dataesr/scientific-paragraphs-categorization huggingface dataset:
Vector quantization Vector Quantization (VQ) is a classical and the most frequently used pattern-matching technique  [2]. We will use the VQ approach, in this paper, due to its easiest implementation and its higher accuracy. This technique consists of extracting a small number of representative feature vectors as an efficient means of characterizing the speaker-specific features. The training data features created by VQ method are combined to create a codebook for each speaker. In the recognition phase, the system compares the difference between a speaker's test data and the codebook of each speaker. Accordingly, it concludes the recognition result  [39] [40] [41]. Figure  3  shows an illustrative diagram of this recognition process. One speaker can be discriminated against from another base of the location of centroids.
These parameters of GMM are computed in training phase to create a speaker model. In testing phase, the speaker model having highest a posteriori probability for the features of an unknown voice is selected as identity of that unknown speaker  [14, 17, 43, 44]. A Gaussian mixture model is a weighted sum of M component Gaussian densities as given by the equation: 1 ( | ) ( | , ) M i i i i p x w g x  = =  (3) The following diagram as shown in Figure  4  illustrates the GMM modelling process of speaker data. It shows the illustrative steps of Gaussian mixture modelling of speaker's database.
Corpus preparation In this paper, we have considered two categories of a database, containing the noisy data and clean data; -Clean database We have registered a database ARBDIGITS of 100 Arab Moroccan speakers including males and females have been built from 'siffer' (zero) to 'Tisaa' (nine) which are used for training purpose. The speaker speaks the word several times isolated. These voices have been recorded at sampling frequencies 8000 Hz. We use the noise removal tool available in "audacity" software to delete background noise from the original recording and then we get the clean data. -Noisy database For the data that we use it in the testing phase, the noise is added by the MATLAB function adding white Gaussian noise (AWGN) to the clean database ARBDIGITS at various levels of signal to noise ratio (SNR) varying from 5 dB to 20 dB.
Results Firstly, the performance of the system is evaluated by recognition rate. It is calculated by: The average recognition rates obtained from the ten Arab digits in clean environment and in noisy environment, for different SNR values, are represented in Table  4  and efficiency chart is shown in Figure  6  respectively. From the results shown in Table  4 , we can conclude that the effect of the noise is not important if the SNR is superior to 20dB, in this case we obtain approximately the same 'average' value of recognition obtained in cleaned data. In testing phase for speaker identification, the spoken samples are recorded by 15 speakers; (10 male speakers and 5 female speakers) chosen from our database ARBDIGITS of 100 Arab Moroccan speakers (the speech wave is with 8 KHz sampling frequency using AUDIORECORD function of MATLAB 2016 environment in windows platform in 64 bit).
The results are implemented under MATLAB R2016b. For this, we have built a GUI interface as illustrated in Figure  10  to simplify the testing process where the speaker can be tested directly by a new voice recording or from test base. During recording, the user adds AWGN noise to her\his voice if he\she desired and he\she selects the SNR level. This GUI enables the recording or plotting of a sound as well as the recording of a new test data and identification of the speaker ID.
DISCUSSIONS In this work, for the experiments tested with the clean data the maximum performance received for MFCC+GMM+VQ is 97. 92%, for MFCC+GMM is 95. 39% % and for MFCC+VQ is 90. 04 %. Also, for the tests with noisy data, the maximum accuracy received for MFCC+GMM+VQ is 92. 50 %, for MFCC+GMM is 86. 57% and for MFCC+VQ is 65. 64 %. It is clearly observed that better performance has been seen when using the three techniques together MFCC+GMM+VQ. From the results, it is clearly found that the Arabic digits speech recognition system and the speaker identification system performed well in both clean and noisy environment using MFCC as feature extraction and vector quantization. For our proposed model of he combination among three methods MFCC+GMM+VQ, we observed that the accuracy of the results obtained is high with either clean or noisy data compared to the results obtained by MFCC+GMM or MFCC+VQ methods.
