dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ka and mtDNAs from the same site with an identical placement. The tree was calculated on 16,528 positions (partial deletion 95%) and with 500 bootstrap iterations using MEGA10  97. To determine the Y-chromosome haplogroups of male individuals, we genotyped the Y-chromosome reads using a Y-SNP list (v. 15. 73) from the International Society of Genetic Genealogy (ISOGG) dataset, ignoring C-to-T and G-to-A transitions on the forward and reverse reads, respectively. This procedure allowed us to manually traverse the ISOGG Y-Haplogroup Tree, checking in a semi-automatic way which positions were covered. This process allowed us to assign an ancestral or derived haplogroup for covered branches, and to make corrections to calls in cases where, for instance, a more derived haplogroup was called because of residual ancient damage (C-to-T or G-to-A mismatches) in terminal read positions at diagnostic SNPs 98 (Supplementary Data 1.
Biological relatedness and population diversity The analysis of biological relatedness was performed by calculating relatedness coefficient (r) based on PMR on the autosomal SNPs (Supplementary Data 1. F and Supplementary Information, section 4). The baseline of each population was determined using the average heterozygosity rate of individuals estimated from pseudo-diploid genotypes (Supplementary Data 1. E). The ROH segments in hunter-gatherer genomes were identified using hapROH  101. As recommended, we analysed individuals with over 400,000 SNPs called on the 1240k panel  101  and we called ROH longer than 4 cM (Supplementary Data 3. B). The effective population sizes (N e ) were then estimated using a maximum likelihood method, after filtering individuals with a signal of close-kin inbreeding (individuals with at least 50 cM of their genome in ROH spanning >20 cM) (Supplementary Information, section 9).
Population genetics analysis The PCA was carried out by smartpca in EIGENSOFT 6. 0. 1  102  , with modern individuals used for calculation and all the ancient individuals projected on the calculated PCs. The "lsqproject: YES" parameter was used to minimize the effect of missing data in ancient individuals. The PCA was calculated with 1379 individuals from 87 western Eurasian modern populations on the 1240k_HO dataset, which was intersected between the 1240k and Human Origins datasets (Supplementary Data 1. K). The MDS analysis showing the genetic affinity among European hunter-gatherers was based on the distance matrix derived from outgroup f 3 -statistics, in the form 1 - f 3 (Mbuti. DG; pop1, pop2) and performed with classical MDS algorithm (cmdscale) implemented in R 3. 5. 1. The hunter-gatherers were grouped based on their geographic origins and dates (Supplementary Information, section 5).
Admixture graphs were reconstructed using qpGraph 6450, with allsnps mode to correct for low-coverage sample and Mbuti. DG set as the outgroup. Admixture events were dated using the ancestry covariance pattern-based DATES 753 program  105  , with a bin size of 0. 1 cM for covariance calculation and the start of exponential fitting at d ≥ 0. 5cM.
West Eurasian PCA showing the genetic positioning of post-LGM hunter-gatherers. Present-day individuals (gray dots) genotyped on the Human Origins dataset are used to define the PCA variation onto which ancient genomes (coloured symbols) are projected. The newly reported individuals with over 15,000 SNPs on the Human Origins dataset are shown in black-outlined and filled symbols, as illustrated in the legend on the right, while representative ancient genomes are shown in outlined symbols, as illustrated in the legend at the bottom of the PCA. Extended Data Fig. 7 | Admixture dates between Oberkassel/Villabruna and ANE ancestries in the oldest individuals from the Sidelkino cluster. The triangles show the average calibrated dates of the tested groups and the dots show the estimated admixture dates with the software DATES 105 using Oberkassel (red dots) or Villabruna (blue dots) clusters as one source and ANE-related individuals as the other source population.
Article Donkalnis human remains and the new radiocarbon dates; the  Italian Ministry of Culture  and  Soprintendenza Archeologia Belle Arti e Paesaggio for the Provinces of Verona, Rovigo , and  Vicenza  for granting access to the human remains of Tagliente 2;  F. Fontana , who carries out investigations of the Riparo Tagliente site (Italy); the  Friuli Venezia Giulia Superintendency  for providing access to the human tooth Pradis 1; and the  Soprintendenza Archeologia Belle Arti e Paesaggio for the Provinces of Barletta-Andria-Trani and Foggia  for providing access to the Paglicci human remains. This project has received funding by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  under grant agreements no. 803147-RESOLUTION (to S.
