dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset We merged our final dataset with publicly available datasets of ancient and modern individuals (de Barros  Damgaard et al. , 2018; Feldman et al. , 2019; Fu et al. , 2016; Gamba et al. , 2014; Gonzalez-Fortes et al. , 2017; Gu ¨nther et al. , 2015; Haber et al. , 2017; Harney et al. , 2018; Hofmanovaet al. , 2016; Jeong et al. , 2019; Jones et al. , 2015; Lazaridis et al. , 2014 Lazaridis et al. , , 2016 Lazaridis et al. , , 2017;; Lipson et al. , 2017; Martiniano et al. , 2017; Mathieson et al. , 2018; McColl et al. , 2018; Meyer et al. , 2012; Mittnik et al. , 2018; Mondal et al. , 2016; Olalde et al. , 2015 Olalde et al. , , 2018 Olalde et al. , , 2019;; Pickrell et al. , 2012; Pru ¨fer et al. , 2017; Raghavan et al. , 2014; Rasmussen et al. , 2014; Seguin-Orlando et al. , 2014; Skoglund et al.
Test of recent admixture We tested for signal of recent admixture events applying the recently developed method DATES (https://github. com/priyamoorjani/ DATES) (M. Chintalapati, N. Patterson, N. Alex, and P. Moorjani, personal communication) with the following parameters: binsize = 0. 001, and fit of decay curve from 0. 0045 (lovalfit) to 1 (maxdist) distance bins (all in Morgan units). DATES is based on the algorithm of the roloffp program, which is specifically designed to test admixture in low-coverage ancient genome data where genotypes are typically haploid and missing rate is high  (Narasimhan et al. , 2019). For each individual in the admixed target population, it first estimates the global admixture proportion by simply fitting the genotype vector of the target individual as a linear combination of the allele frequency vectors of the two source populations.
It can also easily accumulate information across target individuals without information loss, by simply using all SNP pairs from all individuals to calculate the correlation coefficient in each distance bin. Estimated times are given in generations assuming 28 years per generation  (Moorjani et al. , 2016). Compared to admixture LD methods such as ALDER  (Loh et al. , 2013)  and Rolloff  (Moorjani et al. , 2011; Patterson et al. , 2012) , which require a minimum number of samples and coverage of the target population in order to estimate LD with precision, DATES can perform on a single sample from the admixed population. We further tested results where DATES detected a signal of admixture by computing two-reference weighted LD and decay fit with ALDER (v1. 03) and roloffp (https://github. com/ DReichLab/AdmixTools/blob/master/src/rolloffp.
Visualizations We produced all graphs in  Rstudio (v1. 1. 383) and Adobe Illustrator CC 2020 (24. 0. 2). Maps were created in QGIS using the Natural Earth dataset. We produced all graphs in  Rstudio (v1. 1. 383) and Adobe Illustrator CC 2020 (24. 0. 2). Maps were created in QGIS using the Natural Earth dataset. We consulted  Breniquet (1996) ;  Greenberg and Palumbi (2015) ;  Roaf (1998) ;  Sagona (2017) ,  Carter and Philip (2010)  and  Wittke (2010)  for the creation of maps in Figure  1. Heatmap of f 4 -statistics of the form f 4  (Mbuti, p2; p3, p4) , where p3 and p4 are all possible pairs of LC-LBA groups from the present study and published contemporaneous (*), and p2 a selection of ancient populations from West Eurasia. f 4 -statistics that do not deviate significantly from 0 (i. e.
ACKNOWLEDGMENTS We thank  G. Brandt ,  A. Wissgott ,  C. Freund , and  R. Bianco  ( MPI-SHH ) for support in laboratory work. We thank  A. Mo  ¨tsch for support in organization and sample management. We thank  Michal Feldman ,  Stephen Clayton ,  Kay Pru ¨fer , and the members of the population genetics group and the  Max Planck-Harvard Research center  for the Archaeoscience of the ancient Mediterranean (MHAAM) group in the  Department of Archaeogenetics, MPI-SHH  for their input and support. We thank  Michelle O'Reilly  and  Hans Sell  for graphics and video production support and  Jason Ur  for consultation on the manuscript. We thank the  French-German National Agency for Research (ANR) , the  Centre national de la recherchescientifique (CNRS) , and the  French Ministry of Foreign Affairs  for funding the excavation of Mentesh Tepe.
