dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification of cell types and statistical analysis Proportionality tests were run using the R library scProportionTest (https://github. com/rpolicastro/scProportionTest ) using the Seurat object with all annotated cells as the input. One-way ANOVAs, two-way ANOVAs and Tukey's pos hoc tests for main effects and interactions were all run using functions in our R library. A preprocessing function is used to pull the expression data out of the Seurat object and add the desired variables: iPSC line, data of experiment, days in culture, hMO batch. The statistic functions use the base R functions aov and TukeyHSD. The effect of each variable were analyzed separately. A loop is used to analyze each cell type separately. Two-way ANOVAs were performed with one of the variables listed above and protein (13 targeted in the antibody panel) as second variable.
Cell types were selected in full annotated hMO dataset and input into the hypergate function. 28  A table of predictions was output. For each cell type the threshold levels for each antibody required to define the cell type were output. These thresholds are in order from most to least important. For testing the gates, manual gating was applied in FlowJo with the top gate for each cell type in each sample being set as live single cells. The gates were applied in an AIW002 sample and then applied across the other samples. For gating the two antibodies were visualized by scatter plot and a box was drawn selecting the thresholded cells from the antibody pair. The gated cells were then selected and gated with the next pair of antibodies until all thresholds were applied. The final gated cell types from all samples were exported as fsc files and read into R following the CelltypeR workflow.
Single cell sequencing analysis The FASTQ files processed using 10X CellRanger 5. 0. 1 software are installed on the Digital Research Alliance of Canada: Beluga computing cluster. For each of the four sorted populations, the CellRanger output files raw expression matrix, barcode, and feature files were used to create a Seurat data object with minimum filtering of RNA features > 100. After this point data was run locally and all details can be found in the R notebook, 'scRNAseq_processing'. RNA features, RNA counts, and percent mitochondria were checked for quality control for each sample: Neurons1, Neurons2, Glia1(astrocytes) and Glia2 (radial glia). Further filters were applied. For the glia samples there was a large number of cells after filtering. The Seurat function HTODemux was used to assign Hashtag (replicate labels).
For neuron samples and radial glia all cells were selected, for glia1/astrocyte sample the original count was very high. Increase selection of true cells, cells with assigned hashtags were used for further processing. For all samples, doublets were removed using Doublet Finder (ref https://www. cell. com/cell-systems/fulltext/S2405-4712 (19)30073-0). 41  The expected percent of doublets estimation based on the number of cells present after filtering and the 10X version 3 user guide. For each sample data was normalized, variable features selected, PCA and UMAP dimensional reductions were performed, and clusters detected with Louvain network detection (25 dimensions and 43 neighbours selected, and a range of resolutions was run).
Methods Clusters were annotated using a consensus between expression of known cell type markers from gene lists, analysis of cluster markers and cell type predictions of reference data (see below) using Seurat find anchors and label transfer. Subtypes of major cell type groups were observed, at this point these cluster were all merged. The individually processed samples were then merged, samples were down sampled to balance the data and ease processing time. After the four samples were merged the standard processing and clustering was run again using the same settings. Clusters were annotated again, retaining subtypes of each cell type and identifying the DA neurons. Each subtype was analyzed to find subtype markers and analyze using GO biological processes. Reference datasets (see below) using Seurat anchors and label transfer predictions were used to define subtypes of cells.
Acknowledgments T. M. D. received funding through the  McGill Healthy Brains for   Healthy Lives (HBHL) initiative , the  CQDM Quantum Leaps program  with support from  Brain Canada , the  Alain and Sandra Bouchard Foundation , the  Sebastien and Ghislaine Van Berkom Foundation , the  Chamandy Foundation  and the  Mowafaghian Foundation. T. M. D is supported by a project grant from  CIHR  ( PJT-169095 ). E. A. F. is supported by a  CIHR Foundation  grant ( FDN -154301 ), a  Fonds d'Accéleration des Collaborations en Santé (FACS)  grant from  CQDM/MEI  and by a  Canada Research Chair  (Tier 1) in  Parkinson 's disease. R. A. T. received funding through the  McGill Healthy Brains for Healthy Lives (HBHL)   Postdoctoral Fellowship  and  Molson NeuroEngineering Fellowship. The authors thank  David Kalaydjian  and  Nguyen-Vi Mohamed  for helping to test early midbrain organoid differentiation protocols.
