dataesr/scientific-paragraphs-categorization huggingface dataset:
New data and epidemiological analysis Recent data from cohorts of patients receiving ART have been used to inform updated assumptions about the regional patterns of mortality on treatment in Spectrum. These updates are important because they incorporate new data since the advent of the 'treat-all' ARTera, which capture patterns of mortality with earlier ART initiation. Johnson et al. [7]  conduct an updated analysis of the regional patterns of mortality after ART initiation in Africa, Asia and the Americas, analyzing patient cohort data from the International Epidemiology Databases to Evaluate AIDS (IeDEA) network up to 2017. An imputation approach to account for unascertained mortality among those lost to follow-up is applied in the African regions. The authors find that after controlling for treatment duration and baseline CD4 þ count, mortality after ART initiation has declined over time in all regions, although with slower and more modest declines observed in Africa.
[11][12][13][14]atric model in Spectrum has been refined to reflect newly available data about of pregnant women on treatment at delivery, and slightly higher estimates of HIV transmission in the postpartum period. The age-specific pattern of ART initiation among children has also been modified to reflect the increasing proportion of children who start ART under 2 years and over 10 years of age. Finally, a new tool has been added to disaggregate national or provincial HIV estimates to the district level. This tool aims to support local program planning and decision-making while providing a set of localized estimates that are consistent with the estimates obtained at the national level. The Estimation and Projection Package (EPP) within Spectrum is the tool used to estimate HIV incidence from general population surveys and surveillance of HIV prevalence in antenatal care.
We ensured that there was no light reflection on the glass that could hinder interaction. Between each participant, the glass case was cleaned to remove all finger marks. In public context, anti-fingerprint spray can be used to limit any dirt from touches. We implemented the whole setup on an HP EliteBook laptop running Microsoft Windows 10. Our experiment software was implemented in C# and Unity (version 2018 1. 5). Touch events were sent to the application using the TUIO protocol. We modeled the physical environment in Unity to ensure that the interaction feedback was displayed around the target.
Collected Data and Data Processing We logged all touch events from keyboard press until finger land-off (from the glass case). As we were interested in understanding how well users perform the first contact (land-on) with the glass case, we logged for each trial the land-on and land-off finger positions and times (i. e. first and last contact with the glass). We refined the land-on points analysis with two metrics (cf. Fig. 6 ) already used in  [40, 41] : offset and spread. Both metrics rely on the computation of the centroid, i. e. the average land-on position for each target and condition. The offset is defined as the Euclidean distance (i. e. absolute value) between the center of the target and the centroid, for each condition. In this paper, we also compute the offsetX and offsetY, which represents the relative distance in x and y between the centroid and the center of the target (i.
g. Pérez-Ramírez et al. , 2012). Understanding day-tonight aerosol properties from remote-sensing measurements is essential to advances in aerosol dynamics and changes, which eventually will serve to advance our knowledge on aerosol impact on air-quality and climate. Therefore, current efforts are in integrating different measurements that require advancing in the development of retrieval techniques. During the last 2 decades, global and regional networks have been established to get a comprehensive, quantitative and statistically significant database of atmospheric aerosols. The Aerosol Robotic Network (AERONET;  Holben et al. , 1998)  and East Asian SKYNET  (Nakajima et al. , 2007)  use sun-sky photometers to provide aerosol column-integrated properties with high temporal resolution. These networks use retrieval techniques that allow the characterization of aerosol microphysical properties (e.
, 2011 Dubovik et al. , , 2014)) , which includes the GARRLiC code. GRASP is a versatile and opensource algorithm (https://www. grasp-open. com/, last access: 1 July 2019) based in the concept of the  Dubovik and King (2000)  algorithm which has been used successfully by AERONET during the last decades. The GRASP algorithm is divided into two main independent modules: the forward model and numerical inversion modules. The forward model is based on radiative transfer and aerosol models, and it is a convenient tool for sensitivity and tuning studies  (Dubovik et al. , 2014; Torres et al. , 2017). The numerical inversion module is the main part of the core program, which includes general mathematical operations based on the multi-term least square method (LSM) concept  (Dubovik and King, 2000; Dubovik, 2004).
