dataesr/scientific-paragraphs-categorization huggingface dataset:
Geolocation of the pixels The cameras were first corrected from distortion caused by the lens  (Brown, 1966). Ortho-projected images were then generated using a fully automatic processing chain combining commercial off-the-shelf software used for photogrammetric engineering (Agisoft Photoscan, WWW2) and algorithms developed specifically. The process included the identification of ground control points, matching points between the several overlapping images, bundle adjustment to affine geometry of the shot, reconstruction of surface geometry (digital surface model, DSM) and re-projection of images on this DSM  (Hartley & Zisserman, 2004). Ground control points were manually added to the scene before the bundle adjustment, allowing the mosaic and the ortho-photos to be aligned over the ESUs.
Geometric accuracy A dedicated study was carried out to evaluate the absolute accuracy of the geolocation of each pixel. A 13 ha, bare soil agricultural plot in the Versailles region (48°51′ N, 1°51′ E) was scanned by the UAS (Fig. 7a ). Twenty three ground control points (GCP) were setup in the field (Fig. The location of each of these 23 GCPs was measured using a Trimble Pathfinder Power Differential Global Positioning System (GPS). The antenna was fixed on a 2 meter high telescopic rod and a circular level vial that ensures good vertical alignment. At each GCP, GPS positions were averaged during 1 min. To improve the accuracy of the GPS coordinates collected in the field, postprocessed differential correction was made with the GPS Pathfinder Office software (WWW4) by comparing GPS data with base data collected at reference stations (Global Navigation Satellite System (GNSS) permanent network).
Acknowledgments This research was supported by the  Airinov Company, INRA ,  Climate Knowledge  and  Innovation Communities and AgriUAV  ( 32-572 ) project. Aleixandre Verger  is the recipient of a  Juan de la Cierva postdoctoral fellowship  from the  Spanish Ministry of Science and Innovation. The authors would like to thank  P. Burger  for providing the ground measurements of the Auzeville experiment.
Fig. 5A  presents the TL plots obtained for aliquots powdered and heated at 150 °C, 250 °C, 300 °C, 350 °C, 400 °C and 450 °C for one hour; TL curves of a natural material (non-heated aliquot) is also displayed. The natural signal provided the strongest intensity of the whole dataset. For samples annealed at 400 °C and more the intensity of the residual TL signal is negligible compared to the natural signal. Therefore, all radicals and defects have been denatured or emptied respectively. Each plot appears composed of at least two separate peaks. The 150 °C curves clearly show the disappearance of the first peak while the second peak rises. However, it should not be assumed that the intensity difference between the natural curves and the 150 °C curves can be attributed to the disappearance of the R-CH2.
Acknowledgements We would like to thank  Professor Gérard Villeneuve  supervisor of this work at the time, and who passed away several years ago. Gege, since, has been greatly missed by friends and colleagues in Bordeaux and Arcachon were he was living. This is the last work he supervised, before retiring and then sadly a few months later disappearing. We will always remember his kind nature, his gentle behaviour and his great sense of humour. We are also grateful to  Professor Rainer Grün  for his advice and input in the early stage of the manuscript. We would like to thank the  Institutions in Bordeaux (Bordeaux 3 University ,  CNRS ,  Région Aquitaine ) and  Southern Cross University  for supporting part of this work. We would like to thanks also the INRAP archaeologists, especially  Laurence Bourguignon , for providing samples and maps.
Our results show that most of these Bantu-speaking populations are genetically very homogenous, with no genetic division between speakers of Eastern and Western Bantu languages. Most of the mtDNA diversity in our dataset is due to different degrees of admixture with autochthonous populations. Only the pastoralist Himba and Herero stand out due to high frequencies of particular L3f and L3d lineages; the latter are also found in the neighboring Damara, who speak a Khoisan language and were foragers and small-stock herders. In contrast, the close cultural and linguistic relatives of the Herero and Himba, the Kuvale, are genetically similar to other Bantu-speakers. Nevertheless, as demonstrated by resampling tests, the genetic divergence of Herero, Himba, and Kuvale is compatible with a common shared ancestry with high levels of drift, while the similarity of the Herero, Himba, and Damara probably reflects admixture, as also suggested by linguistic analyses.
