dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , MIS 5a and MIS 4). To estimate, evaluate, and compare Neanderthal niches for these two marine isotope stages, we employ the kuenm  34  and ellipsenm  35  R packages (see "Methods"). To provision the different correlative predictive methods, we use the geographic coordinates of archaeological sites within the study region that can be reliably attributed to either MIS 5a or MIS 4, along with climatic and vegetation variables derived from two paleoclimatic simulations primarily constrained by orbital parameters and that correspond to 80 and 60 ka (see "Methods"). These latter data are employed as proxies representing environmental conditions during MIS 5a and MIS 4, respectively. In our region of study, LTCs that incorporate the Levallois concept dominate MIS 5a, and this concept is replaced by the Quina system in certain areas during MIS 4.
Methods Occurrence data. We mined the published literature to obtain an initial corpus of sites in the study area that contained archaeological assemblages reliably attributed to Neanderthal occupations. Next, we eliminated those for which no chronological attribution was possible or for which such attributions were uncertain. Of the remaining sites, we eliminated those for which an unequivocal attribution is not possible to either Marine Isotope Stage (MIS) 5a or MIS 4 via chronological data or proxies. The relatively coarse resolution of dating methods for this period and imprecision associated with other chronological proxies do not allow one to attribute an archaeological site to a specific portion of a MIS (e. g. early MIS 5a versus terminal MIS 5a). Due to this constraint, we grouped archaeological sites by Marine Isotope Stage (MIS).
There exist site levels associated with chronological measurements whose standard error interval place the level within the latter portion of MIS 5a and the early stages of MIS 4. In such instances, we examined other data (faunal spectra, sedimentological characteristics, etc. ), if available, that could aid in determining whether a level in question could be associated with interglacial or glacial environmental conditions, and the appropriate temporal designation was made. In instances, where no other data were available, the site was excluded from the final occurrence dataset (Supplementary Table  S1 ).
Ecological niche modeling. We used ecological niche modeling methods to characterize Neanderthal niches for MIS5a and MIS4. Data preparation for modeling, model calibration, final models and transfers, post-modeling analyses, and assessment of extrapolation risks were performed in R 3. 6. 1 59 using the kuenm R package  34  , which employs Maxent 3. 4. 1  69  as the modeling algorithm. Inputs were the retained variables produced by the climatic and vegetation simulations along with the geographic coordinates of Neanderthal archaeological sites (Table  S1 ) chronologically attributable to one of the targeted scenarios and situated within the defined study region. We first performed model calibration by testing the performance of 2210 candidate models. We produced these models by using 26 distinct variable sets, which were made up of all unique combinations of more than two of the five climatic and vegetation variables described above  70.
MIS4) allowed for the detection of suitable areas that remained stable between MIS5a and MIS4, as well as regions in which suitability was either lost or gained. Such comparisons also allow one to detect niche expansion, under assumptions of niche conservatism, via the identification of suitable areas during MIS4 that are predicted as non-suitable when projecting models from MIS5a. To complement the kuenm analyses, we examined the degree of overlap in environmental dimensions between ellipsoid envelope ecological niches estimated using the ellipsenm R package  35. We created ellipsoids, in a manner similar to Nuñez-Penichet et al. 74  , using environmental values associated with the archaeological occurrences for each period. We obtained environmental values by summarizing variables using a principal component analysis (PCA) performed with kuenm, and we retained the first three principal components (PCs) to measure overlap (Table  S10 ).
We first performed the PCA on the MIS 5a environmental variables. We then transformed (scaled and centered) the MIS 4 variables to PCs using the PCA loadings obtained for MIS 5a (Table  S11 ), thus making them comparable. We calculated overlap values using the cloud of points produced by the union of both periods' environmental conditions-points that did not fill the entire volume of the two ellipsoids but that represent a relevant background to take into account when performing the measurements, sensu  75. The value of overlap, referred to as the Jaccard index (J)  76  , is the proportion of total points contained within the intersection of the two ellipsoids (A and B); J = A ⋂ B/A ⋃ B. To test for statistical significance, we compared the observed value of overlap against a distribution of overlap values calculated for 1000 pairs of ellipsoids created with data sampled randomly from each period's background (background ellipsoids; sample size = number of archaeological occurrences per period).
