dataesr/scientific-paragraphs-categorization huggingface dataset:
We divided the original dataset providing the DNA methylation values of 26 modern samples for the 122 and 59 CpG sites considered into a training (80%) and a test (20%) dataset. The RF-based classifier was built using the randomForest R package based on the algorithm of Breiman and Cutler. 66 A first preliminary RF model was built for the association between methylation values and castration status, for all sites in the training set, using ntree=200 and default values for mtry and cv. fold. The optimized value for the mtry parameter was determined by exploring mtry values comprised between 1 and the number of sites (122 or 59), and selecting the one minimizing the error rate across all sites. We optimized the ntree number following a similar procedure, considering ntree values between 1 and 200.
The optimal values of mtry=1 (122 sites), mtry=5 (59 sites) and ntree=120 and 60 were then implemented in the final RF model used for prediction. The SVM procedure was implemented in the e1071 package in R,  72  using the svm function in classification mode (castrated males vs. intact stallions), with a linear function kernel and the following parameters: cost=2^(-9) and cross=10. For GKNN, we used the gknn function in e1071, with the manhattan method for distance computation  73  and default parameters. The classifications returned by the three models are presented on an MDS plot and a pie chart reporting individual assignment probabilities (Figure  5  and Table  S4 ).
ACKNOWLEDGMENTS We thank  Claudia Gillet  and  Dr Naveed Khan  for technical assistance. This work was supported by  CNRS  and  University Paul Sabatier  ( AnimalFarm International Research Program , IRP), the  France Genomique ''Grands Projets'' program  (BUCEPHALE), and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  681605 -PEGASUS ). Dr Xuexue Liu  and  Dr Yvette Running Horse Collin  were supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant  agreements  101027750  (HOPE) and 890702 (MethylRIDE), respectively. The Genoscope sequencing platform is partly funded by the  France Genomique National  infrastructure, funded as part of '' Investissement d'avenir'' program  managed by  Agence Nationale pour la Recherche  ( ANR-10-INBS-09 ).
Acknowledgements EGC acknowledges support from grant number  FQXi-RFP-CPW-2019  from the  Foundational Questions Institute and Fetzer Franklin Fund , a donor advised fund of  Silicon Valley Community Foundation (EGC) , and an  Australian Research Council (ARC)   Future Fellowship   FT180100317  (EGC). ADB and CR acknowledge support of the ID#  61466  grant from the  John Templeton Foundation , as part of the " Quantum Information Structure of Spacetime (QISS) " project (qiss. fr). EGC acknowledges the traditional owners of the land at  Griffith University  on which this work was undertaken, the  Yuggera and Yugambeh  peoples.
a b s t r a c t Records of relative sea-level rise for the last deglaciation are mostly limited to coral reef records and geophysical model estimates, but observational data from regions with temperate climates is sparse. We present a new relative climatic and regional sea-level rise record for glacial Termination 1 (Marine Isotope Stages [MIS] 2e1) based on ostracode paleoecology from the upper 8 m of the International Ocean Discovery Program (IODP) Site M0080 collected on Expedition 381, in the Gulf of Alkyonides, eastern Corinth basin of the Mediterranean Sea. Results show a series of major faunal transitions from lacustrine (Ponto-Caspian, Lake Corinth) glacial-age assemblages to fully marine (Mediterranean) interglacial assemblages between 20 and 8 ka. During glacial and early deglacial intervals, the Gulf of Alkyonides was characterized by non-marine lacustrine conditions with episodic sediment input from coastal, saline lake environments.
Acknowledgments We are grateful to the shipboard scientific staff and crew of the  Fugro Synergy , mission-specific platform, for the initial work on IODP 381 Site M0080 and  Holger Kuhlmann  and the  Bremen, Germany IODP  repository for assistance in sampling the core. IM was funded by the  CNR-Short Term Mobility Program 2018  and TMC, ARG, RP, and  MRT  by the  USGS Climate Research & Development Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Evolving and growing need for key services, for utilities, road infrastructure, and housing must all be considered. Such growth can limit the areas available for research, but it may also facilitate it. The same is true of data integration, the complex process whereby diverse sources of information can be brought together in ways that can illuminate the past and facilitate the development of the present. The papers assembled in this volume spring from a larger conference held in association with the Rome Transformed Project. This project aims to use largely non-intrusive methods to understand the development of Rome over eight centuries (from the first to eight centuries CE). It explores 68 hectares of the city in an area that includes, alongside multiple other structures, a 1. 5 km long tract of the Aurelian Walls and 0.
