dataesr/scientific-paragraphs-categorization huggingface dataset:
These goat samples represented 11 of the 15 gene pools determined by Colli et al. [15]  using the Admixture software  [26]. We added one individual from the Creole breed (different from the Creole individuals genotyped in the AdaptMap dataset), as a representative of the American gene pool and one from an inbred breed (Palmera). Variants that fulfilled both of the following conditions were included in the "known variants" file: (1) variants that had at least six genotypes with at least one alternative allele ("snpSift filter 'countVariant() > 6'"); and (2) variants that were called by both Freebayes and GATK-HaplotypeCaller. Variant calling for each individual was done with the GATK-HaplotypeCaller in ERC mode with a minimum read mapping quality of 30 and a minimum Phred-scaled confidence threshold of 30 ("-stand_call_conf 30.
Filtering process A variant quality score recalibration (VQSR) step was performed on the raw VCF files. The same 13 goats that were used in variant calling were also used to establish training resource sets for VQSR calibration. Two training resources of true sites were built. The first set of variants included only the highest quality calls, with variants consistently identified with GATK, Mpileup  [21]  and Freebayes  [23]  ("known=false,training=true,tr uth=true,prior=15. 0"). The second set of variants used the 60,000 SNPs selected by Tosser-Klopp et al. [4]  to generate the set of SNPs included in the GoatSNP50 BeadChip ("known=false,training=true,truth=true,pr   S1 ). If a breed is present in multiple countries, the breed code is followed by the country code (2 letters). Each combination of color and symbol corresponds to domestic goats in a single country.
Wild goats are identified with a specific color and symbol ior=12. 0"). The training resource of non-true sites was built using the variants exclusively called by GATK ("kno wn=false,training=true,truth=false,prior=10. 0"). The last SNP resource, which was not used to train recalibration, was the dbSNP variant database provided by Ensembl version 95 ("known=true,training=false,tru th=false,prior=2. 0"). The variant call annotations (for SNPs and InDels) QD, DP, FS, MQRankSum, ReadPos-RankSum, SOR and MQ (only for SNPs) were used for VariantRecalibrator. Additional file 4: Figures S2 and Additional file 5: Figure  S3  show the pairwise comparisons of the annotations chosen in VQSR as described in the VQSR docum entat ion. Based on the "snps. tranches" files (see Additional file 6: Figure  S4  and Additional files 7 and 8), no false-positive variants were observed in the 99 tranche.
Main features of the dataset The GCF_001704415. 1_ARS1_genomic. gff annotation file was used to annotate variants using the SnpEff software. Thus for each SNP, descriptive information is provided in the INFO field. The exhaustive description of the INFO field can be found in snpEff manual. For example, it contains a description of the position of the variant relative to a gene (upstream, downstream, exon, intron), if the variant hits a transcript (transcript), if it is located in an untranslated region (UTR 3ʹ, UTR 5ʹ), its location and effect regarding splicing (splice acceptor, splice donor, splice region), amino-acid changes (missense, nonsense, silent), etc. Table  1  summarizes the numbers and types of variants found on each chromosome for 14 annotations (downstream, exon, intergenic, intron, splice acceptor, splice donor, splice region, transcript, upstream, UTR 3ʹ, UTR 5ʹ, missense, nonsense, silent).
Genetic differentiation We conducted an exploratory analysis to evaluate the genetic differentiation among goat breeds. We included breeds that were represented by a single individual because we were interested in understanding the relationship among all the breeds in the overall dataset. To perform this genetic differentiation analysis, we used the thin-count function from PLINK  [19]  to extract a subset of random 100,000 SNPs from the reduced set of the 667,949 autosomal SNPs used in the PCA analysis (see "Selection of individual goats" section). Starting from this reduced dataset, a matrix of between-population Reynolds distances was calculated using the hapFLK   [27, 28]  and then it was used to construct a Neighbor-Joining tree (Fig. 6 ). Since bezoar (C. aegagrus) is the wild species closest to domestic goats, it was used as an outgroup to root the tree.