Acknowledgements This project received support from the  Ministère de la culture et de la communication  ( PCR: Espaces et subsistance au Paléolithique moyen dans le Sud du Massif  central: contracts  2010-159 ,  2011-61 ,  2014-142 ), the  LabEx Cluster of Excellence " LaScArBx " ( ANR-10-LABX-52 ), the  ANR -funded  NEANDROOTS  project ( ANR-19-CE27-0011-03 ), and the  Région Nouvelle-Aquitaine -funded project  NATCH  ( 2016-1R40204-00007349-00007350 ). Paleoclimatic simulations were produced in the framework of the  TracSymbols ERC  project (FP7/2007-2013, grant no. 249587 ). FdE's participation is supported by the  Programme Investissements d' Avenir IdEx of the University of Bordeaux  and the  Research Council of Norway through its Centres of Excellence funding scheme, SFF Centre for Early Sapiens Behaviour  ( SapienCE , project no.
A total of 556 participants (42. 0%; 231 males) provided enough hair for analysis. Compared to the initial cohort (n=2,120), fewer non-Caucasian (χ 2 =6. 35, p<. 01) males (χ 2 =17. 56, p<. 001) from lower SES families (r pb =-. 09, p<. 001) participated. To account for this selective attrition, we used inverse probability weights in the analyses. The QLSCD protocol was approved by the ISQ and the University of Montreal ethics committees. Informed consent was obtained at each data collection. Prior to the present hair collection, our team conducted a pilot study  (Ouellet-Morin et al. , 2016)  to validate a home-based adaptation to the widely used lab-based collection protocol for which the hair sample is collected by a trained research assistant  (Kirschbaum et al. , 2009). In this pilot study, we invited 34 youth matched on age, sex, and SES to the present populationbased cohort to ask a family member or a friend to collect hair at home and send it back to our laboratory by mail.
now the Canada Research Chair in the Developmental Origins of Vulnerability and Resilience. Mara Brendgen is supported by the Fonds de Recherche du Québec-Santé (FRSQ), Michel Boivin is a Canada Research Chair in Child Development. Sonia J Lupien is a Canada Research Chair in Human Stress and is supported by the Canadian Institutes of Health Research's Foundation grant. Richard E. Tremblay was funded by the Canadian Institute for Advanced Research and the Canada Research Chair in Child Development. Marie-Claude Geoffroy is a Canada Research Chair in Youth Suicide Prevention. The funding sources had no involvement in the interpretation of data, writing of the report, and the decision to submit the article for publication.
Acknowledgments We are grateful to the participants who have given their time to take part in this study. The present study was funded by the  Canadian Institutes of Health Research , the  Social Sciences and Humanities Research Council of Canada , the  Québec Funds for Research (FQRS, FQRSC) , and the  Government of Quebec  through the  Institut de la Statistique du Québec  who collected the data. Isabelle Ouellet-Morin was a  Canadian Institutes of Health Research  new investigator and.
Data Processing and Molecular Sexing Sequencing reads were trimmed and overlapping reads merged using SeqPrep v1. 137 (https://github. com/jstjohn/SeqPrep (accessed on 5 February 2020)). with default settings but with a minor modification in the source code, allowing us to choose the best quality scores of bases in the merged region instead of aggregating the scores following Palkopoulou et al. [15]. We hard masked repeat regions using RepeatModeller v1. 0. 11 [16]. Merged reads were then mapped to this masked cat genome using BWA-ALN v0. 7. 13  [17] , using the slightly modified default settings of deactivated seeding (-l 16,500), more substitutions (-n 0. 01), and allowing up to two gaps (-o 2). DNA analysis and duplicates were removed (separately for each PCR reaction) using a custom perl script that removes reads with identical start and end positions, keeping the first observed such read.
