dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Implementation Details We wish to learn an equivariant rotation function, which means ∆α • rotation in the input image leads to ∆α • transform in the estimated gradient. In general, we train on sin and cos of α • , as they both are constants in terms of ∆α •. Table  I  shows the network's architecture of the proposed detector. Based on SSD  [10] , VGG-16 is adopted as the backbone network pretrained on the ILSVRC CLS-LOC dataset. The SSD utilizes (RConv4) and fully connected (F C7, transformed to a RConv) layers from the standard VGG-16 network. It reduces the endmost FC layer of the VGG-16 architecture and inserts various ranges of smaller conv layers: [RConv8,. , Rconv11], with different feature sizes and features of different layers' LIPM are integrated with their  corresponding layers in the SSD network (s 1 for (RConv4) and s n for the last layer (RConv9)).
9% are due to the samples whose error rates are above 160 • , which means, the front side of the object has been incorrectly taken instead of its rear side. NWPU VHR10 labels are in a conventional axis-aligned bounding boxes (BBs) form, while DOTA objects' labels are in a quadrilateral form. For adopting the both settings, our proposed REFIPN uses both horizontal and oriented BBs (HBB, OBB) as ground truth, where HBB:{x min , y min , x max , y max }, OBB:{x center , y center , w, h, θ}, here w, h denote width, height and θ is within [0, 90 • ) for each object. In training, the OBB ground truth is produced by a group of rotated rectangles which properly overlap with the given quadrilateral labels. For the NWPU-VHR-10 datasets, REFIPN just produces HBB detection results, because OBB ground truth does not exist in the datasets.
9 and 0. 0005 respectively. The whole set of the filters were initialized from a regular distribution to the one of zero mean and σ = 10 -4. To evaluate the performance of REFIPN for the estimation of the object orientations, we use the DOTA-2 dataset (plane and car classes). Table  II  outlines the mean test error. As the evaluation results show, REFIPN, in average by more than 18% improvements, substantially outperforms the other stateof-the-art models. As shown in Fig. 7 , due to the lack of rotation equivariance, the standard CNN does not properly generalize for orientation changes. By utilizing augmentation (rotation), the error significantly decreases. This is the reason why the network has learnt to detect the rotated samples, leading to improvement of the learning capacity. Despite this turn-out, the proposed RConv has a satisfactory generalization ability across different orientations even without augmentation.
C. Comparison with State-of-the-art Methods In this section, we compare the performance of our proposed REFIPN against that of the other state-of-the-art approaches. In Tables III and IV, we respectively show the results of our detector in comparison with the other models on the DOTA and NWPU VHR-10 datasets. On the DOTA dataset, for the 400 × 400 pixel input image, the standard SSD attains a detection rate of 68. 54 mAP while operating at 134 FPS. Among the current object detectors in RSIs, HSP  [5]  and LR-CNN  [36]  achieve the detection rates of 78. 25 and 76. 59 mAP while operating at 37 and 65 FPS, respectively. Our proposed REFIPN achieves a satisfactory trade-off between the precision of detection and speed with 79. 54 mAP while operating at 112 FPS. Some sample detection results are shown in Fig.
6. Our proposed detector on the NWPU VHR-10 dataset also obtains the state-of-the-art results and outperforms the other rotation equivariance methods. Some sample detection results on the test set of the NWPU VHR-10 dataset are shown in Fig. 8. Our detector on the 800 pixel input image achieves 92. 86% mAP which shows the superiority of our model to the other approaches proposed for RSI object detection. Especially for plane, vehicle, and several other classes, our method achieves substantial performance improvement as reported in Table  IV. This improvement is largely due to the following factors. 1) The proposed RConv layer improves the learning capacity and satisfies self-consistency and invariance in absolute orientation estimation, which leads to more accurate object detection. 2) By integrating the proposed LIPM into the standard SSD architecture, we build a feature pyramid network in which each scale of the image pyramid is featurized that boosts the discriminative ability of the detector.
