dataesr/scientific-paragraphs-categorization huggingface dataset:
docx SI 5. Inherited and borrowed correspondence sets for agropastoral vocabulary across the Transeurasian languages. 20_Eurasia3angle_synthesis_SI 5_subsistence. docx SI 5a. Agropastoral vocabulary shared by the Turkic languages 21_Eurasia3angle_synthesis_SI 5a_Turkic. docx SI 5b. Agropastoral vocabulary shared by the Mongolic languages 22_Eurasia3angle_synthesis_SI 5b_Mongolic. docx SI 5c. Agropastoral vocabulary shared by the Tungusic languages 23_Eurasia3angle_synthesis_SI 5c_Tungusic. docx SI 5d. Agropastoral vocabulary shared by the Koreanic languages 24_Eurasia3angle_synthesis_SI 5d_Koreanic. docx SI 5e. Agropastoral vocabulary shared by the Japonic languages 25_Eurasia3angle_synthesis_SI 5e_Japonic. docx SI 6. Archaeological database 26_Eurasia3angle_synthesis_SI 6_E3a Matrix. xls SI 7 Qualitative analysis of the archaeological database 27_Eurasia3angle_synthesis_SI 7_qualitative analysis SI 8 Interpretation of our Bayesian phylogenetic analysis of the archaeological database in Extended data Fig.
2. 28_Eurasia3angle_synthesis_SI 8_Bayesian cultural interpretation SI 9 Early crop remains with direct C14 dates from Northeast Asia. Compiled from published sources and from the radiocarbon database of the National Museum of Japanese History. Radiocarbon dates on rice from the Nabatake site (Saga) are omitted since several of the results from that site published in the early 1980s appear unreliable. 29_Eurasia3angle_synthesis_SI 9_cerealC14. xls SI 10 List of abbreviations used for present-day Eurasian populations 30_Eurasia3angle_synthesis_SI 10_abbreviations SI 11 Sample information for newly-generated ancient DNA data and for co-analyses of published ancient individuals from East Eurasia. 31_Eurasia3angle_synthesis_SI 11_aDNA sample info SI 12 Archaeological context for ancient DNA samples used in this study 32_Eurasia3angle_synthesis_SI 12_site info SI 13 Archaeological interpretation of our ancient DNA analyses 33_Eurasia3angle_synthesis_SI 13_ archaeogenetic interpretation SI 14 Inventory of excavated skeletal remains from Nagabaka 34_Eurasia3angle_synthesis_SI 14_Nagabaka skeletal SI 15 Isotope analyses of the key samples included in this study 35_Eurasia3angle_synthesis_SI 15_isotope SI 16 qpAdm admixture modeling of ancient and modern populations in this study 36_Eurasia3angle_synthesis_SI 16_qpAdm SI 17 Sequencing details and summary of newly generated aDNA from this study 37_Eurasia3angle_synthesis_SI 17_sequencing SI 18 Substitution model Clock model log ML SD 38_Eurasia3angle_synthesis_SI 18 SI 19 BEAST XML files specifying the models, priors, hyperpriors and settings used to run the analyses of the linguistic database 39_Eurasia3angle_synthesis_SI 19_XML files_languages SI 20 Comparison of fit of different models estimating the marginal likelihoods using nested sampling 40_Eurasia3angle_synthesis_SI 20 SI 21 BEAST XML files specifying the models, priors, hyperpriors and settings used to run the analyses of the archaeological database 41_Eurasia3angle_synthesis_SI 21_XML files_cultures SI 22 Results of filtering contaminated samples included in this study, using PCA for contamination control 42_Eurasia3angle_synthesis_SI 22_filtering contamination Supplementary Files.