In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.
Enireddy et al. [48]  trained the linear SVM classifier on deep features extracted by ResNet50, in other word the linear SVM substitutes for the softmax in deep networks. This study proposes to train an SVM model on top of whole deep networks. We are interested in training a SVM  [18]  model on top of deep networks to detect Covid-19 from chest X-ray images. For this purpose, we gathered a real chest X-ray image dataset from public data sources  [49] [50] [51] [52] [53]  tagged in one of three classes (positive Covid-19, normal cases, and lung disease not caused by. Subsequently, we propose to fine-tune different pre-trained deep network models, such as DenseNet121  [37] , MobileNet v2  [39] , Inception v3  [32] , Xception  [33] , Res-Net50  [38] , VGG16, and VGG19  [34] , to classify chest X-ray images.
A. Chest x-ray Image Dataset We started by gathering a real chest X-ray image dataset from public data sources  [49] [50] [51] [52] [53]. The X-ray images were tagged in one of three classes (positive Covid-19 infected patients, normal cases, and lung diseases not caused by Covid-19, such as lung opacity and viral pneumonia). An example of the chest X-ray images of the three classes are shown in Fig. 1. We obtained a dataset with 19,282 chest X-ray images in the PNG format, as shown in Table  1. The full dataset was randomly split into a training set (15,427 images) and a test set (3,855 images).
B. Fine-tuning Deep Networks In recent years, deep learning networks have focused on classifying images owing to their high accuracy. Recent deep networks, such as DenseNet121  [37] , MobileNet v2  [39] , Inception v3  [32] , Xception  [33] , ResNet50  [38] , and VGG  [34] , have achieved high classification accuracy for the Ima-geNet dataset  [54]. Therefore, we studied these deep networks for the classification of chest X-ray images. Instead of training deep networks from scratch, we propose fine-tuning pre-trained deep network models, such as DenseN-et121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to recognize Covid-19 from chest X-ray images. This approach, referred to as transfer learning  [55, 56] , involves re-using knowledge from pre-trained deep network  models on the ImageNet dataset and performing a similar task, that is, chest X-ray image classification with a new dataset.
A. Experimental Setup First, we implemented the training programs in Python using libraries, such as Keras  [57] , with backend Tensorflow  [58] , Scikit-learn  [59] , and OpenCV  [60]. All experiments were launched on a Linux Fedora 34 machine, Intel(R) Core i7-4790 CPU, 3. 6 GHz, 4 cores, 16 GB main memory, and the Gigabyte GeForce RTX 2080Ti 11GB GDDR6, 4352 CUDA cores. As described in Section II. A, the full X-ray image dataset was randomly divided into training (15,427 images) and test sets  (3,855 images). We used the training set to fine-tune the pre-trained DenseNet121, MobileNet v2, Inception v3, Xception, Res-Net50, VGG16, and VGG19 with the best configurations, as illustrated in Table  2 , learning rate = 0. 001, and number of epochs = 200. The training set is used to determine the best hyper-parameters (with γ = 0.
We have proposed a non-linear combination of deep network outputs obtained by training an SVM model on top of deep networks for detecting Covid-19 from chest X-ray images. We gathered the chest X-ray image dataset from public data sources, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Recent pretrained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, were fine-tuned to classify chest X-ray images. We proposed training an SVM model on top of these fine-tuned deep networks to improve chest X-ray image classification. The proposed SVM-on-Top improved 13. 72, 3. 84, 2. 13, 1. 79, 1. 24, 1. 17, and 0. 60% classification accuracy against fined-tuned ResNet50, MobileNet v2, DenseNet121, VGG19, VGG16, Xception, and Inception v3, respectively.
