolphe, 2001). Although street Orientation and Porosity are also key parameters, they were not examined in this paper since their method of quantification was not satisfactory.
It is the degree to which a wall is exposed to the sky, warming up due to solar radiation during the day, and cooling down at night  (Oke, 1988). It ranges between 0 (closed section) and 1 (horizontal flat surface in complete sky contact). SVF can be measured with street view image methods, numerical models, and fisheye photo methods  (Miao et al. , 2020). This study uses the last. The photographs (obtained on the 13th of March 2020, in the afternoon, for the Ginestou and the Village Grec, and on the 27th-28th of February 2020 for Gaou Bénat and Merlier) were taken in street centres, at 30 cm high. Through image treatment, the sky was represented in white, buildings and other obstructions, like vegetation, in black. The black and white treatment and the SVF calculation were undertaken using Rayman software.
However, some images were pre-treated in Photoshop, since the software interpreted dark skies as walls and white walls as the sky. Analysis of the plans was additionally undertaken to determine typical street widths for each case study. SVF locations were chosen based on the observed widths, and indifferent height situations, that is two buildings of the same height or different heights, a building and a courtyard wall, two courtyard walls, among others. Another criterion was the search for the smallest SVF for every case study, based on intuition and street width. Squares were not considered. Even though this method does not cover the entire residence, it gives an overall image. The lower the SVF, the more protection against summer heat  (Jamei et al. , 2016; Ratti et al. , 2003). As such, the streets remain cool for a longer time during the day.
Acknowledgements: We would like to thank  Agence Nationale des Parcs Nationaux and Olam International in Gabon , which co-initiated and funded this study, as well as the  Institut français du Gabon  for their support, the  Centre National pour la Recherche Scientifique et Technologique  for authorising research in theGabonese national territory, and the two reviewers for their useful comments. Funding statement: Fieldwork was funded by the  Olam Sustainability Framework.
For the determination of grain size, 43 samples were analyzed and classified based on Folk's  [45]  nomenclature. Downcore relative elemental composition of sediment cores were analyzed using an Olympus Vanta handheld XRF scanner equipped with the Olympus, (Tokyo, Japan) "Geochem" Suite. Analysis of the elemental composition was performed using an X-ray source with the voltage set to 8-40 kV, which enabled measurements of major and minor elements (Mg; Al; Si; P; S; K; Rb; Ca; Sr; Ti; V; Cr; Mn; Fe; Co; Ni; Cu; Zn; As; Se; Y; Zr; Nb; Mo; Ag; Cd; Sn; Sb; W; Au; Hg; Pb; Bi; Th and U)  [46]  (Tables  A1  and A2 ). The acquired XRF data were reported as elemental ratios  [15, [47] [48] [49]  (Table  A3 ). For the purposes of this study the elemental ratios of Sr/Ca and Ti/Ca were selected. Sr is an alkaline earth metal fixed by calcifying organisms at the same time as Ca.
Conclusions This study provides data on the middle upper Holocene palaeoenvironmental development of the Akrotiri Salt Lake. The palaeoenvironmental changes were driven by sea level and climate variations and tectonic actions that occurred during the middle-upper part of the Holocene. Sediment cores AKC1, AKC2 and AKC3 revealed the depositional environments in the Salt Lake. The Salt Lake developed in three phases, from an open bay (6000 to 4000 years BP) to a semi closed lagoon (4000 to 2000 years BP) and finally to a restricted lagoon (2000 years.
Conclusions This study provides data on the middle and upper Holocene palaeoenvironmental development of the Akrotiri Salt Lake. The palaeoenvironmental changes were driven by sea level and climate variations and tectonic actions that occurred during the middle-upper part of the Holocene. Sediment cores AKC1, AKC2 and AKC3 revealed the depositional environments in the Salt Lake. The Salt Lake developed in three phases, from an open bay (6000 to 4000 years BP) to a semi closed lagoon (4000 to 2000 years BP) and finally to a restricted lagoon (2000 years to the present day). During the entire time of the evolution of the Salt Lake, the influence of the sea was significant, either by direct connection, high energy events (storms) and/or anthropogenic activity. The combined use of geochemical, sedimentological and palaeontological proxies, combined with radiocarbon dating, are valuable indicators for the interpretation of past environments in these types of geological setting and can shed light on the reconstruction of palaeogeographic evolution.