Acknowledgements The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  646612 ) granted to  Martine Robbeets. We thank  Choongwon Jeong ,  Elena Savelyeva ,  Wayne Lawrence ,  Chuanchao Wang ,  Marta Burri ,  Nikolay Klyuev ,  Irina Zhushchikhovskaya ,  Mark Byington ,  Hiroki Miyagi ,  Yuri Vostretsov ,  Aleksandra Jarosz ,  Jan-Olof Svantesson ,  Maria Levy ,  Julie Lefort ,  Matthew Miller ,  Karina Mishchenkova ,  Elena Perekhvalskaya ,  Irina Nikolaeva ,  Alexander Francis-Ratte ,  Ian Joo ,  Rottar Máté  and  Thomas Pellard  for helping to compile, analyze or interpret data.
Le latin médiéval a surtout employé le terme dans son sens fiscal et censitaire: voir l'entrée descriptio dans la Database of Latin Dictionaries de Brepols. Pour des comparaisons avec des formes plus traditionnelles de description des complexes monastiques, M. Lauwers, Circuitus et figura. Exégèse, images et structuration des complexes monastiques dans l'Occident médiéval (IX e -XII e siècle), dans Lauwers Michel (études réunies par), Monastères et espace social dans l'Occident médiéval, cit. ,.
Materials and methods: In this single-centre study, a 20-year experienced urologist intraoperatively and prospectively examined the surface and section of all kidney stones encountered. Calcium oxalate monohydrate (COM/Ia), dihydrate (COD/IIb) and uric acid (UA/IIIb) morphological criteria were collected and classified to generate annotated datasets. A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones. To explain the predictions of the deep neural network model, coarse localisation heat-maps were plotted to pinpoint key areas identified by the network. Results: This study included 347 and 236 observations of stone surface and stone section, respectively; of all stones, around 80 % exhibited only one morphological type and around 20 % displayed two.
Automatic ESR algorithm A deep CNN was trained to predict the composition of both pure and mixed stones. Used as a multiclass classification model, the deep CNN was a ResNet-152-V2  [18]. The optimizer algorithm for training the deep learning model was Adam (learning_rate=0. 001) [19]. The loss function was a categorical cross-entropy (). The batch size was 8 and 100 epochs were performed. To improve the ability for the network to generalize, the training dataset was expanded through data augmentation. In our implementation, horizontal/vertical flips and affine transformations, including random combinations of scaling (range=0. 3), rotation (range=50°), and translation (range=0. 2 of total width/height) were applied during training. Two networks were built separately: one using the -surface dataset‖ and the other using the -section dataset‖.
Quantitative assessment of automatic ESR For both -surface dataset‖ and -section dataset‖, stones were randomly divided into complementary training (70 %) and testing (30 %) subsets (stratified split/no redundancy). A cross-validation step was repeated 10 times with randomly shuffled combinations for training and testing. The full process was also repeated with different random initialization seeds for the deep CNN algorithm. Average test metrics were reported for each step: accuracy, area under the ROC curve (AUROC), specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), false predictive rate (FPRs) and false negative rate (FNRs). For additional information about these test metrics, see  [21]   [22]. Concerning mixed stones, test metrics were evaluated when at least one of the pure morphologies was predicted (we recall that mixed stones are composed of two pure morphologies in the scope of this study) and when both morphologies were predicted.
Qualitative performance of activation maps Figure  1  and 2 also show image areas where the classification network concentrated attention in surface (figure  1 ) and section datasets (figure  2 ), respectively. Activation maps were overlaid on the digital endoscopic image in order to establish whether the classification model relied on relevant urological regions in the decision-making process. For example, a hot spot was usually observed in (1a) on a mammillary dark-brown area, which is a hallmark of Ia. Hot spots were found on characteristic stone features in 98 % of the correctly classified images (using either surface or section images, see -True positive‖ columns in figure  1  and 2 ). Hot spots outside the stone were found in 33 % and 25 % of misclassified surface and section images, respectively (figure  1d ).
These morphological types cover almost 85% of the most common stones that urologists encounter on a daily practice  [6]. Our datasets of endoscopic images will be supplemented by more pure and mixed stone images in future studies (ongoing at our institution) in order to increase the number of morphologies to be predicted. In addition, the automatic ESR score will likely improve if the network is able to train on a larger set of data. It must be reported that particles flying around in the saline may disturb the morphological stone examination. To properly recognize the colors and textures of the stone surface, it is necessary to wait a few tens of seconds until the saline solution has cleaned the urine in the kidney cavities. Then, once the stone is spitted into two parts, a few seconds are again mandatory before the saline solution has cleaned micro-particles of the stone.
