dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, this kind of data provides information about the constraints that apply at different levels of the spine. Various authors have described the links that exist between low back pain and postural changes of the spine  [17, 18, 24]. Analyzing intersegmental moments in these patients could be of great interest. Recently, Bailey et al. [25]  reported the results of a biomechanical study that evaluated peak sagittal vertical axis, forces, and muscular moments at various joints. According to their results, a significant improvement of dynamic sagittal balance metrics was observed after surgical correction in the setting of adult spinal deformity. e advantage of using a pressure platform instead of a force platform is also related to the fact that this device is portative and can be set up in a radiograph room.
MPO was incubated with both nanomaterials separately at room temperature for 24 h before the electrophoresis. Densitometric analysis of the resulting gel bands was quantified by ImageJ software. The analysis showed a decrease in the intensity of the gel bands corresponding to the enzyme incubated with the GO and GOfMLP compared to the control (MPO alone). Values of 89. 13 ± 4. 75% and 85. 85 ± 5. 85% of band intensities were obtained for GO and GOfMLP, respectively, assuming 100% of band intensity for MPO alone used as control (a molecular weight ladder was also run for reference). This result pointed out that MPO binds both GO and GOfMLP, reducing the amount of protein migrating into the gel. To investigate the changes in the structure and the morphology of the GOfMLP and GO sheets after incubation with MPO, TEM analysis at different time points was carried out.
GO-DOX showed a negligible drug internalization, meanwhile a higher presence of DOX was recorded in the GOfMLP-DOX complex. Confocal microscopy was also used to estimate the quantity of DOX inside the nucleus. For this purpose, unstained HeLa cells were treated with the same amount of DOX (10 µM) in all formulations. DOX quantification was estimated after 8 h of incubation using FIJI software according to the literature (Figure  7 ). [47] e treatment with free DOX showed the highest internalization of the drug inside the nuclei, and this value was designated as 100% taking into account that there are no diffusion barriers regarding desorption from the graphene-based platform (Figure  7a ). GOfMLP-DOX showed a significantly higher drug signal than GO-DOX (Figure  7b  and c, respectively ). Densitometric analysis of the images showed that GO and GOfMLP delivered 9  8% and 30  12% of the drug, respectively, into the nuclei compared to the DOX alone (100  27%) (Figure  7d ).
After the exposure period, the AB assay was performed according to the manufacturer's instruction. Briefly, AB reagent (10% [v/v] solution) was added to each well. After 2 h of incubation at 37 °C, fluorescence was measured at the respective excitation and emission wavelength of 531 nm and 595 nm using a Tecan Infinite F200 plate reader. AB solution in the RPMI-1640 cell medium complete medium alone was included as blank. The experiment was performed with at least three biological replicates and two technical replicates for each concentration of GO and GOfMLP. Results were expressed as percentage cell viability versus control. To control for interference, the GO and GOfMLP degradation reaction mixture was maintained in cell-free medium and mixed with the AB dye; no interference was observed (data not shown).
Cells were detached from the plates using a solution of 0. 25% Trypsin/0. 53 mM EDTA. The cells were washed with PBS, 2% FBS and then were incubated (20 min, 4 °C) with the dye (dil. 1:2000). To evaluate their apoptotic state, the cells were stained with annexin V labeled with FITC (556419, BD Bioscience) as per the instructions from the supplier. Thereafter, the cells were washed with PBS, 2% FBS and resuspended in 300 µL, and immediately acquired on the cytometer (Beckman Coulter, Fullerton, CA). At least 10000 cells were counted for each sample, and experiments were performed in triplicates. Flow cytometry data were generated using FlowJo software (FlowJo LLC, software version 7. 6. 5. Ashland, OR, USA). Cell viability (%) was calculated as [(A/B)×100], where A and B are the number of dead cells of treated and control cells respectively.
