dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Maarten Boudry ,  Luke Glowacki ,  Jonathan Gottschall ,  Patrick Hogan ,  Raymond Mar ,  Keith Oatley , and an anonymous reviewer provided helpful comments on earlier versions of this manuscript. This research was funded by a graduate research fellowship from the  National Science Foundation  and, through IAST, by the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010. p. 21.
Human-Crop Interactions: Carpological Data For seeds/fruits, 271 archaeobotanical remains were recorded. Of these, less than 5% were unidentified. Considering the taxonomically identified remains (258 samples), the highest percentage consisted of cereals (74. 42%); 23. 64% of the samples were legumes; and the remaining 1. 94% can be divided into fruits (0. 78%), a fragmentary seed of Cucurbitaceae (0. 39%), and weeds (0. 78%). These results are shown in Table  1  and Figure 4. Most of the analysed findings were concentrated in layers SU 52 (Period A) and SU 1003 (Period B). These two SU have showed a higher concentration of macroremains; in particular the SU 1003 has been interpreted as a fire place in which also cooking pots has been found. The grains do not appear to be subject to particular fragmentation or granulation activities, but many are unidentified due to the high combustion distortion.
It is possible that during Period B the distribution areas of downy oak forest, which are located in hilly areas with thick and deep soils and are very suitable for cultivation (Figure  9 ), were completely deforested to make more space for agricultural activities. It seems less likely that this absence was caused by voluntary selection, and no longer exploiting these types of wood, rather than to a real absence or drastic decrease in these species in the surrounding plant landscape of the 10th-11th c. AD. This hypothesis seems to fit a more intensive agricultural exploitation, as indicated by the high variety of cultivated species recorded in the carpological dataset for the 10th-11th c. AD. The potentially cultivated species represent a very low percentage, consisting of Rosaceae Prunoideae.
From the Piazza Armerina data it was possible to outline the coeval landscape of this settlementa hillside near a watercourse, characterised by pastures, olive groves, and cereal fields. The authors highlight a distinction between a phase in the 10th-11th c. AD, characterised by extensive arboreal coverage, both in terms of olive groves and woods in general, while in the middle of the Norman period (12th c. AD) the pastures extended and tree cover decreased; these changes can be attributed both to a different use of the land and to climatic variations. The zooarchaeological data at Contrada Castro suggest a strategy of exploiting animal resources exclusively based on livestock, and this would seem to be the effect of a well-structured and organised rural economy across Periods A and B. The recorded taxonomic overlap between Periods A and B in this study seems to be indicative of a certain degree of continuity in the exploitation of animal resources over time.
Acknowledgments This study was supported by funds provided by  Bona Furtuna LLC. Angelo Castrorao Barba  thanks the  Spanish MINECO  for the  Juan de la Cierva-Incorporación research fellowship  ( IJCI-2017-31494 ). The archaeological excavation has been carried out under the  scientific direction of the 'Soprintendenza BB. CC. AA' of Palermo thanks  to the support of  Stefano Vassallo  and  Lucina Gandolfo.
CORRECTION The number of sites included in the database is mistakenly reported in the abstract as over 100 when the correct number is 88  [1]. Similarly, the total number of beads is quoted in the abstract as almost 5000 when the correct number is 7852. The same errors occur in the methods section. An updated version of Figure 3  [1] , a screenshot of the application, is published here to reflect the correct number of records in the database and the content of each record. Finally, Narganes Storde Y. M. [2]  is referenced as published in 1995 when the correct publication date is 1999.
Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube.
e. , hydrate minerals) or low-energy electronic transitions (i. e. , Coand Fe-based compounds), which allows for additional and complementary information on the pigment palette used by an artist. The main challenge remains to extract information Sensors 2021, 21, 6150 2 of 11 from the large datasets created (millions of recorded spectra) in this energy domain, where the observed spectral outline typically emerges from multiple overlapping contributions especially in the case of complex mixtures and overlays of paint materials. While innovative data reduction and classification approaches in the visible range are growing to determine and map significant endmembers within hyperspectral dataset, a few works are dedicated to similar applications in the SWIR domain. Principal component analysis (PCA) and fully constrained least-squares (FCLS) spectral unmixing algorithms recently extended the use of multivariate analysis for automatic pigment and binder classification in this energy range  [14] [15] [16].
