dataesr/scientific-paragraphs-categorization huggingface dataset:
On one side, Xtext helped to us maintain the language infrastructure code (e. g. , parser, editor, etc. ) by separating generated code from manual code. On the other side, we did not experienced major problems to update our video variability model since the video domain is stable and the only changes that we applied were increments in the specification. As a conclusion for the cost-benefit practical consideration, we say that the costs of constructing, using and maintaining VM models are low compared with the benefits of producing automatically suitable videos to test complex video analyzers. Similar achievements were impossible before the introduction of variability techniques. Stakeholders. "Who puts effort into and who gains the benefits of the model? What knowledge about feature modeling methods in general and the product line in question do the stakeholders have?" [24].
1-to-1 mappings between features in the problem space and their realizations in the solution space ease their co-evolution. For example, many features in the video domain VM model have a 1-to-1 relationship with code modules that implemented the video generator. In a similar way, feature attributes tend to match input parameters of Lua functions. Using 1-to-1 mappings is not a strict rule. In fact, we also modeled features that are not mapped to any specific module to group other features or attributes. For example, the feature "objects" does not map directly to any module, but helped to group conceptually the "vehicles" and "man" features that have concrete mappings to the code. One important highlight regarding correspondence was that we decided not to use VM to model all possible variability in a video sequence.
The key difference between VM and the other approaches is the use of meta-information associated to features or attributes. For example, VM users can include: i) default values (R6), ii) deltas (R7), iii) elements definitions (R8), iv) multi-ranges and priorities -multi-deltas (R10), v) meta-information annotations such as "not translatable", "not decidable" (R12), and "runtime" (R11), and vi) objective functions (R12). As reported in Section 3, our industrial experience strongly motivates the introduction of these new constructs. We also show the importance of the constructs in terms of reasoning scalability (see Section 4). Currently, we are evaluating the applicability of VM as a generic attributed feature modeling language such as SXFM, TVL or FAMA. In the VM repository https://github.
com/ ViViD-DiverSE/VM-Source we have VM models that support the point that VM is expressive enough to encode existing boolean and attributed feature models found in the literature and public repositories (e. g. , mobile media  [18]  and the SPLOT repository www. splot-research. org). Berger et al. [6]  studied the modeling of variability in the operating system domain (Linux, eCos, and FreeBSD are the subjects of the study). They showed that well-researched concepts of FODA feature models, comprising Boolean (optional) features, a hierarchy, group and crosstree constraints, are used. They also identified domain-specific concepts beyond FODA feature models, such as: visibility conditions, derived features, derived defaults, and binding modes. Dumitrescu et al. [11, 12]  reported on their experience in an automotive model based systems engineering.
B. SOME MARKER TRACKING SYSTEMS Several systems are available for marker tracking, such as CyberCode  [20] , Cantag  [21] , ARTag  [12] , Vuforia  [22] , AprilTag  [23] , AprilCal  [24] , ArUco  [25] , ARToolKit  [26] ,  [27]  and ARToolKitPlus  [28]. ARToolKit was demonstrated publicly for the first time at SIGGRAPH in 1999  [27]. CyberCode  [20]  was available before ARToolKit, whereas ARTag and ARToolKitPlus were later introduced  [29]. The main advantages with ARTag are low rate of inter-marker confusion, low rate of false negativity, and robustness in various lighting and partial occlusion  [29]. The disadvantage with ARTag is computational complexity which making it incompatible with mobile phones and mini devices  [17]. Compared to ARTag, ARToolKit is faster in terms of marker recognition  [12].
ARToolKitPlus suffers from slow processing  [11]. Meanwhile, the ARToolKit source code is freely available. Although other toolkits provide free access (to executables), they are not open source  [11]. Olson presented an efficient digital coding system called AprilTag which can work robustly in occlusion, warping, and lens distortion. Although it is also open source like ARToolKit, AprilTag remains comparatively expensive in terms of computation  [23]. ARToolKit is widely used in several AR and other interactive applications due to its source code availability  [9]. It is compatible with various operating systems, such as PC Linux, SGI IRIX, Windows, Alao Mac, Android, and iOS  [26]. Furthermore, ARToolKit is fast in processing. Hence, it is feasible for real-time interaction  [11]. Although other toolkits also came into existence and may have their advantages, ARToolKit remains a wide-spread system.