3) The feature fusion module can transform the central attention of the detector from the key parts to the whole objects. Therefore, more accurate detection can be achieved. To show the advantages of REFIPN as compared to LR-CNN  [36]  and HSP  [5] , which show the second and third best performers in our experiments, where qualitative performance comparison of different methods on various scales and orientations of objects is conducted. As Fig. 9  shows, the other two methods are less satisfactory in detecting the objects in images, and the background is mis-detected as the foreground mostly due to the orientations of the objects. As the results show, in the other methods, the BBs are not well fit to the detected objects, however, our detector can stably produces precise results. To evaluate the performance of our proposed model, in Fig.
10  we plot the mean localization error and confusions with BG curves over the DOTA dataset. As the results show the proposed model has higher localization and classification accuracy compared to the other baselines. This performance is due to structure and response of our model to the rotation of an object, constructing rotation-equivariant features. Fig. 11  evaluates the performance of REFIPN and the other approaches with respect to object rotation. As the results show, our proposed model is significantly more robust against rotations as compared to the other methods. Our method substantially outperforms the other methods on the image with tiny angles and still have more than 85% of accurate estimations for the rotations around 45 • while less than 70% is achieved by LR-CNN  [36]  and HSP  [5]  and less than 50% is achieved by PTN  [45]  and CyCNN  [46].
D. Ablation Study To evaluate the significance of each proposed module within the proposed framework, we perform a comprehensive ablation study and show the results in Table V that reports the comparison, with respect to detection accuracy and speed of our proposed model against those of the baseline SSD on the DOTA dataset. 1) Rotation Equvariant Convolution: As shown in Tables  III  and V , the mAP improves from 69. 23% to 72. 46% on the 800 pixel input image by using RConv in SSD. It can be observed that the standard CNN does not perform well for the orientation estimation. By adopting rotational augmentation, its error substantially decreases, however, for tiny angles (near to zero), it grows again. On the other hand, the RConv performs well enough on orientation estimation even without using augmentation.
In Fig. 12 , we evaluate the sensitivity of REFIPN to Λ. To Investigate the sensitivity of REFIPN to the number of rotations Λ, we use Λ = 24 to train the model and test it for different rotation values. We have noticed for Λ > 17 there are small changes in the test error, however there is a significant increase in computation time. 2) Light-weight Image Pyramid and Feature Fusion Module: To evaluate the effect of the proposed LIPM on the SSD, we conduct a set of experiments by continuously inserting layers at the LIPM and combine them with the SSD's layers. Table V reports each layer's detection results, speed and FLOPS. Large improvement (3. 22% mAP) is attained when we combined the feature image pyramid network with the RConv4. The performance of the detection is further improved in the following levels and when we use the feature fusion modules, resulting in detection accuracy of 80.
genotype × year interaction). This linear mixed-effects model was fitted in R using the lme4 package  [61]. Heritability was then estimated using the following equation: H 2 = σ 2 g σ 2 g + σ 2 e n where σ 2 g is the genetic variance, σ 2 e the residual variance (environmental variance) and n is the number of years. Heritabilities were calculated for both populations.
Genotyping and linkage mapping Genomic DNA from parental lines and from hybrids from Population #2 was extracted from young leaves. A subset of 454 R × G hybrids (randomly chosen) from Population #2 was genotyped with the RosBREED cherry 6 + 9 K Illumina Infinium ® SNP array  [23]. The strategy consisting in the genotyping of a subset of Population #2 was followed to reduce the cost of the analyses. SNP genotype analyses and marker filtering were done using GenomeStudio software (v2. 0, Illumina) as described in Klagges et al. [62]. Monomorphic markers, individuals with more than 5% missing data and markers with more than 10% missing data were discarded to construct linkage maps of each parent "Regina" and "Garnet", following the pseudo test-cross methodology used for heterozygous species  [63].
JoinMap ® software  [64]  (version 4. 1) was used to perform linkage analysis using SNPs which were heterozygous in only one of the two parents (classes coded <lm x ll > and < nn x np>). The independence LOD test (threshold = 15. 0) and the regression mapping function were used for markers grouping and maps construction, respectively. SNPs with identical segregation in the population were included in the maps (function "Assign identical loci to their groups").