Samples and mtDNA Sequence Data Details on the samples and DNA extraction are given in  [12, 17, 20, 21]. Full mtDNA sequence data were generated from genome libraries tagged with either single or double indexes, and enriched for mtDNA following protocols described previously  [30, 31]. The libraries were sequenced on the Illumina GAIIx (Solexa) platform, using either single or paired end runs of 76 bp length, resulting in an average coverage of ,400x. Sequences were manually checked with Bioedit (www. mbio. ncsu. edu/ BioEdit/bioedit. html) and read alignments were screened with ma  [32]  to confirm indels. To minimize the impact of missing data, we applied imputation using stringent criteria, replacing missing sites with the nucleotide that was present in at least two otherwise identical haplotypes of the dataset.
Before imputation, 47 individuals presented missing data (8. 4% of the total number of sequences, with a maximum of four sites with missing data per individual); after imputation, the number of individuals with missing data was reduced to 23 (3. 7% of the total number of sequences, with a maximum of three sites with missing data per individual), with a total of 26 missing sites imputed. One hundred and ninety seven sequences from Botswana, Namibia, and Angola were previously included in studies focusing on haplogroups L0d and L0k as well as on the prehistory of Khoisan populations  [18, 33] , while a subset of 169 sequences from Zambia were included in  [20] ; the GenBank accession numbers of these samples can be found in Table  S1. The remaining 446 sequences from Zambia and 170 sequences from Angola have not yet been reported and are available from GenBank with accession numbers KJ185394-KJ186009.
The final alignment consists of 982 sequences of 16465 bp. Positions with missing data as well as the poly-C-regions (np 303-315, 16183-16194) were removed from all analyses; in addition, 45 positions with indels were removed from all analyses run in Arlequin and from sequences used for network construction. Our dataset includes speakers of several Bantu languages belonging to both the Western and the Eastern branches of Bantu according to the classification found on Glottolog 2. 2 (http:// glottolog. org/). While we were able to group most of the samples into 17 ethnolinguistically homogenous populations that correspond to the identification of donors' maternal grandmothers, some ethnolinguistic groups were represented by only a few individuals. In these cases, we united samples in five aggregate ''populations'' of speakers of closely related varieties based mainly on linguistic criteria, but ensuring that the resulting populations were genetically homogeneous as shown by non-significant between-population variance in AMOVA analyses and nonsignificant W ST distances (see Table  S1  for the ethnolinguistic affiliation of each individual and the composition of the aggregate ''populations'').
In addition, 49 individuals sampled in Namibia and Zambia could not be grouped into populations with a meaningful sample size or had an unclear ethnic affiliation; these were labeled as ''others'' and included only in lineage-based analyses (i. e. networks and phylogenetic trees). Table  S1  provides details on the country of sampling, ethnolinguistic affiliation, and GenBank accession number for each sample. We also included the Damara, who speak a Khoe language rather than a Bantu language, because of their known genetic proximity to Herero and Himba  [17, 18]. The rough geographic location of the 23 populations included in the study can be seen in Figure  1 , while Table  1  summarizes the information on their country of origin, linguistic affiliation, and subsistence. To investigate the variation in haplogroups L3d and L3f we further included published data from African populations: for the visual depiction in Surfer maps, haplogroup frequency data was collected from the literature as summarized in Table  S2 , while for networks and BEAST runs, 28 published complete mtDNA genome sequences available from GenBank were added to the alignment  [34] [35] [36] [37] [38].
Statistical Analyses Haplogroup assignment was performed with the online tool Haplogrep  [39]. Haplogroup affiliation of individuals belonging to haplogroup L0d and L0k was further defined following the nomenclature reported in  [33]  (see Table  S1 ). Analyses of Molecular Variance (AMOVA), values of sequence diversity, and matrices of pairwise W ST values were computed in Arlequin ver. 3. 11, while values of nucleotide diversity and variance were calculated in R with the package Pegas  [40]. A correspondence analysis (CA) based on haplogroup frequencies was performed with the package ca  [41] , and non-metric Multi-Dimensional Scaling (MDS) analyses based on pairwise W ST values were performed with the function ''isoMDS'' from the package MASS  [42]. The Africa-wide variation in frequency of haplogroups L3d and L3f was visualized on a map with the software Surfer ver.