Values represent mean ± SD (n = 3). Cell uptake studies: confocal images have been captured with a Microscope Zeiss Axio Observer Z1 Confocal Spinning disk equipped with 63 or 100× oil objective and 40× for quantification. Green Cell-Mask was recorded using 488 nm laser and recording in the green channel (505-555 nm), while DOX was recorded using 561 nm laser in the red channel (600-660 nm). Images have then been treated with FIJI software. For quantification, Region Of Interest (ROI) analysis was applied. ROIs have been selected accordingly for each cell. The intensity average has been calculated after blank subtraction. Statistical analysis: all statistical analyses were performed using the Graph-Pad Prism 7 software (San Diego, CA, USA). Details of the data analysis used are explained in GraphPad Prism 7 user's guide (https://www.
Acknowledgements The authors gratefully acknowledge the financial support from the  EU GRAPHENE   Flagship  project (no. 785219 ), and the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ). This work was partly supported by the  Centre National de la Recherche Scientifique (CNRS) , the  International Center for Frontier Research in Chemistry (icFRC) , the  JST PRESTO , and the  JSPS KAKENHI (Science of Atomic Layers (SATL) , Grant number  16H00915 ). The authors thank  C. Royer  and  V. Demais  for TEM analysis at the  Plateforme Imagerie in Vitro at the Center of Neurochemistry (Strasbourg, France). We thank  Mizanur Rahman ,  Karolinska Institutet , for advice on the flow cytometry data analysis. We also thank  Dr.
Finite Element (FE) discretization The RUCs are discretized and solved with the Finite Element Method (FEM) using the commercial FEM package Abaqus  [34]. The total length and height of a RUC are determined by the number of fibers n in the horizontal direction, the number of fiber rows k across the thickness and the thickness ratio m (see Sec. 2. 1 and Sec. 2. 2). The debond appears symmetrically with respect to the x axis (see Fig. 3 ) and we characterize it with the angular size ∆θ (the full debond size is thus 2∆θ). In the case of large debond sizes (≥ 60 • -80 • ), a region of size ∆Φ to be determined by the solution itself appears at the crack tip. In this region, called the contact zone, the crack faces are in contact and slide on each other. Due to existence of the contact zone, frictionless contact is considered between the two crack faces to avoid interpenetration and allow free sliding.
They are computed using the VCCT  [20]  implemented in a custom Python routine. Glass fiber and epoxy are considered throughout this article, and it is assumed that their response always lies in the linear elastic domain. The effective UD properties are computed using Hashin's Concentric Cylinder Assembly model  [38]  with the self-consistency scheme for the out-ofplane shear modulus of Christensen  [39]. The properties used are listed in Table  1. The model was validated with respect to BEM results of  [40, 29] ; considerations about the order of accuracy can be found in  [36]. Observing Figure  4 , it is possible to notice that the values of G I for the 1 ×.
Then, in the second section of this review, in order to properly compare the performance of the different normalization approaches between them, good practices and significance of selected figures of merit, namely the coefficient of determination (R 2 ); the root mean square error (RMSE) declined as the root mean square error of calibration (RMSEC) when strictly applied to the calibration set, the root mean square error of prediction (RMSEP) when strictly applied to the validation set not involved in the construction of the model, and the root mean square error of cross-validation (RMSECV) after a cross-validation process; the limit of detection (LOD), and the limit of quantification (LOQ) are presented as a key-step to make accurate decisions. Finally, we have decided to illustrate the present discussion through the quantitative analysis of a selected dataset after different normalization approaches, and we have accurately compared the related figures of merit to conclude if a given normalization method can be finally recommended in a general context.
Coefficient of determination, R 2 The coefficient of determination, R 2 , is the figure of merit the most commonly used (cf. Table  1 ) to assess the quality of a calibration model. However, this indicator is reliable only when the points are equally distributed over the concentration range and when there is no extreme point, very far from the other ones. At the opposite, if one of these conditions is not fulfilled, the decision based only on the parameter R 2 might be non-robust and this statement seems not always be taken into account in the LIBS literature  [31, 33] , still nowadays  [52]. Figure  1  illustrates the case of univariate calibration models built from a set of 36 LIBS spectra. This dataset has been specially selected to illustrate the case of non-satisfying conditions to build a robust model.