METHODS AND ANALYSIS Study design Our study is a therapeutic web-based, comparative, randomised controlled trial, 2 arms, single blinded, with 52 weeks of follow-up. Data will be collected from clinical assessments at baseline, and weeks 6 and 14, and gambling account based data extracted from the French online gambling regulation authority (ANJ) at baseline, weeks 6, 14 and 52. The ANJ is the regulatory authority Open access supervising online gambling in France. It approves and controls all online gambling games and stores the player account data of all online gaming operators. With participant consent, only player account data from legal online gaming operators (approved by the ANJ) will be extracted. Participants who do not have a player account from an approved gaming operator will be included in the study, but no player account data will be extracted for them.
34  35 ► TimeLine Follow Back-gambling (money and time including offline gambling). ► EuroQol five dimensions questionnaire. 36  Gambling Quality of Life Scale (adapted from Alcohol quality of life scale, ongoing study). 37 ► Neuropsychological assessment: Stop Signal Task measuring cognitive inhibition (stop signal reaction time criteria). 38 ► We will extract from the ANJ database (data collected automatically and prospectively) the following account player based gambling data, at baseline, 6 weeks, 14 weeks and 52 weeks (last 4 weeks): Total Deposit, Total stake per game, Compulsivity (as defined by three consecutive deposits within 12 hours), Number of deposits in the hour following a stake, Total loss per game, Number of sessions (all games): a session is defined as gambling behaviour, whereby the beginning of a session is defined when a gambling action occurs after no gambling action in at least the last 30 min, and the end of the gambling session is a gambling action followed by no gambling action for 30 min), Session duration (poker only), Gambling time slot (a: 0:00 to 3:59, b: 4:00 to 7:59, c: 8:00 to 11:59, d: 12:00 to 15:59, e: 16:00 to 19:59, f: 20:00 to 23:59).
Data analysis The analysis will include all randomised patients (ITT population). Statistical analyses will be performed with SAS software V. 9. 2 (SAS Institute, Cary, North Carolina). All primary and secondary analyses will also be performed in the modified ITT population, defined as all randomised patients who attend at least one training session. A multiple imputation approach will be used to replace missing values where appropriate for primary and all secondary outcomes. We will create 10 copies of the dataset, with the missing values replaced by imputed values, based on observed data including outcomes and baseline characteristics of participants. Each dataset will be analysed using standard statistical methods, and the results from each dataset will be pooled into a final result using Rubin's rule.
Acknowledgments First I would like to thank  Piotr Stalmaszczyk  for having organised PHILANG2019 in Łódź where I first presented the ideas one can find in this paper, in a friendly atmosphere. Later on, I developed these ideas in my dissertation which I wrote in the Fall 2019. This paper owes a lot to the heated discussions I had with François Récanati who was my PhD supervisor at the  Institut Jean Nicod. I now think that what is presented here is not as incompatible as it first appears with his theory of metafictional statements. Finally, I would also like to thank an anonymous referee for very important comments which made this paper much, much better.
An assessment of different measures and variants according to the standard EN 16883:2017  [27]  can help to find the best solutions for individual cases of renovation and conservation of historic buildings. As the standard was written in general for all types of measures, the use of the criteria calls for more specific information and guidelines when it comes to particular decisions in terms of ventilation. Within the Interrreg Alpine Space project ATLAS  [26]  and TASK 59  [25] , a so-called HiBERtool (Historic Buildings Energy Retrofit Tool)  [66]  has been developed as a systematic approach to have a first pre-selection of measures, which can work in terms of preservation, buildings physics, and technical feasibility. If the user of the tool wants to be more informed about practical implementation in real cases, links to a special database of historic buildings, the so called HiBERatlas  [81] , will give guidance via realized examples and reference buildings, with detailed documentation, photos, and illustrations.
Acknowledgements This is a revised version of a presentation made at the 4th annual workshop on Historical Epistemology at the Sorbonne in Paris in May 2018. Both authors contributed equally to this article. We sincerely thank  Michael Hampe ,  Daniel Strassberg ,  Tim Reese ,  Amélie Verley ,  Ariane Scheuren  and  Philipp Muller  for their support and their critical comments, for which we are very grateful. Many thanks for helpful inputs go to the participants of the Colloquium of the Chair for Philosophy and the  Chair of History and Philosophy of Mathematical Sciences of ETH Zurich. Lastly, we also thank two anonymous reviewers for their critical and fruitful comments.
Until recently, archeological prospection using LiDAR data was based mainly on expertbased and time-consuming visual analyses. Currently, deep learning convolutional neural networks (deep CNN) are showing potential for automatic detection of objects in many fields of application, including cultural heritage. However, these computer-vision based algorithms remain strongly restricted by the large number of samples required to train models and the need to define target classes before using the models. Moreover, the methods used to date for archaeological prospection are limited to detecting objects and cannot (semi-)automatically characterize the structures of interest. In this study, we assess the contribution of deep learning methods for detecting and characterizing archeological structures by performing object segmentation using a deep CNN approach with transfer learning.