However, the mapping of determined pigments/binders of interest still results in a challenging task as it necessitates performing nonlinear unmixing on large and complex datasets  [17, 18]. In this context, deep neural networks (DNNs) have recently proven to provide new opportunities in determining the nonlinear mappings between an input and the corresponding output automatically  [19, 20]. Remote sensing and hyperspectral imaging have more particularly benefitted from many DNN applications such as feature extraction, classification, and unmixing. The recent use of a DNN in cultural heritage for pigment identification in the visible range has paved the way to solve similar problems with an unprecedented processing time and accuracy rate  [21, 22]. In this paper, we thus propose to explore the use of DNN models for automatic pigment identification in SWIR hyperspectral datasets.
In order to improve the robustness of the model, an extended training dataset was built. Whereas in previous papers the dataset was constructed from regions of well-characterized paintings  [21]  or from pigment libraries  [20] , here we explore the combined use of various input data, selected from pure pigment pellets, paint mockups, and historical samples. It allows for the training library to incorporate the inherent variability of the dataset, a crucial step to improve the DNN prediction. Paintings from one mockup and one historical thangka are used to test the robustness of the model. Thangkas are sacred paintings depicting a Buddhist deity; their main constituent materials are quite well known and described in the literature (thanks to literary sources  [23, 24] , oral testimonies of modern-day painters but also previous analytical results  [25, 26] ) as cotton cloth or silk, natural glue sizing and a coating traditionally composed of chalk or kaolinite, and several colored layer(s) made of mineral pigments and organic dyes.
Short-Wave Infrared Hyperspectral Imaging (SWIR) SWIR hyperspectral imaging was carried out by a camera (Specim Corp, Oulu, Finland) SWIR 3 equipped with an OLES 30 lens (focal length: 56 mm) and mounted on a motorized translation to ensure the analysis of the painted surface with a working distance of 1. 0 m. The camera is a push-broom imaging device coupled with a cryogenically cooled MCT detector (384 (spatial) × 288 (spectral) pixels), operating in the 1000-2500 nm range with a 12 nm spectral resolution, and a 5. 6 nm spectral sampling. Diffuse illumination was provided by two 20 W halogen lamps, half a meter away from the painting. The dataset was calibrated to apparent reflectance by subtracting a dark image from the collected reflectance data in digital counts, and dividing it by the illumination irradiance (acquired on a 99% reflectance white reference, Spectralon, LabSphere Inc.
Single Point X-ray Fluorescence (XRF) XRF was performed using an in-house instrument, equipped with a Pd anode transmission tube (Moxtek MAGNUM, Orem, Utah) operated at 30 kV and 0. 1 mA (3 W) and a Silicon Drift Detector (active area of 25 mm 2 , X-123FAST SDD, Amptek, Bedford, MA, USA). The beam size was approximately 1. 2 mm for a typical working distance of 1 cm. The system operated with acquisition time of 300 s. For the evaluation of the raw full spectral XRF data, the dedicated PyMCA software  [27]  was used.
DNN Model 2. 5. 1. Overall DNN Workflow A classical workflow to create the model using a neural network trained and tested on an appropriate dataset is established. The pipeline of the approach to produce labeled qualitative pigment maps follows three main steps. First, a large spectral training dataset is prepared, and each input spectrum is labeled with the related pigment class(es) and relative mass ratio(s). Second, the neural network is trained on 80% of the original dataset to predict the pigments present in the input RIS spectra. The remaining 20% of the original dataset is split into two-a validation and a test set. The validation set is used to determine the optimal neural network architecture and for hyperparameter tuning. The test set is used to estimate the overall classification accuracy of the neural network (predictions of pigments present).