Genetic differenciation between breeds and their geographic position Fig. 6  Neighbor-joining tree representing the genetic diversity of domestic goat populations analysed in the context of the VarGoats project. The three-letter breed code (see Additional file 1: Table  S1 ) is used, followed by the two-letter country code if needed. Colors allow the identification of geographical origin (CAS_AU) goats clustered with Angora goats from France and South Africa because of their common Middle Eastern origin. A similar clustering pattern based on the ancestral geographic origin is also observed in transboundary breeds sampled in different countries (i. e. , Alpine, Boer and Saanen). The first branching among domestic goats separated the Asian breeds from the remaining populations. This branch carried the most ancestral cluster, which was mainly composed of breeds that originated in South-Western Asia (Iranian and Pakistani goats) and were thus geographically close to the center of domestication  [1].
Conclusions This 1159-goat genome dataset (VarGoats dataset) provides an unprecedented resource for research studies on caprine genomics. We provide a detailed methodology for calling SNPs, detecting InDels and filtering these data, which has led to a dataset that was validated through a differentiation study. This allowed us to verify that samples were properly assigned to their corresponding breed and geographical origin (both for samples collected for this study and public data). This VarGoats dataset will enable an unprecedented view of the footprints of the natural and human-mediated evolutionary forces that have shaped the diversity of caprine genomes. The VarGoats Consortium is currently organized in working groups, with each team conducting detailed analyses of these data. Among the various topics that are under study are: (i) analyses of structural variations, (ii) population genetics analysis and population history domestication reconstruction, (iii) detection of selection and adaptation signatures, (iv) identification of loss of function alleles, and (v) comparison of the goat genomes with those of other ruminant species.
Acknowledgements First of all, we acknowledge the sample providers of VarGoats project :  James Kijas ,  CSIRO, Australia ;  Bernt Guldbrandtsen ,  Aarhus University, Denmark ;  Juha Kantanen , Luke, Finland;  Dylan Duby ,  Museum National d'Histoire Naturelle, France ;  Pierre Martin ,  Capgenes, France ;  Coralie Danchin ,  Delphine Duclos ,  Institut de l'Elevage, France ;  Daniel Allain ,  Rémy Arquet ,  Nathalie Mandonnet ,  Michel Naves ,  Isabelle Palhière ,  Rachel Rupp ,  INRAE , France and CABRICOOP breeders; François Pompanon, LECA, France;  Hamid R. Rezaei, Gorgan University of Agricultural Sciences and Natural Resources, Iran ;  Sean Carolan  and  Maeve Foran ,  Old Irish Goats Society, Ireland ;  Alessandra Stella ,  IBBA-CNR, Italy ;  Paolo Ajmone-Marsan ,  Licia Colli, BioDNA, Università Cattolica del Sacro Cuore, Italian Goat Consortium, Italy ;  Alessandra Crisà ,  Council for Agricultural Research and Economics (CREA) ,  Italian Goat Consortium, Italy ;  Donata Marletta ,  University of Catania, Italian Goat Consortium, Italy ;  Paola Crepaldi ,  University of Milan, Italian Goat Consortium, Italy ;  Michele Ottino ,  Parco Nazionale del Gran Paradiso, Italy ;  Ettore Randi ,  ISPRA Istituto Superiore per la Protezione e la Ricerca Ambientale, Italy ;  Badr Benjelloun ,  INRA Maroc, Morocco ;  Hans Lenstra ,  University of Utrecht, The Netherlands ;  Muhammad Moaeen-ud-Din  and  Jim Reecy ,  PMAS-Arid Agriculture University, Pakistan ; We thank  Cédric Cabau  ( INRAE ) for setting up and maintaining the VarGoats website.
Funding We are grateful to France Génomique "Call for high impact projects" (ANR-10-INBS-09-08) for selecting our project and providing us the resources to sequence 400 goats. We would like to mention that APIS-GENE funded some WGS sequences through ACTIVEGOAT & CAPRISNP projects. We thank the Occitanie region and the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment (INRAE-GA) for financing the PhD of ET. We thank the Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation for financing LD. We thank André Eggen (Illumina) for providing chips to genotype 192 animals. We thank the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment.