However, * might not be a reliable indicator of the calibration model in case of the presence of extreme points, namely points exhibiting very high concentration values compared to the other ones  [55]. To overcome this drawback, the value should be calculated on the basis of a crossvalidation (CV) process, and referred to as *+. Among the different techniques of cross-validation, the Leave-One-Out (LOO) method is the most common. It consists in removing one point from the dataset and then calculate the * value from the regression built from the N-1 remaining points. Then the excluded point is reintroduced, another point is excluded, and a new * value is calculated, and so on, until all the points of the calibration set have been excluded. Finally, *+ is the average value of the N values of * calculated during the cross-validation process.
*+ is expected to better reveal the limitations of the calibration model than *. When the calibration set is big, another method of cross-validation, known as bootstrapping, is generally preferred to the LOO, and consists in excluding not only one single point at a time but many. Finally, to fully assess a calibration model, it is necessary to predict the concentration values of a set of known samples different from the calibration set. Thus, it has been suggested  [56]  to split the original dataset into the calibration set (2/3) and the validation set (1/3), both of them equally spread over the concentration range. Then, the RMSE value is calculated over the validation set, and referred as ,, with (P) related to the prediction of the concentration values of the samples belonging to the validation set.
The analysis was made with the sample tilted to 70° under the electron beam towards the EBSD detector, to maximize the data collection. The acquisition was made using the TSL OIM data Collection 6 software, and the results were interpreted using TSL OIM Analysis 6. The step between each measurement point was set to 50 nm, to have at least half a dozen points for each grain. The grain sizes were determined by an automatic intercept method from the software, using 40 horizontal lines. Hardness and fracture toughness were evaluated using a micro-hardness tester Matsuzawa MMT-X7. A first set of ten indentations was realized with a weight of 1 kgf during 13 seconds on the surface of each sample to compare with results from the literature. The hardness is calculated by the Clemex software and is given in Vickers Hardness (HV).
However, the bending of the lattice planes at the QW interfaces can still lead to unwanted di↵raction contrast and as such care must be taken to avoid this. An electron biprism voltage of 350 V was used to provide a fringe spacing of 0. 8 nm which were reconstructed to provide phase images of the QW structure with a spatial resolution of 1. 6 nm. However, when using the Lorentz lens the spatial resolution is limited by the aberrations of the imag-ing system rather than by the fringe spacing, which is nominally 2. 2 nm. It was not possible to obtain results with the required signal to noise from a single electron hologram. Therefore, as both good spatial resolution and sensitivity is required simultaneously, a stack of 32 electron holograms each of 8 s acquisition time was acquired using the Holoview software  [27].
Numerical Set-Up Meteorological conditions The scarcity of meteorological information available to recreate the atmospheric state is one of the main difficulties to simulate the Horns Rev 1 Photo Case phenomenon. Hasager et al. [2]   from a large range of sources. The on-site meteorological mast provided measurements of temperature at 16m and 64m and the sea temperature. It also provided measurements of wind velocity at three different heights (30m, 40m and 70m) and wind direction at 28m and 68m. In order to have a better description of the vertical structure of the atmosphere, this work also uses reanalysis data from the Modern-Era Retrospective analysis for Research and Applications (MERRA). The simulation of this photo case is performed in two stages. First, a precursor simulation is performed.
We propose a learning approach to corner detection for event-based cameras that is stable even under fast and abrupt motions. Event-based cameras offer high temporal resolution, power efficiency, and high dynamic range. However, the properties of event-based data are very different compared to standard intensity images, and simple extensions of corner detection methods designed for these images do not perform well on event-based data. We first introduce an efficient way to compute a time surface that is invariant to the speed of the objects. We then show that we can train a Random Forest to recognize events generated by a moving corner from our time surface. Random Forests are also extremely efficient, and therefore a good choice to deal with the high capture frequency of event-based cameras-our implementation processes up to 1.