Lastly, the neural network prediction of pigments is evaluated on two case painting studies: the entire model system and one historical thangka. The output of the model consists of 11 labels and coefficient maps, one for each of the classes in the training dataset, i. e. , azurite, malachite, minium, kaolinite, vermilion, orpiment, carmine, indigo, calcite, Indian cotton support, and unidentified white paint historical layer. For the label maps, each pixel value of the composite pigment class map is set to 0 or 1, which corresponds to the absence or presence of a class in a determined pixel, respectively. The presence of a pigment in a given pixel is obtained using a threshold of 0. 5 or greater over the probability value determined by the DNN model of a match between the SWIR spectrum and the pigment class.
DNN Input Dataset In this paper, we combine three different types of input data to train and test the DNN model. Data are extracted from (i) single-pigment pellets, (ii) single pigment, 2and 3-pigment mixture paint layers, (iii) the canvas and prepared canvas of the mockup sample, and finally (iv) specific areas of the historical tangka preliminarily characterized by single-point XRF and VIS-RIS. In total, the input dataset represents 12,000 spectra with a number of evenly distributed samples per class.
In the case of pigment unmixing, a second output layer of 11 units is added. It is obtained by multiplying the output layers of the two parallel DNNs for pigment identification and unmixing, and applying a softmax activation function. This output layer corresponds to the estimated coefficient vector α of pigment weight concentrations for the given spectrum. To train the networks proposed for pigment classification and pigment unmixing, we use the binary cross-entropy and the Kullback-Leibler Divergence (KLD)  [29]  loss functions, respectively. The Adam optimizer with default configuration parameter values, e. g. , a learning rate of 0. 001, is used to optimize the objective loss functions. Python utilizing the Keras library  [30]  with Tensorflow  [31]  as the backend is used to define and train the model.
Spectral Angle Mapping Algorithm (SAM) SAM measures the spectral similarity between spectra. The algorithm determines the angle between two spectra, a reference (or endmember) and an experimental spectrum considered as vectors in a space with n-dimensions equal to the number of spectral sampling. For a 3D data cube, SAM compares a matrix of reference spectrum vectors to each pixel vector in the spectral sampling space. As such, for each pixel, a smaller angle value represents a closer match to a reference spectrum  [32]. The spectra used as references in this study are defined as the average per class of the NN input mockup pure pigment layers (Figure  1b ). The spectral angles are computed for each class within all pixels using Spectral Python (SPy)  [33]. The class presenting the smallest spectral angle between its reference spectrum and the pixel spectrum is set to 1, and the remaining classes are set to 0.
Comparison of DNN versus SAM for Pigment Classification and Mapping Tasks The efficiency of the SAM and DNN approaches to classify the different pixels of the mockup sample dataset are compared in Figure  1c  and 1d , respectively. Sensors 2021, 21, x FOR PEER REVIEW 5 of 11 SAM measures the spectral similarity between spectra. The algorithm determines the angle between two spectra, a reference (or endmember) and an experimental spectrum considered as vectors in a space with n-dimensions equal to the number of spectral sampling. For a 3D data cube, SAM compares a matrix of reference spectrum vectors to each pixel vector in the spectral sampling space. As such, for each pixel, a smaller angle value represents a closer match to a reference spectrum  [32]. The spectra used as references in this study are defined as the average per class of the NN input mockup pure pigment layers (Figure  1b ).
Comparison of DNN versus SAM for Pigment Classification and Mapping Tasks The efficiency of the SAM and DNN approaches to classify the different pixels of the mockup sample dataset are compared in Figure  1c  and 1d, respectively. 5 ), azurite  (6) , indigo  (7) , kaolinite  (8) , and ground layer (9)), and 14 mixtures using two pigment types (the mixtures can be split into four groups: (i) 7 mixtures of two colors composed of kaolinite white pigment and a non-white pigment with a 1:2 wt%, (ii) 4 mixtures of two non-white pigments with a 1:2 wt%, (iii) 1 mixture of three non-white pigments with a 1:3 wt%, and (iv) 2 overlays of two single pigment layers); (b) average reflectance spectra of all single-pigment paint layers applied on the prepared canvas; (c) pigment classification maps obtained using SAM; (d) pigment coefficient maps obtained using DNN model.
