dataesr/scientific-paragraphs-categorization huggingface dataset:
Skeletal age estimation Bone development was evaluated on three cervical vertebrae following the Cervical Vertebral Maturation (CVM) method described by Baccetti et al. [34]  and computerized by Decocq et al. [36]. The inferior borders of the C2, C3, and C4 vertebrae were analyzed on lateral cephalometric radiographs in addition to their general shape. The ODRADE® cephalometric analysis extension of the OR-THOKIS® software was used to place the eight points mentioned by Bacetti et al. to obtain precise measurements of the three vertebrae.
Statistical analysis Statistical analyses were performed with R Studio® software  (1. 4. 1106 version). P-values less than 0. 05 were considered as statistically significant. Quantitative variables were tested for a normal distribution using the Shapiro-Wilk test and were expressed as means associated with standard deviations. Qualitative variables were expressed as percentages. Spearman's correlation coefficient was calculated to statistically assess the relation between chronological age, dental calcification Reproducibility and repeatability were assessed by forensic sciences students trained by radiologists and orthodontists who analyzed a second time 20% of the total sample within 30 days of the initial analysis. Lin's concordance correlation coefficient (CCC) method was performed to assess the reliability of the methods used and the results were interpreted using the classifications proposed by Partik et al.
7000 and 6650 cal. BP. These data show a regional incision phase before 7350 cal. BP, and thus confirm firstly the existence of the  I-2 incision between 7500 and 7400 cal. BP, as assumed by the Cha07 archive, and secondly, the role of external climatic forcing on the avulsion and palaeochannel formation processes. These signatures are indicative of a rapid geomorphological evolution of the Charef floodplain occurring after a multi-millennial period of stability (the Early Holocene wetland formation  25  ). The channel trajectory at the western edge of the Early Holocene alluvial plain, marked by a SW-NE palaeocourse, evidences a river migration towards the northwest. This migration was not progressive, but rather involved channel avulsion due to very high discharge increases and fast channel aggradation.
This pattern is particularly relevant in the Moulouya basin, where stronger incision during the 7. 6-7. 3 ka RCC Figure  8. Comparison of the Charef record with mid-European high lake levels  38  , the speleothem record from the Wintimdouine cave in the High Atlas 14 (δ  18  O values in grey, and moving average over 13 points following a 5-year resampling in black using Analyseries software), and the main solar proxies: Z-scored δ 14 C values  52  and total solar insolation (TSI, W m -2 ) derived from 14 C and 10 Be radionuclides  37. Ultimately, our record supports the end of deglaciation coupled with orbital-driven decreases in summer insolation and solar activity variations as the main forcings to explain the millennial hydrological instability in the western Mediterranean. Such relationships raise questions about the role of grand solar minima on the hydroclimatic variability since the Early-Mid Holocene transition.
Acknowledgements This study is financially supported by the  Laboratoire d'Excellence Archimède-Programme Investir l' Avenir   ANR-11-LABX-0032-01 -within the framework of the  PALEOMAR  project, and by the  MISTRALS-PaleoMex program , funded by  CNRS-INEE  ( French National Centre for Scientific Research ). B. D. was supported by a grant from the  LabEx ARCHIMEDE. The authors would like to thank  Karl Embleton  for his work in proofreading, the  UMR 5138 Arar  and the  UMS   2572 LMC14  for 14 C measurements in the frame of the ARTEMIS French national service, the  Poznan Radiocarbon Laboratory , and the  Debrecen Radiocarbon Laboratory.
Illuminated manuscripts are essential iconographic sources for medieval studies. With the massive adoption of IIIF, old and new digital collections of manuscripts are accessible online and provide interoperable image data. However, finding illuminations within the manuscripts' pages is increasingly time consuming. This article proposes an approach based on machine learning and transfer learning that browses IIIF manuscript pages and detects the illuminated ones. To evaluate our approach, a group of domain experts created a new dataset of manually annotated IIIF manuscripts. The preliminary results show that our algorithm detects the main illuminated pages in a manuscript, thus reducing experts' search time.
