dataesr/scientific-paragraphs-categorization huggingface dataset:
In our case, we are starting with a simple original dataset randomly generated from a Gaussian distribution, and we use the GAN to try to replicate the dataset and eventually add more data points where some may lack, i. e. , in the tails (mechanically) (The experimental setup is very similar to the one provided at the following URL https://blog. evjang. com/2016/06/generative-adversarial-nets-in. html, accessed on 19 July 2021). The input fed to the generator is also random, and in our illustration, we used a uniform distribution rather than a normal one, therefore, our generator has to non-linearly remould the data (Note that in a production environment, if the shape of the final dataset is known, one may choose a more informative distribution to drive the generator). The generator takes the form of a feedforward network.
SMOTE Regression Traditionally, imbalanced classification involves developing predictive models on classification datasets for which at least one class is underrepresented (imbalanced). The main issue having imbalanced datasets to work with is that machine learning algorithms have a tendency to ignore the minority class, and therefore perform poorly on this one, although, it is generally where it matters most. A solution to deal with imbalanced datasets is to "over-sample" the class which is underrepresented. A viable approach is to synthesise new data points from existing examples. This approach is called Synthetic Minority Oversampling Technique, or SMOTE for short. The authors of  [36]  suggest creating artificially new data points implementing an interpolation approach. For each data point of the minority set, they propose to choose from the set one of its k-nearest neighbours at random.
The "proposal mentioned in  [37]  is based on the existence of a relevance function and on a user-specified threshold on the relevance values", which leads to the rare set definition. The proposed algorithm over-samples data in the minority set and under-samples the remaining elements. This approach permits the creation of a new and more balanced training dataset. With respect to the second component aforementioned, the generation of synthetic examples, an approach similar to the original algorithm as been implemented only slightly modified to properly deal with both numeric and nominal features. Finally, concerning the third element related to the target variable value selection of the created data points, in their approach, the "cases that are to be over-sampled do not have the same target variable value, although they do have a high relevance score", meaning "that when a pair of examples is used to generate a new synthetic case, they will not have the same target variable value.
Risk Measurement-Application In this section, the results obtained with the various methodologies presented above are compared and discussed such that the pros and cons are clearly displayed. The methodologies have been applied to the Tesla returns. Tesla's daily closing prices are presented in Figure  5 , and the equivalent daily returns are represented in Figure  6. The dataset containing Tesla's closing prices also contains information pertaining to the market conditions, such as the daily price variations, the volumes, the opening prices, etc. The reported prices are from 3 January 2012 to 30 April 2017 (The dataset can be downloaded following the url, https://github. com/mrafayaleem/dive-in-ml/blob/master/tesla-random-forests, accessed on 19 July 2021). For the methodologies assuming independent and identically distributed distributions, the auto-correlation function plot obtained on the series does not show any serial correlation (Figure  7 ), therefore, the assumption of independence cannot be rejected (However, it does not necessarily mean that the returns are independent).
038). Figure  8  represents the Gaussian mixture obtained on the Tesla returns (The Gaussian mixture has been developed in R using the nvmix package. A code somehow similar to the one developed could be found at http://sia. webpopix. org/mixtureModels. html, accessed on 19 July 2021). The EM is used here, considering the lower and upper truncations at, respectively, the maximum and the minimum values contained in the dataset and assuming a Gaussian distribution. Here, with respect to the Gaussian distribution, we obtained risk measures inferior to the one obtained with the empirical distribution (i. e. , we obtained 0. 029 with the EM while we obtained 0. 038 empirically). However, this approach would have provided different results assuming another type of parametric distribution (The EM algorithm has been developed in R.
This is a proprietary version of the algorithm as such the code cannot be disclosed, however, the algorithm is detailed in Section 3. 2. 1). The GANs (The GAN approach has been developed in Python. The code used in this paper has been adapted from the following GIT: https://github. com/ericjang/genadv_ tutorial/blob/master/genadv1. ipynb, accessed on 19 July 2021) are really interesting, as the methodology allows synthesising data in the tails; however, the calibration of the methodology is fairly complicated and capricious, even though, it generates scenario points in the tail. In this paper, we set the number of iterations at 500,000 and the size of minibatch at 2000, and the performance of the network training was evaluated using the mean square error. It generally takes several runs before reaching algorithm stability; however, this method is the most conservative, as we obtained an ES of 0.