The approach provides classification with an accuracy of 93%. SAM is then applied for the classification of the entire mockup dataset composed of pure pigments and mixtures. Several limitations of the approach are observed (Figure  1c ). The pure orpiment layers and large portions of the prepared canvas are classified together, and labeled as orpiment. Whereas the add-in of kaolinite pigment results in absorptions at ca. 1390, 1410, 1910, and 2200 nm  [26] , most pigment mixtures with kaolinite are mislabeled, and are assigned to the carmine class. Similarly, for two-and three-pigment mixtures, azurite is the only pigment properly labeled; moreover, in the specific case of azurite and malachite mixtures, pixels are systematically labeled as azurite, malachite being rarely identified in the mixture.
The SAM classification also fails at classifying pure pigment layers from which the reference spectra were not extracted (layers on the right side of the mockup), as such, the pure layer of azurite is labeled as indigo, and the pure layer of malachite is labeled as azurite. Based on these results, SAM provides good accuracy on the area where the endmembers were defined. However, the pigments present in mixtures and other single-pigment layer areas are not properly classified. Thus, the approach requires good a priori knowledge of the expected endmember prior to the classification, and class mapping should present very restrictive threshold values to prevent false-positive classification results. When the DNN model is used to process the full dataset, more representative and accurate classification is obtained (Figure  1d  and Figure  S3 ).
The classification accuracy of the DNN model is estimated to be 98. 1% for the testing dataset containing pure pigments and pigment mixtures. The results provide efficient mapping of pigments of interest, e. g. , azurite and malachite, but also semi-conductor pigments such as minium and orpiment. Tints of colored pigments mixed with kaolinite, pigment mixtures, and paint layer overlays are labeled accordingly to their expected classes. Thus, the DNN classification provides a straightforward and qualitative mapping of the pigment and pigment mixture signatures present within the data cube. In the region from 1000 to 2500 nm, the approach was expected to provide efficient mapping of pigments that present overtones and combination bands associated with vibrational transitions of molecular bonds involving mainly hydrogen, carbon, nitrogen, oxygen, and related functional groups.
The model properly predicts all orpiment-based areas. In the case of the orpiment, a relatively high reflectance factor in the 1500-1900 nm range compared to the 2000-2400 nm energy domain is observed for both the mockup material and historical dataset, and could explain the appropriate labeling of the pigments (Figure  3f , spectrum Y1). Here, again, more work on the absorption coefficient of the pigment in mixtures and layered systems should be performed. For the red areas, two different pigments are identified thanks to p-XRF and VIS-RIS data, namely minium (XRF: Pb-VIS inflection point: 564 nm) and vermilion (XRF: Hg, S-VIS inflection point: 590 nm). The DNN-based mapping is consistent with the location proposed in the visible range (Figure  3b, d ). Minium (Pb 3 O 4 ) is properly identified as the main pigment in the aureole of the central divinity, some clothing parts of the smaller divinities, and in the red decoration surrounding the bottom-right figure.
The model properly predicts all orpiment-based areas. In the case of the orpiment, a relatively high reflectance factor in the 1500-1900 nm range compared to the 2000-2400 nm energy domain is observed for both the mockup material and historical dataset, and could explain the appropriate labeling of the pigments (Figure  3f , spectrum Y1). Here, again, more work on the absorption coefficient of the pigment in mixtures and layered systems should be performed. The SWIR signals in the white and pink areas are similar and present absorption bands at 2275, 2105, and 1930 nm, and a very broad band centered at ca. 1490 nm (Figure  3c , spectrum W1). Their positions are characteristic of cellulose fibers (such as cotton, most probable in the case of Tibetan artworks)  [26]. Vibrational features from the (C = O), N-H, and CH 2 groups of the animal skin glue, which include features near 2041 nm and 2176 nm, can be differentiated  [11].
