dataesr/scientific-paragraphs-categorization huggingface dataset:
Reduction process The theory of rough sets aims at finding the smallest subset of the conditional attribute set in a way that the resulting reduced database remains consistent with respect to the decision attribute. A database is considered to be consistent in case where for every set of objects, having identical feature values, the corresponding decision features are the same. To achieve this, the theory defines the Reduct concept and the Core concept. Formally, in an information table, the unnecessary attributes can be categorized into either irrelevant features or into redundant features. The point is to define an heuristic that defines a measure to evaluate the necessity of a feature. Nevertheless, it is not easy to define an heuristic based on these qualitative definitions of irrelevance and redundancy.
The MapReduce paradigm MapReduce  [10]  is one of the most popular processing techniques and program models for distributed computing to deal with big data. It was proposed by Google in 2004 and designed to easily scale data processing over multiple computing nodes. The MapReduce paradigm is composed of two main tasks/phases, namely the map phase and the reduce phase. At an abstract level, the map process takes as input a set of data and transforms it into a different set where each element is represented in the form of a tuple key/value pair, producing some intermediate results. Then, the reduce process collects the output from the map task as an input and combines these given key/value tuples into a smaller set of pairs to generate the final output. A representation of the MapReduce framework is given in Fig.
e. , Table  1. The final output is Reduct = {H eadache, T emperature}. In this example, we presented a single iteration of Sp-RST, i. e. , N = 1. Therefore, line 16 on Algorithm 1 will not be covered in this example. Sp-RST could reduce the big information To validate the effectiveness of Sp-RST, we require a data set with a large number of attributes that is also defined by a large number of data instances. The Amazon Commerce reviews data set from the UCI machine learning repository  [4]  fulfills this requirement. The Amazon data set was initially build from several customer reviews on the Amazon commerce Web site. The base was constructed based on the identification of the most active users, aiming at performing authorship identification. The database enclosed a total of 1500 data instances which are described using 10,000 features (linguistic style such punctuation, length of words, sentences, etc.
-The sizeup keeps the number of nodes constant and measures how much the runtime increases as the data set is increased by a factor of m: Sizeup(m) = runtime for data set of size m • s runtime for baseline data set of size s To measure the sizeup, we use the smaller databases described in Sect. 6. 1. We use 1000 features as a baseline and consider 2000, 4000, 6000, 8000, and 10,000 features, respectively. A parallel algorithm with a linear sizeup has a very good sizeup performance: Considering a problem that is m times larger than a baseline problem, the algorithm requires in the order of m times more runtime for the larger problem. -The scaleup evaluates the ability to increase the number of nodes and the size of the data set simultaneously: Scaleup(m) = runtime for data set of size s on 1 node runtime for data set of size s • m on m nodes Again, we use the sub-data set with 1000 features as a baseline.
Scalability We measure the scalability of Sp-RST based on its speedup (Fig. 8 ), sizeup (Fig. 10 ) and scaleup (Fig. 12 ) as discussed in Sect. 6. 2 and additionally plot the measured runtimes by the number of nodes and the different data sets (Figs. 9, 11). We see that the runtime increases considerably with the number of features per partition and decreases with the number of nodes used (Fig. 9 ). However, in the latter case, increasing the number of nodes from 1 to 2  or 4 has a much larger effect than increasing it further (Fig. 9 ). Moreover, it increases with the size of the database (Fig. 11 ), where size is measured in terms of the number of features.
Number of Nodes Runtime (in seconds) (f) Amazon10000 This implies that having more partitions is not beneficial with respect to the parallel running time-even though the runtime for a single partition grows exponentially in the number of its features. This indicates that settings with 4 and 5 features per partition generate too small sub-databases that generate unnecessary large overhead due to high communication cost. Figure  10  shows that Sp-RST has a very good sizeup performance; however, the more nodes are used, the more important the parameterization becomes. We see that 4 or 5 features per partition yield generally better sizeup than 8 or 10 features. While 4, 5 as well as 8 and 10 features show very similar sizeup performance. For the latter, the sizeup deteriorates for m ≥ 6. This is in stark contrast to the speedup results discussed earlier where we have observed better speedups for more features per partition.
