dataesr/scientific-paragraphs-categorization huggingface dataset:
Microsatellite genotype analysis We examined the relationship between genetic differentiation and geographic distance by carrying out partial Mantel tests on the microsatellite data using the Arlequin version 3. 5 software package  [37]. Pairwise F ST values were generated between all sampling locations and genetic differentiation was plotted against straight-line, geographic distance. We fit the data with a linear regression, calculated a correlation coefficient and tested for statistical significance. We used the EIGENSOFT software package  [79]  to perform PCA on individual genotypes to identify individuals that could be grouped into significantly different populations. We converted microsatellite data into a false SNP format by scoring the presence or absence of each of n-1 alleles (where n is the number of alleles in the sample) using a script in MATLAB (The MathWorks, Natick, MA) described previously  [39].
We processed this file in SmartPCA, which produced eigenvectors and eigenvalues. We conducted this analysis blindly to a priori population labels for individuals in the dataset. We tested the statistical significance of each eigenvector using Tracy-Widom statistics. Each significant eigenvector recovered by this PCA approach separates the samples in such a way that the first and subsequent eigenvectors distinguish, in order, the most to least differentiated populations in the sample  [79]. All analyses using EIGENSOFT were performed blinded to a priori population labels. We examined population structure and individual ancestry using a Bayesian clustering approach implemented in the TESS (version 2. 3) software package  [30]. This program estimates the shared population history of individuals based on their genotypes and geographic origin under a model that assumes Hardy-Weinberg equilibrium and linkage equilibrium, thereby making no a priori assumptions regarding population classifications.
TESS estimates individual proportions of ancestry into K clusters, where K is specified for the program in advance across independent runs and corresponds to the number of putative ancestral populations. The program then assigns admixture estimates for each individual (Q) from each inferred ancestral population cluster. It also produces posterior predictive values of Q for geographic areas located in between and around the individual data points. TESS runs were performed: (i) with a model that allows individuals to have ancestry in multiple populations (CAR model  [80] ); (ii) with correlated allele frequencies; and (iii) blinded to a priori population labels. Runs were performed with a burn-in step of 500,000 Markov Chain Monte Carlo (MCMC) iterations and 1,000,000 MCMC iterations. Fifty runs each for K = 2 through K = 8 were performed for all datasets.
We created a template map of the extent of probable chimpanzee ranges in Nigeria and Cameroon in ArcMap Version 10 (ESRI Corp. , Redlands, CA) in the ASCII-raster format and input into TESS in order to generate posterior predictive maps of admixture proportions. Using these predictive maps, we generated spatial interpolations of the Q matrices by implementing a matrix based vector algorithm in the program TESS Ad-Mixer  [33]. These spatial interpolations use color mixing to predict admixture proportions across space in order to better visualize the spatial partitioning of genetic differentiation. We calculated allele size range, observed and expected heterozygosity, and an M-Ratio  [84]  for each locus for three populations as identified by cluster analysis. We tested these values against 10,000 random permutations of the data using the Arlequin version 3.
Population history We used the program IMa  [38]  to estimate: (i) the population mutation parameter, θ, for both descendant populations (θ 1 and θ 2 ) and an ancestral population (θ A ); (ii) rates of migration from population 1 to population 2(m 1 ) and from population 2 to population 1 (m 2 ); (iii) and time of population divergence (t) for all possible pairs of the three assumed populations: P. t. ellioti in western Cameroon & Nigeria, P. t. ellioti in central Cameroon, and P. t. troglodytes in southern Cameroon. We used IMa instead of the more recently released IMa2  [40]  for several reasons. The dataset is composed mostly of microsatellites with poorly characterized mutation rates  [39, 64, 87]. Also, due to the complexity of the IMa2 model, the high number of microsatellite alleles, and few number of microsatellite loci, we used the version of the IM model with the fewest assumptions.