Enhanced high quality images may be obtained from low quality image series using dedicated motion compensated super resolution techniques  [24]. In addition, data augmentation techniques involving simulated blur and motion events may further improve the ability of the network to generalize for motion corrupted endoscopic images  [25]. Any unobserved events/image artifacts during the training step may disturb in turn the predictions of our trained network. In future studies, automated and reliable quality control on the input images must be developed in order to detect potential failure modes of the network. The urologist will then be advised to take LASER-fragmented stones for a post-operative infrared -FTIR -examination in a dedicated laboratory. Processing of both surface and section images provide valuable information about stone morphology for computer-aided diagnosis.
In practice, recognition of the morphological hallmarks in surface images is easier than that in section images (or even at the nucleus), as shown in  [12]  and  [26]. Consequently, our surface dataset was generally more densely populated than our section one (except for mixed Ia+IIIb stones for which section images better reveal the two morphological types). However, the diagnostic performance of the surface dataset was found to be comparable to that obtained in the section dataset (figure  3 , blue cells). On the other hand, the diagnostic performance of the section dataset was better than that obtained with the surface dataset for pure IIIb/UA (see Table  1 ). Surface and section images may thus provide a source of cross-validation of the diagnosis according to both complementary and redundant information.
We believe that paired surface and section images for each stone may be incorporated into the CNN in order to improve the accuracy of the predictions. The annotated datasets must be accurate since any subjectivity in ESR or potential bias of the urologist may be transferred into the network model. A concordance study between endoscopic digital pictures and microscopy may provide confirmed ESR image of stones corresponding to specific aetiologies or lithogenic mechanisms  [12]. In addition to automatic ESR, activation maps could become an important tool to test whether, during the decision-making process, the classification model relied on relevant urological regions. Moreover, our study indicated that a hot spot located outside the stone led to a misclassification. Deep CNNs capable of processing a large number of specific images efficiently are paving the way for automatic ESR on videos, which would further improve the accuracy of classification scores.
ACKNOWLEDGMENT Experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr/). The authors gratefully acknowledge the support of  NVIDIA Corporation  with their donation of a TITAN X GPU used in this research.
Acknowledgements I thank  Camille Bui ,  Eleonora Andriani ,  Samuel Gessner  and  Nicholaus Jacobson  for the very fruitful collective analysis of this manuscript we have done together in 2019-2020. I am also grateful to the referees for their helpful comments and suggestions on earlier draft of the work. The efficient and professional contribution of the language editor and copy-editor deserve also mention. All remaining inaccuracies are mine. Research presented in this article was supported by the  ERC  project  ALFA  ( CoG 723085 ,  PI Matthieu Husson ).
Nanoparticles possess high surface-to-volume ratios that can attach bioactive molecules such as proteins and increase the total protein recovered from microsamples. Protein extracts were quantified with Bradford Assay in the presence of Coomassie blue. The protein recovery results were statistically computed, and the SPSS analysis shows significant (p < 0. 05) increase in protein recovery, above 1. 3 times for NPSiO 2 and above 1. 6 times for MCM-41. The statistical data shows evidence that silica nanoparticles intensify the total protein recovered from paint microsamples. Finally, ELISA was realized on the protein extracts to verify and compare the immunodetection of protein from the paint models with and without the use of silica nanoparticles.
Acknowledgements The authors acknowledge for financial support to ED-ARCHMAT grant agreement number  766311  (Project  ESR2 )  Marie Sklodowska - Curie Innovative Training Networks, Horizon 2020  and  THE SCREAM Project-Touchstone for Heritage Endangered by Salt Crystallization , a Research Enterprise on the Art of Munch, Ref:  ALT20-03-0145-FEDER-031577  and the  City University of Macao endowment for the Sustainable Heritage Chair.
