dataesr/scientific-paragraphs-categorization huggingface dataset:
The first convolutional layers will filter the 150 × 150 × 3 image with 32 kernels of size 3 × 3 × 3. The second layer takes as input the output of first convolutional layers and filters it with 64 kernels of size 3 × 3 × 32. This is followed for the rest of the convolutional layers. The filter outputs for the first two convolutional layers on the input 150 × 150 are given in Fig. 5. It should be mentioned here that whereas the input image example for the depictions in Fig. 4  and Fig. 5  is centralized, as indicated in Section V. A, the synthesized imaging dataset also includes non-centered mmW reconstructions. As a result, the presented technique does not require centralizing the objects to achieve classification. The important criterion is that the objects must be placed within the field-of-view (FoV) of the synthesized radar aperture.
This is to ensure that the object can be seen by the radar aperture. However, this is not a fundamental limitation of the CI technique specifically. Rather, it is a physical phenomenon that applies to all imaging radars. No spatial zero padding is used in the convolutional layer and the convolutional stride is fixed at 1 pixel. Each layer is equipped with an activation layer 'ReLU', which makes sure that the negative values are not activated onto the next layer. It is widely used in CNN and does not saturate whilst also not suffering from the Vanishing Gradient problem  [59] -  [61]. It is worth mentioning that deep neural networks with ReLU units train much faster than other activation units  [62]. The selection is also justified with a study wherein training of the dataset has been carried out with two different activation units: ReLU and tanh for a fixed number of epochs and the training error for both the activation units have been recorded.
C. CNN TRAINING Since the CNN uses synthesized mmW images from the CI physical model for training, an assurance on the accuracy of predictions is needed. In other words, we need to validate the model, i. e. check whether the model is underfitting/over-fitting/well generalised based upon its performances on unseen data. One such technique that can be used for this purpose is the k-folds cross-validation  [67] -  [70]. The k-folds cross-validation results in a less biased model compared to other validation methods such as the train-test split  [71] ,  [72]  because, in this method, it is ensured that every observation from the original dataset has a chance of appearing in the training and validation set. This is extremely helpful in our particular case of limited dataset. For this work, the value of k is chosen to be 5 and the entire dataset is split into k=5 folds.
The average accuracy of 0. 8348 in Table  1  shows that the model has the ability to predict with the desired high precision in an actual testing environment on test images. It should be noted that the variation in the validation accuracies across different number of iterations in Table  1  is the consequence of how the dataset (which is random) is split in each iteration. In this context, every iteration will have a different training and validation set. The validation accuracies are a measure of what the model has learned from the training set at each iteration and the prediction by the model for the validation set chosen. Therefore, the accuracy varies for each iteration step and the variation pattern is random.
A. CLASSIFICATION RESULTS Following the training of the classifier-enabled CI model, two studies are conducted to test the classification performance: numerical and experimental. For the numerical study, different set of CAD models of the classes used for training (grenades, guns, knives and scissors) are imported into the developed CI model to reconstruct the mmW images. Leveraging data augmentation on the generated mmW dataset, a sufficient number of data are obtained for numerically testing the classification accuracy. A total of 520 mmW images from all the four classes were generated for testing the model. In addition to the numerical testing to characterize the classification accuracy, in order to demonstrate that the trained classifier model performs accurately with experimental mmW data, mmW images of real threat data are also considered and used as test samples.
For this experimental demonstration, a millimeter-wave handheld imager  [73]  based on a multistatic sparse array  [74] ,  [75]  is used to image a scissor and a knife, the setup of which is shown in Fig. 8(a). These two images are also geometrically transformed leveraging the data augmentation process highlighted in Section IV. In addition to increasing the size of the dataset for experimental verification of the classification process, this step also enables us to test whether the model is able to accurately predict the class of an image if it is geometrically altered. Hence, after a series of transformations on the two images from Fig. 8  resulting in a classification speed of 264 images per second. Based on the predictions by the system on the test images, performance metrics of the model is determined.