Deposited Data Raw and analyzed data (European nucleotide archive) This study ENA: PRJEB37213 Haploid genotype data for 1240K panel (Edmond Data Repository of the Max Planck Society) This study https://nam03. safelinks. protection. outlook. com/?url=https%3A%2F%2Fedmond. mpdl. mpg. de%2Fimeji%2Fcollection% 2Fzdj1HseOUbd1OaEo&data=02%7C01% 7Ctarpin%40cell. com%7C9eeeee0 ef7c44fa29cf808d7f0cd4e15%7C92 74ee3f94254109a27f9fb15c10675d% 7C0%7C0%7C637242637306430 495&sdata=sua73V0iRT5 WUYnKdCdVszof7MzyI5bA3LkT% 2FM6tQv0%3D&reserved=0 Software and Algorithms AdapterRemoval v2. 2. 0 Schubert et al. , 2016 https://github. com/MikkelSchubert/.
METHOD DETAILS Direct AMS radiocarbon dating All individuals with newly-reported genetic data and without direct dating previously performed on them were dated at the radiocarbon dating facility of the Klaus-Tschira-Archa ¨ometrie-Zentrum at the CEZ Archaeometry gGmbH, Mannheim, Germany using a MICADAS-AMS and $1gr of bone material. With a few exceptions, we dated a sample from the same skeletal element that was sampled for the DNA extraction. Collagen was extracted from the bone samples, purified by ultrafiltration (fraction > 30kD) and freeze-dried. Collagen was combusted to CO 2 in an Elemental Analyzer (EA) and CO 2 was converted catalytically to graphite. 14 C ages were normalized to d 13 C = À25& and were given in BP (before present) years before 1950. The calibration was done using the dataset INTCAL13  (Reimer et al.
The deformation of the initial light beam enables the reconstruction of the sample 3D shape using the FA4 (Fringe Analysis) software with the dll HoloMap. (Figure  1a ). The curvature of the sample is measured each for 70 seconds. The maximum temperature in the oven is set to 400°C to induce the smallest amount of structural modifications to the film material. The heating rate is 25°C/min, the maximum temperature is kept for 5min, after which the oven cools down according to its natural inertia. The evolution of the temperature in the oven during the experiment is recorded by the thermocouple next to the samples (Figure  1b ). The slope film ∕ T is measured during the cooling down phase, as the complete sample is under thermal equilibrium due to slow cooling rate.
Setting and recruitment The neuropsychological data analyzed in this study were collected within the EUropean Gene-Environment Interactions (EU-GEI) study, which aims to identify the interactive genetic, clinical, and environmental determinants of schizophrenia  [16]. EU-GEI is a naturalistic prospective multicenter study that consisted of a baseline and up to three follow-up time points (at 6 months, 12 months, and 24 months). Data were collected from May 1, 2010 to August 6, 2015. For the current analyses, only baseline data, that is, at intake into the study, were used. ARMS participants were recruited from 11 Early Detection and Intervention Centers (London, Amsterdam, The Hague, Vienna, Basel, Cologne, Copenhagen, Paris, Barcelona, Melbourne, Saõ Paulo). They were referred to the EU-GEI study by primary health care services, mental health professionals, or themselves or their families.
Assessment of sociodemographic and clinical characteristics Sociodemographic characteristics (e. g. age, sex, ethnicity) were obtained using the modified Medical Research Council sociodemographic schedule  [19]. Current cannabis frequency was assessed with the modified version of the Cannabis Experience Questionnaire  [20]. Data on comorbid affective and anxiety disorders were assessed with the SCID  [18]. Psychiatric medication (i. e. , use of antipsychotics, antidepressants, and sedatives) was obtained using a medical history questionnaire, designed by the EU-GEI group. The general level of functioning was assessed with the modified version of the Global Assessment of Functioning (GAF) scale  [21].