Method for CT Inspection We acquired the internal structure of each body as image data by X-ray CT examination. This allowed us to non-destructively obtain anatomical features of the body such as the skeletal shape and the residual state of soft tissues. In normal X-ray CT examination, crosssectional images are obtained in the axial, sagittal, and coronal directions with respect to the body axis of the target organism. However, since remains found in the permafrost have been subjected to external forces for long periods of time, they display complicated aspects such as partial twisting of the body axis, local changes in posture, and deformation due to pressure. Therefore, in order to obtain the most anatomically accurate axial image possible, the tomographic image direction has to be slightly changed for each part of the body.
For each object, we acquired a continuous tomographic image with as fine a slice thickness and narrow tomographic interval as possible, with respect to the body axis at the time of CT imaging. We constructed a three-dimensional object with equal resolution in the X, Y, and Z directions by interpolating the voxel data. From these volume data, we interpreted the anatomical features of the hard and soft tissues of each individual. This was achieved by creating an appropriate cross-sectional image in the axial, sagittal, and coronal directions, taking into account the twist of the body axis and the change of posture of the body part being analysed. The volume data were converted into a three-dimensional image using a volume rendering method whereby the original anatomical form was displayed as a three-dimensional stereoscopic image, and the image was read while changing the window value and the level value.
Acknowledgments: The authors would like to express their gratitude to  Anna A. Tikhonova ,  Saint-Petersburg, Russia , for the translation of the manuscript from  Russian into English  and to  David W. G. Stanton  for improving English language of the manuscript. Acknowledgements are addressed to the  French Ministry of Culture  and to the scientific team of Chauvet cave ( Carole Fritz ,  Gilles Tosello ).
Introduction Neural networks have been successful in many different areas, such as vision and language. In this research, we explore the possibility of using neural network architectures to relax the fundamental assumption of database management: the processed data is represented as fields of a pre-defined schema. The question, then, is: can data and queries be represented as short natural language sentences, and can queries be answered from these sentences? This research presents a first step in answering that question. We propose NeuralDB, a database system in which updates and queries are given in natural language. The query processor of a NEURALDB builds on the primitives offered by the state-of-the-art Natural Language Processing (NLP) techniques. Realizing the vision of NeuralDB will offer several benefits that database systems have struggled to support for decades.
The first and most important benefit is that a NEURALDB, by definition, has no pre-defined schema. The database's scope does not need to be defined in advance, and any data that becomes relevant as the application is used can be stored and queried. Also, updates and queries can be posed in various natural language forms, as is convenient to any user. Finally, NEURALDB is based on a pre-trained language model that already contains much knowledge. For example, the fact that London is in the UK is already encoded in the language model. Hence, a query asking who lives in the UK can retrieve people who are known to live in London without having to specify an additional join explicitly. Furthermore, using the same paradigm, we can endow the NEURALDB with more domain knowledge by extending the pre-training corpus to that domain.
By nature, a NEURALDB is not meant to provide the same correctness guarantees of a traditional database system, i. e. , that the answers returned for a query satisfy the query language's precise binary semantics. Hence, NEURALDBs should not be considered an alternative to traditional databases in applications where such guarantees are required. Given its benefits, Neural Databases are well suited for emerging applications where the schema of the data cannot be determined in advance and data can be stated in a wide range of linguistic patterns. One of our contributions is to show that state-of-the-art transformer models  [2]  can be adapted to answer simple natural language queries. Specifically, the models can process facts relevant to a query independent of their specific linguistic form and combine multiple facts to yield correct answers, effectively performing a join.