During the Merovingian period, symbolic ornaments appear in the manuscripts' pages and, in the 11 th and 12 th centuries, with the development of artistic centres in France (Benedictine monasteries, including the remarkable Abbey of Cluny in Burgundy), manuscripts sometimes reflect a local style. Illuminations thus became iconographic elements essential in medieval studies and sources of complementary historical knowledge in other fields of study (archaeology, art history, architecture, musicology, or literature). For example, in musicology, medieval iconography analysis brings useful information on the instruments' nature, instruments' organological characteristics, physical characteristics, or playing methods (the research team proposing this article focuses mainly on music in the Middle Ages; therefore, we have developed the first multi-object database in medieval musical iconography: Musiconis -http://musiconis.
However, few digital tools collect and classify these images. Where such tools exist -as in the CNRS Initiale database (Initiale 2021) -this meticulous work has been carried out manually. Unfortunately, it is limited to specific national, regional, and local collections. With the development of the IIIF since 2011, researchers have been provided with massive digital iconographic data, which is a decisive factor in identifying medieval illustrations of various kinds (drawings, illuminations, diagrams). As of 2019, one billion images have become available, and about 300 million of them date from the medieval period. Some individual projects have emerged recently using deep learning and statistical learning techniques using IIIF files  (Moreux 2019; Nakamura 2019; Eyharabide et al. 2020). However, as far as we know, no team or project has yet undertaken a study to use state-of-the-art deep learning methods to find illuminations in medieval manuscripts across IIIF libraries.
When automatically searching for illuminations, the main difficulty lies in separating the text from the images (the background containing neither text nor images must also be considered). This article proposes an approach based on transfer learning that browses a collection of IIIF manuscript pages and detects the illuminated ones. We used the HBA corpus  (Mehri et al. 2017)  for training, a pixel-based annotated dataset released at the ICDAR 2017 Competition on Historical Book Analysis. To evaluate our approach, a group of domain experts created a new dataset of manually annotated IIIF manuscripts. The preliminary results show that our algorithm detects the main illuminated pages in a given manuscript, thus reducing experts' search time and opening up new avenues for the massive exploration of digitized iconographic data.
It offers a unified access to a set of digital data on ancient documents and allows one to view, consult, and query digitized manuscripts, catalogues, and specialized databases on various aspects of the study of the written heritage. Computer research teams have already analyzed several aspects of the automatic analysis of images in digitized documents. The first experiments in the use of AI and IIIF appeared as early as 2017, particularly in countries that adopted IIIF at an early stage (in France, the USA, or Japan, for example). However, our work shows some novelties since it is not limited to a collection or institution. The work proposed in  Schlecht, Carqué, and Ommer (2011)  presents a model-based detector for four types of gestures visualized in images of the Middle Ages. This is a method for learning a small set of templates representative of the gesture variability in medieval legal manuscripts through the application of an efficient version of normalized cross-correlation to vote for gesture position, scale, and orientation.
1). After preprocessing, we trained and validated YOLO on the HBA dataset (Section 4. 2). Then, for each IIIF manuscript manifest, we extracted the URLs of the page's images (Section 4. 3). After evaluating each page's image on our model (Section 4. 4), we merged all the bounding boxes obtained to discover the illuminations (Section 4. 5). Later, for each illumination found on a page, we generated the corresponding IIIF URL with only the Due to its nature, the HBA dataset contains some specificities that could significantly influence the learning process. The first challenge is the images' large and scattered dimensions, with a maximum of 5500 pixels in width and 8000 pixels in height. A second challenge is the HBA dataset's characteristics, including heterogeneity, illustration style differences, and complex layouts (irregular spacing, varying text column widths, margins).
Extracting illustrated decorations §13 Once the labelled dataset is in YOLO format, we describe our dataset parameters and modify the YOLOv5 configuration, which specifies the information on the names and number of classes. In our case, we chose the smallest and fastest base model of YOLOv5, which is yolov5s. During training, the YOLOv5 pipeline creates training data batches with three kinds of augmentations: scaling, colour space adjustments, and mosaic augmentation. We can visualize the training data ground truth as well as the augmented training data. Once the training is completed, we take our trained model and make inference on test images as illustrated in Figure  3(a). For inference, we invoke the adapted weights with a specification of the model confidence. The higher confidence required, the fewer predictions made.