09787413. The SMOTE regression (The SMOTE regression has been developed in Python. The code used in this paper relies on the packaged smogn that can be found at: https://github. com/nickkunz/smogn, accessed on 19 July 2021) is in fact the most promising, as the methodology permits generating points by reverse engineering the level of the variables considered in the underlying regression such that it not only provides points in the tails but also the market conditions or scenarios for these values to appear. Thus, the values could be interpreted and validated with respect to the underlying state of nature. In our application, the ES obtained is in between the most conservative and the least, as it is equal to 0. 0496, which might be considered as too conservative by banks but is likely to provide some stability to the risk measures over time.
Conclusions In this paper, after clarifying the theoretical foundations of both VaR and ES (in the most extensive manner for the ES, as the measure is still a controversial matter), we presented several methodologies to ensure the reliability of the latter. Indeed, it requires as much information in the tails as possible. If there are no data points beyond the VaR set as a threshold (97. 5% in the case of the newest market risk capital standards), the ES will not be robust, and in the most extreme case, can be equal to the VaR itself. As a consequence, we tested the impact of several methodologies coming from the fields of statistics and AI. Though all seem somehow viable, it might be interesting to select one option over another, depending on the initial situation. If one considers that the data sample as being representative of the general population, then one may want to consider KDE, as its application here will ensure to be as close as possible to the dataset while allowing the creation of a continuous version of it.
If your dataset presents similar characteristics to some parametric distributions, fitting one of these might be of interest. If the underlying distribution appears multimodal, a mixture of distributions might be more suited. If one considers that the datasets are censored, then a conditional fitting using an EM algorithm should be considered. The two AI approaches presented have, to our knowledge, not been used in such an environment before. The GANs could be of interest if the underlying data are considered unreliable and if a synthetic dataset has to be generated to reshape the underlying distribution as a whole. Finally, the use of the SMOTE regression is a powerful option to generate data points in the tails of the profit-and-loss distributions. Besides, this last methodology allows reverse engineering the market conditions, which facilitates the acquirement of the log return of interest in the tails.
Modern humans appeared in Europe by at least 45,000 years ago  [1] [2] [3] [4] [5]  , but the extent of their interactions with Neanderthals, who disappeared by about 40,000 years ago 6 , and their relationship to the broader expansion of modern humans outside Africa are poorly understood. Here we present genome-wide data from three individuals dated to between 45,930 and 42,580 years ago from Bacho Kiro Cave, Bulgaria 1,2. They are the earliest Late Pleistocene modern humans known to have been recovered in Europe so far, and were found in association with an Initial Upper Palaeolithic artefact assemblage. Unlike two previously studied individuals of similar ages from Romania 7 and Siberia 8 who did not contribute detectably to later populations, these individuals are more closely related to present-day and ancient populations in East Asia and the Americas than to later west Eurasian populations.
This is more than the average of 1. 9% (95% CI: 1. 5-2. 4%) found in other ancient or present-day humans, except for the Oase1 individual, who had a close Neanderthal relative (6. 4% (95% CI: 5. 7-7. 1%); Extended Data Fig. 6 , Supplementary Information 7). By contrast, the more recent BK1653 individual carried only 1. 9% (95% CI: 1. 4-2. 4%) Neanderthal DNA, similar to other ancient and present-day humans  10, 41  (Extended Data Fig. 6 ). As has been the case for all humans studied so far, the Neanderthal DNA in BK1653 and the IUP Bacho Kiro Cave individuals was more similar to the Vindija33. 19  10 and Chagyrskaya8  42  Neanderthals than to the Altai Neanderthal  9  (2. 8 ≤ |Z| ≤ 5. 1; Supplementary Information 7). To study the spatial distribution of Neanderthal ancestry in the genomes of the Bacho Kiro Cave individuals, we used around 1.
