dataesr/scientific-paragraphs-categorization huggingface dataset:
2%. On the test set (on  1000 skin images), our method has achieved an average Dice score of 85. 8% and IoU score 78. 2%. An additional comparison has been performed by comparing the FCA-Net model with FCN, U-Net, SegNet, FrCN (reported in  [46]  ), GAN-FCN  [47] , and hand-crafted  [48]. In the case of GAN-FCN model, GAN is used to derive additional training data from ISIC2018 dataset, and then this data is combined with the original training data to train the FCN model for skin lesion segmentation. The authors of  [48]  transform skin images into the RGB space and trained a color classifier to discriminate between lesions and normal skin tissue based only on RGB color vectors. Then, they use a Gaussian mixture model (GMM) to model the probability density functions of skin lesions and a support vector machine regression algorithm to segment the images.
Our model is fully automated and fully self-contained, in the sense that we did not use any data augmentation techniques in the training phase or pre-processing steps. The efficiency of the proposed model is assessed on three publicly available skin lesion segmentation challenge datasets: ISBI2016, ISBI2017, and ISIC2018. Our model outperforms several state-of-the-art methods, such as FCN8, U-Net, Seg-Net, ExB, CUMED, MResNet-Seg, and FrCN, in terms of Dice and IoU metrics. In future work, we will focus on adapting the proposed model to segment lesions in different organs (e. g. , breast, colon and eye) using multi-modal medical images.
Diluted exhaust was then sent to different gaseous and particle-measuring instruments. A CAI 600 series CO2 analyser was used to measure the CO2 concentration directly from the raw exhaust. A second CO2 meter (SABLE, CA-10) connected via a three-way valve between the two diluters was used to record the CO2 concentration from the diluted exhaust. For the particle morphology analysis, the particles on the TEM grids were collected by employing the Nanometer Aerosol Sampler (TSI 3089). Joel 2100 High Resolution Transmission Electron Microscopy (HRTEM) was employed to capture the images for the data analysis. Conventional diesel was used as the baseline fuel, along with coconut biodiesel. This particular biodiesel was chosen due to its dominance in the feedstock of the Pacific Islands. Also, in Europe, around 11% of biodiesel is sourced from coconut feedstock.
DPF particle loading Particle mass concentrations were calculated from the particle size distributions measured by the DMS500. The inversion procedure as recommended by the manufacturer was used  [36]. A key parameter needed to convert the total volume, as calculated from the size distributions, to total mass is the effective density. Although the authors are aware that the effective density of diesel and biodiesel particles are most likely not the same due to the lack of empirical data for biodiesel particles, we have used the same effective density, as recommended by the manufacturer  [37] , for all of the fuels. This may introduce some errors, but it is believed to be insignificant. The total particle mass trapped (Masstrapped) inside the DPF after n seconds was calculated by integrating the trapped particle mass at the DMS 500 sampling frequency of every half-second, as shown in the following formula: Masstrapped (n) = # $𝐶 &'(((*+,-.
TEM analysis and image post processing For the determination of morphology of DPM agglomerates, low resolution images were taken at 30,000 times magnification for TEM micrographs with a resolution of 200. Approximately 40 images per sample were taken from different positions. A MATLAB-based code was developed based on algorithms reported in the literature  [38, 39]  to analyse the TEM images and calculate the morphology characteristics of the DPM. A detailed analysis of the morphology of particles has been explained in another paper  [40].
Acknowledgements: We warmly thank Frédéric Austerlitz, Erkan O. Buzbas, Antoine Cools, Flora Jay, Evelyne Heyer, Margueritte Lapierre, Guillaume Laval, Nina Marchi, Etienne Patin, Noah A. Rosenberg, and Zachary A. Szpiech for useful comments and discussions. This project was funded in part by the French Agence Nationale de la Recherche project METHIS (ANR 15-CE32-0009-01). CFL was funded in part by the Sven and Lilly Lawski's Foundation (N2019-0040). Authors declare no conflict of interest for this work.