Creating a testing IIIF dataset of medieval decorations and illustrations §19 A group of medieval experts creates an IIIF dataset of medieval decorations and illustrations to test and validate our deep learning methods for identifying illustrations in medieval manuscripts. They selected four manuscripts with the particularity of having illustrations of various shapes, sizes, and characteristics. Their approximate production dates range from 800 to 1300. Table  1  summarizes the manuscripts' characteristics. Although this dataset applies only to a limited number of documents, we selected manuscripts from different libraries to demonstrate that the remote IIIF image analysis system could be applied to various collections. In our case, the documents are held at Gallica (Valenciennes/BnF), the Universitätsbibliothek Basel, the University of Cambridge, and the University of Princeton.
§20 The first manuscript (see Figure  4(a) ), which is kept at the library of Valenciennes -part of the Bibliothèque nationale de France (BnF) collections -is a document mainly containing a book of Apocalypse published in the 9 th century and is probably of Spanish origin. This Latin consists of 40 leaves (27×20 cm) bound with embossed calf binding. This document describes about forty paintings. To each painting corresponds a legend borrowed from the text of the Apocalypse. In addition to the IIIF images, each illustration (i. e. , illuminated initials, marginal illustrations) was also identified by human experts in the CNRS/IRHT Initiale database. §21 The second manuscript is the History of Animals (see Figure  4(b) ), a zoological work written in Greek around 343 BC by Aristotle. The document comprises 152 leaves (25x18 cm) and dates from the 13th century.
In terms of precision, we obtained 0. 67. In the case of the whole image containing a small decoration, the system considers it false positive (FP) because it is simply not annotated by the experts. 34 images are then predicted by our model as images where there are illuminations, including images with small decoration as shown in the Figure  6(d ). Because of its significant impact on the precision for detecting illuminations, a post-processing step can therefore be added to specify the minimum dimensions of illuminations. Additionally, as it can be seen in the example of Table  3  (row Princeton), it should be noted that 70 images (at the exception of 2 among 72 images) are predicted true positives (TP). §29 One of the major challenges for the neural network in this work is identifying illustrations on pages in IIIF format, based on training with the HBA dataset, and transfer learning on reduced IIIF data.
Conclusions and perspectives §31 In this work, we trained YOLOv5 on the HBA dataset to detect illustrations in IIIF medieval manuscripts using transfer learning techniques. We have also shown that the proposed approach has positive results, which can be explained by the nature of the digitized documents themselves (front and back of the document, multicoloured sampling scale for shooting). This work is exploratory and deserves to be deepened. Therefore, we propose several future research perspectives that our team will pursue. First, to test the model on a much larger dataset, we propose to use the data proposed by Biblissima. In particular, that of the Initiale database to measure the accuracy of the model at the pixel level. Next, build a virtual library of illuminations through several libraries, and explore the tools available to annotate these images online.
implements an open-source CNN-based pixel-wise predictor coupled with task-dependent post-processing blocks. It is a very flexible tool that has analyzed illustrations (ornaments), measured using the standard Intersection over Union (IoU) metric. The next year, T. Monnier and M. Aubry (Monnier and Aubry 2020) presented docExtractor, a generic tool for extracting visual elements (text lines, illustrations) from historical documents without requiring any real data annotation. They demonstrated an excellent efficiency using an off-the-shelf solution across several datasets. They relied on a fast generator of rich synthetic documents and designed a fully convolutional neural network, which generalized better than a detection-based approach, using an encoder-decoder architecture combining ResNet with U-Net.
§11 For training YOLO, the 366 selected HBA images must be labelled and annotated with bounding boxes surrounding the illuminations. Therefore, for each image, we randomly generated 10000 bounding boxes of various sizes and assigned them, based on background colour threshold, the appropriate class (graphic, text, the_rest) to the corresponding rectangle in the ground-truthed image. This new dataset was split into a training (80%) and a testing (20%) subset. The training subset contains 293 ground-truthed images (80% of images) and their corresponding annotation files. Each annotation is composed of: the object class (0: graphic, 1: text or 2: the_rest), the x and y centre coordinates, and the height and width of the bounding box (values in the range [0. 0, 1. 0]) as per YOLO specifications.