The altitude difference between UGR and SNS (∼ 1. 8 km) and the short horizontal distance make the correlative measurements between both sites ideal in our objective of evaluating different GRASP scheme retrievals. The measurements used in this work were acquired in the framework of the SLOPE I campaign. SLOPE I took place at AGORA from May to September 2016 with the objective validating the vertically resolved aerosol properties retrieved from the combination of active and passive remote-sensing measurements by in situ measurements on the surrounding high mountain area. In this regard, several studies have been done using SLOPE I database: day-to-night evolution of the planetary boundary layer  (de Arruda Moreira et al. , 2018)  and its turbulence behaviour  (de Arruda Moreira et al. , 2019) , aerosol hygroscopic growth  (Bedoya-Velásquez et al.
(2016)  during dust events over Dakar (Senegal), and it is attributed to the use of additional lidar data. Columnar aerosol size distribution parameters at nighttime retrieved by GRASP using different schemes (see Table 2) show a good coherence and smooth variation when they are compared to daytime AERONET and GRASP retrievals (scheme D). In fact, the GRASP night-time retrievals Table  2. The average values (± standard deviations) of volume concentration (VC f , VC c as µm 3 µm -2 ), volume modal radius (r v f , r v c as µm) and standard deviation (σ v f and σ v c ) for fine and coarse modes retrieved by GRASP using different configuration schemes and those provided by AERONET. The retrievals are provided for the first period (P 1 ) and the second period (P 2 ). The subscript "f" denotes the fine mode and "c" denotes the coarse mode.
A similar thing happens for the complex refractive index (CRI) and single-scattering albedo (SSA), although more variability is observed, particularly for the N 2 scheme due likely to the large uncertainties in relative sky radiance measurements at lunar aureole and the higher freedom degrees assumed than in the N 1 scheme. Nevertheless, we were not able to go further in the evaluation of the accuracy of the GRASP retrieved parameters. Doing so would require a large synthetic database that is beyond the scope of the paper. Also, it is needed to study the sensitivity of retrievals to errors in the input optical data, which is the objective of future work. In general, the aerosol extinction from GRASP and Raman measurements agrees quite well, with differences below 30 % at 355, 532 and 1064 nm.
Processing of the signal consists in averaging of 30 consecutive Fast Fourier Transform (FFT) of 2 15 samples. All the data acquisition procedures and processing were automated by a custom-made LabVIEW algorithm. monitor photodiode embedded in the VCSEL package. After amplification by a custom-made transimpedance amplifier, the OFI signal was acquired using a National Instrument acquisition card (NI USB 6251, National Instruments, Austin, TX, USA) at a sampling frequency of 400 kHz. Processing of the signal consists in averaging of 30 consecutive Fast Fourier Transform (FFT) of 2 15  samples. All the data acquisition procedures and processing were automated by a custom-made LabVIEW algorithm. To implement our theoretical model, multiple numerical calculation tools were applied. Firstly, the entire optical part of the OFI system and the microfluidic channel chip were modeled using the commercial ray tracing software Zemax to simulate the laser beam propagation.
All parameters such as channel geometrical sizes, materials, lens film coatings, particle characteristics were chosen to be in a realistic agreement with the experimental parameters in the 3D model. To calculate the OFI signal in different particle positions, a cuboid mesh was built inside the channel, and the fine 3D grid intervals were set to be dx = dy = 0. 5 µm, dz = 1 µm. A PS particle was placed at each node and then the ray tracing processing was repeated at all the mesh node coordinates. By the Monte Carlo statistical approach, the individual particle scattering angular distribution and the feedback light power re-entering-cavity coupling efficiency r ext were computed. Secondly, the flowing velocities at each node were calculated through simulation with computational fluid dynamics software Fluent.