C. EXISTING PROBLEMS WITH MARKER DESIGNING Although ARToolKit is easy to use and has proper documentation, the design and recognition of markers continue to hold a number of issues and challenges. Making decisions while designing a marker lacks a certain level of assistance. For example, the size of an outside border can be thick or thin or black objects in the marker body can be increased or decreased. These decisions alter the marker complexity (see Figure  2 ), which creates difficulty in designing high quality markers. A key challenge for a user in marker designing is to ensure that the new marker has a shape distinct from all existing markers in the library. Handling this issue is very challenging without any automatic module. To the best of our knowledge, a mechanism to ensure marker distinction in the library and reduce possible inter-marker confusion does not currently exist.
III. FIDUCIAL MARKER OPTIMIZER (FMO) The FMO is a GUI-based tool developed in MATLAB to optimize the design attributes of markers, increase detection rate, and reduce inter-marker confusion. Its design attributes are as follows: black to white ratio, which is the ratio of the number of black pixels in the border to that of the interior white region (Sec. III-A); information complexity, which represents the complexity of black objects in the interior white region (Sec. III-B); and edge sharpness, which is the abruptness of the intensity of edge changes (Sec. III-C). The FMO also removes noise from the marker and checks for similarity among the markers of the library to predict intermarker confusion. Figure  3  shows a model of the FMO, which represents the overall procedure of marker quality enhancement and its submodules.
%inter-marker confusion = I (test) I(N ) * 100, (2) Similarly, the similarity of the key marker with each testing marker was also computed in the FMO. Marker similarity equal to 1 means the two markers (key and testing markers) have the same shape. It is to be noted that, the FMO crops the interior white region of a marker for calculation of its degree of similarity with the key marker. Figure  13  presents the markers used in this experiment along with the calculated degree of similarity with the key marker. Table  1  shows the results while Figure  14  shows the graph between the degree of similarity of the FMO and % inter-marker confusion in the ARToolKit; which highlight that marker similarity computed via FMO has a meaningful match with the inter-marker confusion in ARToolKit. Therefore, this module is feasible to control inter-marker confusion in ARToolKit.
V. CONCLUSION We designed a tool called FMO to design high quality ARToolKit markers. The FMO enables the marker designers to individually upload all markers of the library and optimize their design attributes, such as B:W ratio, edge sharpness, and information complexity, and checks for possible confusion with other markers of the library. The reduction of intermarker confusion is the most important factor in this study which has been achieved by ensuring marker distinction in the library. Similarly, to make markers increasingly visible is also a key factor. The visibility of the marker has been enhanced through removal of noise and edge sharpness. FMO provides various cognitive aids for marker designers at various stages of marker enhancement. In addition, the marker classifier module classifies the markers into a given number of classes based on their degree of similarities for reduction of inter-marker confusion.
Results Genetic diversity and dissimilarity patterns across Hispanic/Latino populations. To explore inter-individual genetic variation in Cuban and other Hispanic/Latino populations, we first evaluated allele-sharing dissimilarity (ASD)  21  across all pairs of individuals in the dataset of Cuban and worldwide populations (Supplementary Table  S1 ). The multidimensional scaling (MDS) plot based on the ASD matrix shows highly variable genetic patterns across populations having experienced the TAST (Fig. 1a ), including Hispanic/Latino populations in the Americas. These patterns are consistent with three-way admixture from Native American, European, and, to a lesser extent, African source populations  [22] [23] [24]. The Cuban individuals lie on the European-African trajectory, similarly to African-descendants from USA and Barbados, although most Cuban individuals are much closer to the European cluster than the African cluster (Fig.
three-way admixture among European, African, and Native American populations at the source of Cuban genetic diversity. To further estimate the continental ancestry in our dataset, we used the local ancestry calls from the RFMix analysis, calculating global ancestry proportions in our sample on the basis of three-and four-way continental admixture models. The RFMix (EM = 2) results at the individual, municipal, and provincial scales were largely in agreement with the supervised and unsupervised ADMIXTURE results at K = 3 and K = 4 (see Fig. 2  and Supplementary Table  S2 ). As reported in previous studies  17, 23, 28  , ADMIXTURE and RFMix individual-level admixture estimates are highly correlated (Spearman's rho: 0. 997 European, 0. 986 African, 0. 986 Native American, and 0. 537 East Asian ancestry, all of them are highly significant: P-value < 0.
