dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population Cases and referents were recruited prospectively using the PGRx information system. CD cases were identified within a systematic national registry (« PGRx-Demy» registry) assembled by 43 neurology centres specialized in central demyelination from across France, between December 2007 and July 2014. An on-site audit of recruitment and data quality was performed over a 2-month period. Referent-patients were recruited by a network of 406 general practitioners (GPs) participating in the "PGRx-GP" registry. Patients were offered participation regardless of their reason for consulting a physician. From these registries, patients were retained for this study if they met the following inclusion criteria: (1) aged ≥ 2 years; (2) living in France; (3) able to undergo a telephone interview in French (the participant himself/herself or his/her parents); and (4) consenting to participate to the study (parental consent for minor participants).
The following potential confounders of the relation between the exposure to vaccines and the risk of CD, defined based on prior knowledge and clinical experience, were adjusted for in order to obtain adjusted matched odds ratios and corresponding 95% confidence intervals (CI): family history of auto-immune disorders, region of residence, smoking status and co-vaccinations. For all statistical tests, significance was assessed with respect to the two-sided 0. 05 level. Models were also stratified by age groups (< 25 years old, 25-44 years old, ≥ 45 years old). Statistical analyses were performed using SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
74) to high for 23-valent pneumococcal vaccines (PABAK = 0. 98) and HPV vaccines (PABAK = 0. 92)  [35]. A fourth strength lies in the PGRx information system itself, as it is built to minimize selection bias. Indeed, all participants were first recruited in prospective CD diseases registries and their exposure was assessed without any reference to future studies based on these data such as the current study. Thus, participating physicians and patients were blinded to specific hypotheses regarding vaccination. Fifth, confounding was controlled for key factors both through matching (age, sex, index date and region of residence) and adjustment (family history of auto-immune diseases, region of residence, smoking status and co-vaccinations) so that residual confounding seems unlikely. Finally, in general, referent pools such as that used in this study (PGRx-GP) have been shown to be representative of the general population  [53].
Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.
Before the implementation of the model, the authors applied a data preprocessing step by resizing the images. Then, they removed the "fully connected" layer for both models, keeping the convolution layer, and replace it by a polling layer followed by the flattening layer. The Softmax activation function was used, and as an optimizer, the authors used Adam and RMSProp with a learning rate of 0. 0005. The proposed approach was trained and validated on a dataset of 4000 OCT images labeled in 4 categories, normal, CNV, DME, and drusen, of which 80% (3200 images) were used for training and 20% (800 images) for testing. The experimental results indicate that the proposed method achieved an accuracy of 94%. Yang et al. [13]  presented an end-to-end weakly supervised convolutional neural network (WCNN) model to detect AMD pathology and localize its lesions in OCT images.
They used 3231 images from the first dataset and 5084 images from the second one. Each one was divided into 80% for learning and 20% for testing. The experimental results indicate that DPN and Clique-Net achieved the best accuracy values including 99. 6% and 99% on the first base and 95. 8% and 98. 6% on the second base, respectively. Vaghefi et al. [15]  demonstrated that high diagnostic accuracy can be achieved when deep learning combines analysis of multimodal OCT, OCT-A (OCT angiography), and CFP (color fundus photographs) images. The authors presented a CNN model based on the Inception-ResnetV2 architecture. Each image modality was initially passed through the resizing layer, which was followed by 3 repetitions of convo-lution2D layer, batch normalization, and a RELU activation layer.
Then, the three separate modalities were concatenated using a global pooling layer and then passed to the Inception-ResnetV2 model. The proposed approach of combining imaging modalities into a single "multimodal" CNN was used on data from 75 participants grouped into three categories: young healthy, elderly healthy, and patient with dry intermediate AMD. The results indicate that this method gave an accuracy of 99. 8%, allowing the identification of both aging and disease with a high sensitivity and specificity of about 100% for each class. The authors adapted the pretrained model by "finetuning" after preprocessing the OCT images by the BM3D filter. The experimental results show that their method achieved an accuracy equal to 96%. In Table  1 , we summarize some relevant works. The other database used in this work is a Tunisian database containing 934 OCT images.
Preparation and Preprocessing of Data. Before applying the pretrained CNN model, a data preprocessing step is essential to improve the deep learning and the performance of the results. Hence, the OCT volumes were routed in three steps: (i) Data balancing: first, we balanced the three database classes used. In fact, we increased the number of images using the following effects, randomly applied on the input set: data normalization, rotation, shift in width and height, shear, zoom, mirror effect, and fill empty pixels by neighbors' values (fill mode="nearest") to have the same original size after shifts and rotation The application of these transformations gave us additional images like the ones shown in Figure  2 , so the database becomes balanced and ready for use. To use transfer learning, we should choose a pretrained model and then fine-tuning the model according to the classification problem to be studied or extract features from an intermediate layer and inject them into another CNN.