In this study we make use of Extreme Gradient Boosting Trees with the package XGBoost (Chen & Guestrin 2016). A boosting tree classifier fits many weak tree classifiers (similar to Guzmán Naranjo and Bonami Glossa: a journal of general linguistics DOI: 10. 5334/gjgl. 1626 decision trees) and then combines them to form a stronger classifier. The principle is similar to that of Random Forests  (Breiman 2001 ), but while a Random Forest fits many small classifiers randomly, a boosting tree classifier fits many small tree classifiers in a guided manner trying to achieve the best accuracy possible. Our choice of classification method is purely pragmatic. Alternatives like Analogical Modeling  (Arndt-Lappe 2011; 2014; Skousen 1989; 2013) , or the Minimal Generalization Learner  (Albright & Hayes 2002; 2003; Albright 2009) , or other machine learning frameworks such as neural networks  (Bechtel & Abrahamsen 2002; Churchland 1989; McClelland & Rumelhart 1986; Rumelhart et al.
1986) , could be used to the same effect. On a practical level though, boosting trees have several advantages. The main advantage is that boosting trees of this kind can easily handle the type of data in this problem, i. e. , categorical predictors with a large number of different levels, while at the same time being computationally efficient. Simpler models like logistic regression tend to over or underestimate the importance of low frequency levels in this kind of data. Because we want to know whether the model can predict new items instead of just remembering the items it has seen during training, we perform ten-fold cross-validation on every model. This is done by first splitting the dataset into ten groups. The general model is then fitted using nine of the groups as training data, and testing the predictions of the model on the group not used for fitting it.
The process is repeated for each of the ten subgroups. This way prediction on all the datapoints is examined while preventing any kind of overfitting  (Kohavi 1995). Although it is possible to look inside the models and see what each predictor is doing with respect to the output classes, this is a tedious process that is not crucial to our purposes in this paper. We are more interested in knowing how well we can predict the inflection classes of the items, rather than exactly knowing which segments correlate with which classes and how. Instead, we focus on three metrics to evaluate the models: accuracy, no information rate, and kappa score. These metrics are calculated based on a confusion matrix of the model. As an example consider the fictional confusion matrix in Table  4 , exhibiting the performance of a classifier on a dataset of 67 items belonging to three classes A, B, and C.
ě ě~u u All nouns 643 1766 7059 Normalized frequencies 600 600 600 We fit three distinct models to this dataset: a model with just the phonological predictors, collectively labelled 'shape' predictors, a model with just the distributional vectors as predictors, and a model with both. Table  6  reports the performance of the three models. Note that the No Information Rate (NIR) is 1 3 in all cases, as by design the three classes have the exact same type frequency of 600. The overall observation is that all three models perform considerably better than chance, although they do not reach a spectacular level of accuracy. Hence it is clear that assignment of lexemes to one of the three classes is not fully arbitrary. It does not look to be fully predictable either-at least the predictors used in this study are far from ensuring fully accurate prediction.
Only two differences are worth mentioning. First, the two kinds of predictors seem to differ in how they deal with lexemes that are truly overabundant (middle column): the model based on phonology alone has more of a tendency to conclude that they are instances of -ě only, while the model based on distributional vectors alone has more of a tendency to conclude that they are instances of -u alone. The combined model manages to build on both kinds of predictors to achieve better performance on this part of the dataset. How can we explain the patterns of errors we just observed? For this we must distinguish errors on the middle row from errors on the middle column. On the middle row, the errors correspond to cases where the model predicts a lexeme to be overabundant, while it is found   only in one form in the corpus.
c. No exponent with undeclinable nouns. As the description suggests, there are multiple situations of potential overabundance: between -u and -ě, -u and -ovi, -i and -ovi for ordinary nouns; between -ém and -ým, -é and -ý for converted adjectives; and finally, a small number of nouns exhibit fluidity between genders, hard or soft status, or declinable vs. undeclinable status. As a result, we find evidence in the corpus for 8 non-overabundant behaviors as well as 12 overabundant behaviors, as indicated in Table  8. A detailed analysis of this complex and heterogeneous dataset is beyond the scope of this paper. However, it is worth examining the overall performance of a classifier applied to this 20 class system. We thus fit classifiers with similar characteristics to those discussed in Section 3.