To do so, we merged our Cuban dataset with available publicly genome-wide SNP datasets for European, African, and Native American ancestry (Supplementary Tables S1, S6 and S7, respectively). Figure  4a  shows the fine-scale population structure of European ancestry in Cuba observed with the ancestry-specific MDS approach described in Browning et al. 18  (MDS-based ASPCA). The first principal component (PC1) separates the Southern and Northern European populations, while the PC2 splits the French Basque population from other European populations. In agreement with the known historical sources of European migrations to Cuba 6,9 , the Cuban haplotypes of European ancestry overlap with Southern European populations from Spain and Italy rather than with Northern or Central European populations.
In the MDS-based ASPCA of Native American ancestry (Fig. 4c ), the Surui from Brazil, Cabecar from Costa Rica, and Pima from Mexico are the three major outliers observed among Native American populations, as previously described  17, 31, 32. The Cuban haplotypes of Native American ancestry present a wide overlap with individuals from the remaining Native American populations. The IBD analysis primarily points to a Native American ancestral origin mainly associated with South American populations (Supplementary Fig. S13 ). These findings were supported by the ancestry-specific WC-F ST values (range: 0. 33-0. 40) estimated between the Cuban Native American haplotypes and Native American haplotypes from different regions in South America (Supplementary Table  S8 ). We also increased the number of markers included on the analyses to 244,227 SNPs (Supplementary Methods), after merging the Cuban dataset with available whole-genome data of worldwide populations included in the Simons Genome Diversity Project (hereafter; SGDP)  33.
Methods Data collection and genotyping procedure. The original sample size comprised 1,019 voluntary participants born in Cuba representing all fifteen Cuban provinces (Supplementary Fig. S16 ). These samples constitute an excellent representation of the current distribution of the Cuban population in terms of sex, age, and population density (Supplementary Methods). A detailed comparison of the relative proportions of each category in the studied Cuban sample and the Cuban census 47 was published elsewhere  16. A total of 957 DNA samples were genotyped using the Infinium PsychArray v1. 0 and v1. 1 BeadChips (Illumina Inc. ) at Statens Serum Institut, Denmark (http://www. ssi. dk/english. aspx) (Supplementary Methods). During the genome-wide quality control (QC) procedure, 97 samples were removed due to their high missing call rate (i.
e. >1%). After some basic filtering based on Hardy-Weinberg (p < 10 -6 ) and minor allele frequency (i. e. <1%), we created an initial dataset that includes 432,138 polymorphic autosomal SNP markers. We estimated genetic relatedness for all pairs of individuals using KING  58  and PC-Relate  59  , and no first-degree or second-degree relatives were found (Supplementary Fig. S17 ). After additional QC procedures using PLINK v1. 90  60  (see Supplementary Methods), the remaining linkage disequilibrium (LD-) unpruned dataset includes 292,549 SNPs genotyped in 860 unrelated Cuban individuals (range between 24 and 108 individuals per Cuban province). In addition, we assembled another LD-unpruned dataset only for X-chromosome SNP markers that includes 5,060 SNPs genotyped in 860 Cuban individuals.
Population structure analyses. To better understand the genetic contribution from each continental source population to the genetic landscape of present-day Cuba, we merged our Cuban dataset with other datasets comprising 1,353 unrelated individuals from continental reference populations in Europe, Africa, America, and Asia (hereafter-called "Cuba-World" dataset, see Supplementary Table  S1  and Supplementary Fig. S18 )  22, 26. For the LD-unpruned Cuba-World dataset, we first used asd v1. 0 (https://github. com/szpiech/asd) to compute ASD between all pairs of individuals separately  21. We represent the respective inter-individual pairwise ASD distance matrix into two-or three-dimensional metric MDS projections using the cmdscale function in R  25. We then estimated genome-wide heterozygosities for the Cuba-World dataset.