MATERIAL AND METHODS We aimed at evaluating how ABC model-choice and posterior parameter estimation could allow reconstructing highly complex historical admixture processes using independent genomewide SNPs. To do so, we chose to focus on the recent admixture history of populations of African and European ancestry, descending from European colonization and the TAST in the Americas. This case-study represents an appropriate setting for empirically testing our ABC approach, since this period of history starting in the late 15th century has been extensively studied in population genetics based on the same publicly available datasets. First, we describe the targeted case-study population and genetic datasets. Second, we present in detail the complex admixture processes here investigated and the associated demographic parameters.
Third, we describe the novel simulation and summary statistics calculation software package called MetHis, here proposed to investigate these admixture processes. Fourth, we detail the Random-Forest ABC procedure used for scenario-choice inference and the performance of this approach both in general for the tested models and specifically for the real data here investigated. Finally, we detail the Neural Network ABC procedure deployed to estimate posterior parameter distributions, its parameterization, and the cross-validation procedures conducted to evaluate its power and accuracy.
Population Genetics Dataset We considered the admixture histories of the African American (ASW) and Barbadian (ACB) population samples from the 1000 Genomes Project Phase 3 (1000 GENOMES PROJECT CONSORTIUM 2015). Previous studies identified, within the same database, the West European Great-Britain (GBR) and the West African Yoruba (YRI) population samples as reasonable proxies for the genetic sources of the admixture of both ACB and ASW populations, consistently with the macro-history of the TAST in the former British colonial empire in Africa and the Americas  (BAHARIAN et al. 2016; MARTIN et al. 2017; VERDU et al. 2017). We excluded from our sample set, individuals previously identified to be more closely related than first-degree cousins in the four populations separately  (VERDU et al.
Foundation of the admixed population H For all scenarios (Figure  1  However, MetHis does not allow simulating the source populations for the admixture process. Instead, this can be done efficiently using coalescent-based simulations with existing software such as fastsimcoal2  (EXCOFFIER and FOLL 2011; EXCOFFIER et al. 2013) , or other forward-intime genetic data simulators such as SLIM v3  (HALLER and MESSER 2019).
Simulating source populations Here, we wanted to focus our investigation specifically on the admixture process undergone by the admixed population descending from the TAST. Therefore, we made several ad hoc simplification choices for simulating source population genetic data under the nine competing models described next. We consider that the African and European populations at the source of the admixture processes are very large populations at the drift-mutation equilibrium, accurately represented by the Yoruban YRI and British GBR datasets here investigated. Therefore, we first build two separate datasets each comprising 20,000 haploid genomes of 100,000 independent SNPs, each SNP being randomly drawn in the site frequency spectrum (SFS) observed for the YRI and GBR datasets respectively.
These two datasets are used as fixed gamete reservoirs for the African and European source population datasets separately, at each generation of the forward-in-time admixture process. From these reservoirs, at each generation separately, we build an effective individual gene-pool of diploid size Ng (see below), by randomly pairing gametes avoiding selfing. These virtual source populations provide the parental pool for simulating individuals in the admixed population H, at each generation separately. Thus, while our gamete reservoirs are fixed over the 21 generations of the admixture processes here considered, the parental genetic pools are randomly built anew at each generation of the admixture process.
Simulating the admixed population At each generation, MetHis performs simple  Wright-Fisher (FISHER 1922; WRIGHT 1931)  forward-in-time simulations, individual-centered, in a panmictic admixed population H of diploid effective size Ng. For a given individual in the hybrid population at the following generation (g + 1), MetHis independently draws each parent from the source populations with probability  , (Figure  1 , Table  1 ), or from the hybrid population with probability ℎ  , randomly builds a haploid gamete of 100,000 independent SNPs for each parent, and pairs the two constructed gametes to create the new individual. Here, we decided to neglect mutation over the 21 generations of admixture considered. This is reasonable when studying relatively recent admixture histories. Nevertheless, this will be improved in future versions of the software, in particular to allow studying much more ancient admixture histories.
Sampling simulated unrelated individuals After each simulation, we randomly draw individual samples matching sample sizes in our observed dataset: 90 and 89 individuals respectively from the African and European sources, and 50 individuals in the admixed population H. We sample individuals until our sample set contains no individuals related at the 1 st degree cousin within each population and between the admixed population and either source populations, based on explicit parental flagging during the last 2 generations of the simulations.
