dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Unmanned Aerial Vehicles (UAVs), especially rotary wing unmanned aerial systems, used today in many application fields. An eminent case of use is to reach inaccessible areas autonomously, for example in reconnaissance missions  [1] , the fields of search and rescue  [2]  [3], inspection  [4]  and transportation of data and package  [5]  are greatly interested in multi-copter utilization. One multi-copter has the ability to use embedded sensors, cameras or actuators to accomplish a wide range of tasks. Data collected from embedded sensors includes images and video, environmental measurements, and other types of data acquired from the exploration area. Due to the relatively simple mechanical design, the high maneuverability and hovering capabilities of multi-copters, these are especially useful for the mentioned fields of application.
Numerical experiments In order to validate our optimal control approach, we considered the physical parameters of the DJI Phantom 2 quadrotor with multi-rotor propulsion system (2212/920KV motors). The physical parameters of the Phantom 2 used in the simulation experiment, are l = 0. 175m, m = 1. 3kg, I x = 0. 081kgm 2 , I y = 0. 081kgm 2 , I z = 0. 142kgm 2 , κ b = 3. 8305 10 -6 N/rad/s, κ = 2. 2518 10 -8 Nm/rad/s. We solved the problem (10) numerically using the GPOPS-II optimal control software under Matlab 8. 5. GPOPS-II software employs hp-adaptive Gaussian quadrature collocation methods and sparse nonlinear programming  [24]. We used the nonlinear programming (NLP) solver IPOPT (Interior Point OPTimizer)  [25]  among the two solvers offered by the software. In our first test, we solved problem  (10)  to find the energy-efficiency control inputs α i allows quadrotor to fly from the origin at time t 0 = 0 to the position  [6, 7, 8]  T m with the yaw angle take a null value and fixed final time t f = 10s.
Participants Forty-four people participated in our eye-tracking study. A total of 19 participants had been formally diagnosed with autism and they were considered as ASD-group participants whereas the remaining 24 participants were neurotypical people and they were considered as control-group participants. We recruited our ASD-group participants through a charity organisation in Birmingham and the student enabling centre at the University of Wolverhampton whereas we recruited our control-group participants through snowball sampling. All of the control-group participants were asked to complete the Autism Quotient (AQ) test to ensure that they were not on the autism spectrum  [2]. One of these participants had a high score in the test, and therefore we excluded his/her data from further analysis.
To identify the elements of the pages, we used the extended VIPS algorithm as the VIPS algorithm has been commonly used for dividing web pages into their visual elements based on the source code and visual representation and relating the resultant elements with the underlying source code  [1]. It is a crucial feature of the VIPS algorithm as it allows further processing of web pages based on their elements. This algorithm provides visual elements in a hierarchical form where more and smaller elements are available in deeper levels. Visual complexity level: To identify a visual complexity level, we used the ViCRAM tool to compute a visual complexity score (VCS) for each page which varied between 0 and 10 where higher VCS values represent higher visual complexity  [25, 26]. This tool takes a web page with its URL, counts specific structural elements on the web page (i.
Fixation duration is typically related to information processing  [15, 35]. When users look at unnecessarily more points on web pages while searching for a particular item or a piece of information, their searching behaviours are usually considered as inefficient  [11]. Furthermore, when users make more transitions between elements of web pages while searching for a particular item or a piece of information, this situation is typically interpreted as uncertainty in searching  [11]. Based on a preliminary analysis conducted by Eraslan et al. [13]  with other eye-tracking datasets, we expected that the ASD group participants would have lower fixation durations but they would have higher fixation counts and transitions between different elements. The results of our analyses for the first research question are provided in Tables  3  and 4.
In this study, we applied cell type deconvolution to characterize the cell type specificity of cis-eQTLs and cis-sQTLs for 43 cell type-tissue combinations, using seven cell types across 35 tissues (Fig. 1A ). Estimating the cell type composition of a tissue biospecimen from RNA-seq remains a challenging problem  (15)  and multiple approaches for inferring cell type proportions have been proposed  (16). We performed extensive benchmarking for multiple cell types across several expression datasets (fig. S1 ). The xCell method  (17) , which estimates the enrichment of 64 cell types using reference profiles, was most robust based on correlation with cell counts in blood (fig. S1A ), in silico simulations (fig. S1B ), and correlation with expression of marker genes for each cell type (fig. S1C ).