X-Ray photoelectron spectroscopy (XPS) The X-ray photoelectron spectra were recorded using a Kratos Axis Ultra DLD spectrometer (Kratos Analytical, U. K. ) equipped with a monochromatic Al Kα X-ray source (1486. 6 eV) operated at 120 W. Calculations of XPS elemental compositions were performed using the Kratos Vision 2. 2. 11 software package including the subtraction of Shirley-type background  [45]. For XPS analyses, 100 mg of monazite powder was treated for 30 min in a beaker containing 50 mL of the LaCl 3 solution. Before and during this treatment, the pH was maintained constant at 8. 5, a conventional pH for monazite flotation, using NaOH. The particles were then recovered by filtration through a nylon filter (0. 8 µm pore size) and thoroughly rinsed with deionized water. The obtained powders were then placed in a desiccator under partial vacuum for soft drying prior to their introduction in the spectrometer.
Protocol of modeling and calculation settings The adsorption configurations and energies of species on the (100) monazite surface were determined through DFT calculations using the Vienna Ab initio Simulation Package VASP  [46]. First, for each system, ab initio molecular dynamics (AIMD) simulations were carried out during 20 picoseconds to find the area of the phase space accessible at 300 K, i. e. the atoms positions that corresponded to a plausible initial condition for a subsequent geometry optimization. For AIMD simulations, a Nosé-Hoover thermostat was employed to set the temperature to 300 K with a time step of 1 fs. Then, on this system, a DFT relaxation was performed until all forces were smaller than 0. 01 eV•Å -1 to calculate the energy of the system and the corresponding contribution of dispersion energy.
This research was funded by the  Carnot Institutes ICEEL and BRGM  through the  TERRAFINE Inter-Carnot Action  (Carnot-ICEEL no. 118-IC  and  IC-BRGM  no. 2013-01 ). Financial support from  LabEx RESSOURCES21  is also acknowledged (contract Investissements d' Avenir  no. ANR-10-LABX-0021 ). This work was granted access to the HPC resources of TGCC and CINES under the allocation 2019-A0060910433 made by  GENCI. The authors would like to thank  Martine Mallet  ( LCPME, UMR 7564 CNRS -University of Lorraine ) for fruitful discussion and comments on the XPS analyses and  Hubert Haas  ( BRGM ) for providing the equipment required for thermal treatment of monazite precursors.
Silhouettes represent probability distributions of 14 C dates following age-depth modeling carried out using the Undatable age-depth modeling software  (Lougheed and Obrochta 2019)  for terrestrial macrofossils (green) and (fragments of) bivalves (blue), picked from the piston core. The dark shaded area represents a probability density cloud of the Undatable age-depth model, where darker colors indicate a higher probability. concentrations (at the base of the record at Erstaviken, and halfway through the records of Baggensfjärden and Ingaröfjärden; Fig. 2 ; Supplementary Fig. S2 ) represent a period of pronounced oxygen depletion in the Stockholm Archipelago with sulfide continuously being present in the pore waters and potentially (occasionally) also in the bottom waters, that is, euxinic conditions.
Acknowledgments We thank the captain and crew on board  R/V Ocean Surveyor  for their help with sediment collection in the Stockholm Archipelago in July 2015. Lovisa Zillén  ( Swedish Geology Survey ) is especially thanked for logistics and selection of coring sites. Stefano Bonaglia  ( Lund University ) is thanked for assistance in sediment core collection and processing. Arnold van Dijk,  Coen Mulder , and  Zilla van Aartrijk  ( Utrecht University ) are thanked for analytical assistance. This study was funded by the  BONUS   COCOA  project (grant # 2112932-1 ), funded jointly by the  European Union and Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) , grant # 2013-2056  and by the  Swedish Agency for Marine and Water Management (Havs-och vattenmyndigheten ,  DNR 1960-2018 ).
This research was also supported by the  Netherlands Organisation for Scientific Research (NWO)   Vici grant  # 865. 13. 005  and the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013)/ ERC Starting Grant  # 278364  and the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Ministry of Education, Culture and Science (OCW). B. C. Lougheed kindly thanks  Karin Wallner  and  Hans Schöberg  for their guidance with Sr column chemistry and TIMS measurements. B. C. Lougheed and 87 Sr/ 86 Sr analyses were funded by  Swedish Research Council (VR)  grant number  637-2014-499.