, 2021)  and cold plasma density distribution, in the plasmasphere and the plasma trough, from BIRA's SPM code  (Pierrard et al. , 2021; Pierrard & Stegen, 2008; Pierrard & Voiculescu, 2011). This data will then serve as an input to compute each hour, an ensemble of diffusion coefficients that will serve for Salammbô data assimilation simulations (relying on a Ensemble Kalman filter EnKF), as presented in Figure  1   (Daglis et al. , 2021). As the current computational cost of WAPI is inappropriate with the computational requirements of the SafeSpace project that needs the computation of around 200 sets of diffusion coefficients each hour, the ONERA team had to switch to a much more efficient method to compute the latter diffusion coefficients. This constraint led to implementation of a new suitable code called FARWEST (Fast Radiation diffusion with Waves ESTimator), which will be the subject of this paper.
In addition to the computational upgrade, the diffusive frame of Salammbô becomes dynamic with FARWEST, as VLF waves and plasma density are updated each time step. This represents a major improvement going in the direction of the previously cited studies, showing the impact of the plasma density dynamic on wave particle interactions. This paper is organized as follows: Section 2 is devoted to the presentation of the new FARWEST code and its numerical performances. After a quick overview of the old method used in WAPI, we will present the new interpolation based method. Then, in Section 3, we will assess its numerical accuracy compared to WAPI's output and its computational cost. Then, we present in Section 4 the validation procedure operated on FARWEST and measure its physical relevance. In Section 5, we take a further look at the effect of time dependent plasma density inputs on Salammbô's restitution, as the computation of time dependent diffusion coefficients is now enabled with FARWEST.
The Legacy Implementation of WAPI and Its Performances In its current version, WAPI computes in the equatorial pitch angle, momentum space (α eq ,p) the wave-particle interaction diffusion coefficients following the same implementation adopted in BAS's PADIE code  (Glauert & Horne, 2005). To do so, a local pitch angle α grid (size n α = 100) is first defined, describing the background magnetic field line traveled by the trapped particles represented by each Salammbô  ( eq,  * ) grid point. Over this α grid, the corresponding latitudes are computed, allowing to scale the local cold plasma densities N e and the plasma waves spectrum. In particular, for a given frequency band, a Gaussian power spectral density as a function of the wave frequency ω is assumed 𝐵𝐵 2 (𝜔𝜔) = ⎧ ⎪ ⎨ ⎪ ⎩ 𝐴𝐴 2 exp ( - ( 𝜔𝜔 -𝜔𝜔𝑚𝑚 𝛿𝛿𝜔𝜔 ) 2 ) 𝜔𝜔lc ≤ 𝜔𝜔 ≤ 𝜔𝜔uc 0 otherwise (1) with ω m the frequency of the maximum wave power, δω the bandwidth, w lc , w uc the lower and upper cutoff frequencies, and A a normalization factor with a linear dependence to the wave amplitude B ω (see appendix)  (Glauert & Horne, 2005).
Three Gaussian profiles for B(ω) with ω 1 = 200 Hz, δω 1 = 300 Hz and ω 2 = 800 Hz, δω 2 = 500 Hz and ω 3 = 2500 Hz and δω 3 = 1500 Hz. 2  distributions for the three gaussians are reported in Figure  7. Plasma density inside the plasmasphere following the arbitrary profile presented in Figure  8  and the Carpenter and Anderson model  (Carpenter & Anderson, 1992 ) outside the plasmasphere. The choice of this input data is not motivated to obtain the most accurate estimation of the diffusion coefficients by WAPI or FARWEST, but rather benefit from their analytical and realistic features to conduct the following accuracy analysis and later the validation of the new computing method in FARWEST.