Data availability The aligned sequences of all individuals with new genomic data reported in this study are available at the European Nucleotide Archive (ENA) under study accession number PRJEB51862. The compiled genotype file used for analyses, including re-genotyped published genomes, has been uploaded at the Edmond Data Repository of the Max Planck Society (https://edmond. mpdl. mpg. de/dataset. xhtml?persistentId= doi:10. 17617/3. Y1KJMF).
Code availability The code for the newly developed ROH based contamination estimate method is available at https://github. com/hyl317/hapROH. A user manual including an installation guide is available at https:// haproh. readthedocs. io/en/latest/hapROH_with_contamination. html. The version used for this work is archived at https://edmond. mpdl. mpg. de/dataset. xhtml?persistentId=doi:10. 17617/3. Y1KJMF.
Extended Data Fig. 6 | 6 West Eurasian PCA showing the genetic positioning of post-LGM hunter-gatherers. Present-day individuals (gray dots) genotyped on the Human Origins dataset are used to define the PCA variation onto which ancient genomes (coloured symbols) are projected. The newly reported individuals with over 15,000 SNPs on the Human Origins dataset are shown in black-outlined and filled symbols, as illustrated in the legend on the right, while representative ancient genomes are shown in outlined symbols, as illustrated in the legend at the bottom of the PCA. Extended Data Fig. 7 | Admixture dates between Oberkassel/Villabruna and ANE ancestries in the oldest individuals from the Sidelkino cluster. The triangles show the average calibrated dates of the tested groups and the dots show the estimated admixture dates with the software DATES 105 using Oberkassel (red dots) or Villabruna (blue dots) clusters as one source and ANE-related individuals as the other source population.
Acknowledgments We warmly thank  Franck Pommereau ,  Colin Thomas  from the  IBISC, University of Evry  and  Maximilian Cosme  from the  AMAP, University of Montpellier  for valuable discussions on a preliminary version of this paper. We also thank  Malo Y. Bourget  from the  CNRS Moulis  for the proof-reading of the paper and his support during this study.
Site and measurement zones The studied site is a private habitation located in the Parisian suburb (4˚5'0" N; 2˚47'24" E). Access was permitted by the homeowner. In the Parisian region, the climate is described as hot temperate (Cf. Koppen and Geiger classification) with the presence of rainfall in driest months. The annual average temperature is 11. 6˚C and annual average rainfall is 693. 6 mm for 1981-2010 period (infoclimat. fr). Two campaigns of measurement were carried out: the first one between May 7th 2020 and June 18th 2020 (spring) and the second campaign between November 5th 2020 and December 30th 2020 (fall-winter). During both campaigns, climatic data (daily mean relative humidity (%), daily mean temperature (˚C) and precipitation (mm)) were collected from the Roissy-en-France meteorological station (infoclimat.
Characterization of the materials: Plaster and tiles The studied wall surface is made out of roughcast that is a mix between coarse sand (millimetric clasts) and concrete (Fig 1A  and 1B ). Presence of CaCO 3 on surface was determined by HCl test. The mineralogy was determined by X-Ray Diffraction (XRD) using a Panalytical Empyrean powder diffractometer equipped with a PIXcel detector fitted with a Cu anode tube (Kα1 = 1. 5406 Å) operating at 45 kV and 40 Ma. The crystalline phases were identifying and quantifying with HighScorePlus using ISCD and COD database.
DNA extraction and NGS of 16S and ITS As significant differences have been observed between vertical spots for spring and winter seasons, the sequencing was focused on these vertical positions. Sampling with sterile swab stick has been realized on each point of BenthoTorch measurement on July 23, 2021. Total genomic DNA was extracted from sampling swab with Fast DNA Spinkit for Soil (MPBio1) according to manufacturer's instructions. DNA extract was amplified and sequenced on Eurofins genomics platform for 16S on V3-V5 region for bacteria and ITS on ITS1 region for fungi. Green algae were not sequenced. Sequencing data analyses were performed on FROGS pipeline. FROGS allow to obtain abundance table of operational taxonomic unit (OTU) and their taxonomic affiliations. Databases used for taxonomic affiliation are Silva 138 for 16S and UNITE 8.