one exponent in the corpus. We find that for all 7 formal exponents, but only the most frequent of the 4 informal exponents, namely -ama. This is unsurprising: given the general makeup of our corpus, it is unlikely that a lexeme will be found only with an informal variant. The middle part of the table lists lexemes found in each of the 7 expected combinations of an informal and an formal variant. Finally, the right-hand part lists the few unexpected situations of overabundance, due to hesitations on gender, softness, or declinability. These make up less than 0. 4% of the dataset under examination. Given the general description of overabundance in the instrumental plural above, we do not expect these cases of overabundance to interact with the inflection class system: whether a noun is found in one or two forms in the instrumental plural depends on whether that noun is found in the corpus in both formal and informal contexts.
Our goal in this section is to establish whether such contrasts are general. To this end, we collected from the SYN corpus all 27,768,583 occurrences of a hard masculine inanimate noun in the locative singular immediately preceded by a preposition. We then tabulated how many tokens of each inflectional variant (-u vs. -ě) was found for each of the 21,830 relevant lexemes in combination with each preposition. Then, among the 1733 overabundant lexemes in the dataset, we selected for study the 481 lexemes that are attested in collocation with all five prepositions under examination. Our goal is to establish whether the likelihood of using a locative singular in -ě vs. -u varies across governing prepositions. To approach this question we built a Bayesian binomial model using Stan  (Carpenter et al.
To address this question, we sampled from the dataset described in the previous subsection 400 locative singular noun forms: 100 nouns from the '-u only' class, 100 nouns from the '-ě only' class, 100 -u forms of overabundant nouns, and 100 -ě forms of overabundant nouns. 21 We then built a Bayesian multinomial model predicting proportion of use of each of the five prepositions from the class of nouns. Figure  8  reports the conditional effects of the model, with each subplot corresponding to one of the subclasses of nouns. Observation of the conditional effects suggests the existence of various tendencies grouping the data in both relevant dimensions. On the one hand, the proportion of collocation with three prepositions (o, po and při) is higher for -u nouns than for -ě nouns, irrespective of whether we are talking about one of the two forms of an overabundant lexeme or the only form of a non-overabundant one.
On the other hand, the instrumental plural situation highlights the fact that conditions on overabundance may associate with whole series of exponents rather than individual ones: each Czech noun has two possible forms for the instrumental plural, and the (mostly sociolinguistic) conditions are the same for all nouns, but the identity of the exponents varies depending on the inflection class. This illustrates the deeply paradigmatic nature of the phenomenon of overabundance. That being said, we make no claim as to typological, or even language internal generality. As a case in point, consider the fact that, in the dataset under consideration, we observed a combination of three constrasts: overabundance in the loc. sg is lexically restricted to some corners of the inflection class systems, whereas it is lexically general in the ins.
Note that our model does not directly take into account the preferences of individual nominal lexemes for -ě vs. -u. Unfortunately, because of strong collinearity between predictors, models using both prepositions and nominal lexemes as predictors on the whole dataset consistently fail to converge. A model based on a smaller sample of 100 overabundant nouns gives results that are qualitatively consistent with what we report here, although the effects are not as clearcut.
g. , Tiryns and Mycenae's extramural complexes; see  Shelton 2010). Since the 1960 and 1970s, regional surveys have drawn attention to the broader range of settlement types beyond the palaces  (Bennet 2007 (Bennet , 2013)). Data from these surveys are now becoming available, making comparisons across space and time possible, for example, in Messenia  (Jazwa and Jazwa 2017)  and central East Crete  (Spencer and Bevan 2018). Although mainland Greece appears to lack the settlement complexity of Neopalatial Crete, and despite notable regional variation in the number and distribution of settlements (most with an urban character), settlements in southern mainland Greece and Crete in the LBA were probably hierarchically organized. Studies on demography remain relatively underdeveloped  (Bintliff 2019 ).