NC sectors are enriched in damaging mutations To test the functional significance of NC sectors, we sought experimental datasets with quantitative measurements of the consequences of mutations across a protein of interest. Recent studies have pioneered the use of deep mutational scanning to systematically generate and quantify the phenotypic or fitness effects of a large number of individual mutations spanning entire domains or protein  (29, (51) (52) (53) , providing new insights into structure-function relationships. Thus, we asked whether NC sectors were enriched in residues for which mutation altered protein function and/or fitness. The Ras superfamily of membrane-associated small G-proteins is highly conserved and controls a broad range of cellular processes  (54) , has inactive and active states that are regulated by a GTPase-activated protein  (55) , and has been implicated in cancer  (56).
2 ) and/or for which the available homologous sequences are biased to a particular segment of the phylogenetic tree (Fig. 1B ). In particular, application to the growing database of human exome sequences (58) may improve identification of rare disease-causing mutations. NC may also enhance protein engineering tools by highlighting targets for directed evolution. As we have demonstrated, NC expands our ability to detect functional relationships between residues within proteins and to determine the links between protein evolution and adaptation. In concert with deep mutational scanning and other comprehensive functional screens  (59) , NC and MirrorTree should provide deeper insight into the specific selective pressures under which proteins have evolved. The predominant application of coevolution so far has been structure prediction, from using top DCA-predicted contacts as constraints (  4 ) to employing DCA model parameters as input training features for deep neural networks that seek to predict spatial distances between amino acids  (60).
MSA construction MSAs were constructed with BLAST  (61)  to identify up to 10,000 closest sequences to a reference sequence, using the RefSeq database  (62). Sequences were aligned with Clustal Omega (63). Sequences with a Jukes-Cantor distance >1 from the reference sequence were pruned. Redundant sequences and positions with >25% gaps were removed. Any remaining gaps were filled with the closest amino acid from the closest sequence in terms of Jukes-Cantor distance.
Adaptation of the MirrorTree algorithm Mirrortree was originally developed to predict protein-protein interactions based on the similarity of phylogenetic trees  (37). In brief, MSAs are calculated using protein sequences from the same list of organisms for two proteins. For each MSA, the matrix of pairwise Jukes-Cantor distances is calculated. The MirrorTree score is the Pearson correlation coefficient of these two distance matrices. A high correlation indicates that the two proteins have similar phylogenies and thus are likely to have experienced similar functional selection. We adapted this method to compare the phylogenetic similarity of protein sectors with the entire protein (Fig. 4A ) or other sectors (Fig. 6A ). To compute sector-protein and sector-sector MirrorTree scores, filtered MSAs were created focusing on the positions of a given sector.
Acknowledgments The authors thank the  Huang lab  for useful discussions. This work was supported by  Stanford Graduate Fellowships  (to A. C. and E. A. ), a  Gerald J. Lieberman Fellowship  (to A. C. ), grant  851173  from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (to A. -F. B. ),  NSF   CAREER Award MCB-1149328  (to K. C. H. ), and the  Allen Discovery Center at Stanford on Systems Modeling of Infection  (to K. C. H. ). K. C. H. is a Chan Zuckerberg Biohub Investigator.
| Immunostaining and stereology Localization of proteins in tissue sections was performed using fluorescent immunostaining procedures as described previously. 31 tails of antibodies used can be found in Table  S1. Images were captured using an Olympus Provis AX70 microscope (Olympus, Japan) with Zen imaging software (Carl Zeiss Ltd, UK). A minimum of three different animals from each group were utilized to ensure reproducibility of results. Negative controls lacking primary antibody were used throughout. Estimates of viral targeting of Leydig cells were determined using whole slide scans (taken using a Zeiss Axio Scan. Z1 (Lentiviral and adenoviral-injected tissue) or with a Leica Aperio AT2 (adeno-associated virus injected tissue) of testis sections immunolabelled with GFP and a Leydig cell-specific marker (HSD3β1 or CYP17a1).
Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and userfriendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr.