Fashion image analysis has attracted significant research attention owing to the availability of large-scale fashion datasets with rich annotations. However, existing deep learning models for fashion datasets often have high computational requirements. In this study, we propose a new model suitable for low-power devices. The proposed network is a one-stage detector that rapidly detects multiple cloths and landmarks in fashion images. The network is designed as a modification of the EfficientDet originally proposed by Google Brain. The proposed network simultaneously trains the core input features with different resolutions and applies compound scaling to the backbone feature network. The bounding box/class/landmark prediction networks maintain the balance between the speed and accuracy. Moreover, a low number of parameters and low computational cost make it efficient.
fashion image analysis. The accurate clothing detection is one of the performance measures for this analysis; however, technical challenges, such as the availability of large datasets and inference time, should be addressed. To solve these problems, Huang et al. [1]  and Kiapour et al. [2]  looked for informative regions by detecting bounding boxes; Chen et al. [3]  and Bossard et al. [4]  detected human joints. Liu et al. [6]  presented the fashion landmark concept, assuming that the clothing bounding boxes are fed as prior information for both training and testing sets. Fashion landmark detection is helpful to predict useful key points on the human body. The pipeline first detects the human body parts where the clothing regions are followed by synthesizing the clothing on that part  [5].
In contrast, the single-stage detector, such as YOLO, shows high inference speed  [13]. These factors make the two-stage detector more flexible and accurate than the single-stage detector, while the single-stage detectors are more efficient and have faster inference time than the former. Fig. 1  shows the basic structure of both the single-stage detector and the two-stage detector. Most previous clothing detection and landmark detection studies have used the DeepFashion dataset, which had one item of clothing with 4 to 8 landmarks per image. Neural networks trained on such datasets cannot detect multiple clothes in images. Instead, we used the DeepFashion2 dataset, which includes more than 200,000 fashion images with annotations for 13 different classes. Each of its classes has specific keypoints with 294 unique landmarks.
A. FashionNet FashionNet  [6]  is a deep learning-based fashion image analyzer using a DeepFashion data set proposed by Liu in 2016. The network is designed based on VGG-16 and predicts landmark estimation and fashion attributes by modifying the last layer. Fig. 2 (a)  shows the model structure of the last layer of FashionNet. The last convolution layer has three branches, the first one capturing the entire clothing item's global feature while the second capturing the local feature for the expected fashion landmark. The first and second branches' outputs are linked together to predict the clothing category attributes together and model the clothing pair. The last branch predicts the location and visibility of the landmarks. A key component of the FashionNet is the landmark pooling layer in the second branch that captures the landmark local feature.
After apprehending the landmarks' visibility, perform max pooling inside the region to obtain a local feature map. This local feature map is stacked to form the final feature maps. This is similar to the RoI pooling layer introduced in  [12] , but it is meaningful to consider the interaction between fashion landmarks by connecting local feature without treating the pooled area independently. FashionNet was meaningful because it used the first largescale fashion dataset called DeepFashion to perform a fashion image analysis and later contributed to the active research of fashion image analysis. However, the DeepFashion dataset has only the same number of landmarks for each clothing category, which is not enough to characterize many clothing categories. The Deepfashion dataset contains only one clothing per image, so the FashionNet cannot perform multi-clothes analysis.
B. MATCH R-CNN Match R-CNN  [18] , proposed by Ge, is a fashion image analysis network using the DeepFashion2 dataset. Designed based on Mask R-CNN  [39] , it performs clothing detection, landmark estimation, and clothing segmentation. It consists of three networks called Feature Network(FN), Perception Network(PN), and Matching Network(MN). FN extracts the features and delivers the PN's output feature map through the RoIAlign proposed by  [39]. Based on the feature maps received from the FN, the PN performs three predictions: landmark estimation, clothing detection, and segmentation. Fig. 2 (b)  shows the structure of the PN. For bounding boxe and mask, it have the same structure as Mask R-CNN. Landmark estimation consists of a fully convolution network just like a clothing segmentation. MN is a matching network to learn the similarities of clothing as a network for clothing retrieval.