However, we identify two significant limitations of these models: (1) they do not perform well on aggregation queries (e. g. , counting, max/min), and (2) since the input size to the transformer is bounded, and the complexity of the transformer is quadratic in the size of its input, they only work on a relatively small collection of facts. Another contribution is to propose an architecture for neural databases that uses the power of transformers at its core but puts several other components in place to address the scalability and aggregation issues. Our architecture runs multiple instances of a Neural SPJ operator in parallel. The operator results are either the answer to the query or the input to an aggregation operator, which is done traditionally. Underlying this architecture is a novel algorithm for generating the small sets of database sentences fed to each Neural SPJ operator.
Acknowledgements While carrying out the analyses making up this research DCS was financed by the  Fyssen Foundation  through a postdoctoral grant from  UMR 6566 CReAAH. During the period of writing of the manuscript DCS was financed by the  Ministerio de Ciencia, Innovación y Universidades  through the  Juan de la Cierva Research Programme -Incorporación  ( IJCI-2014-20590 ). Ll. Lloveras was financed by a postdoctoral contract ( BP-B00140-2014 ) from the  Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya  and the  COFUND programme (Marie Curie Actions ). We are also grateful for the finance received for the projects  HAR2017-86509  and  SGR2017-00011. The use-wear analyses and the photographic registers were carried out by  DCS  and  ICC  in the laboratories of the  Institution Milá y Fontanals (IMF-CSIC ).
Data Collection Data collection for identifying real-world global decisions was carried out in the INSeaPTION project, a collaborative research project, which had, among others, the aim of co-developing coastal climate services at the global scale. To this end, two workshops (September 2018 and June 2020) were conducted with global stakeholders. The initial workshop (n = 22) included stakeholders from governments (n = 8), and private companies (n = 4), as well as coastal domain experts and researchers (n = 10). Participants were asked to present current decisions they faced that were influenced by climate risks, and these were discussed among the wider group with the aim of distilling key characteristics and information needs. Based on the results of the initial workshop, interview candidates were identified and in-depth semi-structured interviews (n = 6) were conducted to further elaborate decisions and climate services.
The time horizon of such direct investments is thus at least 10-15 years, and typically longer. For specific investments, detailed local information is needed to conduct risk assessments and thus global climate information is not strictly required. However, for the illiquid investment portfolio more broadly, the reinsurer assesses their exposure to particular natural catastrophe scenarios, particularly considering the (re-)insurance policies they have issued. Thus, similar to the examples of multilateral risk pools above, reinsurers require assessments of current and future frequency and severity of hurricanes to assess their overall exposure. A global climate service supporting this decision is generally provided internally by such investors through their own experts making use of experience and datasets from the insurance side of their business.
Re-insurers core business involves assessing physical climate-related risks, and thus they make use of this expertise in risk assessments for investment decisions. The investment arm of a major European re-insurer convenes an internal expert group that is involved in due diligence for every major investment decision in physical assets. Further, where relevant, the due diligence process of the re-insurer makes use of historical databases, e. g. , on storm or flood events, claims, etc, from the insurance side of business to conduct the physical risk assessments. However, the re-insurer identifies a salient gap in their risk assessments. Their current approaches focus only on historical data sets, whereas equity "buy and hold" investors require assessments of how risks will develop under climate change over much longer time horizons, i.
, 2018) , they face several limitations, e. g. , lack of data, lack of access to propriety data on productive assets, etc. , and do not yet comprehensively assess climate risk. Another example involves financial services companies certifying "Green Bonds" linked to adaptation projects. Currently, certification decisions are supported by "Green Bond taxonomies, " which define activities and benefit thresholds that qualify a bond for certification  (EC, 2019). While conducting detailed local assessments for individual projects is too resource intensive to implement at the scale required for the large and growing Green Bond market (CPI, 2019), Green Bond taxonomy activities could be extended by global scale SLR impact assessments. Such assessments would be particularly valuable outside of the US and Western Europe, where data needed to assess coastal risk is often scarce.