Algorithms such as Random Forest only work on data that are in tabular form, whereas CNN can handle any type of data, regardless of being in a tabular form or as a collection of images. Also, such traditional machine learning algorithms are more suitable for smaller datasets. In our case, we have 2655 images with labels associated with them. Therefore, it is advisable to use neural networks for such a size of dataset. In addition, algorithms such as Random Forest are generally slow in generating predictions because they use multiple decision trees. When making a prediction, all the trees have to make a decision for the same given input and then perform voting. As a result, the more the number of trees used, the slower the prediction process becomes. Hence, real-time decision making is difficult to achieve with such algorithms.
10. Two different test datasets were constructed out of the previous test dataset by considering the two SNR levels. The classification was carried out on both the datasets separately and the performances were recorded. From the confusion matrices in Fig. 11 , the weighted average f1-scores for all the predictions were compared. In case of images with 15 dB SNR level, an accuracy of 81% was observed, whereas for images with 10 dB SNR level, 73% was recorded. From these results, we can conclude that accuracy of image classification is highly affected by noise. The variation of the classification accuracy with the change in SNR level is shown in Fig. 12. Analyzing the SNR v/s accuracy curve in Fig. 12 , it can be concluded that the classification accuracy drops substantially as the system SNR is reduced.
Underlying reason behind this behaviour can be seen in Fig. 13 , where the output of the first layer is shown in case of an input image, similar to one in Fig. 5 , but with 10 dB SNR level. By comparing the qualities of the filter outputs in these two figures, it is evident that the input image in Fig. 13  is distorted, posing a challenge for the model to extract meaningful features from it. A similar study was conducted on the experimental images as well. These images were introduced to speckle noises as a function of varying the SNR level. The noise effect is shown in Fig. 14. The same test dataset was used as in the case of the experimental study (Section VI. A. ). But in this case, a noise element was introduced to each image. The predictions were carried out by the model on those images and the confusion matrix is generated as shown in Fig.
1. The same training parameters such as number of layers in the CNN architecture, learning rate, optimizers, etc. were used as in the previous case. After training, the model was tested with the same noisy test dataset. The following predictions were recorded in both the noisy simulated as well as the experimental images, as shown in Fig. 16. From the confusion matrices in Fig. 16 , the classification accuracies on the simulated and experimental images were recorded to be 88% and 93%, respectively. This proves that the performance of the classifier on images with higher noise content can be improved if the system is trained with images having a similar noise content. This is also a testament to the importance of using the physical model which accurately represents the actual system parameters of a radar rather than using readily available datasets to generate the training data.
The predictions record a high accuracy as shown in Tables  2  and 3. The obtained results validated that the integrated CNN algorithm, trained with synthesized images from the developed CI physical model, can accurately classify images of threat objects. This system has the potential to be used in security-screening applications, wherein different threat objects present in reconstructed mmW images can be detected and classified into their appropriate classes. Since it is known that real-world images are susceptible to noise, the effect of presence of measurement noise in input images on the classification accuracy was also studied. For this analysis, no changes were made to the training dataset, but the test images were subjected to noise. It was observed that the accuracy decreases as the noise level increases.
Hence, from these findings, it is recommended that in order to perform classification on noisy mmW images, the model must be trained with higher noise images. We justified this outcome by doing another study wherein the model was trained with images having a higher presence of noise. The performances of this model on the noisy test dataset clearly suggest that the accuracy improves when trained with images of higher noise content. It is worth mentioning that achieving training using the synthesized mmW images from the developed CI physical model made it possible to carry out this systematic analysis and understand the system classification performance as a function of varying SNR levels.
Over-damping (less than 1 oscillation after the flush test) or underdamping (more than 2 oscillations after the flush test) were identified and corrected before measurement. The invasive and NIBP data were collected simultaneously three times with 1 h between each measurement at T1, T2, and T3. In most cases, T1 occurs in the first hours of the ICU stay. Notification of the invasive and non-invasive arterial pressure was not recorded blindly. At each timepoints, the heart rate, the ventilatory support variables, the doses of vasopressor and inotropes were recorded. The age, height, weight, reason for admission to the ICU and associated medical conditions were also collected. Lactate, creatinine, central venous oxygen saturation (ScVO 2 ), cerebral near-infrared spectroscopy (NIRS), fluid balance at the time of the measurement if available and central body temperature values were noted.