3 , Extended Data Fig. 7a ). On the basis of the distribution of the long Neanderthal segments, we estimate that individual F6-620 had a Neanderthal ancestor less than six generations back in his family tree (Extended Data Table  2 , Supplementary Information 8). Both the CC7-335 and BB7-240 individuals had Neanderthal ancestors about seven generations back in their families, with upper confidence intervals of ten and seventeen generations, respectively (Extended Data Table  2 , Extended Data Fig. 7b , Supplementary Information 8). Thus, all IUP Bacho Kiro Cave individuals had recent Neanderthal ancestors in their immediate family histories. To further explore the extent to which the Bacho Kiro Cave individuals contributed ancestry to later populations in Eurasia, we investigated whether the Neanderthal DNA segments in Bacho Kiro Cave genomes overlapped with Neanderthal segments in present-day populations more than expected by chance.
Sequencing of capture products and data processing For all sequencing runs we trimmed the adapters and merged overlapping forward and reverse reads into single sequences using leeHom  51  (version: https://bioinf. eva. mpg. de/leehom/). The Burrows-Wheeler Aligner  52  (BWA, version: 0. 5. 10-evan. 9-1-g44db244; https://github. com/mpieva/network-aware-bwa) with the parameters adjusted for ancient DNA ("-n 0. 01 -o 2 -l 16500")  43  was used to align the data from all sequencing runs to the human reference genome (GRCh37/1000 Genomes release; ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/). Only reads that showed perfect matches to the expected index combinations were used for all downstream analyses. PCR duplicates were removed using bam-rmdup (version: 0.
6. 3; https://github. com/mpieva/ biohazard-tools) and SAMtools (version: 1. 3. 1)  53 was used to filter for fragments that were at least 35 bp long and that had a mapping quality equal to or greater than 25. BAM files of the libraries enriched for the specific subset of the nuclear genome were further intersected with the BED files containing target SNP positions (390k, 840k, 1000k, Archaic admixture, a merged set of SNP Panels 1 and 2 or 1240k, and a merged set of SNP Panels 1, 2 and 3 or 2200k) and regions (Y chromosome) using BEDtools  54  (version: 2. 24. 0). In order to filter for endogenous ancient DNA or putatively deaminated fragments, we used elevated C-to-T substitutions relative to the reference genome at the first three and/or last three positions of the alignment ends  20. We merged libraries originating from the same specimen using samtools merge  53  to produce the final datasets for downstream analyses (Extended Data Table  1 , Supplementary Information 2).
Merging of the Bacho Kiro Cave and Oase1 data with other genomes We performed random read sampling using bam-caller (https://github. com/bodkan/bam-caller, version: 0. 1) by picking a base with a base quality of at least 30 at each position in the 1240k and 2200k SNP Panels that was covered by at least one fragment longer than 35 bp with a mapping quality equal to or higher than 25 (L ≥ 35 bp, MQ ≥ 25, BQ ≥ 30). To mitigate the effect of deamination-derived substitutions on downstream analyses, we did not sample any Ts on the forward strands (in the orientation as sequenced) or any As on the reverse strands in the first five and/or last five positions from the alignment ends. Owing to the haploid nature of the Y chromosome, we called genotypes across the approximately 6. 9 Mb of the Y chromosome for the enriched libraries of male individuals by calling a consensus allele at each position by majority call requiring a minimum coverage of 3 for specimens F6-620 and BB7-240 and of 2 for specimen CC7-335 using using bam-caller (https://github.
com/bodkan/bam-caller, version: 0. 1) (Supplementary Information 2). We merged the data from the newly sequenced specimens with datasets of previously published ancient and present-day humans, as well as archaic humans, for three SNP panels (1240k, 2240k and Archaic admixture; Supplementary Information 3). Data from the 1240k panel include genotypes of 2,109 ancient and 2,974 present-day individuals compiled from published studies and available in the EIGENSTRAT format  28  at https://reich. hms. harvard. edu/allen-ancient-dna-resource-aadr-downloadable-genotypespresent-day-and-ancient-dna-data (version 37. 2, released 22 February  2019). Data from the 2240k panel include published genetic data of ancient modern humans obtained through hybridization captures  7, 13, 29  and a range of present-day  9, 31  and ancient modern humans  8, 30, [32] [33] [34] [35] [36] [37] [55] [56] [57] [58] [59] [60]  , as well as the archaics  9, 10, 42, 43, 61  , for which whole-genome shotgun data of varying coverage are available (Supplementary Information 3).