Acknowledgements We thank the governments of Cameroon and Nigeria for permission to conduct this research. We thank the  Zoological Society of San Diego , the  Ebo Forest Research Project , the  Wildlife Conservation Society  and the  World Wildlife Fund  for their support in Cameroon. We thank  Joe Kujawski ,  Anne Shelton , and the  State University of New York -College of Nanoscale Science and Engineering (CNSE)  for their assistance with running IMa on the CNSE servers. We thank  Caro-Beth Stewart ,  George Robinson ,  Jeremy Kirchman  and  Adam Freedman  for helpful comments on early drafts of this manuscript. National Science Foundation awards 0755823 and  1243524  (to MKG),  National Institutes of Health  award  R37 AI 50529  (to BHH and MP), funding from  National Geographic  (to AN), the  Offield Family Foundation  (to BJM), the  USFWS Great Apes Conservation Fund  (to BJM), the  Arcus Foundation  (to BJM), and the  University at Albany -State University of New York  (to MKG) supported this work.
The quality of biometric raw data is one of the main factors affecting the overall performance of biometric systems. Poor biometric samples increase the enrollment failure, and decrease the system performance. Hence, controlling the quality of the acquired biometric raw data is essential in order to have useful biometric authentication systems. Towards this goal, we present a generic methodology for the quality assessment of image-based biometric modality combining two types of information: 1) image quality and 2) pattern-based quality using the SIFT descriptor. The associated metric has the advantages of being multimodal (face, fingerprint and hand veins), and independent from the used authentication system. Six benchmark databases and one biometric verification system are used to illustrate the benefits of.
This verification inaccuracy is due to many reasons such as the variations of human characteristics (e. g. , occlusions  [1] ), environmental factors (e. g. , illuminations  [2] ) and cross-device matching  [3]. This kind of acquisition artifacts may deeply affect the performance of biometric systems and hence, decrease their use in real life applications. Moreover, the impact of quality on the system overall performace is also presented by the results of the FVC seriers of competitions  (FVC in 2000 (FVC in , 2002 (FVC in , 2004 (FVC in and 2006) )   [4]. More specifically, the used databases in FVC 2004 and FVC 2006 are more difficult than FVC 2002 and FVC 2000 ones, due to the perturbations deliberately introduced. The results show that the EER in average of the best matching algorithm has increased from 0.
96 in FVC 2000 and FVC 2002 to 2. 115 in FVC 2004 and FVC 2006. Therefore, controlling the quality of the acquired biometric raw data is considered as an essential step in both enrollment and verification phases. Using the quality information, poor quality samples can be removed during the enrollment or rejected during the verification. Such information could be also used for soft biometrics and multimodal approaches  [5, 6]. We present in this paper a quality assessment metric of image-based biometric raw data using both information: 1) image quality and 2) patternbased quality using the SIFT keyspoints extracted from the image. The presented metric has the advantages of being multimodal (face, fingerprint and hand veins), and independent from the used authentication system. The outline of the paper is given as follows: Section 2 presents related previous works on quality assessment of biometric raw data.
Other iris quality metrics are presented in  [15, 16]. However, these methods are used to measure the quality of iris image, and cannot be used for other types of modalities. He et al. [17]  present a hierarchical model to compute the biometric sample quality at three levels: database, class and image quality levels. The method is based on the quantiles of genuine and impostor matching score distributions. However, their model could not be used directly on a single capture (i. e. , requires a pre-acquired database). [2]  present an asymmetry-based quality assessment method of face images. The method uses SIFT descriptor for quality assessment. The presented method has shown its robustness against illumination and pose variations. Another asymmetry-based method is presented in  [18, 19]. However, this approach supposes the asymmetry hypothesis hence, could not be used for the others types of modalities.
The α * i which optimize this criterion are obtained by solving the following problem :                max α i i=1 α i -1 2 i,j=1 α i α j y i K(x i , x j y j ) with constraints, 0 ≤ α i ≤ C , i=1 α i y i = 0. ( 9 ) where C is a penalization coefficient for data points located in or beyond the margin and provides a compromise between their numbers and the width of the margin. In this paper, we use the RBF kernel: k(x i , x j ) = exp(-γ x i -x j 2 ) (10) In order to train models with RBF kernels, we use a python script provided by the libsvm library  [35].