QTL analyses for f lowering date QTL analyses were based on linkage maps already available for Population #1  [8]  and on the new maps for Population #2. For both populations, QTL analyses were performed for the three FD stages using the Multiple Interval Mapping (MIM) method implemented in Mul-tiQTL V2. 6 software (http://www. multiqtl. com). Detections were carried out separately for "Regina" and "Garnet" parental maps by using the "single QTL model" (one-QTL per linkage group). We tested the "two-linked QTLs model" but no consistent detections were observed (results not shown). Both single-year (or year-by-year) and multi-year models were utilized (multi-environment model available in MultiQTL). When performing multiyear analysis, a single position (in cM) and a single LOD value are given while values of percentage of variation explained (PVE) are estimated for each year.
For ease of reading, the mean PVE value across years is presented. This study gives an update of the QTL analyses presented in Castède et al. [8]  by using five additional years of FD phenotyping to perform a multi-year analysis with the 10 years available in total, from 2008 to 2017. Graphical representation of QTLs on linkage maps was generated with MapChart software v2. 3  [65]. Only QTLs for FF were presented in this paper. In accordance with to the GDR nomenclature (see www. rosaceae. org for more details), they were called "qP-FD".
Fine mapping of the QTL on "Regina" LG4 with KASP markers SNPs located within the confidence interval of the FD QTL on LG4 were selected from different sources: i) SNPs within the RosBREED cherry 6 + 9 K Illumina Infinium ® SNP array, based on their position on the sweet cherry physical map; ii) SNPs from GBS analyses described in a previous study  [66] ; and iii) SNPs identified through the mapping of available "Regina" RNA-sequencing data on the "Regina" genome sequence (Table  S4 ). In this last case, SNPs were identified using IGV software (http://software. broadinstitute. org/software/igv/), and heterozygous markers with high RNA-seq coverage were selected. Well-distributed SNPs were then selected to be transformed into Kompetitive Allele Specific PCR (KASP) markers based on the dual Fluorescence Resonance Energy Transfer (FRET) method, in which the sample DNA is amplified with allele specific primers conjugated to fluorometric dyes HEX and FAM at their 5 end.
For each KASP, three primers were developed by the BioGEVES laboratory (Beaucouzé, France) and the reactions were performed as described in Bernard et al  [67]. Fine mapping of the QTL region on LG R4 was carried out into two steps. Firstly, nine KASPs were used to genotype the complete Population #2 (1386 individuals): KASP_9. 269, KASP_9. 271, KASP_9. 456, KASP_9. 727, KASP_9. 780, KASP_9. 890, KASP_9. 935, KASP_10. 090 and KASP_10. 159 (named accordingly to their physical position on sweet cherry LG4 in kb). Recombinant individuals detected in this region were then genotyped with eight new KASP markers, selected on IGV software and located within genes between KASP_9. 780 and KASP_10. 159, named KASP_9. 781 (gene PAV04_REGINAg0203151), KASP_9. 801 (gene PAV04_REGINAg0203181), KASP_9. 814 (gene PAV04_REGINAg0203191), KASP_9.
In silico candidate genes identification Chromosomal region for in silico CG analysis was selected based on the QTL on LG4 detected with MultiQTL within Population #2 (QTL analyses using 454 R × G hybrids). Predicted cherry gene models and corresponding protein sequences of this genomic region were retrieved from "Regina" sweet cherry genome repository  [68]  (https:// doi. org/10. 15454/KEW474). Data mining on the gene ontology terms associated with candidate genes was done using Blast2GO Version 1. 4. 4  [69]. Predicted cherry peptides were used for similarity search in a nonredundant genebank protein database with blastp algorithm with a minimum e-value <10 -6 before gene ontology mapping and annotation. When no functional annotation from Blast2GO was available, an annotation from the Arabidopsis database TAIR (The Arabidopsis Information Resource) was added.
For the 2015/2016 samples, total RNA was extracted and sequenced as described in Vimont et al. [24]. Eleven dates spanning from July 2015 to the end of March 2016 were used, including the three mentioned dormancy stages covered with the 2009/2010 sampling. Sequencing data are available online (Bio-Project PRJNA756935 and Gene Expression Omnibus GSE130426). The quality of raw reads was assessed using FastQC (www. bioinformatics. babraham. ac. uk/pro jects/fastqc/) and possible adaptor contaminations and low quality trailing sequences were removed using Trimmomatic  [70]. Raw reads sequences were mapped on the sweet cherry "Regina" reference genome  [68]  (v1. 0) using STAR as previously described in Vimont et al. [71]. Raw counts and transcripts per million reads (TPM) for each transcript were calculated using HTSeq  [72, 73].