Following the seminal idea of  Tukey (1975) , data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most popular one. The R-package ddalpha is a software directed to fuse experience of the applicant with recent achievements in the area of data depth and depth-based classification. ddalpha provides an implementation for exact and approximate computation of most reasonable and widely applied notions of data depth. These can be further used in the depth-based multivariate and functional classifiers implemented in the package, where the DDα-procedure is in the main focus. The package is expandable with user-defined custom depth methods and separators.
must be set. Rich built-in benchmark procedures allow to estimate the empirical risk and error rates of the DD-classifier and the portion of outsiders help in making the decision concerning the settings. ddalpha possesses tools for immediate classification of functional data in which the measurements are first brought onto a finite dimensional basis, and then fed to the depth-classifier. In addition, the componentwise classification technique by  Delaigle et al. (2012)  is implemented. Unlike other packages, ddalpha implements under one roof various depth functions and classifiers for multivariate and functional data. ddalpha is the only package that implements zonoid depth and efficient exact halfspace depth. All depths in the package are implemented for any dimension d ≥ 2; except for the projection depth all implemented algorithms are exact, and supplemented by their approximating versions to deal with the increasing computational burden for large samples and higher dimensions.
It also supports user-friendly definitions of depths and classifiers. In addition, the package contains 50 multivariate and 4 functional ready-to-use classification problems and data generators for a palette of distributions. Most of the functions of the package are programmed in C++, in order to be fast and efficient. The package has a module structure, which makes it expandable and allows user-defined custom depth methods and separators. ddalpha employs boost (package  BH Eddelbuettel et al. (2016a) ), a well known fast and widely applied library, and resorts to Rcpp  (Eddelbuettel et al. , 2016b)  allowing for calls of R functions from C++.
Existing R-functionality on data depth Having proved to be useful in many areas, data depth and its applications find implementation in a number of  R-packages: aplpack, depth, localdepth, fda. usc, rsdepth, depthTools, MFHD, depth. plot, DepthProc, WMTregions, modQR, OjaNP. Functions of aplpack  (Wolf, 2014)  allow to exactly compute bivariate halfspace depth and construct a bagplot. R-package depth  (Genest et al. , 2012)  provides implementation for the exact halfspace depth for d ≤ 3 and for approximate halfspace depth when d ≥ 3, exact simplicial depth in R 2 , and exact simplicial volume depth in any dimension. DepthProc  (Kosiorowski et al. , 2016)  calculates Mahalanobis, Euclidean, LP, bivariate regression, and modified band depth and approximates halfspace and projection depth, provides implementation for their local versions w.
r. t. Paindaveine and Van Bever (2013)  and for the corresponding depth median estimators. It also contains functions for depth visualization and produces DD-plots. Agostinelli and Romanazzi (2011)  proposed local versions for several depth notions. Their R-package localdepth  (Agostinelli et al. , 2013)  evaluates simplicial, univariate halfspace, Mahalanobis, ellipsoid depths and their localization. The package fda. usc (Febrero-Bande and Oviedo de la Fuente, 2012) provides methods for exploratory and descriptive analysis of functional data. It contains functions for functional data representation, functional outliers detection, functional regression models and analysis of variance model, functional supervised and unsupervised classification. The package calculates the following depth functions for multivariate data: simplicial depth in R 2 , halfspace depth (aproximate for d > 3), Mahalanobis depth, approximate projection depth, likelihood depth; and for functional data: Fraiman and Muniz depth, h-modal depth, random Tukey depth, random projection depth, double random projection depth.
In addition the package suggests a number of classifiers acting in the functional depth space, namely maximum depth, polynomial, logistic regression, LDA, QDA, kNN and KDA ones. Several packages have specific purpose or implement only one depth. Among the others are: depthTools (Lopez-Pintado and Torrente, 2013), implementing different statistical tools for the description and analysis of gene expression data based on the modified band depth; depth. plot (Mahalanobish and Karmakar, 2015) , containing the implementations of spatial depth and spatial ranks and constructing corresponding DD-plots; MFHD  (Hubert and Vakili, 2013) , calculating multivariate functional halfspace depth and median for bivarite functional data; rsdepth  (Mustafa et al. , 2014) , implementing the ray shooting depth and the corresponding median in R 2 ; WMTregions  (Bazovkin, 2013) , computing weighted-mean trimmed regions with zonoid regions as a special case; modQR  ( Šiman and Boček, 2016) , calculating multiple-output regression quantiles with halfspace trimmed regions as a special case; and OjaNP  (Fischer et al.