Climatic data The climatic data are presented in Table  1. The total precipitation quantity is 57. 8 mm for the spring period (from May 7th 2020 to June 18th 2020) and 133. 4 mm for the fall-winter period (from November 5th 2020 to December 30th 2020). The average temperature is 16. 4˚C during the spring period and 7. 9˚C during the fall-winter one. During the studied spring season, daily temperature varies between 4. 4˚C and 15. 9˚C and during fall-winter season, between 2. 1˚C and 9. 7˚C. Relative humidity (RH) data also show significant differences for both periods. The percentage of time corresponding to the 3 categories RH < 40% (dry air), 40 < RH < 80% (medium air humidity), and RH > 80% (wet air) was calculated. During spring season, the major part of time (65%) air has a RH between 40% and 80%.
On the contrary, the correlation with temperature is negative. Based on this correlation matrix, a dose-response function (DRF) that correlates meteorological parameters and the biological colonization on vertical surfaces was developed. On horizontal surfaces, contamination can occur. It was also decided to focus on green algae concentration as cyanobacteria concentration is relatively similar. The dataset is given in S1 Table. Then, the green algae concentration as a function of RH (Fig  7 ) was fitted using a power law and only considering the data for days without rain: green algae ½ � ¼ a � RH b ð1Þ Four sets of data were used separately: VA1 and VA2; VA3; VNA4 and VNA5; VNA6. Parameters of Eq (1) are given in Table  6. As green algae concentration is generally higher for rainy days, the rain parameter was added in the DRF: green algae ½ � ¼ a � RH b þ rain c ð2Þ Different values of c were tested to fit the data.
Descriptions were based on the protocols developed by  Roberts (1991)  and  Grauer and Roberts (1996)  for trauma analysis in paleopathology, and interpretation of the mechanism of injury was based on the forensic, medical, and paleopathology literature (e. g. Lovell, 1997; Galloway, 1999; Ambade and Godbole, 2006; Kimmerle and Baraybar, 2008; Court-Brown et al. , 2015; Buckely et al. , 2017; Hahn and Chong, 2017). The left forearm and the left fibula were X-rayed at the Radiology Unit of the Policlinico GB Rossi in Verona (Italy). All skeletal elements were photographed, and surface 3D models of the main skeletal elements were created using a structured-light scanner (DAVID ®  SLS-3, DAVID Group 2007 -2015). The 3D models were uploaded to MorphoSource, a freely accessible online repository for 3D data provided by Duke University, USA (https://www.
Acknowledgements Thanks to the  Department of Diagnostics and Public Health, Policlinico GB Rossi, University of Verona , and in particular to  Maria Cristina Ambrosetti ,  Institute of Radiology. We thank  Sireen El Zaatari ,  Agnes Fatz ,  Gabriel Ferreira  and  Christina Kyriakouli  at the  Institute for Archeological Sciences and Senckenberg Centre for Human Evolution  and.
In order to decrease the statistical error, from five to six craters were made for each fluence value. To obtain measurable crater depths given the axial resolution of the OCT, each crater was the result of 5 pulses between 1 J/cm 2 and 600 mJ/cm 2 , 10 pulses between 600 and 340 mJ/cm 2 , and 15 pulses between 300 and 100 mJ/cm 2. The crater depths were then measured by OCT and plotted as a function of the fluence. On this plot (Figure  12B ), the error bars are 1 standard deviation on from 5 to 6 crater depths. The ablation rate data obtained were fitted to the "blow-off" model. The "blowoff" model is often used to predict the laser ablation rate of organic tissues and polymers in a moderate fluence range  [14, 25]. This very simple model requires several conditions to be valid. First, the Beer-Lambert law must correctly describe the spatial distribution of the laser energy absorbed in the material.
Conclusions In this study, we proposed a comprehensive investigation of the laser removal of dammar varnish from easel paintings using a 4ω ns Nd:YAG laser at 266 nm. The lasermatter interaction was optimized by shaping the intensity profile of the laser used. This resulted in a clear and well-defined square homogeneous beam for the precise ablation of the varnish surfaces. The overlapping rate during the ablation of an extended surface was also studied to obtain a homogeneous residual surface. A calibrated procedure for precise varnish ablation was then proposed. The procedure involves the use of OCT to measure the ablation rate. The data obtained, when fitted to the photoablative "blow-off" model, allow for the controlled micrometric removal of varnish. Laser-induced luminescence was performed on ablated varnished mock-ups to assess the photochemical integrity of the remaining material after laser ablation.