Acknowledgements This work was supported by the  "Région Nouvelle-Aquitaine "  in the project  " CerGEn " reference 2018-1R20203, numbers  00019006  and  00019007. INRAE BAP division and the "Région Nouvelle-Aquitaine " co-funded the PhD scholarship of C. B and funded the Postdoctoral fellowship of A. D. The " Groupement d'Intérêt Scientifique Fruits ", GIS Fruits (https://www. gis-fruits. org/),funded the Master thesis scholarship of N. H. Z. E. We thank  Hélène Christmann  and  Jacques Joly  for their contribution to the phenotyping of the f lowering date of the accessions of the  INRAE's Prunus Genetic Resources Center in Bourran  and of the cultivars panel in Toulenne, respectively. We thank  INRAE's Experimental Unit UEA  for the management of the populations and cultivars in Toulenne and for preserving and managing the collections of the  INRAE's Prunus Genetic Resources Center in Bourran.
Data availability Phenotypic data are available in the excel file "Raw data -Phenotyping data". Assembly and annotation of the "Regina" sweet cherry genome is available at https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/KEW474 RNA-seq data from 2015-2016 have been deposited in the NCBI Gene Expression Omnibus under the accession code GSE130426. RNA-seq data from 2009-2010 have been deposited in the NCBI Short Read Archive under the accession code PRJNA756935 (https://www. ncbi. nlm. nih. gov/sra/ PRJNA756935).
Buildings For building extraction, we train a deep learning model based on U-Net  (Ronneberger et al. , 2015)  with a ResNet-101  (He et al. , 2015)  encoder to output a pixel-wise classification of the input image. Predicted classes are: rooftop, contour/wall (used to separate adjacent buildings), facade, and shadow of buildings on the ground. 3. 1. 1 Data preparation Our dataset spans orthoimages across the world. For each image we have manually-curated ground truth polygons for individual building rooftops. Each polygon has a ground truth height attribute. The image metadata include azimuth and elevation angles for the sun and the satellite. From the rooftop polygons we rasterize rooftop and contour/wall ground truth as binary masks. Rooftop polygons are projected on the footprint using the satellite angles and the height attribute.
Trees For trees, we train the same model as for buildings but with a different output head to predict two classes: tree and ground shadow of trees. Our dataset is a collection of various satellite images across the globe for which we have vegetation ground truth polygons. For the ground truth ground shadow of trees however, we had to resort to manual annotation. As this is resource-intensive, we only labeled a fraction of our dataset. Training is adapted to apply the loss on the shadow class only when the ground truth for it is available. Images for which the shadow ground truth is available are sampled more often during training. We are thus able to train for tree segmentation on the whole dataset (to increase generalisation), while also being able to train for tree shadow on the small shadow-labeled portion of the dataset, all with the same model.
2D segmentation We have tested our pipeline on more than twenty cities and rural areas on which metrics have been carefully calculated as well as comparison with other state-of-the-art automatic methods. Regarding the evaluation of the semantic segmentation, the straightforward common metric Intersection over Union (IoU) also known as Jaccard index  (Rahman and Wang, 2016) , was calculated for each class. It measures the number of pixels in common divided by the total number of pixels present in both predictions. A very precise, manually labeled, ground truth dataset over twelve areas on four continents was used to evaluate results. We have reported an IoU of 0. 83 on the final segmentation for trees, 0. 89 for water and 0. 85 for buildings. Moreover, it can be pointed out that the percentage of missing polygons for buildings is 1.
The Normandy health data warehouse EDSaN integrates the medication orders from the University Hospital of Rouen (France). This study aims at describing the design and the evaluation of an information retrieval system founded on a complex and semantically augmented knowledge graph dedicated to EDSaN drugs' prescriptions. The system is intended to help the selection of drugs in the search process by health professionals. The manual evaluation of the relevance of the returned drugs showed encouraging results as expected. A deeper analysis in order to improve the ranking method is needed and will be performed in a future work.