Funding: The  GTEx Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health (NIH)  and by the  National Cancer Institute (NCI) , the  National Human Genome Research Institute (NHGRI) , the  National Heart, Lung, and Blood Institute (NHLBI) , the  National Institute on Drug Abuse (NIDA) , the  National Institute of Mental Health (NIMH)  and the  National Institute of Neurological Disorders and Stroke (NINDS). This work was funded by following funding sources:  Marie-Skłodowska Curie  fellowship  H2020 Grant 706636 (S. K-H. ),HHSN268201000029C  (F. A. ,K. G. A. ,A. V. S. ) 5U41HG009494 (F. A. ,K. G. A. ),  U01HG007593 U01HG007598  (M. O. ,B. E. S. ),  1K99HG009916-01  (S. E. C. ),  R01MH107666  (H. I. ),  P30DK020595  (H. I. ),  R01HG002585  (G.
W. ,M. S. ),  R01MH101814  (M. M-A. ,V. W. ,R. G. ,D. G-M. ,A. V. ,E. T. D. ),  BIO2015-70777-P ,  Ministerio de Economia y Competitividad and FEDER  funds (M. M-A. ,V. W. ,R. G. ,D. G-M. ), FPU15/03635 ,  Ministerio de Educación, Cultura y Deporte  (M. M-A. ), R01MH109905  ( A. Ba. ), Searle Scholar Program  ( A. Ba. ), EU IMI program  ( UE7-DIRECT-115317-1 ) (A. V. ,E. T. D. ),  FNS  funded project  RNA1  ( 31003A_149984 ) (A. V. ,E. T. D. ),  Massachusetts Lions Eye Research Fund  Grant (A. R. H. ),  MRC  Grants  MR/R023131/1  and  MR/M004422/1  (K. S. S),  R01MH106842  (T. L. ), Biomedical Big Data Training Grant  5T32LM012424-03  (B. N. ),  R01HL142028  (T. L. ,S. K. ),  R01GM122924  (T. L. ,S. E. C. ),  UM1HG008901  (T. L. ),  R01GM124486  (T. L. ). The TwinsUK study was funded by the  Wellcome Trust and European Community 's  Seventh Framework Programme  (FP7/2007-2013).
Two major drawbacks commonly arise when 1) recombination is present or 2) sample sizes are large. The first major drawback, often encountered in analyses of within-species variation, is that recombination breaks the assumption of a single phylogeny, and instead generates a network of ancestral relationships in which different genes, and different positions within genes, can have different phylogenetic histories  (Schierup and Hein 2000). It is well established that inappropriate application of phylogeny-based methods to recombining data can produce highly misleading biological inferences, including false signals of adaptive evolution in the form of artificially elevated d N =d S  (Anisimova et al. 2003; Shriner et al. 2003). The second major drawback is the computational cost of estimating a phylogeny when the number of sequences becomes large, for example, the 10,209 genomes recently published by CRyPTIC Consortium and 100,000 Genomes Project (2018) that bear witness to the relentless evolution of antimicrobial resistance in tuberculosis.
Open Access implicitly integrates over phylogenetic relatedness using diffusion theory and the coalescent  (Wright 1949; Kingman 1982). Since genomegaMap interprets codon count information, its computational cost remains constant even as the sample size increases arbitrarily, making it a viable approach for extremely large data sets. The method assumes independence between sites, yet simulations show that the method performs well even when the absence of recombination causes strong linkage disequilibrium. I demonstrate the utility of the method by estimating variation in d N =d S ratios in 3,979 genes sequenced in 10,209 Mycobacterium tuberculosis genomes (CRyPTIC Consortium and 100,000 Genomes Project 2018).