Images were taken at different time points (0, 6 and 9 h) and analyzed with ImageJ software (version 1. 51s, NIH Image, NIH, Bethesda, MD, USA). Wound closure was determined by the equation as follows: Wound closure (%) = (Original wound area-area at each time point)/Original wound area. 2. 7. 2. Transwell Assay 8 × 10 4 HUVECs were seeded in each transwell insert (Corning Incorporated, Corning, NY, USA), pre-coated with 1% gelatin and treated with blue light. After 6 h [18-20], the cells in the inserts were fixed with 4% paraformaldehyde for 30 min, washed with PBS two times and stained with 0. 1% Crystal violet for 30 min. Five representative fields were randomly chosen, and the numbers of penetrated cells were counted using ImageJ software (version 1. 51s, NIH Image, NIH, Bethesda, MD, USA).
2. 8. Angiogenic Assays 2. 8. 1. Tube Formation Assay 5 × 10 3 HUVECs were seeded per well in the 15-well µ-angiogenesis slides (Ibidi GmbH, Munich, Germany) and pre-coated with reduced growth factor Matrigel (Corning Incorporated, Corning, NY, USA). Then the cells were treated with blue light. Tubelike structures were imaged with an inverted Microscope (Leica Mikroskopie & Systeme, Wetzlar, Germany) after 6 h incubation. Total tube length of five random fields was quantified with "Angiogenesis Analyzer" for ImageJ software (version 1. 51s, NIH Image, NIH, Bethesda, MD, USA)  [21].
Bioinformatic Analysis of RNA Sequencing To analyze the RNA sequencing data, the R software (version 3. 6. 3) and NGS analysis R package systemPipeR (version 1. 22. 0) were used. Quality control and adapter trimming of raw data were performed using FastQC (version 0. 11. 5) and Trim-galore (version 0. 4. 1) (www. bioinformatics. babraham. ac. uk, accessed on 27 April 2020). The resulting reads were then aligned to the reference human genome GRCh38. p13 and counted using Kallisto (version 0. 46. 1). The count data were transformed to log2-counts per million (logCPM) with the limma package (version 3. 44. 3). Differential gene expression analysis was performed using the DESeq2 package (version 1. 28. 1). Gene enrichment analysis was made with the fgsea package (version 1. 14. 0) and the EnrichmentBrowser package (version 2.
Real-Time PCR Verifies Genes in Enriched Pathways Real-time PCR validation of RNA sequencing data was performed for selected genes in the enriched VEGF and p53 pathway. The validation was performed with RNA samples derived 24 h after blue light irradiation. Real-time PCR results partially matched the RNA sequencing results. We found in the VEGF pathway two genes enriched in the low fluence group; PLA2G4A and PTGS2 were highly expressed after low fluence blue light irradiation. In the enriched p53 pathway of the high fluence group, CASP9 and BAX were significantly upregulated (Figure  6C, D ).
Acknowledgments: We would like to thank  URGO RID  ( Chenôve, France ) for financial support. We also want to thank the  China Scholarship Council (CSC)  for supporting  Kejia Kan  and  Yifei Mu. We gratefully acknowledge the excellent technical support of  Stefanie Uhlig , the  FlowCore Mannheim and Institute of Transfusion Medicine and Immunology. We acknowledge the support of the  Microscopy Core Facility Mannheim at the Medical Faculty Mannheim.
Equibiaxial stretches ranging between 5-20% were performed, as well as non-equibiaxial stretches of varying ratios between the mean-fiber and sheet directions, (e f : e s ) ∈ {(1:1), (1:0. 75), (1:0. 5), (0. 75:1), (0. 5:1)}. Four preconditioning cycles were performed before acquiring the data during the fifth cycle, at a loading speed of 3 mm/min. Triaxial shear tests were performed on cubes extracted from adjacent locations of the biaxial samples. The cube sides were cut in order to align with the tissue microstructure (figure  3a ). Shear deformations between 10 and 50% were performed, with a 10% increment and loading speed of 1 mm/min. Shear relaxation tests were also performed at 50% at a ramp speed of 100 mm/min and a duration of 5 min. As a cube can be used to test two orthogonal directions, a total of three specimens were needed to test all six modes of simple shear.