Funding: This research was funded by the  French Ministry Research Program EquipEx PATRIMEX  ( ANR-EQPX-0034 ) and  ESR ESPADON-PATRIMEX  ( ANR-21-ESRE-00050 ). The research work had the support of a grant under the  Decree of the Government of the Russian Federation , No. 220 of 9 April 2010 (Agreement No. 075-15-2021-593  of  1 June 2021 ). Maxime Lopez  thanks the  Fondation des Sciences du Patrimoine (FSP)  for funding his PhD thesis.
During the drilling, we use a sideview system composed of a green diode emitting at 520 nm for illumination and a Basler camera (Basler acA1920-25mu) coupled with a longdistance microscope (InfiniMax KX with MX-6 objective) for real-time imaging. The latter is equipped with a 520 nm-bandpass filter in order to directly visualize through the samples and not being blinded by the processing laser wavelength. The focusing head is mounted on a Z-motorized stage (VP25X, MKS Instruments), whereas the sample is fixed on a motorized XY-monolithic stage (One-XY60, MKS Instruments). The XYZ-stages and the laser gate are controlled by the DMCpro software (Direct Machining Control, Vilnius, Lithuania). The latter includes an autofocus function allowing for a precision in the focus position down to a few microns, reducing the positioning uncertainty and leading to the same conditions for all drilling experiments.
The workstation has a granite base and gantry ensuring a high stability and an excellent repeatability of the experiments. for illumination and a Basler camera (Basler acA1920-25mu) coupled with a long-distance microscope (InfiniMax KX with MX-6 objective) for real-time imaging. The latter is equipped with a 520 nm-bandpass filter in order to directly visualize through the samples and not being blinded by the processing laser wavelength. The focusing head is mounted on a Z-motorized stage (VP25X, MKS Instruments), whereas the sample is fixed on a motorized XY-monolithic stage (One-XY60, MKS Instruments). The XYZ-stages and the laser gate are controlled by the DMCpro software (Direct Machining Control, Vilnius, Lithuania). The latter includes an autofocus function allowing for a precision in the focus position down to a few microns, reducing the positioning uncertainty and leading to the same conditions for all drilling experiments.
g. , images, point clouds, drawings, and so on). When the digital model is modified, further usage, exploitation and updating of crucial information concerning the considered heritage object is often precluded. More adequate systems for the proper documentation and continuity of digital heritage data are sought after, to solve existing problems of accessibility, retrieval and traceability of digital information and models. For this task, while on the one hand the Aïoli platform ( aioli. cloud , Fig. 1  ) offers a collaborative and hybrid approach for the annotation of realitybased models of CH  [ 3 , 4 ] , while on the other hand AI technologies Fig. 1. 2D/3D propagation and information insertion within the aioli platform. Adapted from  [ 16 , 30 ]. e. g. : National Museum of San Matteo, Pisa.
The predictive algorithm is capable of appropriately classifying the entire dataset, based on the identification of architectural feature classes, materials, decay or other conditions over non-previously annotated portions. For this first phase, we leverage the RF (supervised ML) and convolutional neural networks (DL), following the successful workflows already presented in  [ 23 , 24 , 30 , 31  ]. • At a later stage, the classified data resulting from the completion of the first process, whether in 2D (images) or 3D (point  clouds), are suitably exploited to propagate the information to different types of representations. In other words, if classification algorithms are applied to images, the 2D annotation is used to semantically enrich the 3D representation; conversely, if the point cloud is classified, the annotation is propagated backwards from the 3D to the image set.
The annotated portion of the data is transferred to the point cloud by 2D-to-3D projection, to perform semantic segmentation by RF on the 3D datum; the annotation result is finally back-projected on the remaining images. • 2 nd method: here, a reduced annotated image set generated with Aïoli is used to first classify the entire image dataset by RF, and the resulting annotations are transferred to 3D by back projection. • 3 rd method: in this last, more automated case, the operator no longer intervenes in the manual annotation phase and the semantic segmentation of the images is performed using a pretrained CNN-based semantic segmentation model, DeepLabv3 +  [35]  , with ResNet-18  [36]  as the base architecture. The segmentation result is later back-projected to 3D. All methods build on scripts developed in MATLAB, which is the informative basis of the platform.