Acknowledgments. We thank all patients who participated in the study as well as the referring specialists. Financial Support. This work was supported by the  European Union (European Community 's  Seventh Framework Program  [grant number  HEALTH-F2-2010-241909 ; Project  EU-GEI ]). M. J. K. was supported by a  Medical Research Council Fellowship  (grant number  MR/J008915/1 ). N. B. -V. was supported by the  Ministerio de Ciencia, Innovación e Universidades  ( PSI2017-87512-C2-1-R ) and the  Generalitat de Catalunya  ( 2017SGR1612  and  ICREA Academia Award ). The French cohort was supported by the  French Ministry  grant ( PHRC AOM07-118 ) and by  Fondation Pierre Deniker (CMLF). B. P. R. was supported by the  Netherlands Organization for Scientific (VIDI  grant number  91718336 ).
2). In order to obtain objective data to compare the different blue variations we used colorimetry. Measurements were carried out on the surface of the wares and yielded different colour groups. Among these groups, nine representative samples (TZ05, TZ07, TZ08, TZ09, TZ10, TZ12, TZ16, TZ19, TZ20) were selected and cut on the exact location of the colorimetric measurements to gain access to the underglaze microstructure. The other five sherds were not cut because of the size of the fragments and the place of the colorimetry measurements. Sampling would have been too damaging for the sherds. The obtained cuts were mounted in epoxy resin and polished to obtain clean crosssections.
Colorimetry Colours of blue and white zones were measured on the surface of the wares using a Konica Minolta CM-700d spectrometer with a 10° angle. For each measurement, data for specular components included (SCI) and excluded (SCE) were taken simultaneously. We used a 3 mm aperture to adapt to small zones. Each zone underwent 3 to 5 measurements. Mean value of measurements are presented in Appendices (Table  A. 3). The colorimetric data was exploited using the CIELab 1976 system. Each colour is defined by three values: a* expresses the green (-100) and red (+100) components, b* the blue (-100) and yellow (+100) components and L* the brightness between black (0) and white (100).
Size and distributions of particles The pigment zones show different size and distribution of pigment particles. On SEM images, we selected an area of 100 x 75 µm and we performed a particle counting using Image J software (Appendice A. 7). A threshold range is set to discriminate the objects of interest from the background, in particular the pigments particles from the glassy matrix and the acicular crystals. Eight samples were analysed (TZ08, TZ09, TZ10, TZ16, TZ19, TZ20). Table  A. 8  shows the number of particles on the analysed zone, the average size of particles and the number of particles depending on their size. In Tian WANG et al. [4] , it appears that the number of cobalt aluminate particles influences the blue intensity, with dark blue decorations containing higher number of particles than light blue zones.
Acknowledgments The authors gratefully acknowledge  Sébastien Moyano  and  Frédéric Neumayer  ( CEMES, Toulouse ) for their technical assistance during the Raman spectroscopy experiments, as well as  Christophe Deshayes  ( CEMES, Toulouse ) for his assistance during the SEM-EDS experiments. They also acknowledge  Guirec Ollivier ,  Luc Robbiola  and the  TRACES laboratory (University Toulouse-Jean Jaurès, Toulouse)  for their assistance with the colorimetric measurements. This work was supported by the  PHC   CAI YUANPEI 2016-2018 program  No. 36708RD.
This software project should make it possible to address the issue of phonemic awareness in its entirety (time-consuming in the case of certain children) as revealed by a study of syllables presented in two different modalities, oral and visual. In this way, young children who will subsequently learn to read are exposed at an early age, in SyllaboCod, to orthographic syllables that will be stored for later reading. The second software program, which is known as Chassymo, was developed to help children who experience difficulties in learning to read by helping them to automate the process of word reading. The aim of Chassymo is to stimulate word reading by focusing on the syllables, which are the ortho-phonological units used by readers, including young readers, once the grapheme-phoneme correspondences are mastered.
The general principle of this software is to present a single written syllable together with its phonological value. Next, a spoken word is heard and the child has to say the position in the word at which the syllable is located. Whatever the child's response, feedback is given by coloring the syllable in the written word: in green if the response is correct, in red if the child's response is wrong. Chassymo has been used with poor readers in first and second grade as well as with secondary school students. The effects of Chassymo on the development of word recognition and reading comprehension in French poor readers during Grade 1 and Grade 2 have been tested in several studies. The results have revealed an effect of graphosyllabic training on silent word recognition, word reading aloud and reading comprehension  (Ecalle, Kleinsz, & Magnan, 2013; Kleinsz et al.