Thus, depending on the size of the database in terms of features and the number of nodes available different parameter settings will be more appropriate. Figure  12  shows the scaleup for 1, 2, 4, and 8 nodes and the corresponding data sets Amazon1000, Amazon2000, Amazon4000, and Amazon8000. For up to 4 nodes, the scaleup is close to 1 for most parameter settings; however, it drops below 1 for 8 nodes. Therefore, we can conclude that using a very large number of nodes does not necessarily yield much better runtimes while we obtain large improvements with moderate parallelization. This is in line with the speedup results in Fig. 8  where the speedup was close to linear for most parameter settings for up to 4 or 8 nodes and only deteriorates quickly with more nodes.
There, nano-XRF mapping was performed on ROIs defined on medium-resolution maps. For orientation purpose, an overview scan was performed Table  1. Sample list, location of intraoperative sample extraction, and quantitative comparison of the individual samples regarding Co, Cr, and Ti content. Percentage of spatial mass fraction greater than background spatial mass fractions (= Q 0. 999 of all pixel intensities from control samples) and maximum spatial mass fractions of Co, Cr, and Ti. Data were obtained from all XRF-scans with 2-10 µm spatial resolution. with steps of 1 µm before scanning at resolutions of 60 and 30 nm (Figure  1E ).
Identification of the Overall Exposure: Metal Mass Fractions in Analyzed Samples For all samples analyzed, the underlying clinical case and secondary patient characteristics including implant data (Table  S1 , Supporting Information), prerevisonal radiographs (Figure  S1 , Supporting Information) and, if available in clinical records, local and systemic metal levels (Table  S2 , Supporting Information) are reported. The nano-and micron-resolved XRF-analyses revealed that metals released from endoprostheses are abundantly present in acetabular, femoral, and tibial peri-implant cancellous bone including BM with Co, Cr, and Ti particles of various sizes and shapes dominating the overall metal exposure. Supraphysiological metal quantities were identified in specimens from the proximity of various hip and knee endoprostheses independent of their specific design.
To elucidate whether Cr accumulates within extracellular matrix or cellular structures of the marrow, the co-localization of Cr with sulfur (S) and phosphorus (P) was investigated. S is a generally accepted indicator for extracellular matrix, while P is more specific for cellular structures. These low Z-elements are detectable in the course of micro-XRF analyses under vacuum conditions. Plotting of the spatial mass fractions indicated a distinct co-localization of Cr with P (Figure  4D ) and S (Figure  4E ) within the analyzed peri-implant BM of both implant types. Notably, Cr was exclusively detected co-localized with S and P, indicating pronounced binding affinity of the released Cr to BM matrix and cellular structures. The Cr content within the P-and S-rich data points are statistically significant higher if compared to the Co content in these data points (Figure  4F ).
Confocal Fluorescence and Second Harmonic Imaging Microscopy: Confocal and second harmonic microscopy was performed on a Leica SP5 II microscope equipped with a Spectra Physics Multiphoton Laser (Mai Tai HP) using a 25× water immersion objective as described before. [77] ytotoxicity Assay: Semiquantitative detection of LDH was performed by using a commercially available cytotoxicity detection kit (Roche) according to the manufacturer's instructions at every media change. Data Presentation and Statistics: XRF-and XANES-spectra were fitted using the software PyMca. [78] To assess absolute and relative mass fractions, the spectra of regions allocated to cells were averaged and the peak areas corresponding to individual XRF-lines were computed into mass fractions, taking into account the matrix composition.
The overall density was assumed to be 1. 2 g cm -3 for intertrabecular soft tissue and 1. 9 g cm -3 for bone trabeculae. [79] Low matrix components were assessed to be 0. 5 g (carbon), 0. 21 g (oxygen), 0. 14 g (hydrogen), and 0. 17 g (nitrogen) per cm 3. [80, 81] PyMca was used for descriptive statistics regarding RGB imaging, heat maps, and peak spectra. ImageJ was used for the thresholding of the Ca-maps and for reconstruction of the 3D two-photon excitation images. GraphPad Prism 8. 0. 0 was used for statistical analysis and data plotting. No samples or data were excluded from the analysis and all data points are shown as individual values. The Shapiro-Wilk test was used to test normality and the Levene test was used to assess the homogeneity of variances of the data for the indicated groups.