Outline of the article To facilitate understanding and keep the presentation solid, the functionality of the R-package ddalpha is illustrated through the article on the same functional data set "ECG Five Days" from  Chen et al. (2015) , which is a long ECG time series constituting two classes. The data set originally contains 890 objects. We took a subset consisting of 70 objects only (35 from each of the days) which best demonstrates the general and complete aspects of the proposed procedures (e. g. , existence of outliers in its bivariate projection or necessity of three features in the α-procedure). In Section 5 functional data are transformed into a finite dimensional space using the LStransform, as it is shown in Figure  1  presenting the functions and their derivatives. In this example we choose L = S = 1, and thus the LS-transform produces the two-dimensional discrete space, where each function is described by the area under the function and under its derivative as it is shown in Figure  6 , left.
Data depth This section regards depth functions. First (Section 2. 1), we briefly review the concept of data depth and its fundamental properties. Then (Section 2. 2), we give the definitions in their empirical versions for several depth notions: Mahalanobis, projection, spatial, halfspace, simplicial, simplicial volume, zonoid depths. For each notion, we shortly discuss relevant computational aspects, leaving motivations, ideas, and details to the corresponding literature and the software manual. We do not touch the question of computation of depth-trimmed regions for the following reasons: first, for a number of depth notions there exist no algorithms; then, for some depth notions these can be computed using different R-packages, e. g. WMTregions for the family of weighted-mean regions including zonoid depth  (Bazovkin and Mosler, 2012)  or modQR for multiple-output quantile regression including halfspace depth as a particular case; finally, this is not required in classification.
Implemented notions The R-package ddalpha implements a number of depths. Below we consider their empirical versions. For each implemented notion of data depth, the depth surface (left) and depth contours (right) are plotted in Figures  2  and 3  for bivariate data used in Section 5. Mahalanobis depth is based on an outlyingness measure, viz. the Mahalanobis distance  (Mahalanobis, 1936)  between z and a center of X, µ(X) say: d 2 M ah z; µ(X), Σ(X) = z -µ(X) Σ(X) -1 z -µ(X). The depth of a point z w. r. t. X is then defined as  (Liu, 1992 ) D M ah (z|X) = 1 1 + d 2 M ah z; µ(X), Σ(X) , (1) q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -0. 006 -0. 004 -0. 002 0. 000 -0. 015 -0. 010 -0. 005 0. 000 0. 005 data Bivariate data q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -0.
2 , and median absolute deviation from the median, M AD(y) = med(|y -med(y)1 n |), are used, as they are robust. Projection depth is then obtained as D prj (z|X) = 1 1 + o prj (z|X). (2) This depth satisfies all the above postulates and quasiconcavity. By involving the symmetric scale factor M AD its contours are centrally symmetric and thus are not well suited for describing skewed data. Exact computation of the projection depth is a nontrivial task, which fast becomes intractable for large n and d. Liu and Zuo (2014)  suggest an algorithm (and a MATLAB implementation, see  Liu and Zuo, 2015). In practice one may approximate the projection depth from above by minimizing it over projections on k random lines, which has time complexity O(knd). It can be shown that finding the exact value is a zero-probability event though.