Analysis of 10,209 M. tuberculosis Genomes CRyPTIC Consortium and 100,000 Genomes Project (2018) collected and whole-genome sequenced 10,209 M. tuberculosis samples from 16 countries across six continents comprising strains enriched for antimicrobial resistance and unenriched strains collected for routine clinical diagnostics. They mapped all genomes to the H37Rv reference genome  (Cole et al. 1998 ) (GenBank accession number NC_000962. 2). I downloaded the alignment of every genome to H37Rv and combined these to create a multiple sequence alignment for each of the 3,979 CDSs in the GenBank annotation, ignoring insertions relative to H37Rv and masking nonsense mutations. Inference of x, j, and h for an individual gene can be improved by gleaning information from other genes. Often this is implemented through a hierarchical model, for example, estimating a distribution for the selection parameters across all sites in all genes  (Wilson et al.
2 for j and h to avoid overinformative priors. This produced a prior median and (2. 5%, 97. 5%) quantiles of 0. 45 (0. 0098, 21) for x, 3. 2 (0. 90, 12) for j, and 0. 057 (0. 023, 0. 14) for h. I analyzed the data under a mixture of two models with equal prior probability: 1) the sliding window model with mean block length p À1 x ¼ 33 codons and 2) the independent codon model. For each gene, I ran two independent MCMC chains of 500,000 or 1,000,000 iterations for the two models, respectively, with 50,000 iterations removed as burnin. The chain lengths were chosen from preliminary runs where convergence and burn-in were assessed visually. The median run times per gene per chain were 233 and 172 min for the two models, respectively. I used the harmonic mean estimate of the Bayes factors to merge the results for each gene and to obtain posterior model probabilities.
CRyPTIC Consortium and 100,000 Genomes Project (2018) collected and whole-genome sequenced 10,209 M. tuberculosis genomes to quantify the performance of genomic prediction of drug resistance. The predictions were correct in 91. 3-97. 5% of resistant isolates and 93. 6-99. 0% of susceptible isolates for the four first-line drugs. These predictions rely on existing knowledge of the genetic mechanisms of drug resistance. Vast data sets have the potential to reveal novel mechanisms of drug resistance through genome-wide association studies (GWAS). Such studies can benefit from an understanding of the selection pressures shaping genetic diversity and the identification of sites under positive selection because often that selection is driven by drug therapy (e. g. ,  Farhat et al. 2013; Os orio et al.
In very few genes, such as pncA encoding pyrazinamidase, did positive selection appear to be more common. More often, the strongest evidence for positive selection was found in a small number of codons within genes dominated by negative selection, such as gyrA, encoding DNA gyrase subunit A. This shows how positive selection occurs against backdrops of both rapid amino acid change and functional constraint, so the mean Prðx > 1Þ per gene provides limited insight. Instead, I identified every gene with one or more codons exhibiting a posterior probability of positive selection of at least 90% (i. e. , Prðx > 1Þ ! 0:9) (supplementary table  S1 , Supplementary Material online). The genes are annotated by their descriptions in GenBank and MycoBrowser  (Kapopoulou et al. 2011). In total, 15,931/1,330,612 codons (1.
Wilson and the CRyPTIC Consortium. doi:10. 1093/molbev/msaa069 MBE counts for every annotated CDS https://doi. org/10. 6084/m9. figshare. 7599020. v1 and a summary of the Bayesian analysis at the gene level (supplementary table S1, Supplementary Material online) and codon level https://doi. org/10. 6084/ m9. figshare. 10329311. GenomegaMap is available as a Docker container and Cþþ source code from https://hub. docker. com/r/dannywilson/ genomegamap and https://github. com/danny-wilson/ genomegaMap. The following data are available: codon.
Data Our methodology is based on a diachronic analysis of one image before and one image after the event. For the two S2 images, they have to be chosen according to three main criteria to ensure the best quality of the results. Reference image has to be as close as possible before the event to prevent changes possibly occurring during this time laps not directly linked to it. Both images have to contain very few clouds (as their masking effect is cumulative), which could be difficult to obtain for the postcyclone image as it needs to be very close after the event, during a period often very cloudy. S1 images are less constraining. We have chosen the dates closest to the optical images in order to be able to compare them as much as possible with each other. Table  1. Sentinel-1 and 2 images used for change detection.