Acknowledgements This study was realizable through allocation of beamtime at the  European Synchrotron Radiation Facility (ESRF)  beamlines  ID21 & ID16B  (proposal number:  MD-1161 ). The authors would like to thank all beamline staff at ID21 and ID16B for their support and hospitality. Further, the authors wish to thank  Prof. Dr. med. G. Wassilew, PD, Dr. med. V. Janz , and  Dr. med. U. Sentürk  for intraoperative sample harvesting. The authors gratefully acknowledge  M. Princ  and  K. Mika  for technical assistance in the course of sample preparation,  Dr. J. Braun  for the support regarding descriptive statistics,  S. Goerlitz  for the support with confocal and two-photon microscopy and  C. Naujok  at the  Charité media center  for the realization of the graphical abstract. The authors would like to thank the  Core Unit "Cell Harvesting" of the Berlin Institute of Health Center for Regenerative Therapies (BCRT)  for their support with sample processing and cell analysis.
3, 0. 6, 0. 9) of Y. enterocolitica (aptamer concentration 150 nM, bacteria concentration 10 8 cfu mL À1 ). background, which demonstrated a high binding ability of the selected candidates to the Y. enterocolitica as compared to control and other chosen candidates (Fig. 4 ). A similar binding affinity curve was obtained by spectrophotometric analysis (data not shown). The binding affinity of each candidate was determined by conducting the series of binding analysis using different concentrations of aptamers (0-400 nM) and a constant number of bacterial cells (10 8 cfu mL À1 ). The binding assay results were utilized to draw the saturation curve and dissociation constant (K d ) of each aptamer by non-linear regression analysis by using Graph Pad Prism Soware 5. 0 (Fig. 5 ). Among the sequences, M1, M5 and M7 exhibited higher uorescence intensities with 79, 70, and 77%, respectively, which was above the background.
Acknowledgements This work was partly funded by the  National Natural Science Foundation of China  ( 31871881 ),  Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF) , (CX(18)2025),  S&T Support Program of Jiangsu Province  ( BE2017623 ), the  National First-class Discipline Program of Food Science and Technology  ( JUFSTR20180303 ), and the  Distinguished Professor Program of Jiangsu Province  ( JUSRP51714B ).
Content Based Image Retrieval(CBIR) is the task of finding the images from the datasets that consider similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD,. ) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. To tackle this problem, we present in this paper an efficient CBIR framework based on incorporation between deep learning based semantic segmentation and visual features. We show experimentally that the incorporate leads to the increase of accuracy of our CBIR framework.
Introduction Content Based Image Retrieval(CBIR) is a fundamental step in many computer vision applications such as pose estimation, virtual reality, Medical diagnosis, remote sensing, crime detection, video analysis and military surveillance. CBIR is the task of retrieving the images similar to the input query from the dataset based on their contents. CBIR system (see figure  1 ) based on three main steps:  (1)  Feature extraction (2) Signature construction (3) Retrieval. The performance of any proposed approach depends on the way in an image signature is constructed. Therefore, construction image signature is a key step and the core of CBIR system. State of the art mentions two main contributions used to retrieve the closest image: BoVW (Bag of Visual Words) and CNN (Convolutional Neural Networks) descriptors for image retrieval.
Both contributions represent images as vector of valued features. This vector encodes the primitive image such as color, texture and shape. In this paper, we present a new idea to improve the potential of recovering the relevant images. Our work incorporate the extracted visual features with the semantic information to build a robust semantic signature. Before computing the distance between the query and the datasets, we have proposed also an efficient test for checking the semantic similarity. This step keeps only the images with the same semantic content with the query and penalize the rest. Our results on different database highlight the power of our approach. This article is structured as follows: we provide a brief overview of convolutional neural networks descriptors and bag of visual words related works in Sect.