Minimisation problem In this paper, we consider the fit of three models: the HO model, Costa model, and proposed viscoelastic model from section 2. 6. All models considered were fit to data by minimizing the objective function across all 14 tests simultaneously (Eq. (  23 )) to determine the model's set of N-parameters (denoted as θ) θ = arg min y∈R N + min β∈R 3 χ(y, β) , ( 23 ) where the objective function, χ, is given as χ(y, β) = i,kl 1 R i kl 1 β i σ i kl (y) -σi kl 2 2 1/2 i,kl 1 R i kl σi kl 2 2 1/2 , ( 24 ) and R i kl = σi kl 2 is introduced to give tests equivalent weights (irrespective of the magnitude or number of time points). Note that χ gives the relative error across all tests, with χ = 1 denoting 100% error. In this minimization, a parameter set is found to best match all datasets.
Errors were also quantified on a test by test basis to understand areas of model strength and weakness using the related test-specific objective function, χ i kl (θ) = 1 R i kl 1 β i σ i kl (θ) -σi kl 2 1 R i kl σi kl 2 , ( 25 ) where the β i and θ are determined from the minimization in Eq. (23). Minimization for the Costa and HO models were done using the nonlinear lsqnonlin minimization routine in MATLAB. Multiple initial guesses were selected for the model parameters, with the reported results selected using the best fit. To examine the behavior and uniqueness of the parameter space of the proposed viscoelastic model, a parameter sweep was performed. To generate model predictions, each experiment had to be simulated based on the given kinematics to solve the fractional differential equation forward in time (see  [91, 90]  for details).
Model sensitivity analysis Sensitivity was assessed from two perspectives: the effect of noise in the data on the best-fit material parameters, and the effect of parameter perturbations on the fitting error. In the first instance, 100 noisy datasets were produced via adding unbiased uniformly distributed noise vector over time η, to the measured stress values: σi kl = σi kl + η. The noise level was set to 10% of the peak stress across a test for a specific mode, e. g. , η ∈ 0. 1[-| σi kl | ∞ , | σi kl | ∞ ], E(η) = 0. For each noisy dataset σi kl , a model fit with a different set of optimal parameters θ were obtained, following the same parameter fit procedure. Next, the optimal parameters θ min were perturbed by ±10% of the original value, by turn. The error function χ was computed for each local perturbation, according to Eq.
Results Figure  4  shows the best fit for HO, Costa and proposed viscoelastic (VE) models to the data. Example curves for the relaxation, cyclic shear and biaxial tests are shown along with bar plots showing test-specific errors for each of the 14 datasets, computed according to Eq. (  25 ). The overall errors, computed using Eq. (  24 ), were 24. 17, 24. 95 and 7. 65% for HO, Costa and VE models, respectively. In addition, figure  5  explores the VE model error across the α (0. 2 to 0. 4), b 1 (0 to 20) and b 2 (0 to 10) parameter space. The error across all datasets, computed according to Eq. (  24 ), is shown in the bottom right panel. The error computed separately for the relaxation, cyclic shear and biaxial tests is shown in the top left, top right and bottom left panels, respectively.
The isosurfaces help visualizing the error behavior in the 3D space. In each of the four panels, the minimum error is indicated by the white sphere with the overall minimum uniquely identified at α = 0. 23, b 1 = 9. 5 and b 2 = 2. 25 over all datasets. The errors over the individual tests were computed using Eq. (25). Figure  7  shows the prediction of the models under biaxial stretches of various ff:ss ratios (1:1 -blue, 1:0. 75 -cyan, 0. 75:1 -green, 1:0. 5 -red and 0. 5:1 -black). The models are shown in the top left (HO), top right  (Costa)  and bottom left (VE) panels, while the original data, from  [77] , are presented in the bottom right panel. The solid curves indicate the fiber response, and the dashed curves show the response in sheet direction. Note that here none of the models was fit to data (except for ff:ss ratio of 1:1 which was part of the original fitting dataset), and the curves represent predictions obtained by using the parameters shown in figure  4.