Population genetic analyses To determine the relationship of the Bacho Kiro Cave and Oase1 individuals to other modern and archaic humans we used a range of f-statistics from ADMIXTOOLS  28  (version: v5. 1) and as implemented in the R package admixr  62  (version: 0. 7. 1; Supplementary Information 4). We used qpGraph program (Admixture Graph) from ADMIXTOOLS  28  (version: v5. 1) to test models of the relationship among Initial Upper Palaeolithic Bacho Kiro Cave individuals, the roughly 35,000-year-old Bacho Kiro Cave individual BK1653 and other ancient modern humans from Eurasia older than 30,000 years bp (Fig. 2d , Supplementary Information 6).
Neanderthal ancestry We estimated the proportion of Neanderthal DNA in the genomes of present-day and ancient modern humans by computing a direct f 4 ratio 28 that takes advantage of the two high-quality Neanderthal genomes  9, 10, 41  (Extended Data Fig. 6 , Supplementary Information 7). We used admixfrog  44  (version: 0. 5. 6, https://github. com/BenjaminPeter/ admixfrog/) to detect archaic introgressed segments in the genomes of the Bacho Kiro Cave and Oase1 individuals, as well as in other ancient modern humans and 254 present-day non-African individuals from the SGDP  31  as a direct comparison (Supplementary Information 8). (c; nsnps = 1,676,430) and present-day human populations from the SGDP 31 calculated as f 3 (Bacho Kiro BK1653/Kostenki14, present-day humans; Mbuti). Three Mbuti individuals from the same panel  31  were used as an outgroup.
Acknowledgements We thank  A. Weihmann  and  B. Schellbach  for their help with DNA sequencing;  R. Barr ,  P. Korlević  and  S. Tüpke  for help with graphics;  D. Reich  and  M. Slatkin  for discussions and input; the  Tourist Association STD "Bacho Kiro " in Dryanovo; the History Museum in Dryanovo; the  Regional History Museum in Gabrovo ; the  National Museum of Natural History (NMNH)  in Sofia; and  N. Spassov. M. H. is supported by a  Marie Skłodowska Curie Individual Fellowship  (no. 844014 ). Q. F. was supported by the  Strategic Priority Research Program (B)  ( XDB26000000 ) of  CAS ,  NSFC  ( 41925009 ,  41630102 ,  41672021 ). O. T. M. and S. C. were supported by a grant from the  Ministry of Research and Innovation ,  CNCS -UEFISCDI , project number  PN-III-P4-ID-PCCF-2016-0016 , within  PNCDI III  and the  EEA Grants   2014-2021 , under Project contract no.
3/2019. F. W. received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 948365 ). P. S. was supported by the  Vallee Foundation , the  European Research Council  (grant no. 852558 ), the  Wellcome Trust  ( 217223/Z/19/Z ) and  Francis Crick Institute  core funding ( FC001595 ) from  Cancer Research UK , the  UK Medical Research Council  and the  Wellcome Trust. This study was funded by the  Max Planck Society  and the  European Research Council  (grant agreement no. 694707  to S. P. ).
Data availability The aligned sequences of the Bacho Kiro Cave and Oase1 individuals have been deposited in the European Nucleotide Archive under accession number PRJEB39134. Comparative data of present-day human genomes from the SGDP that were used in this study are available at https://www. simonsfoundation. org/simons-genome-diversity-project/. Comparative data used in this study, which include genotypes of 2,109 ancient and 2,974 present-day individuals compiled from published studies, are available in the EIGENSTRAT file format at https://reich. hms. harvard. edu/allen-ancient-dna-resource-aadr-downloadable-genotypespresent-day-and-ancient-dna-data (version 37. 2, released 22 February.
Loading control: Ponceau staining. d, f Quantification of paxillin within β3 integrin-positive adhesions, expressed as red/green ratio measured upon co-expression of mCherry_paxillin and GFP-tagged integrins in d NIH-3T3 and f paxillin-null fibroblasts. Statistical analysis is provided in Supplementary Dataset 1. e WB of the paxillin-null clone and the parental cells, showing the loss of paxillin expression. g Quantification of endogenous paxillin within β3 integrin-positive adhesions, expressed as red/green ratio measured upon expression of GFP-tagged integrins and staining with the αpaxillin mAb. Statistical analysis is provided in Supplementary Dataset 1. h, i Quantification of the (h) area and (i) maximal length of β3 integrin-positive adhesions in NIH-3T3 cells co-transfected with mCherry_paxillin and β3_GFP integrins, expressed as mean value per acquisition.