10. 4. 799 (Golden Software Inc. ). Median-joining networks  [43]  with no pre-or post-processing steps were computed with Network 4. 11 (www. fluxus-engineering. com) and visualized in Network publisher 1. 3. 0. 0. For the networks of haplogroups L3d and L3f all sites were given equal weights, while for the network of haplogroup L0d the hypervariable positions were downweighted by one third in order to reduce the effect of reticulation. A Mantel test was performed between genetic (Wst) and geographic distances with the R package vegan  [44] ; geographic distances between populations were averaged over GPS data from the individual sampling locations with the function rdist. earth of the package fields  [45]. BEAST (v1. 7. 2;  [46] ) was used to construct Bayesian Skyline Plots and phylogenetic trees, based on the complete mtDNA sequence and using the mutation rate of 1.
665610 28 from Soares et al. [47]. A Generalized Time Reversible model was applied, and multiple runs were performed for each dataset, using 10, 20 or 30 million chains for single haplogroups and populations. For the schematic tree of the whole dataset 40 million chains and a GTR mutation model were used. The most probable tree from the BEAST runs was assembled with TreeAnnotator and drawn with FigTree v 1. 4. 0. Resampling tests were performed in R to investigate the possible shared ancestry of the Herero, Himba, and Damara on the one hand, and the Herero, Himba, and Kuvale on the other, notwithstanding the absence of haplogroup L3f in the Damara and the absence of L3d in the Kuvale and the concomitant high frequencies of these haplogroups in the Herero and Himba. In both cases we proceeded as follows: the Herero and the Himba were considered a single population with a sample size of 51 individuals, while for the Damara and Kuvale we used the actual sample sizes included in the study (i.
Genetic Structure of Southern African Bantu-speakers As can be seen from Table  S3  and Figure  S1  in File S1, haplogroups found in relatively high frequency across most of the populations of the dataset are L0a, L1c, L2a, and L3e. Other haplogroups, however, are more restricted, being found in only a few populations; of these, L3d and L3f (discussed in detail below) show a particularly striking distribution, being found in very high frequency only in the populations of Namibia. There is very little discernible structure in the maternal genepool of the Bantu-speaking populations of southern Africa, as shown by a three-dimensional MDS analysis. Only two distinct groups of populations emerge (Figure  2A ): the Himba, Herero, and the non-Bantu-speaking Damara from Namibia on the one hand, and the Kuvale from Angola as well as the Tswana and Kgalagadi from Botswana on the other hand; the Tswana and Kgalagadi are separated from their geographic neighbors the Wider Shona.
Admixture with Autochthonous Populations The degree of admixture between the immigrating Bantuspeaking agriculturalists and autochthonous populations is highly variable. On the one hand, some Zambian populations, such as the Nkoya, the Eastern Tonga, or the Totela, carry no autochthonous lineages at all, while on the other hand the Kuvale, Fwe, Tswana, Wider_Shona, and Kgalagadi have 21-53% of  haplogroups L0d and/or L0k. There is a noticeable geographical cline in the presence of these two different autochthonous haplogroups (cf. [33] ), with L0d being present at high frequency in Bantu-speaking populations from the southern range of our dataset (Botswana, Namibia, and southern Angola), while L0k is practically absent from these populations (Figure  S3A  in File S1). While the frequency distribution of L0d in our Bantu-speaking populations matches that of extant Khoisan populations (where the highest frequencies of L0d are found in populations of South Africa, Botswana, and Namibia), the highest frequencies of L0k in extant Khoisan populations are found in western Botswana and northeastern Namibia  [33].
The admixture between Herero and Khoisan populations is likely to have taken place quite recently, as they share four out of their five L0d sequences (Table  S4 ). In contrast, the Tswana share only two of their five L0d sequences with different Khoisan populations, while two sequences are at least four to five mutational steps distant from any Khoisan haplotypes. Rather surprisingly, the Kgalagadi, who are the Bantu-speaking population with the highest level of autochthonous haplogroups, share only one out of their ten L0d sequences directly with the neighboring Khoe-speaking G|ui; the other haplotypes are between one and at least eight mutational steps distant from the closest Khoisan sequence type (Table  S4 ). These data indicate that the gene flow from autochthonous populations into most of the Bantu-speaking populations included in the dataset took place a long time ago and/or involved Khoisan populations who did not survive into the present.