Setting q = 2, and n = 24, 50, 100, 250, 500, 1000, n i = n/2, i = 1, 2, we sample X i from a Student-t distribution with location parameters µ 1 = [0, 0], µ 2 = [1, 1] and common scale parameter Σ = [ 1 1 1 4 ], setting the degrees of freedom to t = 1, 5, 10, ∞. Average error rates over 250 samples each checked on 1000 observations are indicated in Figure  5. The testing observations were sampled inside the convex hull of the training set. The problem of outsiders is addressed in Section 4. For n = 1000, experiments have not been conducted with the simplicial depth due to high computation time. As expected, with increasing n and t classification error and difference between various depths decrease. As the classes stem from elliptical family, depths accounting explicitly for ellipticity (Mahalanobis and spatial due to covariance matrix), symmetry of the data (projection), and also volume, form the error frontier.
Alternative separators in the DD-plot Besides the DDα-separator, the package ddalpha allows for two alternative separators in the depth space: a polynomial rule and the k-nearest-neighbor (kNN) procedure. When  Li et al. (2012)  introduce the DD-classifier, they suggest to use a polynomial of certain degree passing through the origin of the DD-plot to separate the two training classes. Based on the fact that by choosing the polynomial order appropriately the empirical risk can be approximated arbitrarily well, they prove the consistency of the DD-classifier for a wide range of distributions including some important cases of the elliptically symmetric family. In practice, the minimal error is searched by smoothing the empirical loss with a logistic function and then optimizing the parameter of this function.
Point "3" on the other hand has zero depth w. r. t. both training classes, and thus classification rule in the DD-plot is helpless. Nevertheless, visually it clearly belongs to the "blue" class, and most probably would be correctly classified by a very simple classifier, say a poorly tuned kNN (e. g. 1NN). The suggestion thus is to apply an additional fast classifier to the outsiders. The R-package ddalpha implements a number of outsider treatments: linear (LDA) and quadratic (QDA) discriminant analysis, kNN, maximum depth classifier based on Mahalanobis depth; and additionally random classification or identification of outsiders for statistical analysis or passing to another procedure. For the same experimental setting as in Section 2. 4, we contrast these treatments in Figure  9 , comparing classification errors on outsiders only.
As the functional data depth reduces space dimensionality from infinity to one, the final performance is sensitive to the choice of the depth representation and of the finite-dimensional separator, and thus both constituents should be chosen very carefully. Potentially, this lacks quantitative flexibility because of the finite set of existing components. Nevertheless, in many cases this solution provides satisfactory results; see a comprehensive discussion by  Cuesta-Albertos et al. (2016)  with experimental comparisons involving a number of functional depth notions and q-dimensional classifiers, as well as their implementation in the R-package fda. usc. Corresponding functional depth procedures can also be used with R-package ddalpha, see Section 6 for a detailed explanation. ddalpha suggests two implementations of the strategy of immediate functional data projection onto a finite-dimensional space with further application of a multivariate depth-based classifier: componentwise classification by  Delaigle et al.
Custom depths and separators As mentioned above, the user can amplify the existing variety by defining his own depth functions and separators. Custom depth functions and separators are defined by implementing three functions: parameters validator, learning, and calculating functions, see Tables  2  and 3. Usage examples are found in the manual of the package ddalpha. Validator is a nonmandatory function that validates the input parameters and checks if the depth calculating procedure is applicable to the data. All the parameters of a user-defined depth or separator must be returned by a validator as a named list, otherwise they will not be saved in the ddalpha object. Definition of a custom depth function is done as follows: The depth-training function. <name>_learn(ddalpha) calculates any data-based statistics that the depth function needs (e.
) Definition of a custom separator is similar. Recall that there exist binary separators applicable to two classes, and multiclass ones that separate more than two classes at once. In case if the custom method is binary, the package takes care of the voting procedures, and the user only has to implement a method that separates two classes. The training method for a binary separator. <name>_learn(ddalpha, index1, index2, depths1, depths2) accepts the depths of the objects w. r. t. two classes and returns a trained classifier. A multiclass separator has to implement another interface:. <name>_learn(ddalpha), accessing the depths of the different classes via ddalpha$patterns  [[i] ]$depths. The binary classifier can utilize the whole depth space (i. e. depths w. r. t. other classes than the two currently under consideration) to get more information like the α-separator does, or restrict to the DD-plot w.