Simulating by randomly drawing parameter values from prior distributions With this implementation of MetHis, we performed 10,000 independent simulations under each nine competing scenarios described above and in Figure  1 , drawing the corresponding modelparameters (pulse-times and associated admixture intensities, "steepness" of the recurring admixture-increases or decreases and associated initial and final admixture intensities), in priordistributions detailed in Table  1. Although the user can perform MetHis simulations with an external parameter list, we readily provide ad hoc scripts in MetHis, which allow to easily generate parameter lists for a large number of complex admixture scenarios set by the user. For the best models identified using Random-Forest ABC model-choice approach  (PUDLO et al.
Summary Statistics We considered 24 summary statistics for ABC model-choice and posterior parameter inference, computed on each simulated dataset with MetHis. Four statistics were strictly withinpopulations; four statistics were strictly between-populations; and 16 statistics were specifically calculated to describe the distribution of admixture among individuals within the admixed population H. Indeed, previous theoretical works have shown that this distribution and all its moments carried signatures of the underlying complex historical process  (VERDU and ROSENBERG 2011; GRAVEL 2012). Numerous descriptive statistical approaches have been successfully developed to estimate admixture fractions from genetic data in admixed populations (e. g. ALEXANDER et al. 2009; PATTERSON et al. 2012; PICKRELL and PRITCHARD 2012).
However, most methods remain computationally costly when iterated for large to very large sets of simulated genetic data. Therefore, only a few previous ABC historical inference approaches have considered the distribution of admixture fraction as a summary statistics  (BUZBAS and ROSENBERG 2015; BUZBAS and VERDU 2018) , although some admixture-related statistics have been embedded in ABC software packages  (CORNUET et al. 2014).
Distribution of admixture fractions as a summary statistic We estimated individual admixture distribution based on allele-sharing-dissimilarity (ASD)  (BOWCOCK et al. 1994 ) and multidimensional scaling (MDS)  (PASCHOU et al. 2007; PRICE et al. 2009). For each simulated dataset, we first calculated a pairwise inter-individual ASD matrix using asd software (https://github. com/szpiech/asd) on all pairs of sampled individuals and all 100,000 independent SNPs. Then we projected in two dimensions this pairwise ASD matrix with classical unsupervised metric MDS using the cmdscale function R (R DEVELOPMENT CORE TEAM 2017). We expect individuals in population H to be dispersed along an axis joining the centroids of the two proxy source populations on the two-dimensional MDS plot. We projected individuals orthogonally on this axis, and calculate individual's relative distance to each centroid.
Model-choice with Random-Forest Approximate Bayesian Computation We used Random-Forest ABC (RF-ABC) for model-choice implemented in the abcrf function of the abcrf R package to obtain the cross-validation table and associated prior error rate using an out-of-bag approach (Figure  2 ). We considered the same prior probability for the nine competing models each represented by 10,000 simulations in the reference table. For the ACB and ASW observed data separately, we performed model-choice prediction and estimation of posterior probabilities of the winning model using the predict. abcrf function in the same R package, using the complete simulated reference table for training the Random-Forest algorithm (Figure  3 , Supplementary Table  S1 ). Both sets of analyses were performed considering 1,000 decision trees in the forest after visually checking that error-rates converged appropriately (Supplementary Figure  S6 ), using the err.