Method Two experiments were conducted at school with newcomer migrant children as a function of their initial French literacy level as assessed by their teachers using their own pedagogical tools. We then assigned the children to one of the software programs depending on their teachers. The children with very little knowledge of the French alphabetic code worked with Syl-laboCod and those who had begun to learn to read and had mastered some phoneme-grapheme correspondences worked with Chassymo. Because of the high level of heterogeneity in this population, we adopted an experimental design that compared the children with themselves by first using a baseline indicating their progress without any specific intervention and then evaluating their progress after a period of training. To summarize, three assessment sessions were conducted at five-week intervals: between t0 and t1 no intervention (baseline), and between t1 and t2 training with the software 4.
Participants Eleven migrant children were involved at the start of this study. However, complete data could be collected for only six of them, schooled in two different classes. Only one child was in France for more than a year but he had already been attending school for a few months (see Tab. 1). Only two children had attended school in their country of origin. Their global academic level in their first language (L1) was assessed by a specialist teacher using academic tools (in mathematics and reading). With the exception of one child, it can be seen that they had a low level in L1. Their French literacy skills at pre-test at t0 were assessed by an experimenter. Their teachers were willing to take part in this study and to implement the training in the classroom as part of their pedagogical activities.
Participants Eleven children (6 boys; 5 girls) participated in this study. They were registered in four schools. With the exception of two young girls and one boy, they were all more than 12 years old (Tab. 2). Eight of them had attended school in their country of origin and three had not attended school before their arrival in France. The experiment was conducted in the same conditions as were used in Experiment 1 but with the Chassymo software, which is known to be efficient with French children. Note: The academic level was assessed from A = «low» to C = «good»; * = the other country of origin was not known.
Training: material and implementation The Chassymo program has been described in detail by  Ecalle et al. (2013). The objective is to stimulate grapho-syllabic processing, which is known to be one of the core components of word reading in French. It should be remembered that with this program, children hear a spoken syllable and then have to say whether or not this syllable is present in the word and also indicate the position of the syllable (initial, median or final position). Feedback indicating the highlighted syllable in the written word is always given, irrespective of whether the answer is right or wrong. Once again, each child sat alone with headphones in front of the screen for one (30 min) or two sessions per day (2*15 mn) for 4 days per week during five weeks, giving a total of 10 hours.
Acknowledgments This article is based upon work from  COST Action CA15209 EURELAX "European Network on NMR Relaxometry ", supported by  COST (European Cooperation in Science and Technology). They acknowledge the Institute of Materials of Paris (IMPC)  for the access of the NMR relaxometers (RELAXOME facility). Mohamed Selmane is gratefully acknowledged for conducting XRD. The NMR relaxometers and the X-ray diffractometer were funded by  Sorbonne Université ,  CNRS  and  Région Ile de France , and is part of The  Institute of Materials of Paris (IMPC). The authours are grateful to  Eva Balaven ,  Santiago Braley ,  Lorie Chahmirian ,  Augustin Lavirotte ,  Laura Maiche ,  Pablo Mas  and  Pauline Sourd , for their technical help. This work was supported by the  Observatoire des Patrimoines of Sorbonne Université (OPUS).
ACKNOWLEDGMENTS This study derives from a workshop funded by the  Wenner-Gren Foundation for Anthropological Research  (Grant number  CONF-788 ) on the theme " Neanderthal Notions of Death  and its Aftermath: The Contribution of New Data from  Shanidar Cave " held at the  McDonald Institute for Archaeological Research at the University of Cambridge, UK , in January 2019. G. B. and E. P. would like to thank in particular.
Infrared images were recorded with a Musis multispectral camera at 1000 nm. Fourier Transform Infrared spectra were recorded with an Agilent 4300 handheld spectrometer in Diffuse Reflectance and an Attenuated Total Reflectance modes. The spectral range was 650-4000 cm -1. X-ray fluorescence spectra were collected with an Artax200 µXRF using a 0. 65 mm collimator, a Mo anode, 50 kV, 700 µA, 30 s, helium flush for the better detection of the low-Z elements and a Mo filter (12. 5 µm). The filter is used in order to separate the signal of sulfur coming from the pictorial and ground layers and molybdenum from the anode material (Mo Lα at 2. 29 keV and S Kα at 2. 31 keV). The spectra were recorded with Spectra 5. 3 software and processed with OriginPro 2015.