Existing medicinal drug databases such as Wikidata  [5] , Drug Bank 2 , or GoodRx 3 contain valuable information but lack of comprehensiveness when taken separately and/or store some of this information as unstructured data  [6]. In this study, the design of a system enabling the retrieval of prescription orders contained in the Normandy's Health Data Warehouse (EDSaN)  [7]  at the Rouen University Hospital (Normandy, France) is described. A conceptual graph of drug knowledge data was designed and used in the information retrieval process to retrieve the French Common Dispensing Unit (UCD) codes that are used to encode and bill administrated drugs in France. A first evaluation is conducted to assess the consistency of the concepts resulting from traversing the graph. The evaluation focused in this study on the UCD codes only.
The EDSaN Data Warehouse and HeTOP The EDSaN data warehouse currently integrates 6,978,586 atomic prescriptions, i. e. of a single pharmaceutical specialty, distributed over 1,452,616 prescriptions' orders between 2011 and the end of August 2021. The data are loaded and maintained into EDSaN from the Rouen University Hospital Information System (HIS) and originate from either a dedicated database or in the care plan. Although various structured fields exist in the HIS to describe the prescriptions and administrations of drugs (dosage, time, number of administrations, etc. ), the poor quality of these data, e. g. empty fields, wrong values, does not allow an accurate structured search. In EDSaN, every atomic prescription is associated to a UCD code that identifies the prescribed pharmaceutical product.
The prescription orders include patient information (id, age, gender, and birthdate), stay information (id, entry and leaving dates, units) and prescription information (id, date), and a list of atomic prescriptions that corresponds to a single prescribed drug. The Health Terminology/Ontology Portal (HeTOP) (https://hetop. eu) is used as primary data source for drug knowledge data. It currently integrates terminological concepts from over than 70 health terminologies and/or ontologies as well as semantic relationships between those concepts. Since 2019, it includes a formal drug model suited for French specificities.
Structured Information Retrieval The free and open-source search engine software library Apache Lucene was used to enable the querying of prescription orders. Lucene documents were generated for each order to provide Boolean search functionalities. A graphical module specifically dedicated to prescriptions were also added to the Java web application of EDSaN. It enables users to query the Lucene indexes through a form that provides a search field for each prescription order metadata. This form enables an accurate structured search since each metadata could be targeted. The search for prescription orders based on what drug were prescribed can notably be done by using the search field dedicated to UCD codes. This is essential to handle user queries for which the full text search does not return any answers.
Modelling the Knowledge Graph To assist in the selection of UCD codes, a knowledge graph was designed thanks to drug and related data (e. g. , diseases) from HeTOP and its semantic network. Vertices are selected among HeTOP concepts and include MeSH (Medical Subject Heading) Descriptors, Anatomical Therapeutic Chemical (ATC) concepts, International Nonproprietary Names (INNs), virtual drugs from the MédicaBase (https://www. medicabase. fr/index. htm) database, pharmacological roots, pharmacological specialties, drug components, drug components groups, and UCD codes. The edges between vertices were also taken from semantic relationships in the HeTOP. Some types of relationships were excluded due to their technical nature or low relevance in the context of this study (e. g. , "not to be confused with").
int/mscw/) in Europe, EANET (East Asia acid deposition NETwork) in Southeastern Asia (https://www. eanet. asia/) and AMoN (Ammonia Monitoring Network in the US, AMoN-US; National Air Pollution Surveillance Program (NAPS) sites in Canada) in North America (http://nadp. slh. wisc. edu/data/AMoN/). The SRMs for ammonia calculated with the backward "retroplume" mode express the emission sensitivity (in seconds), which yields a modelled concentration in the receptor point (station) when coupled with gridded emissions (in kg m-2 s-1) at the lowest model level (100 m). Here, we used the EGG emissions  [3] [4] [5]  (see previous section) to get backward FLEXPART concentrations. To check the consistency of the proxy used in the SRMs of ammonia, we also simulated surface concentrations of ammonia with FLEXPART in forward mode using the same emissions.