However, the original data was acquired following a protocol of progressive increases in shear strain, with preconditioning cycles at each level. This means that the sample was not preconditioned to the largest shear levels until later cycles. To demonstrate this impact, the test was repeated using porcine myocardium (using the same experimental protocol) whereby the shear protocol was stepped from largest to smallest shear strain (right plot). In this case, the passive tissue response resembles that predicted by the VE model.
Another important comparison is with the previous viscoelastic model published by Gültekin et al. [26]  that fits the data utilized here. This paper extended the HO model using a nonlinear Maxwell approach  [90]  that relied on 18 parameters fit to each test separately. Because this model utilized a single relaxation time, the best fit to the relaxation data showed challenges capturing the entire decay spectrum. In contrast, the proposed VE model captures this decay spectrum while also fitting other datasets. The model also shows difficulty reproducing the cyclic shear data, particularly compared to the model proposed. In addition, the model fitting for the Gültekin model requires determination of 14 nonlinear parameters, significantly increasing the cost of parameterization and analysis. Examining the predictive responses, a key observation comes when comparing the biaxial data at varying levels of stretch, see figure  7.
Stable isotopes of water molecule Stable isotopes of the water molecule (δ 18 O vs δ 2 H) are presented in Fig. 6. The isotopic composition of CI groundwater in Algeria and Tunisia ranges from -9. 2 to -2. 3‰ for δ 18 O and from -69. 2 to -34. 8‰ for δ 2 H. Most of the samples from the CI aquifer lay below both the GMWL of Craig  (Craig, 1961)  and Sfax local meteoric lines (South Tunisia) defined by  Maliki et al. (2000)  and  Abid et al. (2009) , the closest dataset for rainwater available in the literature. The scatter plot of CI groundwater situated inside of the SASS basin in Algeria follows the trendline δ 2 H ¼ 3. 01 δ 18 O -36. 70 showing that the CI groundwater has undergone evaporative processes. In addition, the samples from bordering regions (Saharan Atlas and Saoura Valley) follow the trendline δ 2 H ¼ 3.
Multivariate analysis with a self-organizing map (SOM) The self-organizing map (SOM) technique is an artificial neural network (ANN) algorithm based on unsupervised learning. It is a powerful tool for exploring and clustering large and multivariate datasets  (Kohonen, 1995). This technique has been widely applied is various fields of groundwater quality (e. g. ,  Hong and Rosen, 2001; Jurado et al. , 2018; Tobiszewski et al. , 2012). The main objective of SOM is to reduce the dimensionality of datasets for easier visualization of their variability. First, all samples are normalized and introduced as "input vectors", and then the algorithm runs an iterative "training process" to join each sample to one "node" of the artificially created network corresponding to "output vectors". The "Euclidean distance" between these two vectors should be as small as possible.
The fixation of F following anionic exchange induces the release of OH, which would increase the pH. Paradoxically, for the oriental clusters, a decrease in pH can be observed downgradient with a decrease in F concentrations. Thus, the hypothesis of anionic F/OH change as a cause of decrease in F levels can be excluded. The negative correlation of the F with δ 18 O is also observable in our dataset. This reinforces the hypothesis of  Edmunds et al. (2003)  that F contents are influenced by palaeoclimatic conditions during recharge. Samples located upgradient of the oriental clusters (OR1 and OR2) were exposed to more intense evaporation than those located downgradient (OR4). On the other hand, the lowest levels were observed in the occidental cluster (OCD), whereas the stable isotope data show that these waters are the most evaporated.
INTRODUCTION Publicly available human RNA-sequencing (RNA-seq) datasets are precious resources for biomedical research. RNA-seq data are widely used to identify actively transcribed genes, quantify gene or transcript expression, identify new fusion transcripts or identify alternative splicing or mutation events. The search for specific transcriptional events or RNAs across large-scale data has become essential in precision medicine. Advanced tools such as recount2  (1)  have achieved transcript counts in large datasets, available in an online resource. However, these tools are reference based and only provide counts for precomputed transcripts. An increasing number of studies attempt to analyze in a retrospective fashion the vast repository of RNA-seq data, including normal and pathological conditions, to discover or validate RNA biomarkers for disease diagnosis  (2, 3).