Thus, overall it can be noticed that with a small training dataset and with the issues affecting test image layout, our model pre-trained on the HBA dataset can detect the specified class in the IIIF manuscript of Valenciennes/BnF. §26 A second implementation is carried out on the Basel manuscript to test our detection system's performance. In this case, we can see that our model can predict 16 of 19 true positives (TP) and detect illuminations with a precision of 0. 62. As presented in Table3(row Basel), our object detector generates the IIIF illumination correctly.
However, upon close examination, there are arguments that would suggest that proper names are not the special lexical items they were made out to be, and that they could enjoy the same lexical characterization as other units. 1. 1. A prominent place in language 11 From a strictly numerical view, proper names occupy a prominent place in language. In their report on the multilingual resource JRC Names 1 , Ehrmann et al. [2016]  describe proper names  (of person, location, organization)  as "an open word class which evolves endlessly". With 700 new named entities entering the dataset weekly, amounting to more than 1. 75 million people identified, 10, 000 organization names as well as about 390, 000 lexical variants registered as of January 2015, proper names are indeed pervasive in everyday language.
In comparison, the OED totals about 600, 000 words (past and present) for the English language, proper names excluded. It has been estimated that in a large expanding dataset of a language, "70% of lexical entries already are proper names, and this percentage continues to increase"  [Hanks 2013: 35-36]. Despite their numerical prominence, or maybe precisely because of it, their lexicographic treatment makes proper names seemingly outside the lexical system of the English language.
Introduction: Proper names and the lexicon -an exposition Lexis, 20 | 2022 valence, arousal and concreteness norms on English compounding, with a noticeable impact on response times across lexical decision and naming. VANLANGENDONCK Willy & VAN DE VELDE Mark, 2016, "Names and grammar", in HOUGH Carole (Ed. ),TheOxfordHandbook of Names and Naming, Oxford: Oxford University Press, 17-38. Jean-Louis, 2001, Pour une lexicologie du nom propre, Doctoral Thesis, Paris : Université Paris-Diderot. Jean-Louis, 2009, « Lexicologie du nom propre et onomastique », Nouvelle Revue d'Onomastique 51 (1), 301-315. Linguistics in Philosophy, Ithaca: Cornell University Press. JRC Names is a cross-language dataset compiling named entities, i. e. , expressions used to refer to individuals that need to be singled out in context.
For people, places and organizations, named entities are virtually reduced to proper names, but various modifiers or key terms like titles, occupation names or places / nationalities are used to retrieve names as they frequently cooccur. The JRC Names dataset was started in 2004, made available to the public in 2011 and has been enriched since. Of all the languages, English has the highest precision rate (96,83%)(Ehrmann,   Jacquet & Steinberger [2016: 4]).
For people, places and organizations, named entities are virtually reduced to proper names, but various modifiers or key terms like titles, occupation names or places / nationalities are used to retrieve names as they frequently cooccur. The JRC Names dataset was started in 2004, made available to the public in 2011 and has been enriched since. Of all the languages, English has the highest precision rate (96,83%)(Ehrmann,   Jacquet & Steinberger [2016: 4]). AUTHORS ÉLISE MIGNOT Sorbonne Université, CELISO (Centre de Linguistique en Sorbonne) elise. mignot@sorbonne-universite. fr MANON PHILIPPE Université Rennes 2, ACE (Anglophonie : communautés, écritures) manon. philippe@univ-rennes2. fr VAXELAIRE VAXELAIRE VENDLER Zeno, 1967, NOTES 1. Introduction: Proper names and the lexicon -an exposition Lexis, 20 | 2022.