Construction of a CNN from Scratch. We have created a CNN model from scratch, using the PyTorch Library. We gave the name "OCTorch-Net" for our created CNN model. The details of this model are given in Table  3. Creating this CNN model with fewer layers than the pretrained CNN networks (Xception and Inception-ResnetV2) allows us to reduce the number of layers compared to the pretrained models. Indeed, the built CNN architecture is composed of a small number of layers, 8 convolution layers, only 3 MaxPooling layers, one flatten layer, one dropout layer, and one dense layer. This reduction of the number of layers of the network allows us to reduce the use of hardware systems (CPU/GPU) and the minimization of the learning time of the OCT image characteristics. where TP is the number of positive correct results in the dataset, TN is the number of negative correct results, FP is the number of false positive results, and FN is the number of false negative results.
Evaluation Metrics First, we used the following pretrained CNN models: Xception and InceptionResnetV2. The results obtained on the Duke dataset are detailed in Table  4. Also, Figure  3  presents the evolution of the accuracy and the loss function for the model built "OCTorch-Net" on the public database "Duke. " The results obtained on the Tunisian dataset are detailed in Table  5. In addition, Figure  4  presents the evolution of the accuracy and the loss function for the BCNN model (Xception, Xception) on the Tunisian dataset.
However, this introduces a complexity in the number of parameters used. The use of a CNN from scratch has  7 International Journal of Biomedical Imaging allowed to overcome this problem, since the number of parameters is lower, although the results found are very satisfactory and even exceed the results obtained by finetuning of standard architectures. This new architecture OCTorch-Net can be validated on a larger number of data to be integrated in a real clinical context. In fact, the OCTorch-Net is very effective for the prediction of the two retinal pathologies with an accuracy equal to 99. 68% thanks to data augmentation, which generated an acceptable number of images. But, we are working currently on obtaining a large Tunisian database which will allow us to better enhance the reliability of the results and to test the models on a large number of data.
In Table  6 , we present a comparison of the performances of the proposed methods and two methods from the literature. × × × ×× ×××××××××× × ×× × ××× × ×× × × ×××××××× × × × × × × × × × ×××××××××× × ×× × ××× × × × × × ×× × ×× × ×× × × × × × × × × × × ×××××× ××××× ××× × × × × × ×× × ×× ××××××× × × Figure 3: The performance of "OCTorch-Net" on the public database "Duke. "  It is clear that the proposed system achieved high classification accuracy and prediction ability of AMD and DME diseases compared to other state-of-the-art methods. The results obtained prove that our architectures surpass most methods proposed in the literature. In particular, the new architecture has given excellent results that can be used in internal regions that suffer from a lack of specialized doctors.
Conclusion We generated a deep CNN-based method to address the problems of multiclass classification of retinal pathologies (AMD, DME, and normal), which are the direct causes of vision problems, leading to total blindness in adulthood. Therefore, to avoid vision problems, our method presents an automated system to detect patients with macular edema or age-related macular degeneration using OCT scans because it is the common imaging method applied in ophthalmology in most countries of the world. In fact, on Duke dataset the from scratch CNN "OCTorch-Net" gave the best classification score with an accuracy of 99. 68%, while on Tunisian dataset, the BCNN model (Xception, Xception) gave the best performance with an accuracy of 98. 56%. We believe that the method can be further improved by adding spatial attention modules whose goal is to focus on the most information-rich regions that may be more significant than other regions.
Data Availability We used a public dataset cited in P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images", BioMedical Optics Express, 5  (10) , pp. 3568-3577, 2014.
3. 1. Dataset. In this work, we implemented and tested our CNN models on a public dataset and on a private one. The public dataset was provided by Srinivasan et al. and was acquired using SD-OCT imaging (Heidelberg Engineering Inc. in Germany) at Duke University, Harvard University, and the University of Michigan. The dataset includes 45 SD-OCT volumes: 15 normal, 15 AMD, and 15 DME patients [17]. All volumes contain multiple B scans that range from 31 to 97 with a resolution of 496 × 768 pixels that we had grouped into 706 AMD, 1101 DME, and 1405 normal.
ii) ROI and image resizing: ROI selection (region of interest) in the OCT images was made; then, an image resizing was done Transfer learning mainly consists in applying the knowledge acquired from a pretrained CNN model (which was created for a source task) to a similar target task. It allows us to avoid reinventing a CNN from scratch, and it helps us to create AI applications in a very short time. (iii) Data splitting: the dataset was meticulously parti- tioned into training and test sets to optimize the learning process. Specifically, 70% of the images from each dataset were randomly allocated to the training set, allowing the models to learn from the inherent patterns and underlying features in the images. Additionally, a validation set was created from a portion of the training data, acting as a checkpoint to monitor the model performance on unseen data and prevent overfitting on the training set.