RIS in the visible range (commonly the visible to near-infrared range in commercial cameras, VNIR, from 350 to 1000 nm) may provide information on the electronic transitions of molecules occurring in that spectral range, which can provide insight as to the visible color of some compounds. RIS in the short wavelength infrared range (SWIR; from 1000 to 2500 nm), which is the focus of this research, provides information on the distribution of many chemical species using characteristic frequencies related to vibrational modes of certain functional groups, like O H. 11  However, modeling of the reflectance response of complex material matrices, like paint layers, in the SWIR spectral range is challenging,  12, 13  making the extraction of chemical distribution maps from SWIR RIS data more difficult than for MA-XRF.
Novel methods have also been described for RIS in certain sections of the mid-infrared range (MIR, from 2500 to 25,000 nm), like 2500-3750 nm in  14  and 8064-13,158 nm in,  15  which provides access to even further vibrational modes, such as C H and C O, respectively. However, these have not yet seen widespread use. Currently, most RIS data processing methods used in cultural heritage science revolve primarily around dimensionality reduction methods, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE),  16  or uniform manifold approximation and projection (UMAP),  17, 18  followed by manual endmember selection and a mapping approach, such as spectral angle mapping (SAM). 19, 20  While these methods are commonly used in the analysis of RIS datasets (in particular for the VNIR range, presenting less sharp spectral features), their unsupervised machine-learning nature makes the results often dataset-specific and the manual user intervention steps tend to be rather laborintensive, and difficult to reproduce.
| Ma-XRF MA-XRF data were acquired using an M6 Jetstream MA-XRF scanner equipped with two 60 mm 2 SDD detectors (Bruker Nano GmbH, Berlin, Germany). 31  The Rhodium x-ray tube was operated at 50 kV and 200 μA, and the scan was conducted with a dwell time of 400 ms and a step size of 500 μm for the paint mock-ups and a dwell time of 70 ms and a step size of 450 μm for the painting. This resulted in data sets of 342 Â 892 pixels for the mixture samples, 372 Â 1232 pixels for the layer samples, and 485 Â 374 pixels for the painting. The spectral data were processed using DataMuncher  32  and PyMCA  33  to produce elemental signal intensity maps. To evaluate the potential for semi-quantitative analysis, the arithmetic means of signal intensities of each paint sample were taken and compared to the known pigment areal density of each sample.
To determine the limits of detection (LODs), the critical signal value y c was calculated using Equation  1 , where y BL is the average blank signal and s BL is the standard deviation of the blank signal. 34  The blank signal was taken from areas of the data set where each considered pigment is not present. y c ¼ y BL þ 3s BL ð1Þ For these datasets, it was observed that the relationships between signal intensity and areal density for the single layer 50 μm samples of both pigments were approximately linear. This happens since the samples are thin enough for matrix effects to not play a very prominent role in signal intensity. A linear fit of these 50 μm samples was used to describe these relations and the areal density value at which this linear fit crosses y c was taken as the LOD. An alternative, more traditional method of calculating the LODs for MA-XRF compares the intensity of a fluorescence signal to its spectral background 31 using: LOD ¼ 3 ffiffiffiffiffiffiffiffiffiffiffi N back p N signal c ffiffi t p ð2Þ where N back and N signal are the background and peak intensities, respectively, c is the elemental concentration and t is the measurement time.
| SWIR RIS SWIR RIS data was acquired using a Micro-Hyperspec SWIR 640 hyperspectral camera (Headwall Photonics, Boston, MA), covering a spectral range from 900 to 2500 nm, with a spectral resolution of 6 nm (267 channels) and a custom-built motorized easel (LG Motion, Basingstoke, UK). The samples were illuminated by two Gulliver 30 lamps (Ianiro LED, New Taipei City, Taiwan) with 150 W halogen 3000 K bulbs set at 50 of the surface normal. The camera was operated using an integration time of 120 ms per line. The datasets were calibrated using a Spectralon 99% diffuse white reflectance standard (Labsphere, North Sutton, NH). The mixture samples were too large to be acquired in a single scan, and thus two scans were taken and later stitched together using DataHandlerP. 35  The datasets were scaled to match the resolution of the MA-XRF scans, resulting in datasets of 342 Â 892 pixels for the mixture samples and 357 Â 1232 pixels for the layer samples, with a pixel size of approximately 500 μm.