6Mev/s on a single CPU. Thanks to our time surface formulation and this learning approach, our method is significantly more robust to abrupt changes of direction of the corners compared to previous ones. Our method also naturally assigns a confidence score for the corners, which can be useful for postprocessing. Moreover, we introduce a highresolution dataset suitable for quantitative evaluation and comparison of corner detection methods for event-based cameras. We call our approach SILC, for Speed Invariant Learned Corners, and compare it to the state-of-the-art with extensive experiments, showing better performance.
Our current implementation processes up to 1. 6 • 10 6 events per second on a single CPU. To evaluate the quality of our detector, we also release a new high-resolution benchmark dataset. We propose a metric which is independent of ground truth keypoints extracted from gray level images, which are often used but would introduce a strong bias in the evaluation. Specifically, we compare our approach to different detectors in combination with a simple nearest neighbor based tracking, showing that, thanks to the temporal continuity of the events, a very simple tracking rule can lead to state-of-the-art results.
Thanks to these characteristics, event-based cameras have temporal resolution in the order of the microsecond, high dynamic range, and are well suited for low-power applications. Some types of event-based cameras also provide graylevel measurements together with the change detection events. The DAVIS  [10]  sensor for example, is a 240 × 180 sensor able to output intensity frames at standard frame rates. The ATIS sensor  [46]  instead provides asynchronous intensity measurements in form of time differences at the same temporal resolution of the events. Our method can be applied to any type of event-based cameras and does not require gray-level information at run time. Intensity information has been used in this work only to build the dataset used to train our classifier, as explained in Section 4.
During training, each tree is trained independently in a greedy manner, one node at the time. For a given node, let D N be the training set of samples that reached this node. The optimal θ = (k, th) for the split are obtained by minimizing the Gini impurity index  [21]  by exhaustive search. The dataset is then split in left and right D L , D R and the children nodes are trained recursively. Training stops when a maximum depth is reached, when a minimum number of samples is reached or if the impurity index is too low. A Random Forest improves the accuracy of a single decision tree by training multiple trees. The trees need to be For each incoming event (x 0 , y 0 , t 0 , p 0 ) in the input stream (a) we compute the Speed Invariant Time Surface (b). The Speed Invariant Time Surface is used as input to a Random Forest trained to discriminate corner points (c).
Building a Dataset for Event-based Corner Detection To train our classifier F, we need a labeled training set {(s j , c j )}, where the binary class c j of event e j is 1 if e j is a feature event, 0 otherwise. Building such a dataset in the case of event-based cameras is not trivial. Manual annotation is impractical, since it would require to label single events in a sequence of million of events per second. We propose to leverage the graylevel measurement provided by a HVGA ATIS sensor, already discussed in Section 2. For every event, we apply the Harris corner detector to its location only, in the graylevel image. If a corner is detected at this location, we add the event to our training set as a positive sample, and as a negative sample otherwise. In practice, even the Harris detector applied to graylevel images can sometimes fail in presence of noise, and we therefore decided to acquire our dataset by recording well contrasted geometric patterns.
Examples of this dataset are shown in the supplemen tary material The dataset can be downloaded at the following URL 1. In this way, we greatly reduce the number of outliers in the dataset. As we will see in Section 6, even if the pattern we use is quite simple, the classifier learnt on such data can generalize well in more complex real situations. We also extend this protocol to a DAVIS sensor. In this case the corners are detected on entire frames at regular intervals and all the events falling within 2 pixels form a detected corner in a time interval of 5ms are marked as corner events.