Acknowledgments This study focuses on the prehistory of populations as reflected in their genetic variation. It does not intend to evaluate the self-identification or cultural identity of any group, which consist of much more than just genetic ancestry. We sincerely thank: all the sample donors for their participation in this study and the  governments of Botswana, Angola, Namibia, and Zambia  for supporting our research;  Roland Schro  ¨der and  Madhusudan Reddy Nandineni  for assistance with library preparation;  Hongyang Xu  for help with the imputation process;  Ana Duggan  for assistance with the tree analysis; and  Mingkun Li  for assistance with the bioinformatics analysis.
This work was funded by the  Max Planck Society , by  FCT  Project Grant  PTDC/BIA-EVF/2907/2012 , by the  EU   FP7 REGPOT  grant  CIBIO-New-Gen-28643 , and by the '' Genomics and Evolutionary Biology '' project co-financed by the North Portugal Regional Operational Programme 2007/2013 (ON. 2-ONovo Norte), under the National Strategic Reference Framework (NSRF), through the  European Regional Development Fund (ERDF) ; CB was supported by the  European Research Council   ERC-2011-AdG 295733  grant (Langelin); MV was supported by  STAB VIDA, Investigaca ˜o e Servicos em Ciencias Biolo  ´gicas, Lda; MV and SO were supported by the  Portuguese Ministry for Science, Technology and Higher Education  through PhD grants  SFRH/BDE/51828/2012  and  SFRH/BD/85776/2012 , respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We will not try to categorize points as being either "linguistic" or "gestural". Symbolic gestures are part of our broad, integrative view of language as a multimodal interactive system and we will focus on the development of pointing within the communication systems that children develop, be they spoken or signed. Two studies conducted in the framework of the CoLaJE project financed by the French National Agency  (Morgenstern et. al 2010)  are presented here in order to make an attempt at establishing comparisons between two longitudinal follow--ups in two different situations. The researchers 3  coded the data together in order to conduct comparable analyses 4. 5   Sekali 2012). For this study, we used the data up to two years old.
Results We began with an analysis of population mixture in southern Africa, using the data from Pickrell et al. [3]  supplemented with an additional 32 individuals from seven Khoisan populations genotyped on the Affymetrix Human Origins Array (Supplementary Table  1 ); note that the Damara are excluded from most of the subsequent analyses as they genetically resemble southern African Bantu-speaking groups  [3]. These southern African data were then combined with previously-published worldwide data  [5]  (SI Methods). After removing individuals who appeared to be genetic outliers with respect to others in their population (SI Methods), we analyzed a final data set consisting of 1,040 individuals from 75 worldwide populations, all genotyped on the Affymetrix Human Origins array at 565,259 single nucleotide polymorphisms (SNPs).
The three populations with the largest amplitude (and thus the closest inferred relationship to the true mixing population) are listed. Note that the only populations from western Africa in these data are the Yoruba and Mandenka. q q q q q q q q q q q q q q q q q 0e+00 2e-04 4e-04 0e+00 2e-04 4e-04 Amplitude (French reference) Amplitude (Yoruba reference) q q q q q q q q q q q q q q q q q A. ALDER amplitudes q q central African Khoisan q q q q q q q q q q q q q q q q q 0 10 20 30 40 50 60 0 20 40 60 Mixture time in gen. (French reference) Mixture time in gen. (Yoruba reference) q q q q q q q q q q q q q q q q q B. Inferred mixture times Figure  2 : : Relationship with west Eurasia is shared by all Khoisan populations. We generated weighted LD decay curves in each Khoisan (or central African hunter-gatherer) population, using weights computed using the test population as one reference and either the French or the Yoruba as the other reference.
3% to 1. 1%, with an average of 0. 7%. We additionally estimated the Eurasian ancestry proportion in the Mandenka from western Africa using this method as 2. 0 %. The main southern African dataset in this paper is from  Pickrell et al. [2012]  (the dataset before filtering for outlier individuals). We genotyped an additional 32 samples from these populations on the Affymetrix Human Origins Array (Supplementary Table  1 ). As with the original dataset  [Pickrell et al. , 2012] , these additional samples were obtained as part of a multidisciplinary project investigating the prehistory of the Khoisan peoples and languages (http://www2. hu-berlin. de/kba/) with prior informed written consent from all donors, and with ethical clearance from the Review Board of the University of Leipzig and with the permission of the Ministry of Youth, Sport and Culture of Botswana and the Ministry of Health and Social Services of Namibia.