C-Method The theoretical spectral data related to the V-groove grating shown in Fig. 1  are computed using the C-Method, also known as Chandezon Method, through the commercial software McGrating  [17]. The C-method is a well known and powerful tool especially suited for the computation of diffraction efficiencies of shallow sinusoidal or pseudo-sinusoidal gratings. It solves Maxwell's equations using the following coordinates transformation  [18, 19] :              v = x; w = y; u = z -s(x) (2) where s(x) is the grating's profile. Although this transformation complicates the functional form of Maxwell's equations, the surface roughness is flattened, thus making the matching of boundary conditions simpler. In the new (u,v,w) coordinates system, the problem is reduced to an eigenvalues problem whose resolution determines the propagative and evanescent modes of the system.
Mathematically, the meta-model can be written as f : R 5 → R 3 [CIE L * a * b * ]; y i = f (x i ); i = 1. N; (3) which, in continuity with our previous work  [12] , was obtained using an artificial neural network (ANN) as intensive learning meta-model  [24, 25]. In this contribution we consider three hidden layers with 100 neurons each. The weights are determined through the minimization of the mean square error on the training set and the learning method employed is the gradient descent, which is calculated by the back-propagation principle. To train the meta-model we generated a color database, composed of 14000 elements, using the spectra numerically obtained with the C-method, considering TM-polarized light and zeroth order diffraction in reflection configuration. Throughout our numerical experiments we found that this size of the database was enough to minimize the average prediction error and, at the same time, to have a sufficient number of elements to generate the database in a reasonable time.
Each geometrical parameter (p l ) l=1. 5 lies within the lower and upper bounds shown in Table  1. These values were chosen taking into account industrial feasibility considerations. The chromatic response of each structure in the database was calculated using the CIE 2deg color matching functions and the standard illuminant D65. The related perceived colors are depicted in Fig. 4  in the CIE L*a*b* space. We can notice that by adjusting the structure's parameters, we can reach a wide and vivid visible color gamut. The difference between the initial color database and the predicted one is shown in Fig. 5(a). We quantify the prediction error through the color difference ∆E f , f = ∆E(f (x i ), f (x i )) with the formula given by CIEDE2000  [26]. The histogram in Fig. 5 (b) illustrates the performance of the meta-model prediction, we notice that 78% of the initial elements of the database are predicted with a ∆E f , f <2 and more than 98% with a ∆E f , f ≤ 4, a difference that is barely perceptible to the human eye.
The NSGA-II is one of the most popular population-based algorithms used for evolutionary multi-objective optimization (EMO). It is a very efficient method for solving problems with a small number of objective functions. We used the implementation of NSGA-II provided in Python via the library Platypus  [34]  with 1000 initial individuals and 50000 iterations, the optimal 3D-Pareto front obtained is shown in Fig. 8(a). In order to assess the convergence behavior of the NSGA-II, we searched for the target colors in Fig. 6  considering different randomly generated initial populations. To facilitate the visualization, we show in Fig. 8 (a) only two of the 3D-Pareto fronts obtained. This result suggests that the NSGA-II is not sensitive to initialization and presents a good reproducibility. Furthermore, despite the large number of evaluations needed because of the population size, the meta-model f makes possible to obtain a 3D-Pareto front in about 311 seconds.
Furthermore, the post-optimization stage made evident that among the geometrical parameters optimized, the groove's angle of aperture had the strongest effect on the generation of color. The inclusion of a meta-model in the optimization process is a practical way to significantly decrease the computing time. As it has been shown throughout this work, the iterative nature of the multi-objective approach makes the optimization of the diffraction gratings extremely long when done with the rigorous electromagnetic method. It should be noted that generating the database to train and validate the neural network can be time consuming; however, this is done just once. Afterwards, the meta-model can be used without being necessarily included it in an optimization process. At last, the modular structure of the optimization scheme presented in this work not only facilitates its numerical implementation, but it also makes of it a generic and suitable tool that could be easily used for the solution of inverse or optimization problems in other branches of optics and photonics.
The AMS was generally operated in the high sensitivity V-mode to facilitate constant measurements during flights. The accuracy of these measurements was estimated to 50% with 10% precision during ORACLES. A more thorough description of the University of Hawaii AMS and data processing techniques using data analysis toolkit SQUIRREL v. 1. 57l and PIKA v. 1. 16l can be found elsewhere  (Shank et al. , 2012; Sueper, 2018).