The painting was also too large for a single scan acquisition, and instead four scans were stitched together, resulting in a dataset of 3250 Â 2170 pixels with a pixel size of $168 μm. To be able to fit the reflectance features, the data needed to be pre-processed. First, the data set was cropped to the desired spectral range (1333-1591 nm). Then, a 5-pixel special median filter perpendicular to the scanning direction was used to get rid of single-pixel measurement artifacts, followed by a Savitzky-Golay filter (window length of 5 and polynomial degree of 2) in the spectral direction to reduce noise and smooth the SWIR spectral profile. The flat-field normalized reflectance signal (R) was then inverted (1-R). Afterward, the specific features were isolated from the background absorption using a variant of the SNIP filter,  36  as shown in Figure  2a.
To account for that, a third component was added for gypsum (Figure  2d ). The component was derived using a method similar to that used for the other components but using a function consisting of 3 Gaussian profiles of which the height and width are calculated using the curve_fit function on a sample spectrum extracted from the painting's data set. For the semi-quantitative analysis and calculation of SWIR LODs, an approach similar to that used for MA-XRF signals was utilized on the SWIR signals, with the mean signal intensities of each paint sample being compared to pigment areal density. To clearly show which areas of an elemental or chemical map are above or below its corresponding limit of detection we devised a non-standard color scheme in that signals above the LOD are shown in a red-orange heat map and signals below in a greyscale.
In both datasets, areas with large concentrations of azurite present a hydrocerussite signal, which means the method is more reliable for the identification of blue verditer than for lead white. Regarding semi-quantification, Figure  7d ,e shows the average sample signal intensity plotted against the areal density of the corresponding pigments. The relationship between the SWIR RIS signal intensity and the pigment areal density in the single-layer samples is much better described by a linear fit than for MA-XRF. The azurite results show a closer correlation than the hydrocerussite results, but this could be due to the previously mentioned azurite misidentifications or due to the overlap between the support and kaolinite features and the hydrocerussite feature. The calculated LODs are 0. 93 mg/cm 2 for blue verditer and 5.
16 mg/cm 2 (14 μm). For azurite, the sky (area 1) has a pigment areal density of 16. 1 mg/cm 2 (the equivalent of a 50 μm layer of pure blue verditer paint), the background trees (area 2) have 12. 4 mg/cm 2 (38 μm), and the background water (area 4) has 20. 1 mg/cm 2 (62 μm). All these areas present the considered pigments in either mixture with other pigments or as underlayers and have average areal densities above the LODs and values within the common areal density values described earlier. The achieved results of the novel SWIR RIS data processing method in both specially prepared samples and historical samples show promise in the general applicability of the method for mapping the considered pigments. The production of reasonable chemical distribution maps using standard fitting features based on reference materials provides an improvement in both reproducibility and automation over the highly datasetspecific methods currently used for RIS data processing.
While pigments often lack reflectance features in visible to near-infrared range (VNIR, 400-1000 nm) that could be fit with this method, the mid-infrared range (MIR, 2500-25,000 nm) is rich in these kinds of features. 40  MIR RIS is not currently a commonly used method in the analysis of cultural heritage objects, but as advances in MIR spectrometers make their use more practical in museum contexts, the method is likely to become more relevant and the development of data processing methods well-suited for the analysis of artist's pigments will be of great interest. However, whilst the method shows promise for its applicability for the identification of other pigments based on prominent and characteristic reflectance features, it is inherently limited to pigments that exhibit such prominent features, and it must be tailored to these specific features.