Conclusions The DNN approach proposed in this study offers new possibilities in the SWIR range to identify and map pigments in complex materials either for unknown mixtures or multilayered systems. Applied to both mockup and historical paintings, the DNN model has been proven to outperform the most common classification technique in the field known as SAM. DNN allows a more representative mapping of the pigments of interest, which results from the multi-labeling proposed by the approach (i. e. , a single pixel can contain several classes) and the multiple inputs from mockup and historical paintings used as a training dataset. This study confirms the potential of SWIR for pigments with characteristic absorption features such as malachite and azurite, even when they are mixed and in degraded systems.
In the case of semi-conductor-type pigments, more research is needed to understand the overall spectral shape/intensity in the SWIR domain, but it is definitely to be considered as it allows efficient mapping of such pigments and brings complementary information to the VIS-RIS spectra, in particular when multi-layered systems are investigated. As recently outlined in numerous studies, combined access to VIS and SWIR imaging modalities improves pigment classification in historical datasets; it is thus expected that neural network models trained on combined datasets will solve more challenging tasks in the near future.
Acknowledgments: The authors are grateful to  Marion Boyer  for providing access to the investigated thangka, and for her help in the interpretation of the results with regard to the materiality of the thangka. The authors would also like to thank  Nicolas Baskiotis  ( MLIA, Sorbonne University ) for his help, insightful comments, and advice on deep learning. Anne Michelin  ( CRC ,  USR 3224 ) is thanked for providing access to the SWIR camera belonging to the  Centre de Recherche sur la Conservation (Paris)  to carry out the analysis of the mockup sample. Finally,  Philippe Walter  is thanked for discussion and advices on SAM calculation using SPy.
constructing the MAPs publication data about the translations was initially sourced from WorldCat and unesCo's Index Translationum, but it soon became apparent that these databases, though large, provide only a very partial account of the field. so the whole research group collaborated in improving the data, searching national and local library catalogues both online and on paper, and also using bookshops: for instance the two (abridged) gujarati translations known to us were found in bookshops by Abhishek Jain. the work is ongoing, as the digital humanities (dh), archival and literary-critical strands of the project are running simultaneously, with a dialogic relationship between them. this is an untidy, and in some ways frustrating method: where the dh researcher prefers a complete dataset to work with, archival scholars and readers are always excited to uncover new things.
nevertheless, our practice has the benefit that a dh intervention can prompt a new archival or literary-critical discovery, and vice versa-a process of interaction that will be described further below. the provisional dataset that underpinned the maps we have so far created (and currently-March 2021-available on our website) consisted of 593 acts of translation into 57 languages. We defined an 'act of translation' as including both the first publication of a new translation and its republication in a different place, for reasons we will explain below; and we did not attempt to distinguish between strict translations and abridgements or freer versions, so long as they remained within the genre of prose fiction. this is both because such categories are historically and culturally contingent (toury, 62) and because we would not have been able to establish our own definition of them without close study of every text in the dataset-an impossibility given the quantities involved.
the number of acts of translation per language in our provisional dataset varied from fifty-six for russian to fifteen languages boasting only one translation each. however, we now know that those figures were in many respects wide of the mark, and we are currently aware of 668 acts of translation into 62 languages. the biggest disparity concerns translations into Chinese: when we made the provisional dataset we knew of forty-two Chinese translations; but as we were writing this article hongtao Wang and a team of researchers at Beijing Foreign studies university completed work that uncovered a further eighty-eight translations. With a total of 130 translations, we now know Chinese to be, by some distance, the language that has most welcomed and remade Jane Eyre. the russian data has shifted in the opposite direction, as it transpired that many texts where the translator's name was not given were in fact reprints of earlier versions.