We then examined all populations in this merged dataset for outliers, and removed 17 individuals (Supplementary Figure  1 ). We merged these data with those from  Patterson et al. [2012]  and  Meyer et al. [2012] , and all other samples genotyped on the same array. In analyses of multiple mixture events, we used a set of 51 populations (Supplementary Table  2 ). For most analyses of the Khoisan, we excluded the Damara because they appear genetically similar to Niger-Congo speakers  [Pickrell et al. , 2012]. These data consist of 565,259 SNPs; for most analyses, we use all of these SNPs. However, in some places (where noted) we used only subsets of these SNPs from known ascertainment panels. For analysis of multiple mixture dates we used the set of populations listed in Supplementary Table  2.
1. 2. 3 Conditions under which the mixture weights do not identify the true admixing populations. When fitting the above model, for a given pair of populations i and j on admixture event k, we estimate a parameter C ijk. The relative values of these parameters for different populations reflect the differential relationships of i and j to the true admixing populations. We asked in what situation the maximum values of these parameters do not identify the reference populations most closely related to the true admixing populations. Consider the two-admixture situation presented in Supplementary Figure  4 , and let the earliest admixture event (between populations C and D) be too old to detect by LD, and so the only curve in the data has a decay rate of t 2 generations. Now consider the LD curve computed using populations A and B as references (these are the true admixing populations) and that computed using B and C as references.
Simulations To test the performance of our approach to estimate multiple mixture dates, we used coalescent simulations implemented in the software macs  [Chen et al. , 2009]. The basic simulation setup is shown in Supplementary Figure  5. For each simulation, we simulated 30 individuals from each of the nine populations, and each individual consisted of 10 independent chromosomes of 200Mb. We thus simulated many aspects of real data, including hundreds of thousands of SNPs in linkage disequilibrium. We simulated three scenarios: t 1 = 20 and t 2 = 100, t 1 = 20 and t 2 = 60, and t 1 = 40 and t 2 = 100. We additionally performed simulations of a single episode of population mixture. The exact macs command used was (for, e. g. , t 1 = 40 and t 2 = 100): macs 540 200000000 -t 0. 00004 -r 0. 0004 -I 9 60 60 60 60 60 60 60 60 60 0 -em 0.
We model f j as: f j = f X ŵX + fS ŵS , ( 18 ) where ŵX is the estimated proportion of west Eurasian ancestry in population j from Table  1  in the main text and ŵS = 1 -ŵX. For a given f X , the sum of squared errors is: SS(f X ) = N j=1 ( fj -f j ) 2 , ( 19 ) where fj is the estimated allele frequency at the SNP in population j. We then search over values of f X to minimize Equation  19. In the set of east African populations, we included the six with the most west Eurasian ancestry: the Tygray, Amhara, Afar, Oromo, Somali, and Ethiopian Somali. We then minimized Equation  19 for each SNP in the Illumina data using the optimize() function in R [R Development Core Team, 2011]. To compare to an ancestral Middle Eastern population, we performed the same analysis on the Bedouin, Druze, and Palestinian populations, using the estimated African ancestry proportions from  Moorjani et al.
We merged data from several published sources[Altshuler et al. ,  2010; Behar et al. , 2010; Henn et al. , 2011; Li et al. , 2008; Pagani et al. , 2012; Schlebusch et al. , 2012]. The merged dataset consisted of 2,935 individuals genotyped at 256,540 SNPs. For analyses of multiple mixture events, we used a set of 55 populations (Supplementary Table.
Acknowledgements We thank  Richard Durbin  for suggesting that we impute the allele frequencies of the ancestral west Eurasian population in eastern Africa, and  Carlos Bustamante  for helpful discussions on the interpretation of the admixture signals. We thank two anonymous reviewers and commenters on the blogs  Gene Expression and Haldane's Sieve  for helpful suggestions. The Khoisan samples analysed here were collected in the framework of a multidisciplinary project investigating the prehistory of the Khoisan peoples and languages (http://www2. hu-berlin. de/kba/), and we thank  Tom Gldemann ,  Christfried Naumann ,  Chiara Barbieri ,  Linda Gerlach ,  Falko Berthold , and  Hirosi Nakagawa  for assistance with sample collection. JP was supported by  NIH postdoctoral  fellowship  GM103098.