Acknowledgements. This research was supported by the  International Research and Collaboration Award for Histories of AI : A  Genealogy of Power, Cambridge University  (2020). I am indebted to the organizers and members of this project for their encouragement and insightful exchanges at the project's seminar and summer schools. I'm also grateful to the participants in the research seminar and project on Algorithmic Rationalities in the postwar Soviet Union at the  Higher School of Economics, Moscow  (2020-2). Both have greatly broadened my vision and enriched my understanding of the histories of AI on both sides of the Iron Curtain.
In corpus data, it is costly to identify all-focus sentences and/or to include the information structure of sentences as a factor in the quantitative study. We, therefore, carried out constrained experimental sentence production studies to collect out-of-the-blue sentences. In addition, the experimental approach allows us to test the hypotheses drawn from the corpus data in a controlled manner. Indeed, corpora do not always provide us with balanced datasets, with enough tokens for each contrast to draw reliable conclusions. Furthermore, attested sentences display idiosyncrasies, not only at the discursive level, that may explain a particular order. For example, in our data, on the one hand, we have sentences where the preverbal (definite) object and the verb form an idiom, on the other hand, we have sentences with long and complex postverbal (non-definite) DOs.
Acknowledgments: We would like to express our deep gratitude to  Tork Dalalyan  and  Nvard Vardanyan  from the  Institute of Archeology and Ethnography of the National Academy of Sciences of Armenia , without whose support and hospitality this research could not have been carried out. They offered us the opportunity to conduct our experiments during a one-week stay in October 2019 at the Academy by providing us with an office and by helping us recruit participants. We also thank all participants, undergraduate and graduate students, researchers and professors, who volunteered to take part in our experiments. Our special thanks to  Jorj Abrahamian , whose contribution was invaluable to our online experiment. He helped us post our call for participants on social networks and recruited most of the participants for our second experiment.
We are also thankful to two anonymous reviewers whose comments and suggestions greatly improved the paper. This work is partially supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the program " Investissements d'Avenir " (reference:  ANR-10-LABX-0083 ). It contributes to the  IdEx Université de Paris  ( ANR-18-IDEX-0001 ). This work was completed while the second author held a postdoctoral position successively at  Universität zu Köln and Université Sorbonne Nouvelle.
, 2008; Debono Spiteri et al. , 2016). Hints of similar variation have been observed in late 6 th to mid-5 th millennium BC central Europe, with dairy fat residues recovered from vessels on some settlement sites but not others  (Salque et al. , 2012) , while in the Western Baltic in the 4 th millennium BC, milk consumption occurs alongside continued exploitation of traditional hunter-gatherer aquatic resources  (Craig et al. , 2011). To better apprehend the complex nature of these human-animalenvironment interactions, a multi-proxy approach is essential-each analytical technique either supports or flags inconsistencies within other datasets or reflects different aspects of management strategies. For example, dairying is commonly identified in the archaeological record via mortality profiles based on domesticated ruminant teeth.
The phenomenon of marrow-extracted cattle bone from clay pits has also been observed on other Kuyavian LBK sites (e. g. ,  Marciniak, 2004 Marciniak, , 2005, 188), 188). If indeed the remains of episodic feasting events, this activity may have had a role in maintaining social ties across the region, even sustaining a specific Kuyavian group identity  (Marciniak, 2005; Pyzel, 2009, 77). Wider stable isotope analyses on LBK human and animal bone collagen (Bickle and Whittle, 2013) provides additional context for activity at Ludwinowo, sampling some 617 humans and 366 animals from LBK cemeteries and settlements across a 1,200 km range. From this data there emerges a human-faunal trophic level difference that varies in magnitude across the LBK territory: while the overall impression remains one of dietary consistency, humanfauna 15 N values for eastern sites (within the Danube catchment) are smaller than those for western sites (within the Rhine catchment) and when modelled offer the possibility that western LBK communities were consuming a greater proportion of animalbased dietary protein (c.