To avoid SNP-chip geographical ascertainment bias when calculating population SNP-by-SNP heterozygosities, we employed a haplotype heterozygosity approach following Verdu et al. 25  , which was derived from the method developed by Li et al. 26  , and using the recombination map from the HapMap Phase 2 project Build GRCh37/hg19. To estimate three-and four-way continental ancestry for each individual, we first used the clustering algorithm implemented in the software ADMIXTURE v1. 30  61. For the LD-pruned Cuba-World dataset, we performed both supervised and unsupervised ADMIXTURE analysis at K = 3 and K = 4. We then conducted the local ancestry deconvolution approach at the within-individual genomic level using RFMix v1. 5. 4  62  , based on the autosomal haploid genomes in the LD-unpruned Cuba-World dataset (see Supplementary Methods).
We first used SHAPEIT2  63  to generate haplotypic phased data for each assembled dataset, and then we ran RFMix in "PopPhased" mode using two steps of Expectation-Maximization algorithm (EM = 2)  62. We collapsed inferred ancestry calls to calculate the average ancestry proportions for each individual haploid genome, Cuban municipality, and Cuban province. We focused on average ancestry proportions across all, short (between >5 and ≤50 cM), and long (>50 cM) ancestry tracts  17, 28. To investigate the distribution of genomic IBD segments shared between pairs of individuals from the Cuba-World dataset, we use the Refined-IBD  64  tool implemented in the software BEAGLE v4. 1  65. The minimum length threshold was set to 3 cM 66. We then estimated the total length of IBD segments (in Mb) shared IBD between pairs of individuals from the same province and different provinces in Cuba.
Estimating sex-biased gene flow. To explore if there is evidence of sex-specific admixture patterns in Cuba, we estimated admixture proportions in the X-chromosome dataset using RFMix (EM = 2), and compared them to the estimates based on autosomal data using three approaches. First, we applied the method proposed by Rishishwar et al. 29  to calculate the ΔAdmix ratio. We expect ΔAdmix ratios to be positive when there is an excess of female-specific admixture contributions from one specific ancestry; or negative when there is an excess of male-specific admixture instead  29. Second, we applied Wilcoxon signed-rank two-sided unpaired test to assess the difference between the paired X-chromosome and autosomal ancestry proportions of individuals from each Cuban province  17. Third, we estimated sex-specific admixture under the sex-specific admixture mechanistic modelling framework developed in (p.
271 in  30  ), with a fixed number of 15 generations  30, 67. Inferring subcontinental ancestry. To investigate the ancestral origins of the Cuban population, we used haplotype-based methods for individual Cuban haploid genomes masked for one specific ancestry. To do so, we merged our Cuban dataset with publicly available whole-genome and genome-wide SNP datasets for European, African, and Native American ancestries (called "Cuba-World", "Cuba-Africa", and "Cuba-America" SNP dataset, respectively) (see Supplementary Fig. S17  and Supplementary Tables S1, S6, and S7), and we then inferred genetic links between the Cuban population and putative ancestral populations included in each reference population panel using three genome-wide methods. First, we used asd v1. 0 to compute ASD between all pairs of individuals separately for each assembled SNP dataset and visualize them in the MDS based on ASD, as described above.
Second, we analysed ancestry calls estimated for each haploid individual in the RFMix analysis, to generate new datasets with non-masked reference haplotypes and masked Cuban haplotypes with different thresholds for each continental ancestry (i. e. 75%, 60%, 50%, 40%, 30%, and 15%). The only exception was the analysis of Native American ancestry, in which we only used a 15% cutoff due to the fact that only one Cuban sample has more than 30% Native American ancestry. For each dataset, we used the MDS approach described in Browning et al. 18  , to perform MDS-based ASPCA for within-continental ancestry inference  18, 28. We found better overlapping for non-masked reference haplotypes and masked Cuban haplotypes with at least 40% of European ancestry, or 75% of African ancestry, or 15% of Native American ancestry, and plots from those thresholds were included in the manuscript (see Fig.