, 1995 ). Trace element and LI data were recently published for two Classic Kerma daggers by  Rademakers et al. (2021b)  and for one Classic Kerma artefact by  Odler and Kmo š ek (2020). LI ratios for two Middle Kerma daggers reported by  Young (1996)  are insufficiently precise for provenance analysis (cf. Rademakers et al. , 2021b ) , 3 and elemental analysis of Kerma artefacts is otherwise limited (yet see Dunham, 1943 ; 4  Vercoutter et al. , 1960 ). Masson-Berghoff et al. (2018)  provide further LI data and discussion for the Late Period (roughly coinciding with the Napatan and Meroitic periods at Kerma), expanding on limited earlier analyses for that period (e. g. ,  Fleming, 1982 ). These earlier studies provide essential comparatives to the dataset presented here. Indeed, one of the main issues to investigate is whether copper alloy compositions at Kerma evolved similarly to those in Egypt and to what degree copper provisioning systems were comparable and perhaps interrelated over time.
Artefact ÄMUL 2167 does sit on the edge of the Limassol Forest range but is similar to Kerma copper alloys and New Kingdom "Intermediate copper " from Pi-Ramesse  ( Rademakers et al. , 2017 ). Again, there is no specific evidence indicating a Cypriot origin (for example, analysed Limassol Forest copper ores have high cobalt and nickel concentrations; Stos-  Gale et al. , 1997 ). Nonetheless,  Odler and Kmo š ek (2020 : 155)  assert that it was "made of Cypriot ore. " ÄMUL 2178 is similarly stated to have been "made solely of Cypriot ore, " yet arguments against a Cypriot provenance for a Pi-Ramesse dagger with nearly identical LI ratios are provided by  Rademakers et al. (2017). Such positive attributions are thus in need of further nuance (see also  Rademakers et al. , 2021a ). 11 If links with Cyprus are to be proposed, comparisons between Kerma-period artefacts and datasets on Early and Middle Cypriot (and Late Cypriot I) copper alloys may be more instructive.
Nonetheless, tin concentrations of about 0. 1% to 0. 3% likely reveal the recycling and mixing of tin bronze during this period. It is expected that higher tin bronzes equally existed at Kerma during this period, yet surprisingly little analytical data are currently available to validate this. The blades of two Classic Kerma daggers analysed by  Rademakers et al. (2021b)  are made of low arsenical copper, while  Vercoutter et al. (1960)  report on the analysis of a Classic Kerma dagger (from Grave K. 1620, subsidiary to Tumulus XVI;  Bonnet, 2000 : 16-21 ) made of tin bronze with minor lead and arsenic, and traces of nickel, silver, antimony, and bismuth (concentrations are not specified). Cowell (1987)  further reports on the composition of two (probably Classic) Kerma daggers from the British Museum (EA55442 and EA55443), with the former containing 1.
4% arsenic and 0. 4% tin (its rivet has only 0. 6% arsenic; see observations by  Rademakers et al. , 2021b )  and the latter containing 0. 8% arsenic, 8. 9% tin, and 1. 5% lead. Some Classic Kerma artefacts analysed by  Dunham (1943)  have percentage levels of tin too. It thus appears that Classic Kerma dagger alloy compositions varied significantly, perhaps reflecting differences in functionality, personal expression (see Walsh, 2021 on metal vessels), or alloy availability. Therefore the lack of tin bronzes in the presented Classic Kerma assemblage, which includes small unalloyed copper prills, fragments, and rods, may simply be a sampling artefact. While it is possible that tin was not widely used for smaller object types and was mainly reserved for prestige items (such as daggers), a wider dataset is required to assess this more confidently.
Current data are similarly insufficient to speculate on the supply chain of tin for Kerma (as part of long-distance exchange networks or more local production from Eastern Desert cassiterite; cf. Rademakers et al. , 2018a  ) and its possible disruption over time. While there is a significant time gap between the Kerma and Meroitic periods, for which no analytical data are currently available, arsenic content is observed to decrease over time, with tin bronzes dominating the Meroitic assemblage. This may be explained by the recycling of "old copper " during tin alloying, whereby arsenic is progressively lost over time. However, the very low (less than 0. 1%) arsenic concentrations in several artefacts argue against this and point to the alloying of fresh copper with tin at that time. Indeed, there appears to be no shift towards strongly clustered LI ratios, as would be expected for a system relying heavily on recycling.