Acknowledgments Many thanks to  Iwona Sobkowiak-Tabaka  who provided access to the Ludwinowo archive at the depot in the  Polish Academy of Sciences. David Altoft ,  Borys Banecki , and  Dagmara Miernecka  are thanked for the sampling and/or analysis of some potsherds from this study at the  University of Bristol. We thank  Denis Fiorillo  at the  Muséum National d'Histoire Naturelle  mass spectrometry facilities for isotope measurements on cattle teeth. MR-S thanks the  Royal Society  for funding her  Dorothy Hodgkin Fellowship  ( DHF\R1\180064  and  RGF\EA\181067 ). We wish to thank the  NERC  ( NE/V003917/1 ), the  ERC  ( FP7-IDEAS-ERC/340923 ), the  Higher Education Funding Council for England (HEFCE) Science Research Investment Fund (SRIF) , and the  University of Bristol  for funding GC-MS and GC-IRMS capabilities used for this work.
X-ray Diffraction (XRD) Identi cation of crystalline phases was performed through X-ray diffraction analysis on raw samples (A1-A6) using a Bruker D8 Advance diffractometer with monochromatized CuK α radiation (1. 54 Å) at 40 kV and 40 mA. Synchrotron Radiation (SR) µXRD mapping on micro cross-sections (samples A4, A7, A9 and A12) was performed at the ID13 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble), bene tting from the Historical Materials BAG  [21]. The µXRD branch was used to perform crystalline phase mapping using a 2. 5 × 2. 5 µm 2 beam with an energy of 13 keV. Two-dimensional (2D) diffraction patterns were collected in transmission mode at every pixel of 2D maps and converted into 1D diffractograms by azimuthal integration, using the Jupyter Notebooks based on the PyFAI software package  [22].
In both cases, the identi cation of the crystalline phases was performed using Match! and QualX software, while maps analysis was carried out with PyMca software  [23]. Micro X-ray Fluorescence (µXRF) and X-ray Absorption Near Edge Structure (µXANES) High lateral resolution information on the distribution and speciation of S of samples A7, A9, A5, A4 and A12 was obtained by performing hyperspectral 2D µXRF mapping and µXANES spectroscopy analysis in XRF mode at S K-edge at the scanning X-ray microscope end-station hosted at the beamline ID21  [24, 25]  of the ESRF (Grenoble, France). The energy calibration was performed using reference powders of CaSO 4 ⋅2H 2 O. The incident beam was focused with Kirkpatrick-Baez mirrors down to a beam of 0. 5 × 0. 6 µm 2 (h × v). Hyperspectral 2D µXRF maps were recorded by scanning the energy across the S K-edge employing the following conditions: from 2.
46 to 2. 4675 keV, 2. 5 eV step; from 2. 468 to 2. 485 keV, 0. 5 eV step; from 2. 49 to 2. 53 keV, 2 eV step. An additional map employing a monochromatic primary beam of xed energy at 3. 573 keV was acquired, to excite Cd at its L 3 -edge. The software PyMca was used to t the XRF spectra and to separate the contribution of different elements. S - II and S VI chemical state maps are obtained by extracting the maps obtained at the energy of 2. 473 keV and 2. 482 keV to favour the excitation of the S - II and S VI species respectively and following the procedure described in literature  [15]. XANES spectra from selected ROIs of hyperspectral 2D µXRF maps were also extracted and compared.
Fourier-Transformed Infrared spectroscopy (FTIR) FTIR analyses were performed on raw samples A1-A6 in transmission mode with a Bruker Tensor 27 system in the spectral range 4000 cm - 1 to 400 cm - 1 , with 3 cm - 1 of resolution and accumulating 32 scans. Micro Attenuated Total Re ectance (µ-ATR) FTIR was carried out on points of the cross-sections using a Thermo Nicolet iN10 MX spectrometer equipped with an MCT/A detector cooled with liquid Nitrogen and a Germanium crystal, with a real aperture of 25 × 25 µm². Data were collected in the range 4000 - 750 cm - 1 , at a spectral resolution of 4 cm - 1 and with 256 average scans. Spectra are displayed without correction for ATR.
Raman microscopy Raman spectra were acquired using an inVia Qontor confocal Raman microscope (Renishaw plc, Wotton-under-Edge, UK). The spectra were recorded using the 785 nm laser excitation and 50x long-distance magni cation with 1 cm - 1 spectral resolution and 0. 958 µm spatial resolution using the WiRE 5. 5 software. The acquisitions varied between 5 and 20 seconds, 2 to 118 mW power, 5 and 20 accumulations.