Alteration process In this study, we introduce three types of synthetic alterations as well as three levels for each type using the MATLAB tool: • Blurring alteration: blurring images are obtained using a two-dimensional Gaussian filter. To do so, we use the fspecial ('gaussian', hsize, σ) method which returns a rotationally symmetric Gaussian lowpass filter of size hsize with standard deviation σ. • Gaussian noise alteration: noisy images are obtained using the imnoise (I, 'gaussian', m, v) method. It adds Gaussian white noise of mean m and variance v to the image I. • Resize alteration: such kind of altered images are obtained using the imresize (I, scale, 'nearest') method. It resizes the image I using a nearest-neighbor interpolation. Using these alterations, the input vector to SVM is the five retained quality criteria (one for image quality and four pattern-based quality) and the output can belong to ten different classes defined as follows (see Table  2 ): • class 1 illustrates a reference image.
Quality criteria behavior with alterations In this section, we show the robustness of the used criteria in detecting alterations presented in the previous section. To do so, we use the Pearson's correlation coefficient between two variables as defined in Equation  13. It is defined as the covariance of the two variables (X and Y) divided by the product of their standard deviation (σ X and σ Y ): P earson(X, Y ) = Cov(X, Y ) σ X σ Y (13) In order to compute the correlation of the used criteria with the three types of alterations, we define for each type of alteration and for each criterion p the variables as follows: • X p = {X pk | k = 1 : 4} where X p1 is the set of values of criterion p for the reference databases images, (X p2 , X p3 , X p4 ) are the sets of values of criterion p for the altered databases level 1, 2 and 3, respectively.
• Alteration levels are represented by the variable Y (1: for the reference databases, 2, 3 and 4: for the altered databases level 1, 2 and 3). More precisely, Y = {y k |y k = 1 f or k = 1 : N, y k = 2 f or k = N + 1 : 2N, y k = 3 f or k = 2N + 1 : 3N and y k = 4 f or k = 3N + 1 : 4N } where N is the size of the 4 reference databases. Using the extracted SIFT keypoints and the Pearson's correlation coefficient, four pattern-based quality criteria are retained to contribute to quality assessment: 1. Keypoints: the number of keypoints detected from image im. 2. DC coefficient: DC coefficient of the matrix M s , with N (im) rows and 128 columns, related to SIFT invariant descriptor for s i , i = 1 : N (im) where N (im) is the number of detected keypoints for image im.
Learning the multi-class SVM models We learned 7 multi-class SVM models: 5 for face databases, and 2 for hand veins and fingerprint databases. Table  4  presents the accuracy of the learned multi-class SVM models on both training and test sets. We have put the symbol "×" at the last two lines, since we have only 1 multi-class SVM generated per database. Table  4  shows the efficiency of the proposed metric in predicting the three synthetic alterations (blurring, Gaussian noise and resize) of data, with a valuable 10-class classification accuracy (going from 82. 29% to 97. 73 on the training set, and from 81. 16% to 91. 1% on the test set). Results for the different databases are similar but not exactly the same. The reason is related to the complexity of the databases incorporating more or less artifacts.
In order to test more the generalization of these results, we have tested the following: • we used one of the four face databases for training, and the rest three for tests. We obtained a valuable 4-class accuracy of 87. 84%. The four classes are 1: original, 2, 3 and 4: blurring, gaussian noise and resize level 3, respectively. For a 10-class classification, this accuracy decreased to 50. 78% due to the different resolution of images in each database. However, the resolution of images to be used for training should be as much as similar for the test images in order to maintain a high accuracy. This is illustrated by the results of the multi-class SVM (SV M all ) presented in Table  4  (third and fourth columns). We have obtained good classification results since the images used for training this model contains a set of images from each database.