Traditional ML and DL algorithms are run as MATLAB live scripts, using functions from the Math-Works' Statistics & Machine Learning Toolbox and Deep Learning  Toolbox, and the semantic segmentation of the 2 nd method leverages the Ilastik tool  [32]. Aïoli integrates its information propagation scripts in MATLAB, which are executed by the platform via Docker containers on a dedicated calculation server. However, for this experiment, the developed scripts were tested outside the platform, respecting the data hierarchy, in order to verify the feasibility of the process before considering, for the next stages of implementation, their integration within Aïoli, as this implies solving interface and ergonomic issues for the use of these new functionalities. In the fu-ture, Aïoli aims to include a plug-in system that will allow users to add their own segmentation algorithms.
In detail, the training set is first constructed by annotating (in 2D) some relevant images of the dataset and by associating each annotation with a specific class into which the dataset should be divided. For the San Matteo case, classes of recurring architectural elements are sorted based on the ArCH benchmark  [33]  ; through the manipulation of textual and numerical descriptors, a class name and index are assigned to each annotation as userdefined attributes (e. g. , Fig. 5  ). In particular, to properly characterize the 2D/3D regions of interest so constructed: • Annotations are grouped according to the relative class they belong to, through the project layer control panel ( Fig. 6  ). • A dedicated numerical user descriptor called 'Label' is created to provide a class index for each annotation.
Thanks to the correspondence created in Aïoli between image pixels and 3D points, the manually-annotated training set is propagated from the images to the point cloud. The resulting hybrid 2D/3D training set (Figs. 2-6 , 3-6 ) is used to classify the entire point cloud using the RF. Finally, the annotations on the semantically annotated point cloud are exploited to annotate the remaining image set by backprojection. Again, by recovering the projective relationships between the images and the 3D model, the semantic segmentation is semi-automatically propagated to the remaining part of the image dataset, via back information transfer 2D → 3D ( Fig. 7  ).
From classified image sets to 3D point clouds: training on few images for 2D/3D semantic segmentation and propagation As another possible approach to the semantic enrichment of digital heritage data, the second method also relies on photogrammetric processing. This approach starts with the manual annotation of one or a few images -considered representative of the entire dataset -, on which the user annotates the classes into which the data should be divided. Thanks to this set of sample data, and relying on the identification of common features associated with the images, a RF  [29]  (a supervised ML algorithm) can be trained. As in the first approach, even in this second method the user intervenes by manually annotating a part of the image and the data is classified by a supervised ML procedure; however, the classifier is trained here on 2D information (pixels) rather than on 3D data (point clouds).
From classified image sets to 3D point clouds: pre-trained models and existing labeled sets for deep learning applications The third case is conceptually similar to the second one, as it is still formulated as an image classification problem; however, at this stage, the intention is to investigate the possibility of making the annotation even more automatic by using DL-based semantic segmentation methods. In this case, pre-trained models and existing labeled datasets are considered to increase the automation of the annotation process, to solve the classification problem via transfer learning. The aim is to avoid the manual input of classes by the user and thus the manual labeling of a part of the data (at least in a very first step of the process), if and when a huge amount of training samples already exists and is available via the web.
To this extent, the third method relies on the use of convolutional neural networks (CNNs)  [34]  , trained on existing data that  are already labeled. In this case, a pre-trained image classification network, i. e. , a saved network that was previously trained to extract powerful and informative features from other images, is used as a starting point and adapted to learn a new task on new input images. The neural network is enhanced, via transfer learning, by considering the addition of an existing dataset of labeled images  [35]. For the example below, which illustrates the case of the façade of the School of Engineering in Pisa ( Fig. 9  ), the CamVid dataset  [36]  is considered for training; it consists of a collection of images of street-level views obtained while driving ( Fig. 9 -1  ), and each ground-truth image has its own associated pixel-labeled image for 32 semantic classes including buildings, sky, vegetation etc.