C. DFA FRAMEWORK Liu et al. [7]  presented a DFA framework with a fashion landmark dataset of over 120,000 fashion images. Each image had eight correctly labeled landmarks. Furthermore, images are separated into five subsets with respect to their groundtruth position and visibility. These subsets include normal, medium, and large poses; medium and large zoom-ins. Both the spatial and appearance domains of these subsets show vast variations (e. g. , more than 30% images have large pose and zoom-in variations). To overcome these limitations, the DFA has three stages, wherein each stage refines the previous stage's predictions. DFA deals with the full images and achieves better performance with less computational expense than other models such as DeepPose  [21].
III. PROPOSED METHOD Fashion image analysis should take advantage of the domain of fashion's features into account for scalability to the real world. Therefore, when designing a fashion image analysis network, we focused on the utilization of real applications. This should be an appropriate balance between accuracy and speed, and efficiency in terms of resource consumption. The EfficientDet proposed by Google Brain achieved state-of-theart in the COCO dataset and is one of the best models to meet these conditions. The low number of parameters makes it efficient in terms of resource consumption, while at the same time being fast inference and high accuracy. Based on EfficientDet, we have modified the network globally to fit the fashion domain, added a prediction head, and designed a loss function for a new task called fashion landmark estimation.
B. PREDICTION HEAD A prediction network has been designed to fit the fashion image domain. Fig. 4  shows the overall structure of the prediction network. It performs 3 × 3 convolution three times for each task by inputting a feature map Pi that has gone through BiFPN. Next, it adjusts the output channel by convolution with filter size 3, stride 1, and padding 1. Because it is a single-stage detector with no region proposal module, it uses nine anchors (of three sizes and three aspect ratios). The DeepFashion2 dataset has 13 clothing categories and 294 unique fashion landmarks. Therefore, the class prediction network's final output channel is ca(number of classes × number of anchors). The boundary box prediction network predicts four coordinates (x 1 , y 1 , x 2 , y 2 ) for regression, so it is 4a.
D. TRAINING EfficientDet is configured from D0 to D7 according to the coefficient so that lowering the value lowers the computational cost and decreases accuracy. In contrast, higher GPU resource consumption implies lower speed but higher accuracy. We have trained using two models, D0 and D1, because we aim to achieve near-real-time inference speed. Table  1  shows a detailed configuration of D0 and D1 models. The input resolutions are 512 × 512, 640 × 640, and the backbone network uses EfficientNet-B0, B1. The batch size is 16, and the data augmentation only used flip. With the learning rate of 1e -3 and the AdamP optimizer, we obtained better results than using the common Adam. The training was conducted using Nvidia Tesla V100 32GB GPU 3-way system.
A. BACKBONE Fashion landmark estimation is complex and difficult because of the large spatial variation depending on the pose, occlusions, and style. In a single clothing landmark estimation, Lee et al. [15]  tried to mitigate this using the global-local embedding module using a Gaussian map. In multi-human keypoints estimation, which is a similar task, Chen et al. [16]  proposed to localize easy keypoints using the FPNs called the GlobalNet and then localize difficult keypoints through the RefineNet. Although there was a problem of low resolution when using FPN, it was solved element-wise using 1×1 conv after upsampling. As a result of the previous two cases, we evaluated the feature extractor on the estimation of landmarks and keypoints. EfficientDet has models of different sizes, from D0 (faster and lighter) to D7 (larger and heavier).
A. DeepFashion2 DATASET In this research, DeepFashion2 dataset was used to train the proposed model. The DeepFashion2 dataset was compiled by Liu et al. in 2019  [18]  with multipurpose benchmarks. These multipurpose benchmarks are clothing detection, landmark estimation, segmentation, and retrieval. DeepFashion2 fills the DeepFashion dataset gap as it contains multiple clothing items per image with rich annotations. The dataset contains more than 200,000 fashion images with 191,961 training samples and 32,153 testing samples.