• we used a subset of the CASIA-FingerprintV5  [43]  (first 100 persons, 5 images per person, left thumb) of 500 images. The volunteers of CASIA-FingerprintV5 were asked to rotate their fingers with various levels of pressure to generate significant intra-class variations. Using the FVC2002 DB2 database for learning the multi-class SVM, we classified each of the CASIA-FingerprintV5 500 images into the four categories presented in Table  5  (1 for good, 2 for fair, 3 for poor and 4 for very poor). We then computed the intra-class matching scores, using the matching algorithm presented in Section 4. 1. 3, of the CASIA-FingerprintV5 database (by taking the first image as reference and the rest for the test). Using the pearson correlation coefficient between the obtained intra-class matching scores and the four categories, we obtained a significant correlation of 0.
EER value of each quality set In order to validate the proposed quality metric in predicting the used matching algorithm performance, according to Grother and Tabassi  [8] , we calculate the EER value of each quality set predicted by the learned multiclass SVM models. Figure  17  shows the EER values of each quality set among the used biometric databases. From this figure, we can deduce several points: • The proposed metric has shown its efficiency in predicting the used matching system among the 6 biometric databases. More generally speaking, the more the images are altered, the more the EER values are increasing. This is demonstrated by the increasing curves presented in Figure  17. For the hand veins database, we have obtained a slight increase of EER values (0%: good category to 0. 05%: very poor category).
Conclusion and perspectives The quality assessment of biometric raw data is a key factor to take into account during the enrollment step when using biometric systems. Such kind of information may be used to enhance the overall performance of biometric systems, as well as in fusion approaches. However, few are the works exist in comparison to the performance ones. Toward contributing in this research area, we have presented an image-based quality assessment metric of biometric raw data using two types of information (image and patternbased quality). The proposed metric is independent from the used matching system, and could be used to several kind of modalities. Using six public biometric databases (face, fingerprint and hand veins), we have shown its efficiency in detecting three kinds of synthetic alterations (blurring, Gaussian noise and resolution).
ations) and three levels for each type of alteration. The presented alterations are commonly realistic during the acquisition of biometric raw data, which may deeply affect the overall performance of biometric systems. Finally, we get 60 databases: 6 reference and 54 altered databases (i. e. , 9 for each reference database): 1. Reference databases • FACES94 Database [36]: This database is composed of 152 individuals and 20 samples per individual. These images have been captured in regulated illumination and the variation of expression is moderated.
Altered databasesUsing the introduced alterations presented in Section 4. 1. 1, we generated 54 databases from the 6 reference databases: FACES94, ENSIB, FERET, AR, FVC2002 DB 2 and the hand veins databases. Figure12shows these alterations on a sample from FACES94 database. (a) Blurring alteration (b) Gaussian noise alteration (c) Resize alteration.
Learning the multi-class SVM models: for face databases, we learn 4 multi-class SVM models using the images from the four benchmark databases (1 multi-class SVM per benchmark database illustrated by SV M each ), and 1 multi-class SVM model containing examples from the four benchmark databases (illustrated by SV M all ). For the fingerprint and hand veins databases, we learn 2 multi-class SVM models, respectively. In order to train and to test the multi-class SVM models, we split each benchmark database images into two sets S training and S test in a balanced way (i. e. , both sets contain the same ratio of reference and altered images). The choice of the kernel and the selection of the parameters required are presented in Section 3. 3.
20 2. 2 Altered databasesUsing the introduced alterations presented in Section 4. 1. 1, we generated 54 databases from the 6 reference databases: FACES94, ENSIB, FERET, AR, FVC2002 DB 2 and the hand veins databases. Figure12shows these alterations on a sample from FACES94 database. (a) Blurring alteration (b) Gaussian noise alteration (c) Resize alteration.
• Learning the multi-class SVM models: for face databases, we learn 4 multi-class SVM models using the images from the four benchmark databases (1 multi-class SVM per benchmark database illustrated by SV M each ), and 1 multi-class SVM model containing examples from the four benchmark databases (illustrated by SV M all ). For the fingerprint and hand veins databases, we learn 2 multi-class SVM models, respectively. In order to train and to test the multi-class SVM models, we split each benchmark database images into two sets S training and S test in a balanced way (i. e. , both sets contain the same ratio of reference and altered images). The choice of the kernel and the selection of the parameters required are presented in Section 3. 3.