01, 0. 05, 0. 1, and 0. 2), and a number of neurons ranging between four and seven (the number of free parameters in the winning scenarios, see Results). For each pair of tolerance level and number of neurons values, we conducted cross-validation checking of posterior parameter estimations with 1,000 randomly chosen simulated datasets in turn used as pseudo-observed data with the "cv4abc" function of the R package abc. We considered the median point-estimate of each posterior parameter ( ^) to be compared with the true parameter value used for simulation (  ). The cross-validation parameter prediction error was then calculated across the 1,000 separate posterior estimations for pseudo-observed datasets for each pair of tolerance level and number of neurons, and for each parameter   , as ∑ ( ^ -  ) 2 1000 1 (1000 × (  )) ⁄ , allowing to compare errors for scenarios-parameters across NN tolerance-levels and numbers of hidden neurons, using the summary.
cv4abc function in the R package abc  (CSILLÉRY et al. 2012). Results showed that, a priori, all numbers of neurons considered performed very similarly for a given tolerance level (Supplementary Table  S2 ). Furthermore, results showed that considering 1% closest simulations to the pseudo-observed ones, to train the NN for parameter estimation, reduces the average error for each tested number of neurons. Thus, we decided to opt for four neurons in the hidden layer and a 1% tolerance level for training the NN in all subsequent NN-ABC analyses, in order to avoid overfitting in parameter posterior estimations.
Estimation of model-parameters posterior distributions for ACB and ASW We jointly estimated model-parameters posterior distributions for the ACB and ASW admixed population separately, using 100,000 simulations for the best scenarios identified for each admixed population separately, using NN-ABC ("neuralnet" methods' option in the R package abc) based on the logit-transformed ("logit" transformation option in the R package abc) summary statistics using a 1% tolerance level to train the NN (i. e. considering only the 1,000 closest simulations to the observed data), fitted using a single-hidden-layer neural network with four hidden neurons (Figure  4 , Table  2 ).
We then compared these estimates with the true parameter used for each 1,000 pseudo-observed target in the vicinity of our observed data and provide three types of error measurements in Finally, based on these cross-validation procedures, we evaluated a posteriori if, in the vicinity of the ACB and ASW observed datasets respectively, the lengths of the estimated 95% credibility intervals for each parameter was accurately estimated or not  (JAY et al. 2019). To do so, we calculated how many times the true parameter (  ) was found inside the estimated 95% credibility interval [2. 5%quantile(  ^) ; 97. 5%quantile(  ^)], among the 1,000 out-of-bag NN-ABC posterior parameter estimation, separately for the ACB and ASW (Supplementary Table  S3 ). For each parameter, if less than 95% of the true parameter values are found inside the 95% credibility interval estimated for the observed data, we consider the length of this credibility interval as underestimated indicative of a non-conservative behavior of the parameter estimation.
Finally, the observed vectors of 24 summary statistics computed for the ACB and ASW, respectively, were not significantly different (p-value = 0. 468 and 0. 710 respectively) from the 90,000 simulated sets of statistics using a goodness-of-fit approach (Supplementary Figure  S5 ). Therefore, we successfully simulated datasets producing sets of summary statistics reasonably close to the observed ones, despite considering constant effective population sizes, fixed virtual source population genetic pool-sets, and neglecting mutation during the 21 generations of forward-in-time simulations performed using MetHis.
Complex admixture scenarios cross-validation with RF-ABC We trained the RF-ABC model-choice algorithm using 1,000 trees, which guaranteed the convergence of the model-choice prior error rates (Supplementary Figure  S6 ). Based on this training, the complete out-of-bag cross-validation matrix showed that the nine competing scenarios of complex historical admixture could be relatively reasonably distinguished using our set of 24 summary statistics and 10,000 simulations under each competing scenario, despite the high level of nestedness of the scenarios here considered (see Material and Methods). Indeed, we calculated an out-of-bag prior error rate of 32. 41%, considering each 90,000 simulation, in turn, as out-of-bag pseudo-observed target dataset and the rest of simulations (89,999) as the training dataset for RF-ABC scenario-choice.
DISCUSSION We evaluated how machine-learning Approximate Bayesian Computation methods can bring new insights to the reconstruction of highly complex admixture histories using genetic data. To illustrate our proof of concept and thoroughly investigate the power and accuracy of our approach using real data, we aimed at reconstructing the recent complex admixture history for the African American (ASW) and Barbadian (ACB) population samples from the 1000 Genomes project (Phase 3). Our results demonstrated that our novel MetHis forward-in-time simulator and summary statistics calculator coupled with RF-ABC scenario-choice can often clearly infer the best class of highly complex admixture histories underlying independent SNP data diversity, in a reasonable-size sample and genetic dataset. In the two source-populations admixture models here investigated, we distinguished scenarios encompassing two pulses of admixture from each source, after the founding admixture event, monotonically increasing or decreasing admixture intensities over time, or a combination of these three scenarios.