UHSAS Particle size distributions from 60 to 1000 nm were measured with a Ultra-High Sensitivity Aerosol Spectrometer (Droplet Measurement Technologies, Boulder CO, USA). It uses scattered light from a 1054 nm laser to determine particle size. The long wavelength suppresses the ambiguity due to Mie scattering, though the highly absorbing nature of the ORACLES aerosol may result in substantial undersizing of particles > 300 nm. It was calibrated with monodisperse polystyrene latex spheres. The inlet system included a 400ºC thermal denuder that could be switched in and out to identify the refractory fraction of the aerosol, though those data are not presented here. The inlet system had significant losses, particularly for particles <80nm.
GEOS-5 The Goddard Earth Observing System version 5, is a global modeling system developed at NASA Global Modeling and Assimilation Office (GMAO)  (Molod et al. , 2015; Rienecker et al. , 2008). It is a state-of-art modeling tool used for near-real time weather and air quality forecasts. It also serves as tool for climate variability studies and reanalysis for research (MERRA-2)  (Randles et al. , 2017). GEOS-5 includes modules for solving atmospheric circulation and composition, chemistry, ocean circulation and land surface processes. Furthermore, GEOS-5 uses a robust atmospheric data assimilation system using the Grid-point Statistical Interpolation (GSI) algorithm, which includes AOD assimilation from MODIS (Terra and Aqua), among others. Aerosols are treated online using GOCART (Goddard Chemistry Aerosol Radiation and Transport)  (Chin et al.
GEOS-Chem GEOS-Chem version 12. 0. 0 (http://www. geos-chem. org/) is a global 3-D model of atmospheric composition driven by assimilated meteorological data GEOS-FP data  (Lucchesi, 2013)  from the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. The GEOS-FP data have 1-hourly and 3-hourly temporal resolution, 72 vertical layers, and 0. 25x0. 3125º horizontal resolution. The original horizontal resolution is then degraded to 2º x2. 5º for the input to GEOS-Chem. Aerosol types simulated in GEOS-Chem include sulfatenitrate-ammonium aerosols, carbonaceous aerosols, sea salt, and mineral dust. The simulation of carbonaceous aerosols was originally described by  Park et al. (2003). Daily smoke emissions have been updated to the Quick Fire Emission Data set version 2 (QFED2)  (Darmenov and da Silva, 2015)  and a diurnal cycle representative of daytime burning is applied.
The peak area for each detected compound was divided by the peak area of the internal standard (Tetralin ® , Sigma-Aldrich, St. Gallen, Switzerland), and the root emissions of volatiles were finally given as tetralin equivalent nanograms of compound released by gram of root biomass collected  66. For subsequent statistical analyses, we calculated the total amount of VOCs produced per species, and the Shannon diversity index H (function diversity from the package vegan  67  ). We did not include the number of individual peaks (i. e. peak richness), since it is highly correlated with both the total amount (t 1,158 = 2. 92, p < 0. 001), and the Shannon diversity (t 1,158 = 6. 30, p < 0. 001).
Statistical analyses. Effect of herbivory on root biomass, nematode recruitment and VOCs production. To address the impact of the herbivore treatment, we first calculated standardized effect sizes for root biomass and EPNs recruited between herbivore-infested plants minus the control plants using Hedge's g statistics (function cohen. d in package effsize in R  68  ). We next used Bayesian phylogenetic mixed models (BPMMs), as implemented in the R package MCMCglmm  69  , to test the effect of the herbivore treatment on average root biomass, EPNs recruitment, total and all individual VOCs emitted across all species, while taking into account phylogenetic relationships among Festuca species. Differences of VOCs production across species and herbivore treatment were also assessed with a permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distance matrices (function adonis from the package vegan  67  ), and average species and treatment differences were visualized using non-metric multidimensional scaling (NMDS) (function metaMDS in vegan) (Fig.
S3 ). All VOCs data were log-transformed to normalize their distribution (function decostand in vegan). To test for potential negative correlations between constitutive VOCs (species means) and their inducibility (i. e. , the difference in mean VOCs values for each species between control and damaged plants), we conducted tests to control for potential spurious correlations  70  in Matlab (version 7. 5. 0. 342 -R2007b, MathWorks Inc. , USA). We also performed phylogenetically controlled analyses to tests for correlations between mean constitutive VOCs levels and their inducibility.