4 ). Moreover, for the Native American ancestry we repeated the analysis using a dataset with an increased number of SNPs, but lower number of Native American populations. After merging the Cuban dataset with 40 worldwide populations included in the SGDP, the QC-filtered LD-unpruned dataset included 244,227 SNPs (called "Cuba-SGDP" SNP dataset) (see Supplementary Table  S9 ). We then estimated genetic affinities between pairwise populations based on ancestry-specific WC-F ST 28,68 using VCFTools  69. Third, we estimated the total length of IBD segments (in Mb) shared between Cuban individuals and individuals from different European, African and Native American populations. Modelling demographic history and admixture timing. To explore the recent migration history in Cuba, we applied the model-testing approach implemented in the TRACTS software  37.
This analysis allows fitting tract-length distributions expected under predefined complex admixture scenarios to all the observed tract-length distributions in our Cuban dataset using RFMix (EM = 2), to infer the admixture parameters (timing and intensity of admixture events) underlying the observed data  37. We first pooled Cuban individuals into the three historical regions (Western, Central, and Eastern departments) that have administratively structured Cuba from 1827 to 1878  34. For each historical department, we then tested five admixture models with different admixture pulses. We started the first model with a founding admixture event between European and indigenous groups fifteen generations ago followed by a pulse of African admixture (1 st EUR,1 st NAM +1 st AFR). Separately, we evaluated models adding an additional: African pulse (1 st EUR, 1 st NAM +1 st AFR +2 nd AFR), Native American pulse (1 st EUR, 1 st NAM +2 nd NAM +1 st AFR); Native American and African pulse (1 st EUR, 1 st NAM +2 nd NAM +1 st AFR +2 nd AFR), and Native American, African, and European pulse (1 st EUR, 1 st NAM +2 nd NAM +1 st AFR +2 nd AFR +2 nd EUR).
Acknowledgements The authors would like to acknowledge the volunteers from  Cuba , who voluntarily donated DNA samples and whose genome-wide SNP data are reported in this study for the first time. We thank  Alicia Martin ,  Petr Triska ,  Miguel G Vilar ,  Jesús Guanche , and  Marial Iglesias  for helpful comments and discussions on the analyses. We are grateful to the  SciNet High-Performance Computing Centre  ( University of Toronto, Canada ) for providing help and computing resources. C. F. L. and P. V. were funded in part by the  French "Agence Nationale pour la Recherche"  grant  METHIS  ( ANR-15-CE32-0009-01 ).
The shaft furnace description included in the plant model, though based on similar equations, was intentionally made simpler and faster to run, on process simulation software. Thus, REDUCTOR is more detailed and more precise, but, of course, requires longer computation times. The modeling of a shaft furnace, simulating the reduction of iron ore by syngas, is a powerful tool for defining optimal operating conditions. Use of such a model can lead to the maximization of conversion or the minimization of energy consumption, among other effects capable of reducing carbon dioxide emissions. As such, numerous iron ore shaft furnace models have been proposed in the literature. Initial studies addressed the reduction of a single pellet by H 2 , CO, or H 2 -CO mixtures  [4] [5] [6] [7] [8] [9]. Subsequent studies developed models that simulated the reduction zone of the shaft furnace in one dimension  [10, 11].
The model includes a sophisticated, pellet sub-model. We consider eight heterogeneous and two homogeneous chemical reactions. These features represent a more advanced and detailed model, compared to previous studies. Moreover, the results were validated against two sets of plant data. The present model, REDUCTOR, differs from the other model we recently reported  [18]  on the following points. REDUCTOR is a computational fluid dynamics (CFD)-type, two-dimensional model, which describes the shaft furnace alone. The other model is of the systemic type, is one-dimensional, and aims to simulate the whole DR plant. The shaft furnace description included in the plant model, though based on similar equations, was intentionally made simpler and faster to run, on process simulation software. Thus, REDUCTOR is more detailed and more precise, but, of course, requires longer computation times.
Funding: This research was supported by the  French State , through two programs: 'Investment in the future': (i) one, operated by the  French Environment and Energy Management Agency (ADEME) , 'Valorization of CO 2 in industry', 2014-18, VALORCO, No  1382C0245 ; the authors thank  Mrs. Nathalie Thybaud  and  Aïcha El Khamlichi , and the coordinator, Eric de Coninck; and (ii) one operated by the  National Research Agency (ANR)  and referenced by  ANR-11-LABX-0008-01  ( LabEx DAMAS ).