Evaluation of Event-based Detectors 1 http://prophesee. ai/hvga-atis-corner-dataset Event-based Datasets for Corner Detection We evaluate our method on two event-based datasets. The first is the commonly used Event-Camera dataset  [40]. It is composed by recordings of different scenes taken with a DAVIS sensor. As done by previous works, we consider the boxes, shapes, hdr and dynamic subsets for evaluation, for which camera poses groundtruth are provided, but we keep the simplest shapes dataset to train our model. The second dataset we use is a new dataset that we introduce for the first time in this paper. Compared to the Event-Camera dataset, ours was acquired using a higher resolution HVGA ATIS sensor, thus allowing better localization and finer feature detection, together with higher data rate.
While the Event-Camera dataset is more generic and adapted to test visual odometry and SLAM applications, the intent of our dataset is to specifically evaluate the accuracy of eventbased feature detection and tracking algorithms. We call this dataset the HVGA ATIS Corner Dataset. It consists of 7 sequences of increasing difficulty, from a standard checkerboard to a complex natural image (Fig. 6 ). We record planar patterns, so that ground truth acquisition is simple and the evaluation is less affected by triangulation errors. Evaluation Metrics Previous approaches for event-based feature detection rely on Harris corners extracted from graylevel images  [61, 64, 23]. This approach has the shortcoming of penalizing event-based features when not matched to any graylevel one, even if these features might correspond to stable points in the events stream.
In the case of the planar dataset, the reprojection error is computed by estimating a homography between two different timestamps. More precisely, given two timestamps t 1 and t 2 , we collect all the features that fall within a 5ms timewindow from these times. We then consider the last feature for each track. This gives two sets of 2D correspondences that can be used to estimate the homography between the two views of the camera. We use RANSAC for a robust estimation. Once the homography is computed, we reproject points from time t 2 to the reference time t 1 and compute the average distance between the reference points and the projected ones. During this process, we exclude points detected outside the planar pattern. In the case of the Event-Camera dataset, which contains 3D scenes, the reprojection error is computed by triangulating the tracked points, using the available 3d poses.
For all of these methods we use the publicly available implementation provided by the authors. As done in  [4] , we also apply an event-based 'trail' filter before each detector. This filter removes the multiple events generated by a contrast step in intensity which caused multiple threshold crossing. The timings of Table  3  are reported taking into account this filtering. Ablation Study In the first set of experiments, we quantify the improvement brought by our Speed Invariant Time Surface formulation against the standard Time Surface. We train two Random Forests, one using local patches extracted from the Time Surface of  [6] , and the second one on Speed Invariant Time Surface patches. Then, we apply these detectors to the HVGA dataset and track the features using nearest neighbor data association with a radius of 3 pixels and a temporal window of 10ms.
From the tracked features we estimate the homography using different time steps, as explained in Section 5. The corresponding reprojection errors are reported in Table  1. As we can see our method has lower reprojection error, the reason is that the detector trained on the Time Surface can not generalize well, and, a part for the simple chessboard pattern returns very noisy detections. We refer to the supplementary material for a visual comparison of the detected features. Evaluation on the HVGA ATIS Corner Dataset In this section, we compare the stability of event-based feature detectors against the baseline methods using the homography reprojection error. Since this metric can be applied only to planar scenes, we use it for our dataset and not on the Event-Camera dataset. For nearest neighbor data association, we used a radius of 3 pixels and a temporal window of 10ms.
7. A snapshot example can be found in Fig. 6. Corresponding video sequences are provided in the supplementary material. Arc  [4]  evHarris  [61]  evFast [39] SILC Figure  6 : Comparison of event-based detectors on the HVGA ATIS Corner Dataset. Positive and negative events accumulated during 5ms showed in black and white with corresponding corner events in red. Our method is more robust to noise and can detect stable corners even in complex scenes. Evaluation on the Event-Camera Dataset We repeat similar experiments for the Event-Camera Dataset. Since the resolution of the camera is lower for this dataset, we use a larger temporal window, equal to 50ms, for the tracking. Because of the lower resolution and the higher level of noise, this dataset is more challenging. The reprojection error and the percentage of valid tracks are given in Tab.