2010; ROBERT et al. 2010; SISSON et al. 2018). Importantly, the current MetHis -ABC approach does not make use of admixture linkagedisequilibrium patterns in the admixed population, and only relies on independent genetic markers. Nevertheless, admixture LD has consistently proved to bring massive information about the complex admixture history of numerous populations worldwide  (GRAVEL 2012; HELLENTHAL et al. 2014; MEDINA et al. 2018; NI et al. 2019). However, existing methods to calculate admixture LD patterns remain computationally intensive and require numerous markers and accurate phasing, which is difficult under ABC where such statistics have to be calculated for each one of the numerous simulated datasets. In this context, RF-ABC  (PUDLO et al. 2016; RAYNAL et al. 2019)  or AABC  (BUZBAS and ROSENBERG 2015)  methods allow substantially diminishing the number of simulations required for satisfactory scenario-choice and posterior parameter inference, which makes both approaches promising tools for using, in the future, admixture LD patterns to reconstruct complex admixture processes from genomic data.
Approximate Bayesian Computation mean posterior parameter errors under the winning Scenario AfrDE-EurDE, for the ACB and ASW populations separately, using four different methods: NN estimation of the parameters taken jointly as a vector, NN estimation of the parameters taken separately, Random Forest (parameters taken separately), and Rejection the 1,000 simulations closest to our real data, separately for the ACB and ASW, as pseudoobserved datasets for four separate methods : NN estimation of the parameters taken jointly as a vector, NN estimation of the parameters taken independently, Random Forest (parameters are taken independently), and Rejection (parameters are taken independently).
Forward-in-time simulations with MetHis 2012). In this context, we developed MetHis, a C open-source software package available at https://github. com/romain-laurent/MetHis, to simulate large amounts of genetic data under the two-source populations VR2011 model and calculate summary statistics of interest to population geneticists interested in complex admixture processes. MetHis, in its current form, can be used to simulate any number of independent SNPs in the admixed population H. Simulation of genome-wide independent SNPs under highly complex admixture histories is often not trivial under the coalescent and using classical existing software (WAKELEY et al.
2009; Cordier et al. 2012; Hibbett & Taylor 2013; Schmidt et al. 2013; Hibbett et al. 2016 ). The internal transcribed spacer (ITS) region is now recognized as the universal barcode for fungi  (Schoch et al. 2012) , and is conventionally used to sequence fungal communities  (Lindahl et al. 2013; Bálint et al. 2014) , in combination with large taxonomic reference databases, such as UNITE  (Abarenkov et al. 2010; Kõljalg et al. 2013). Despite their widespread use, metabarcoding approaches suffer from various biases due to the sampling process, molecular biology steps and bioinformatic analyses used  (Lindahl et al. 2013; Schmidt et al. 2013; Bálint et al. 2016; Sommeria-Klein et al. 2016; Palmer and Jusino et al. 2018 ). These biases can prevent accurate recovery of the fungal community. For instance, the fungi identified may differ according to the barcode region chosen and the primers used for its amplification  (Tedersoo et al.
Bioinformatic approaches We analyzed the MiSeq sequences with 360 combinations of bioinformatic softwares and parameters (referred to hereafter as bioinformatic approaches). These approaches differed in (1) the paired-end read assembly algorithm used (Assembly), (2) the fungal ITS1 extraction method (Extraction), (3) the method of sequence variation analysis (Variation), (4) the treatment of chimeric sequences (Chimeras) and (5) the final filtering of the community (Filtering). The various steps of the bioinformatic analyses are described below and in Figure  2. (1) Paired-end read assembly algorithm (Assembly) Two paired-end read assembly algorithms were compared (Fig. 2 ). Paired-end sequences were joined with the FASTQ-JOIN function of QIIME v1. 8. 0 (Assembly=FASTQ-JOIN_OL) or with PEAR v0.