While the lack of ground-truth samples (reference data) is a known constraint in remote-sensing archaeological prospection, two strategies can address this limitation: transfer learning and data augmentation. The first strategy applies a pre-trained source domain model to initialize a targeted domain model  (Weiss, Khoshgoftaar and Wang 2016) , while the second strategy uses transformers that modify input data for training. These strategies are known to improve model performance for small datasets and to increase model generalization  (Shorten and Khoshgoftaar 2019). Defining target classes before using a model is based on one-class approaches that define only a generic "archeological structure" class without dividing it into several sub-classes, assuming that the object characterization can identify types of archaeological structures.
LiDAR-derived visualization image The workflow for processing LiDAR data consisted of several steps (Figure  3 ). The image dataset was derived from a LiDAR point-cloud collected over the area in 2016 (200 km², excluding water area). The raw point-cloud was collected from a bispectral (1064 and 532 nm) Optech Titan LiDAR sensor operated from a fixed-wing vector 1300 m above ground level at a pulse repetition frequency of 300 kHz per channel and a 26° field of view to obtain a nominal point density of 14 points/m². The 3D point-cloud recorded was processed with LasTools (rapidlasso GmbH, Gilchin, Germany) to perform groundfiltering and gridding operations to create a Digital Terrain Model (DTM) at a spatial resolution of 50 cm  (Guyot, Hubert-Moy and Lorho 2018). The terrain model was then used to perform two VTs.
Archaeological annotated reference data The reference dataset consisted of 195 georeferenced polygons that represented footprints of known archaeological sites in the study area. The sites were selected from the regional archaeological reference dataset provided by the Service Régional de l'Archéologie (SRA Bretagne). Only archaeological structures of which topographic characteristics could be perceived on the LiDAR-derived DTM were kept (thus excluding sites related to small-object deposits, such as potsherds, and sites considered as above-ground structures with no influence on the bare-earth topography, such as standing stones). The selected archaeological sites had diverse chronologies, morphologies, and landscape contexts. Their state of conservation also varied greatly, from long-known restored monuments to unexcavated little-documented structures.
The reference dataset included 195 archaeological structures, including 176 funeral structures attributed to the Neolithic, 10 funeral structures attributed to protohistoric periods, 1 motte, 3 promontory forts and 5 ruined windmills. Given the highly imbalanced dataset (overrepresentation of Neolithic structures) and the tasks to evaluate (object detection and segmentation), the annotations were intentionally grouped into a single "archaeological structure" class with no further distinction. The reference dataset was converted from a geospatial format to an annotation one (json COCO) in which each annotation was associated with its corresponding eMSTP tile to be input into the deep CNN architecture. Due to spatial proximity between some archaeological sites, 150 eMSTP images covered the 195 annotations (a mean of 1.
(Semi-)automatic object detection and segmentation We used the open-source implementation of Mask R-CNN developed by Matterport  (Abdulla 2017). The featureextraction phase (backbone) was performed using the Resnet-101 deep CNN initialized with weights pre-trained on the COCO dataset  (Lin et al. 2014)  for the transferlearning strategy. To limit overfitting due to the small training dataset, data augmentation (DA) was activated in the Mask R-CNN workflow using the imgaug library  (Jung et al. 2020). For each epoch, input images were randomly augmented with affine transformations (scaling: 80-120% of the original image size; translation: -20% to 20% of the original image position; rotation: -25° to 25° of the original image orientation). These transformations were defined within limited ranges of scaling, translation and rotation to avoid unrealistic versions of the eMSTP images.
The augmentation process was applied 50% of the time to ensure that the deep CNN received both augmented and non-augmented versions of the training dataset. A   16, 32, 64, 128] (in px)  and RPN_  ANCHOR_RATIOS = [0. 5, 1, 2]  (width/height ratio). The training process was set to fine-tune the head layers of the network (RPN, classifier and mask) (the other layers were frozen) to maximize use of transfer learning within the backbone network. The validation dataset was used to monitor the loss at the end of each epoch. For each experimental configuration, the model was run in inference mode to predict results from the test dataset (20 images). The inference returned a BBOX, confidence score and binary mask (or segment) for each object detected in the images of the test dataset. Model performance was evaluated both statistically and visually.