2. We explain our proposals in Sect. 3. We present the experimental part on four different datasets and discuss the results of our work in Sect. 4. Section 5 conclusion. [8]  is the popular model used for image classification and image similarity. BoVW treated as following. For each image, the visual features detected then extracted using a visual descriptors such as SIFT  [15]. This step will be repeated in a recursive way on all images dataset until collecting all visual descriptors dataset. Then a clustering step using K-MEANS  [11]  will be applied on the descriptors to build the visual vocabulary (visual words) from the center of each cluster. In order to obtain the visual words, the features query replaced by the index of the visual words that consider the nearest using euclidean distance.
Semantic histogram Except that the semantics provides us a class by label, we can also know the objects in the image and their proportion. We exploit this information to check the semantic similarity between the images. Then, we assume that if two images share the same classes label are then semantically similar otherwise the content of the images is different. Consequently, using the semantic information we can select the images which are similar in content with the query. In other side, we can neutral then penalize in the calculation step the dissimilar images with Sim(I query ,I dataset ) ≤. To deal with this problem, we proposed to construct for each image a semantic histogram. As shown in figure  4 , we define the image as a vector of N unit contains the proportion of each class in the image.
Then, we measure the semantic similarity between two images using equation  (1). Sim(query, candidate) = n i=0 |P queryi -P dataseti | (1) where P are the the proportion of a class in the image. The main advantage of the checking phase is makes us able to increase the CBIR accuracy by keeping only the images that have the same semantic content with the query and to penalize the rest that consider semantically different(figure  5 ). In this section, we present the potential of our approach on four different datasets (Table  1 ). Our goal is to increase the CBIR accuracy and reduce the execution time. To evaluate our proposition, we test on the following datasets : • MSRC v1 • Corel 1000  [27]  or Wang is a dataset of 1000 images divided into 10 categories and each category contains 100 image.
4. 3 Benchmark datasets for semantic segmentation. Many semantic segmentation datasets have been proposed in the last years such as Cityscapes  [7] , Mapillary  [16] , COCO  [14] , ADE20K  [29] , Coco-stuff  [5] , Mseg  [13]  and others. The semantic representation is divided into two main categories: Stuff and Things. Things objects have characteristic shapes like vehicle, dog, computer. Stuff is the description of amorphous objects like sea, sky, tree,. Therefore, the semantic segmentation datasets are divided into three main categories: (i) Stuff-only (ii) Thing-only (iii) Stuff and Things. To obtain a robust prediction, we use the recent implementation HRNet-W48  [24]  architecture trained on Coco-stuff  [5]  and Mseg  [13]  datasets. The main advantage of using Coco-stuff  [5]  and Mseg  [13]  datasets is that they are able to predict for both thing and stuff with high number of class predicted for an image.
0. 61 0. 58 0. 60 Linnaeus  [6]  0. 71 0. 73 0. 71 Wang  [27]  0. 73 0. 74 0. 71 using semantic filter MSRC v1 0. 81 0. 86 0. 87 MSRC v2 0. 74 0. 73 0. 71 Linnaeus  [6]  0. 73 0. 75 0. 74 Wang  [27]  0. 84 0. 84 0. 83 0. 57 0. 55 0. 61 Linnaeus  [6]  0. 67 0. 68 0. 66 Wang  [27]  0. 71 0. 72 0. 69 using semantic filter MSRC v1 0. 80 0. 85 0. 86 MSRC v2 0. 71 0. 72 0. 71 Linnaeus  [6]  0. 72 0. 75 0. 73 Wang  [27]  0. 82 0. 83 0. 80 Table  4. MAP evaluations using Coco-stuff datasets We conducted our experimentation on two different semantic dataset (Table  2 ) and four retrieval datasets (Table  1 ). We test our approach using three different descriptors (Kaze, Surf, Kaze). In addition, we compare there with two categories of methods : (i) Local visual Feature: methods that are based on local features like Surf, Sift included the inherited methods such as BoVW, Vlad, Fisher.
(ii) Learning based features: methods that based on learning the features using deep learning algorithms. Tables  3, 4  present the performance of the retrieval on the 4 datasets with three different descriptors. Above, we show the map (mean average precision) scores using only the semantic signature ( figure  3 ). Down, we present the results by adding the semantic filter. In experimentation we set epsilon ( ) at 0. 9 to keep only the images that are considered semantically similar to the input query and we assign a negative score to the rest. It clearly indicates that adding the semantic filter improves the accuracy.