FUNDING We acknowledge funding from the project INSeaPTION as part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by BMBF (DE), MINECO (ES), NWO (NL), and ANR (FR) with cofunding by the European Union (Grant 690462). This publication was also supported by PROTECT. This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement no. 869304. This is PROTECT contribution number 24.
Statistical analysis We weighted data for age and sex, according to the Ministry of Health's Shared Directory of Health Professionals to match the HSP population in France. In a sensitivity analysis, we separately weighted data on regional HSP numbers and hospital size (in addition to age and sex) and found no significant differences in the distribution of HSPs' responses compared to that after age-and sexweighting. The following results are thus presented with the latter weighting only. To estimate vaccine hesitancy prevalence among HSPs, we constructed a typology of vaccine-related attitudes, based on items on trust in sources of information about vaccines, perceived usefulness and risks of vaccines, and self-efficacy (Table  A2 , Appendix), adapting a previously published approach used for GPs  [9].
Finally, all data were self-reported and a social desirability bias cannot be ruled out. The field survey nonetheless took place during a period not marked by intense vaccine controversies. Memory bias might have affected the reliability of responses to selfreported vaccination: authors consider that it could slightly overestimate actual uptake  [29] ; it should not, however, have influenced the relation between self-declared uptake and VH. In conclusion, the results of this study contribute to a more complete assessment and understanding of VH among HCPs. Concerns or uncertainties about the safety of specific vaccines were observed in most specialties in hospitals, with the exception of infectious disease specialists and pediatricians. Our results, combined with those of previous studies of HCPs, suggest that hesitant HSPs may have less proactive practices than non-hesitant HSPs.
Acknowledgments We warmly thank the  SPECIVAC Investigating group: Hôpital Cochin, Paris: Odile LAUNAY, Mamadou Salif CISSE. Hôpital Bichat Claude-Bernard, Paris: Xavier DUVAL, Yolande COSTA DE BEAURE-GARD. CHU de Clermont-Ferrand: Sylvie SONNEVILLE. Hôpital Saint-Eloi , Montpellier:  Florence GALTIER ,  Philippe GERAUD. CHU Limoges :  Christine VALLEJO ,  Rachel FROGET , Caroline CAILLE.
, 2017). Whereas several researchers have studied the effect of context on cues to deceit  (Hauch et al. , 2015; Markowitz & Hancock, 2019) , the question remains how well the techniques for a whole population can be tailored to a single person. This is important because practitioners must regularly determine whether a specific individual is being deceptive. Answering this question requires a broad set of statements of which the veracity is known, all made by a single individual. To date, this has proven challenging because the fact-checking efforts needed to acquire such a data set are significant. In the present article, we put language-based lie detection to the test in a unique real-world setting. We analyzed a multitude of statements made by a single individual, the 45th U. S. president.
Method Ethical approval was granted by the internal review board for nonexperimental research of the Erasmus School of Economics, Erasmus University Rotterdam. Data collection. To start, we collected a data set (Data Set 1) of all presidential tweets sent by @realDonaldTrump over the 3-month period from February to April 2018. Data for Study 1 were gathered in May 2018. We examined all tweets posted in the 3 most recent, yet completed,.
Our personalized model outperformed existing models from the literature, thereby highlighting the added value of an individualized approach. months. The choice of 3 months was a deliberate tradeoff between collecting a sufficient number of tweets and the manual labor involved in data screening. In total, 605 tweets were gathered. The Washington Post provided us with a data set comprising fact-checked communications from this period that we matched to our Twitter data file. As a result, we could identify which tweets were deemed factually incorrect and labeled the remainder as correct. We named the obtained variable veracity. Because fact checking may contain an element of subjectivity, we compared the judged veracity of Data Set 1 with the veracity determined by a second factchecking source, PolitiFact (see the Supplemental Material available online).