Computational Cost We report in Table  1 , the computational cost of each task in FARWEST (as schematized in Figure  5 ), evaluated on 8 × 3. 5 GHz machine (Intel® Xeon(R) CPU E5-1620 v3) with 8 GB RAM. Compared to WAPI's computation cost, the computational gain of the new code is substantial. In fact, the computing time required to estimate one set of diffusion coefficients over the Salammbô grid was reduced from several hours with WAPI to almost 1 minute with FARWEST. Note that, the computation of one pre-computed table set for 1 Gaussian among 3 (as described in step 2 in 2. 2) lasted around 15 hr (but realized once and for all). Nevertheless, considering the data assimilation application planned within the SafeSpace chain and operating downstream of FARWEST (see Figure  1 ), a parallel implementation will be required in order to compute the 200 sets of diffusion coefficients.
SemiDefinite Programming (SDP)-based relaxations, which require the matrix variable to lie in the cone of positive-semidefinite matrices, represent a powerful tool in this context. SDP is particularly interesting because it enjoys polynomial complexity and usually leads to relatively tight dual bounds (see, e. g. ,  Rendl, 2010). We refer the reader to  Helmberg (2002)  and  Laurent and Rendl (2005)  for a survey on SDP. Specific software has been developed for SDP, for example the commercial solver MOSEK, or SeDuMi  (Sturm, 1999) , SDPT3  (Tütüncü et al. , 2003) , or CSDP  (Borchers, 1999) , among others. The opensource software BiqCrunch is a SDP-based solver for binary quadratic problems  (Roupin, 2004). In this work we will present a new relaxation of problem (1) yielding bounds even tighter than those provided by SDP.
To generate real-world-based evidence and allow direct comparison, we initiated a crossover randomized controlled multicentre trial, conducted at three rheumatology centers in Germany, where patients completed two DDSS, with different questioning approaches (Ada and Rheport), consecutively before their regular appointment  (10). Furthermore, patients completed a questionnaire to assess previous online symptom assessment and DDSS perception. The results of a first interim analysis  (10) , focusing on diagnostic accuracy and including 164 patients from the first recruiting center suggested that the majority of patients would recommend both tools to other patients and friends, with a slight preference for Rheport (67. 1 vs. 64. 0%). The aim of this analysis was to evaluate patient's previous online assessment experiences, to compare the acceptability, usability, usefulness and potential impact of artificial intelligence (AI)-based symptom checker (Ada) and an online questionnairebased self-referral tool (Rheport) using the final dataset from all three rheumatology centers.
Participant Demographics Seven hundred and fifty five consecutive patients newly presenting to three German recruiting rheumatology outpatient clinics with musculoskeletal symptoms and unknown diagnosis were approached between September 2019 and April 2021. Six hundred and fifty four agreed to participate, 54 patients were excluded due to major data missing (one of the DDSS not completed, or questionnaire not started), so that 600 patients were included into the main analysis, see Figure  1. The demographic characteristics are displayed in Table  1. Median age was 52 Years (37. 0-61. 0), 418/600 (69. 7%) patients were female and 214/600 (35. 7%) of the patients were diagnosed with an Inflammatory Rheumatic Disease (IRD) based on physician's judgment. 531/600 (88. 5%) of the Patients regularly used a smartphone or tablet.
FUNDING This study was supported by  Novartis Pharma GmbH, Nürnberg, Germany  (Grant Number:  33419272 ). The APC was funded by  Novartis Pharma GmbH, Nürnberg, Germany  (Grant Number:  33419272 ). Novartis employees were not involved in the design and conduct of the study. They did not contribute to the collection, analysis, and interpretation of data. They did not support the authors in the development of the manuscript.
The purpose of this work is to evaluate the performance of some wear models, either with different mathematical formulation or different definition of the unknown wear coefficients, on the prediction of the work-roll wear amplitude in Hot Strip Mills (HSM). To achieve this goal, a classical model calibration approach based on inverse optimization has been developed to calibrate these wear models. A large industrial hot rolling database composed by roll wear amplitude measurements for both later finishing mill stands (F6 and F7) from ArcelorMittal Dofasco HSM was considered and a least-square cost function was applied to minimize the differences between both numerical and experimental results during the optimization process. The averaged roll wear gap between measurements and optimized numerical predictions was then used as a quantitative indicator to compare the performance between the wear models and identify the most suitable one for roll wear prediction.