Statistical analysis is provided in Supplementary Dataset 1. j Number of β3 integrin-positive adhesions per acquisition in NIH-3T3 cells co-transfected with mCherry_paxillin and β3_GFP integrins. Statistical analysis is provided in Supplementary Dataset 1. k, l Quantification of the k area and l maximal length of β3 integrin-positive adhesions in paxillin-null cells co-transfected with mCherry_paxillin and β3_GFP integrins, expressed as mean value per acquisition. Statistical analysis is provided in Supplementary Dataset 1. m Number of β3 integrin-positive adhesions per acquisition in paxillin-null cells co-transfected with mCherry_paxillin and β3_GFP integrins. Statistical analysis is provided in Supplementary Dataset 1. n Quantification of the β3 integrin flow in adhesions for the Movies 1, 2, and 3.
Data points represent the mean of the optical flow of all pixels analyzed (i. e. within adhesions) between two consecutive frames; error bar ±SD. ns (not significant) p > 0. 05; *p ≤ 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. Sample size, complete statistical analysis, and p values are provided in tables in supplementary datasets. between the substrate-bound integrin and the retrogradely moving F-actin network  4. Bimolecular Fluorescence Complementation (BiFC) was used as a tool to measure paxillin recruitment to β3 integrin-positive adhesions. Accordingly, mCherry_paxillin and β3 WT /β3 VE/YA integrins were C-terminally tagged with complementary citrine fragments (CN, citrine N-terminal fragment and CC, citrine Cterminal fragment) and co-expressed, as shown in Fig. 2c. Transfected NIH-3T3 cells, recognized in virtue of their mCherry emission, were imaged via total internal reflection fluorescence (TIRF) microscopy, in order to detect the BiFC-derived citrine fluorescence.
To validate the Acyl-RAC results, proteins recovered in the palmitoylated fraction from three independent experiments were subjected to proteomic analysis and the following comparison with proteins in the SwissPalm database  65. Among murine proteins recovered in at least two out of three experiments (Fig. 8g ), 96. 5% were annotated in the database as S-acylated proteins (Fig. 8h  and Supplementary Dataset 5). Overall, these data highlighted the relevance of LIM4 positive charges and demonstrated the palmitoylation of the paxillin protein, which is in line with a possible membrane-binding function of the LIM4 domain. BiFC shows a defined spatial orientation of paxillin LIM domains within β3 integrin-positive FAs. A possible function of the proposed membrane-binding activity of LIM4, is to consolidate the specific orientation of the LIM domain-array within β3 integrin-containing FAs.
Previous iPALM studies revealed a close membrane association of both paxillin termini  66. However, based on various structures of two tandemly-organized LIM domains (e. g. PDB ID: 1RUT and PDB ID: 2DFY), it is conceivable that the paxillin LIM1-LIM2-LIM3-LIM4 array has an extended conformation. Because the BiFC efficiency is imposed by the relative position of the proteins under analysis, as well as by the spatial orientation of the complementary fluorophore fragments, we attempted to reveal the orientation of paxillin in respect to the PM by BiFC. Accordingly, as summarized in Fig. 9a , differentially tagged recombinant paxillins were tested for their ability to complement, in the context of β3 integrinexpressing fibroblasts (Swiss-3T3  44  ), a membrane-anchored citrine fragment so localized via the PM-targeting peptide of Lck  67.
Gray: zinc-binding residues; blue: positive residues; red: negative residues; bold: non-conserved residues in respect to the human paxillin LIM4 domain; arrows indicate the conserved free cysteines; yellow: conserved GRC motif. b, c Expression analysis of paxillin LIM4 mutants b via western blot and c TIRF microscopy. Loading control: Ponceau staining. d Box plot of the halflives of PA-GFP_paxillin wt and LIM4 mutants in β3 WT _mCherry-positive FAs. Statistical analysis is provided in Supplementary Dataset 4. e, f Acyl-Resin Assisted Capture assays, e from cell transiently transfected with mCherry_paxillin wt or mCherry_paxillin-CAIL, untreated (DMSO) or treated with 2bromopalmitate and f from cell transiently transfected with mCherry_paxillin wt or Cys-mutants, (top) without and (bottom) with the CaaX-box.