Methods Top 20 ElAlami  [9]  0. 76 Guo and Prasetyo  [1]   For the methods  [9] [1] [28]  [20]  in table 5, we compare the precision of the top 20 retrieved images for all categories for Wang dataset. In Table  6  we compare our results with a large state of the art methods. For  [25] ,  [23] ,  [12]  we extract from their architecture the features vector from the features layer then we evaluate the their performance on the datasets using L2 distance. As indicate the results our proposed present good performance for all datasets.
458 For experimental data, the whole nested cross-validation process was repeated 10 times (r=10), whereas for simulated data it was performed only once, but on 10 different simulation replicates (r=1 and t=10). The 14 traits were analyzed jointly for MTV_RR, MTV_LASSO and MTV_EN. But for SPRING, since analyzing all traits together was computationally too heavy, we split traits into three groups by hierarchical clustering (Figure  S3 ) performed with R/hclust applied to genotypic BLUPs. All traits within each group were analyzed together. For simulated data with the same heritability values for both traits, performance results were averaged not only over simulation replicates and partitions of outer CV, but also over traits, because both traits were equivalent in terms of simulation parameters.
Candidate genes After merging the QTLs colocalized between traits, we obtained 12 intervals, located on chromosomes 1, 4, 10, 12, 13, 17 and 18, harboring a total of 3,461 genes according to the VCost. v3 annotation  (Canaguier et al. 2017 ). Among them, 2,379 had a NCBI Refseq identifier and 1,757 a putative function (Table  S24 ). We then focused our analysis on the eight "new" intervals, i. e. those which were not overlapping with CIM SSR intervals. They encompassed 1,155 genes, half of which were annotated. We were able to retrieve from TAIR and/or UniProt a more precise description of the genes function for 86% of the annotated genes (Table  S24 ). The remaining ones either did not have any homolo-818 gous gene in Arabidopsis thaliana or were not described in the 819 above-mentioned databases.
Candidate gene analysis The thorough methodology deployed for candidate genes analysis allowed us not only to retrieve a list of the genes un-   S25 ). One of these genes, the trihelix transcription factor GT-2 (Vitvi04g01604), was specifically shown to impact transpiration and transpiration efficiency in Arabidopsis by acting as a negative regulator of stomatal density. On the other hand, 27 genes among the list are directly related to growth, development, or photosynthesis, meaning a possible direct impact on DeltaBiomass. A histidine kinase 1 (Vitvi04g01483) may be a particularly interesting candidate for its multiple roles in ABA signalling, stomatal development and plant growth known in Arabidopsis, hence potentially simultaneously acting on both components of TE. Both these likely candidates were often highly expressed in grapevine leaves according to the data retrieved from the RNA-seq database.
Acknowledgments We thank  Marie Perrot-Dockès  for her help concerning penalized regression methods, the  South Green  platform for computing, notably  Bertrand Pitollat , and the GREAT platform, notably  Amandine Velt  and  Camille Rustenholz. We also acknowledge the funding of the  SelVi and FruitSelGen projects (BAP department and Selgen metaprogram of INRAE). Partial funding of the PhD was provided by  ANRT  (grant number  2018/0577 ), IFV and  Inter-Rhône.
546 inra. fr/Species/Vitis/Annotations) to get putative functions from 547 NCBI, when available. For those genes with a putative func-548 tion, we then refined the analysis to retrieve additional informa-549 tion about their function and expression. We searched UniProt 550 (https://www. uniprot. org/) and TAIR (https://www. arabidopsis. org/) 551 databases to get a complete description of the genes function, 552 their name and the corresponding locus in Arabidopsis. In addi-553 tion, we used the GREAT (GRape Expression Atlas) RNA-seq 554 data analysis workflow (https://great. colmar. inrae. fr/app/GREAT), 555 which gathers published expression data, to assess the level 556 of expression of our candidate genes in grapevine leaves and 557 shoots, the organs relevant for the traits considered in this study.