Introduction Principal Component Analysis (PCA) has been widely used in genetics for many years and in many contexts. For instance, adding PCs as covariates is routinely used to adjust for population structure in Genome-Wide Association Studies (GWAS)  (Price et al. 2006; Novembre and Stephens 2008). PCA has also been used to detect loci under selection  (Galinsky et al. 2016; Luu et al. 2017)  and in heritability analyses  Yang et al. (2010) ;  Loh et al. (2015a). Recently, the advent of large population-scale genetic datasets, such as the UK biobank data, has prompted research on developing scalable algorithms to compute PCA on very large data  (Bycroft et al. 2018). It is now possible to efficiently approximate PCA on very large datasets thanks to software such as FastPCA (fast mode of EIGENSOFT), FlashPCA2, PLINK 2.
2008; Abdellaoui et al. 2013; Privé et al. 2018). Including PCs that capture LD as covariates in genetic analyses can lead to reduced power for detecting genetic association within these LD regions. Second, another issue may arise when projecting PCs of a reference dataset to another study dataset: projected PCs are shrunk towards 0 in the new dataset  (Lee et al. 2010; Wang et al. 2015; Zhang et al. 2019). This shrinkage makes it potentially dangerous to use the projected PCs for analyses such as PC regression, ancestry detection and correction for ancestry. This is also an issue when projecting individuals from the same dataset that were discarded from the PCA computation (e. g. related individuals). Third, PC scores may capture outliers that are due to family structure, population structure or other reasons; it might be beneficial to detect and remove these individuals to maximise the population structure captured by PCA (in the case of removing a few outliers) or to restrict analyses to genetically homogeneous samples (e.
g. "White British" people in the UK Biobank). Finally, efficient methods for PCA use approximations, which can results in some lack of precision of computed PCs. This has been shown to be a possible issue for software such as FastPCA and PLINK 2. 0, but not for FlashPCA2 and bigstatsr/bigsnpr  (Abraham et al. 2017; Privé et al. 2018 ). An overview of existing methods with their respective advantages and limitations is presented in table 1. For this paper, we derive implementations of truncated PCA and other useful functions for e. g. performing LD thinning and computing various statistics. We make these available in a new release of R package bigsnpr (v1. 0. 0); what differs from previously available functions presented in  Privé et al. (2018)  is that these new functions can be used directly on PLINK bed/bim/fam files with some missing values.
We use these new functions to analyse the UK Biobank data, and show that they are both very fast and easy to use. We also point out that many PCs currently reported by the UK Biobank capture LD structure instead of population structure. Interestingly, subsetting the UK Biobank data enables to get more PCs that capture population structure than when using the whole sample (~40 instead of ~16). Then, we project the other individuals that were not used in the PCA calculation, show that this projection is biased and provide an efficient solution to estimate unbiased projections instead. Finally, we explore options to detect outlier samples in PCA, either a few outlier samples that may correspond to e. g. batch effects or family structure, or when the goal is to restrict the data to individuals of homogeneous ancestry.
Note that this decomposition is equivalent to the decomposition presented above after imputation by the variant means. To compute this decomposition easily and efficiently, we implement an accessor that memory-map the PLINK bed file to use it directly as if it were a standard matrix. Then, we apply the same algorithm for partial SVD that is used in R packages bigstatsr and FlashPCA2, namely the implicitly restarted Arnoldi method  (Lehoucq and Sorensen 1996; Abraham et al. 2017; Privé et al. 2018). This algorithm is implemented in R package RSpectra, and requires a function that computes the matrix-vector multiplication of the scaled genotype matrix with a given vector, which we implement for PLINK bed files and parallelise.
Robust Mahalanobis distance Mahalanobis distances are computed as d(x) 2 = (x-µ) T Σ -1 (x-µ), where µ and Σ are (robust) estimators of location and covariance of x. We use these distances for many applications in this paper. When x is multivariate Gaussian data with K dimensions, the squared distances follow a χ 2 (K) distribution. If x are PC scores of centered data and if we use standard estimates, then µ = 0 and Σ = I K. Yet, here we use the pairwise orthogonalised Gnanadesikan-Kettenrin robust estimates of these parameters, as implemented in function covRob of R package robust with parameter estim = "pairwiseGK"  (Gnanadesikan and Kettenring 1972; Yohai and Zamar 1988; Maronna and Zamar 2002; Todorov et al. 2009). We reexport function covRob in R package bigutilsr for convenience.