Supermesh between atmospheric grids and HDEM The HTUs are built from the supermesh  (Farrell et al. , 2009)  between the atmospheric grid and the hydrological digital elevation model (HDEM). The initial step is to create this supermesh by calculating the set of polygons of the HDEM grid overlapping with the coarser mesh. The result is for each atmospheric grid point the list of polygons of intersecting polygones and their area. At the borders of the atmospheric grid a number of small polygons with their areas and flow direction will be created. This generates some numerical noise but which will be absorbed during the HTU construction process. More importantly it allows to preserve catchment areas and the diversity of flow directions out of the atmospheric grid. This process is performed using the SphericalGeometry library (https://github.
, 2013; Schneider et al. , 2014)  is used here. Figure  11  shows on a Taylor diagram the quality of the simulated discharge when compared to observations at the 35 stations presented in appendix A. For discharge, the database of GRDC  (Fekete et al. , 2000)  (http://grdc. bafg. de) is used. It has been enhanced with national sources of information for a number of European countries. The stream temperatures were obtained through the GESM database (Programme, 2017) (https://gemstat. org/). The diagnostics are computed with the available observations over the period 1983-2002. For discharge these results are well in-line with previous validations of ORCHIDEE  (Ngo-Duc et al. , 2005)  and the experience gained during the data assimilation experiments performed  (Wang et al. , 2018). The discrepancies between the models in the Euro-Mediterranean region can be attributed to missing processes in the land surface model.
, 2021). We have noted that probably only a more realistic representation of groundwater transport will enable to correctly simulate temperatures and water quality parameters in areas with complex hydrogeological structures. It will have to be seen if a specific HTU graph taking into account the hydrogeology needs to be implemented or if a full three dimensional representation will be required  (Maxwell and Miller, 2005). Code and data availability. The code and its evolution are freely available on the GitLab of the French Research organisations : https://gitlab. in2p3. fr/ipsl/lmd/intro/routingpp. The hydrological digital elevation models used will be made available upon request. The code version and data used for this study are available at https://doi. org/10. 5281/zenodo. 7058895 Acknowledgements.
The muon momentum measurement was previously published for early Run 2 data, corresponding to 3. 2 fb -1 collected in 2015  [10]. This paper describes the methods used to calibrate the momentum measurement of muons reconstructed by the ATLAS detector for the full Run 2 data sample. First, the potential charge-dependent bias on the scale of the muon momentum measurement, introduced by the imperfect knowledge of the real detector geometry, is measured in reconstructed collision data with a sample of  →  events. 1 A dedicated correction is derived and applied to the data. Then, J/ →  and  →  decays are selected in data and used to measure the resolution and scale of the muon momentum, which are compared with those predicted by simulation. A calibration procedure is applied to simulated events, to improve the agreement between the simulation and the data.
Data sample The analysis is performed using the √  =13 TeV   collision data sample recorded by the ATLAS detector between 2015 and 2018. Only events collected in stable beam conditions and with all relevant ATLAS magnets and detector subsystems fully operational are used in the analysis. A combination of single-muon trigger algorithms (for  →  events) and trigger algorithms dedicated to J/ →  and  →  topologies  [15]  were used. Starting from the middle of 2016, due to the increasing instantaneous luminosity of the LHC, events associated with J/ →  and  →  triggers were collected with a dedicated data acquisition stream for -physics and low-mass final states, separated from the main physics stream so that their reconstruction could be delayed if the availability of processing resources was limited  [16].
7 in the event. The samples of  → , Z → , W → , and  * / * (→ ) events were simulated using the Powheg Box v1  [23] [24] [25] [26]  generator at next-to-leading-order (NLO) accuracy in perturbative QCD, interfaced to Pythia 8. 186 with the AZNLO  [27]  set of tuned generator parameters. The CT10nlo PDF set  [28]  was used for the hard-scattering processes, whereas the CTEQ6L1 PDF set was used for the modelling of the parton showering, hadronisation, and underlying event. The effect of QED final-state radiation was simulated with Photos++. The EvtGen 1. 2. 0 program  [29]  was used to decay bottom and charm hadrons. To improve the agreement with the data, the simulated  samples are reweighted to reproduce the muon-pair tranverse momentum and rapidity distributions measured in data. A configuration similar to the one used for the  samples described above was used to simulate diboson processes decaying into final states containing muons, ( ,  , ), using Powheg Box v2 and Pythia 8.