Kwang Nam Choi  (knchoiThis work was supported in part by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  under Grant  2019R1F1A1062612 , and in part by the  HPC Support  Project funded by the  Ministry of Science and ICT  and the  National IT Industry Promotion Agency (NIPA) of Korea.
Computational Results Numerical experiments were computed with a quadcore computer with Intel(R) Core(TM) i7-4790S CPU 3. 20 GHz with 8 threads and running Linux Ubuntu, with 16 Gb of RAM memory. [1, 8] ] ∪  [[11, 18] ]distinguishes instances generated with the same previous characteristics. For each instance, jobs have the same penalization cost. Penalties are larger than transportation costs so that the optimal solutions serve a maximal number of jobs. For only instance gotic_15_20_40_ex7. txt, it was impossible to realize all the jobs; the other optimal solutions visit every customer. In the instances, localisation of depots and customers is given with 2D coordinates, and the distance from localisation coordinates (x, y) and (x , y ) shall be computed using the following rule (different from  [19] ).
Hence, instances with c < c max are less constrained than the original ones with c = c max and have a better optimal value. In the case c = 1, there are no skill constraints, and the model is similar with a MDVRPTW. For computational analyses, we investigate the difficulty of instances regarding speed and competence parameters. This defines four datasets: Hence, datasets s1 and S1 are MDVRPTW instances, with a correspondence and more constrained instances due to speed with dataset s1. Dataset S1 is less constrained than the three other datasets. Original dataset sc max is the more constrained dataset, comparable to the three other datasets. Comparing the difficulty induced for instances sc max and S1 allows analyzing the difficulty induced by speed and rare competence separately. In order to compare optimization algorithms in a given dataset, the following indicators are provided:.
Implementation and Characteristics of MILP Solving The numerical experiments reported here used Cplex version 12. 6 to solve MILP and LP. The analysis of B&B characteristics was provided by using the OPL modeling language to call Cplex. The matheuristics of Section 4 were implemented with OPL script to iterate successive MILP computations. A first CG scheme was implemented with OPL script also by using the populate mode to observe the impact of keeping several columns with negative reduced cost per sub-problem resolution. The final CG scheme and heuristics for subproblems were coded in Python by using the PuLP library to call for MILP and LP. Using PuLP allows using Cplex or open source tools (such COIN-OR) the same programming interface. However, using PuLP resulted in important computation time to load MILP models, similarly to https://python-mip.
LocalSolver As a benchmark local search solver for our study, we used LocalSolver version 7. 5. We note that the results are better now by using the last versions of LocalSolver; we provide here the results with version 7. 5, and our instances were given to the LocalSolver Company for their internal benchmark. For the latest versions of LocalSolver (at least with version 10), BKSs are found for most of the instances within 600 s solving time. Note that our instances may be interesting for benchmarking general local search solvers, especially to test the accuracy of local search facing highly constrained instances where some multi-start strategies may be useful. Benchmark codes with LocalSolver are also given in https://github. com/ndupin/SiteDepTRPTW (accessed on 25 October 2021). For the efficiency of the LocalSolver model, we used the definition of variables using LocalSolver lists, and TW constraints are modeled as an objective function accumulating TW infeasibilities, with a hierarchical optimization considering firstly feasibility and then the costs of the routes.
Contrary to  [4] , such large neighborhoods are not sufficient to converge to local minimums of excellent quality. The quality of the local minimum varies significantly following the different initialization. Considering the four matheuristic initialization and the null initialization, followed by the MILP-VND provided a multi-start matheuristic that is accurate for finding good solutions, the most difficult dataset sc max provided the worst solutions. Dataset sc max is generally the more difficult one for local search approaches, with less feasible neighbor moves because of the hard constraints. This was not the case of the CG-based heuristics. Using MILP-VND after the LAG_RINS heuristic of an excellent quality allowed polishing the results and increases the number of BKS found, illustrating the advantages, drawbacks and complementarity of local search approaches and CG heuristics depending on the structures of the instances.