The standard upper limit defined by Tukey's rule is q 75% (x) + 1. 5 • IQR(x), where x is the vector of computed statistics and IQR(x) = q 75% (x)-q 25% (x) is the interquartile range. One assumption of Tukey's rule is that the sample is normally distributed; we account for skewness in the data using the medcouple as implemented in function adjboxStats of R package robustbase  (Brys et al. 2004; Hubert and Vandervieren 2008). Standard Tukey's rule also uses a fixed coefficient (1. 5) that does not account for multiple testing, which means that there are always some outliers detected when using 1. 5 for large samples. To solve these two potential issues, we implement tukey_mc_up in R package bigutilsr and use it here, which accounts both for skewness and multiple testing by default. We remove the detected outlier variants, compute the PC scores and loadings again, and iterate until there is no detected outlier variant anymore.
Detecting outlier samples in PCA For detecting outlier samples in PCA, we use a modified version of the Probabilistic Local Outlier Factor (PLOF) statistic on PCs  (Kriegel et al. 2009). Using K nearest neighbours (KNN), this consists in comparing the distance from a point j to its KNNs (pd j = 1 K K k=1 d 2 j↔j k , where j k is the k-th NN of j) with the distances from its KNNs to their respective KNNs ( 1 K K k=1 pd j k ). The idea is that an outlier should be far from all other points, and is even more outlier if its KNNs are in a very dense cluster. Here, we use pd j / 1 K K k=1 pd j k. Note the square root, as it otherwise detects as outlier any point that is next to a very dense cluster. We implement (the two parts of) this statistic in function prob_dist of R package bigutilsr. To make it fast, we use the fast K nearest neighbours implementation of R package nabor  (Elseberg et al.
Projecting PCs from a reference dataset To project PCs of a reference dataset (e. g. 1000G) to a target genotype dataset, we implement the following 3 steps in function bed_projectPCA of package bigsnpr: 1) matching the variants of each dataset, including removing ambiguous alleles [A/T] and [C/G], and matching strand and direction of the alleles; 2) computing PCA of the reference dataset using the matched variants only; 3) projecting computed PCs to the target data using an optimised implementation (see Supplementary Materials) of the Online Augmentation, Decomposition, and Procrustes (OADP) transformation  (Zhang et al. 2019). To project individuals from the same dataset as the ones used for computing PCA, you can use function bed_projectSelfPCA. Note that the new individuals to be projected should not be related to the ones used for computing PCA (Section 3.
Data We provide and use a subsetted version of the 1000 genomes (1000G) project data (1000 Genomes Project  Consortium et al. 2015; Meyer 2019). Variants are restricted to the ones in common with HapMap3 or UK Biobank  (International HapMap 3 Consortium et al. 2010; Bycroft et al. 2018). Moreover, we apply some quality control filters; we remove variants having a minor allele frequency < 0. 01, variants with P-value of the Hardy-Weinberg exact test < 10 -50 , and non-autosomal variants. To remove related individuals with second-degree relationship or more, we apply KING-relatedness cutoff of 0. 0884 to the data using PLINK 2. 0  (Manichaikul et al. 2010; Chang et al. 2015). This results in 2490 individuals and 1,664,852 variants of the 1000G project (phase 3) in PLINK bed/bim/fam format. Resulting PLINK files and R code to generate these files are made available at https://doi.
org/10. 6084/m9. figshare. 9208979. v3. To easily download these data, we provide function download_1000G in R package bigsnpr. In this paper, we also analyse the UK Biobank data (https://www. ukbiobank. ac. uk/). We apply some quality control filters; we remove individuals with more than 10% missing values, variants with more than 1% missing values, variants having a minor allele frequency < 0. 01, variants with P-value of the Hardy-Weinberg exact test < 10 -50 , and non-autosomal variants. This results in 488,371 individuals and 504,139 variants. When removing related individuals, we use the list of individual pairs reported by the UK Biobank.