I. THE IPOL JOURNAL This article discusses the development of the online journal IPOL (Image Processing on Line), which seems to be one of the first journals which publishes peer-reviewed articles associating each of them with an online execution demo which allows users to test the algorithms with their own images and parameter values. Both the source code of the algorithm and the source code of the demo are also peer-reviewed. The journal was founded with the support of an Advanced Grant of the European Research Council to develop the mathematical theory and algorithms in the Image Processing field. Its main goal was to demonstrate the ability to mount complex image processing chains for image blind restoration, automatic 3D reconstruction from several photographs of a scene, and automatic analysis and detection of features in images.
Before the last 10 years, the initiatives on reproducible research were not really common in the Image Processing domain. In the field of wavelet analysis, the initiative of Buckheit and Donoho highlights the reproducibility with the development of some Matlab routines called "WaveLab" defined as a base to reproduce all content of their published wavelet articles  [3]. Later a similar approach was also provided by Fadili et al. with the "MCALab" routine dedicated to the Matlab implementation of image decomposition and inpainting algorithms. These approaches were also studied in the signal processing field  [4]  and in the cloud computing domain  [5]. We can also mention the work of Gentleman which proposes a new modern format of publication with a mixture of code and text applied in bioinformatics  [6].
Rooted in a credibility crisis, as pointed out by Donoho et al. in the scientific computation field  [7] , the initiative of the IPOL journal should potentially imply numerous benefits as those described by Donoho  [8] : improve work habits, improved teamwork, great impact, less inadvertent competition, greater continuity, and cumulative impact. The IPOL model requires authors to write the article including pseudo-code of the algorithm and release associated open source code. The code does not need to be optimized, but must implement faithfully the described algorithm, and is designed to be a representative implementation. The demonstration can also be written by the authors, but help can be asked from the technical staff. The reviewers role is to read carefully the article and the source code, and especially verify that they are in accordance.
A. Reference programming language Some rules were needed to precise how algorithms can be implemented and how they should be evaluated by reviewers. The IPOL editorial board prepared a set of software guidelines with the list of authorized programming languages and libraries, and basic criteria of software quality expected by the journal. The first accepted programming language was C/C++, since these compiled languages are well standardized, reliable, fast, common, and stable libraries exist. Two issues remain in author requests. The first is a demand for Matlab support which is to become effective in 2015. The second is related to the use of less known libraries, which can still be used by authors if they are provided entirely within their source code.
B. Online publication It was necessary to design a web interface managing all online articles. Its first classic role was to serve articles in PDF format and the corresponding source code package. But the main software design questions were raised by the online interface of the algorithmic demos, applicable on any image submitted by the users. The system also stores the results of every user experiment in the archives of each algorithm. This program managing all articles was designed in 2009 by N. Limare  [16]  and revised in 2013, before being placed in the hands of other editors. It was necessary to design a web interface adapted to each paper and its particular demo. This framework is a Pythonbased webserver handling inputs and generating web pages in result of every user interaction with a demo: selecting or updating a file, changing a parameter of the algorithm, choosing a part of an image, a point, a line, etc.
C. Improvements in the demo system architecture After more than four years of existence of IPOL, we could identify some parts of the initial architecture of the demo system which need to be improved or extended. In its first version, the demo system was designed as an object-oriented monolithic kernel which performs each of the required operations, including web interaction, format conversion, or managing the archive of experiments. Therefore, the source code of the demo system becomes too complex, with tightly interface-coupled components. And it is not easy to distribute the system over different machines. To solve these problems, the next version of the demo system will have a modular architecture, where each specialized functionality will be put in a separate module. The monolithic kernel becomes then a controller, which executes operations in the specialized modules using a webservice.