Charge-dependent momentum scale calibration in data After dedicated detector alignment procedures are applied, residual effects can still introduce a bias in the momentum measurement of the muon. These effects arise from both the ID and the MS sub-detectors. The ID alignment is derived by a global  2 minimisation of the track-hit residuals  [49]. Residual detector displacements relative to the nominal detector geometry may however still be present, the weak modes, that are not fully corrected for by the current alignment procedures. ID alignment studies of these modes are periodically performed and data is reconstructed with an improved description of the detector geometry. Nevertheless, residual effects after this correction procedure still prevent the best possible precision being attained, motivating the study discussed in the following.
Material and methods The study was conducted in compliance with the tenets of the Declaration of Helsinki. The collection of all study-related data was approved by the Institutional Review Board (CEERB d'Ile de France, Paris, France). Subjects provided their written informed consent. This prospective study was conducted in Lariboisière Hospital, Paris, France, between July 1, 2019 and January 30, 2020. Twenty-two healthy volunteers without ocular disease and with normal (i. e. , 20/20) best-corrected visual acuity were included. Ocular fundus examination was normal in all eyes, and their refraction ranged between -4 and +2 diopters. All eyes were imaged with SS-OCTA using the PLEX1 Elite 9000 Swept-Source OCTA device (Carl Zeiss Meditec Inc. , Dublin, CA, USA). Scans were analyzed using a modified 2.
9 mm at the pupil plane. This beam size, in combination with the effects of the effective aperture at the galvanometers, produces a beam diameter estimated to be about 14 μm at the retinal plane. As a consequence, with the beam expander in place, the depth of focus at the retina is reduced. The Rayleigh range of the OCT beam, which corresponds to the tolerance on the position of the beam waist at the retina, is about 105 μm in the system with the BE in place. This may be compared to about 510 μm for the system without the BE. For this study, each eye was therefore imaged with 3×3-mm (standard scan) and 2. 25x2. 25-mm (BE scan) OCTA scans. Acquisitions for both the standard and BE scan were repeated three times per eye for the purpose of analyzing image repeatibility. Of note, data analysis (qualitative and quantitative) was performed on single scans, as regularly done in clinical practice.
Both scans consisted of 300 B-scans of 300 A-scans. All scans were captured using the FastTrack eye motion correction software (Carl Zeiss Meditec, Inc, USA). The scan pattern for the configuration with the BE consists of 300x300 samples over a 2. 25x2. 25 mm field of view i. e. 7. 5 μm sampling resolution. The scan pattern for the configuration without the beam expander consists of 300x300 samples over a 3 mm x 3mm field of view or a 10 μm sampling resolution. The quality of all OCTA scans was verified in the quality check screen after completion of the scan. Scans were not included in this analysis if a signal strength lower than 7, significant motion artifacts or evidence of defocus or blur were present on more than 10% of the image. Exported OCTA volumes were uploaded to the ARI Network platform (https:// arinetworkhub.
com), a cloud-based collaborative and processing solution provided by Zeiss and available to clinical and research institutions who are part of the A R I network. The three repeated standard and BE scans were processed off-line on Matlab (The MathWorks, Inc. , Natick, Massachusetts, USA) using an algorithm (created by Carl Zeiss Meditec) that registers the cube scans relative to each other and then averages them. The layer segmentation algorithm is a proprietary algorithm that outlines the location of the retinal layers within the OCT data. This algorithm is provided by the device manufacturer and part of the review software available in the device. The algorithm selects a reference retinal slab and calculates the transformation vectors to correctly register the slabs from the other repeated scans relative to the reference slab.
Qualitative analysis of OCTA images The BE and standard OCTA scans were aligned using ImageJ software (National Institutes of Health, Bethesda, MD): the large vessels in the superficial capillary plexus (SCP) scans were used as a reference for the alignment of all the corresponding scans (i. e. the deep capillary complex [DCC], and choriocapillaris scans). The standard scans were cropped to obtain the same size as that of the BE scans, so that they were undistinguishable for grading purposes of the qualitative criteria. Eight quality criteria were assessed for the standard and BE scans (in both single and average images), for the analysis of the SCP and DCC scans (Fig  1 ). Comparisons were made between single standard versus BE scans, and between average standard versus BE scans. The eight SCP and DCC image quality criteria were: 1) vessel sharpness (i.
e. , the presence of a false flow signal in no-flow areas, particularly visible within the FAZ); 8) presence of motion artifacts (i. e. , the presence of any misalignment visible as a discontinuity in the vessel course <10% of the total scan area) (Fig  1). Criteria 1, 5, 6, 7 and 8 were assessed on both the SCP and DCC slabs. Criterion 2 was assessed on the full-retina slab. Criterion 3 was assessed on the SCP slab. Criterion 4 was assessed on the DCC slab. The grading process was set up by three OCT masked experts (SB, CL, MN) and reference images were used for comparisons for the entire grading process. Two experts graded the retinal criteria (SB, CL) and two experts graded the choriocapillaris criteria (CL, MN). Data from qualitative analysis obtained from the two graders were averaged to perform statistical analysis.