Predictions were assessed statistically per experiment by using metrics adapted to object detection and segmentation. The AP (average precision) for an IoU (intersection over union) threshold of 0. 5 was used to assess each image and averaged as mAP to assess each dataset of the experimental configurations. IoU refers to the overlapping score of the predicted mask compared to the reference data: area of intersection IoU area of union = (1) AP refers to the area under the precision-recall curve, with: TP precision TP FP = + (2) TP recall TP FN = + (3) with TP and FP and FN the true positives, false positives and false negatives, respectively. mAP@IoU v refers to the mean APs at a IoU threshold v for a given dataset with:  1 1 n v i i mAP@IoU AP n = = (4) with n the number of images i for a given dataset.
Sensitivity of deep CNN to the amount of sample data The overall performances of the deep CNN approach applied to 110 experimental datasets (i. e. 10 datasets × 11 training sizes) were measured using the mean average precision (mAP) metric. The creation of the experimental datasets from 150 images and the evaluation metric (mAP@IoU. 5) used to assess performance are described in the Materials and methods section. The mAP@IoU. 5 ranged from 0. 29 (experiment F train10 ) to 0. 77 (experiment A train80 ), with a mean of 0. 50 and standard deviation of 0. 10 (Figure  6a  and 6b ). The sensitivity analysis of the number of training images available (Figure  6b ) showed that mean mAP@IoU. 5 increased from 0. 37 to 0. 55 as the number of training images increased from 10 to 110, respectively. Mean mAP@IoU.
Area 3: Le Net, St Gildas de Rhuys A train10 predicted that the monument was contained in one (obj. 1) of the three objects detected (Figure  7k ). However, visual analysis revealed that obj. 1 was a large (>3 ha) irregular stain that covered most of the image. The commission error associated with this single object was 99%. A train110 predicted also three objects (Figure  7l ). The passage grave was predicted (obj. 3) with a confidence of 0. 93 and an IoU of 0. 79, indicating that it corresponded to the footprint of the archaeological structure provided by the reference dataset. The other two objects (obj. 1 and 2), which had higher  confidence scores (1. 0 and 0. 96, respectively), are perfect examples of false positives. Obj. 1 is a traffic roundabout with a perfectly circular mound landscape design as the central element, while obj.
The hyperscale topographic position signatures and morphometric descriptors were then used to provide a data-driven description of the predicted objects, which was then compared to the archaeological reference dataset and additional archaeological documentation  (Gouezin 2017; Le Rouzic 1933)  as follows: -Object 1 was a pseudo-circular element 16-20 m in diameter composed of two main topographical units (groups of signatures). The first unit largely dominated its environment at the mesoscale (10-100 m) and, to a lesser extent, macroscale (100-1000 m). The second unit, with only few pixels, had a negative value of topographic position at the micro-/meso-scale, indicating the presence of a pit or trench. This object corresponded to Dolmen A and described its visible inner structures (e. g. corridor, central position of the chamber).
This was mostly due to the conversion of the topographic metric DEVs from relative to absolute values during the calculation of the eMSTP image. Object D was also considered as a false positive. This object, which corresponded to a horse training arena with flat elevation and surrounding embankments, shared shape characteristics with reference data, but not topographic or texture characteristics. The model did not predict any potential structure on the hill located North-East of the area (point E). While the yellow-reddish color in the eMSTP image -associated to the meso-macro dominating topographic signaturecorresponded to the specific position of many tumulus in the study area, the model did not predict any object, which was probably due to the absence of local morphological anomalies. Figure  15  Example of prediction results outside the reference dataset, Goah Lêron area, Carnac (France).
SENSITIVITY OF THE APPROACH AND GENERALIZATION ABILITY The deep CNN approach resulted in high detection and segmentation performances (mAP up to 0. 77) with relatively small training datasets. The largest training dataset contained 110 images, which is small training set for deep learning. This confirms the approach's ability to perform well in archaeological contexts in which sparse reference data are a common limitation. Nonetheless, the model's sensitivity to the images selected for the training and test datasets (with mAP@ IoU. 5 varying from 0. 29 (model E train110 ) to 0. 77 (model A train110 ) for the same number of training images) raises some concerns. A previous study that focused on (semi-) automatic archaeological mapping  (Verschoof-van der Vaart et al. 2020 ) also mentioned this sensitivity.
Some of the variability is related to the metrics used to evaluate detection and segmentation performances, but the main sources of variability seem to be the images selected for model evaluation (the complexity of the test dataset) and training (whether the training dataset is representative and comprehensive)  (Soroush et al. 2020). The deep CNN approach showed adaptability in detecting and segmenting different archaeological structures within the region. However, model training and evaluation were limited to a region that has particular topographic and archaeological characteristics. Most of the archaeological structures contained in the reference dataset have a topographically dominant position (burial mounds, hillforts, wind mills), but their local dominance is highly variable in magnitude and scale.