Nowadays, cultural heritage is more than ever linked to the present. It links us to our cultural past through the conscious act of preserving and bequeathing to future generations, turning society into its custodian. The appreciation of cultural heritage happens not only because of its communicative power, but also because of its economic power, through sustainable development and the promotion of creative industries. This paper presents SILKNOW, an EU-H2002 funded project and its application to cultural heritage, as well as to creative industries and design innovation. To this end, it presents the use of image recognition tools applied to cultural heritage, through the interoperability of data in the open-access registers of silk museums and its presentation, analysis and creative process carried out by the design students of EASD Valencia as a case study, in the branches of jewellery and fashion project, inspired by the heritage of silk.
), and preserving an intangible heritage (historical weaving techniques) for younger generations. Its research activities and outputs will have a direct impact on computer science and big data management, focusing on searching digital content in heterogeneous, multilingual and multimodal databases. We can affirm that cultural heritage and technology are united in an interdisciplinary gaze that is gaining strength. Digital access can serve as a mediation to integrate citizens in the challenges of heritage and its preservation. This contributes to the understanding of cultural heritage diversity and the socio-cultural reflection connected to historical, artistic and cultural dimensions. Anna María Guasch  [30]  defends that the processes of globalization, such as the consolidation of new technologies, and the changes in communication and access to information have allowed the widening new horizons.
Multi-Task Learning with Training Samples for the Image-Based Prediction of Variables Describing Silk Fabrics This section gives an overview of how we use artificial intelligence for learning abstract knowledge about the images of silk fabrics, and how we can use this knowledge to support experts in cultural heritage and improve understanding of the European silk heritage. Knowledge about silk fabrics, e. g. , about certain properties of such fabrics such as the production timespan or the production place, is often collected in databases (e. g. ,  [67, 68] ) and can usually be accessed via the internet. Whereas such collections can offer information for up to tens of thousands of silk fabrics, the provided information is often very heterogeneous, as it is given as free text. This is rather problematic, because in order to develop and use digital tools for understanding cultural heritage, it is essential that annotations about the properties of fabrics are represented in a homogeneous, standardized way.
Accessibility in this sense will not only be for non-specialists, but also for people with vision or hearing difficulties and intellectual understanding. SILKNOW is working with data coming from several museums that are not homogenized. On the one hand, collections vary in size and type of collection. We are working with huge databases (as shown in Table  1 ) such as the Victoria and Albert Museum (VAM) or the Metropolitan Art Museum (MET) and the Museos estatales (CERES) Spanish museums database, but we are also working with smaller collections such as OADI-Osservatorio per le Arti Decorativi in Italia. On the other hand, these collections are not homogenized, they correspond to different typologies such as Decoratif Art Museums (MAD, VAM, Rhode Island School of Design -RISD-) and the Fine Art Museum (MET), as well as textile museums (IMATEX -CDMT Terrassa-, GARIN).
Finally, we are working with national databases (Joconde, CERES-MCU, UNIPA). We are working with a total of 38,380 records and 67,417 images. In the next table, there is a relation between museums and their records and images, for SILKNOW we only took those records that were useful for the project, which includes silk fabrics from the 15th to the 19th century. Due to record heterogeneity, data quality varies according to the degree of specialization in each museum. For example, it is not the same when a collection corresponds to textile museums where conservators are highly specialized in several fabrics, designs and weaving techniques to when a collection corresponds to a small ecclesiastical museum (e. g. , those coming from OADI). In this sense, quality also differs not only in the way of cataloguing (e.
g. , an absence of standardization in dates and techniques due to the lack of controlled vocabularies and different fields when writing a catalographic records, etc. ) but also in the image quality. Another challenge is the fact that not all the collections are open access collections. In this sense, data in their catalogues are the raw material, not only for a project like ours but for any serious digital development in heritage institutions. Although some large museums are leading the way (like the Metropolitan) by making large amounts of data about their collections freely available, in other cases, it is government agencies that are collecting information on some or many collections, such as CERES. In SILKNOW, working mainly with small and medium-sized museums, we are finding a whole range of possible situations: from the absolute lack of interest in sharing anything to an absolute data cession.