Such wear coefficients may theoretically depend on rolling process factors such as, strip temperature, strip grade, roll type, roll oxidation or roll-bite lubrication  [9, 12, 14]. Though, neither analytical relation linking these factors nor details about the parametrization of the wear coefficients (by considering the variation of these factors during the rolling process) are generally provided. Thus, the main goal of this work is to compare and evaluate the performance of 3 wear models and identify the most suitable parametrization approach to correlate the unknown wear coefficient of these models with the following rolling process factors: (i) roll stand, (ii) roll type/supplier and (iii) strip grade. To do this, a large rolling database and corresponding wear measurements from ArcelorMittal Dofasco HSM are used to analyze and quantify the performance of both wear equations and wear coefficient parametrization approaches on the prediction of the roll wear amplitude.
Experiments An industrial rolling database from ArcelorMittal Dofasco HSM for the stands F6 and F7 top and bottom rolls was used for this study. This database is composed by 395 rolling campaigns which correspond to a total of 51710 rolled strips. Roll wear profiles were measured on roll grinders at the end of each rolling campaign and after work-rolls fully cooled down, to avoid any effect of the roll thermal expansion. Thereby, a total of 1580 roll wear profile measurements have been acquired (395 campaigns x 4 roll stands). From these measured wear profiles, only the wear amplitude at the roll center is used to be compared with the numerical calculations of each wear model. Moreover, 9 different strip grades have been rolled and 8 different roll type/suppliers have been used over this rolling campaign database.
In this way, the wear parametrization options 1, 2 and 3 lead respectively to 21, 76 and 12 wear coefficients to calibrate. Note that roll wear model analysis found in the literature were usually carried out only considering a few rolling campaigns  [8, [10] [11] [12]. Thus, this database composed by a larger number of rolling campaigns with distinct strip rolled schedules enables a more reliable evaluation of roll wear model predictions.
Model Calibration Framework A classical calibration approach based on inverse parameters optimization is applied to tune the wear models. However, not all the rolling database is used on the model's calibration. In fact, the rolling database and corresponding wear measurements from Dofasco HSM are randomly split into two sets. The first dataset, which corresponds to 2/3 of the rolling campaigns, is named as training set and is applied for the calibration of the wear models. While the second dataset, which consists in the remaining 1/3 rolling campaigns, is used for the validation of the models through a performance analysis of both wear equations and wear coefficient parametrization approaches on the prediction of the roll wear amplitude. This performance analysis is made by quantifying the averaged wear gap between measurements and predictions.
Note that the validation dataset allows for a more robust comparison and evaluation between the different wear models since it was not used to fit the wear coefficients and so, gives an unbiased effectiveness estimation of the calibrated wear models. Inverse parameters optimization. The wear models are calibrated by an inverse optimization process which searches for the set of input wear coefficients leading to the smallest gap between wear amplitude measurements and calculations. This inverse optimization process combines an optimization algorithm, responsible for updating the unknown wear coefficients, with a cost function, responsible for calculating the difference between experimental and numerical wear amplitudes. During the optimization, the wear coefficients set is iteratively updated with the aim of minimizing the cost function value.
So, the calibrated model results obtained from initial set 1 were used to evaluate the performance of the different roll wear formulations. Table  2  lists (i) the final cost function values as well as (ii) the averaged gap between predicted and measured roll wear amplitudes obtained by both training and validation datasets for each model formulation. From this table, it is shown that no very significant differences exist between the 3 wear model predictions since the final cost function values as well as the averaged wear gaps obtained are quite similar. Indeed, the averaged wear gap between the measured wear amplitudes and the predictions of the 3 wear formulations are in-between ≈46-51 µm for the validation dataset. However, Wang's model calculations tend to promote better roll wear amplitude predictions (≈46 µm) while, in opposition, Cao's model calculations led to the worst ones (≈51 µm).