dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: Most of the computations presented in this paper were performed using the CIMENT/GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr). The authors acknowledge the  EpiMed core facility  (http://epimed. univ-grenoble-alpes. fr) for their support and assistance in this work. This study was supported by the  ANR  ( ANR-17-CE13-022 ), the  Fondation pour la Recherche Médicale FRM  ( DEQ20170336702 ), the  International Emerging Action CNRS , the  association Espoir contre le Cancer Isère , the  FWO  project  G087018N , infrastructure grant  I009718N ,  Hercules Foundation  ( G0H6316N ), the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/2007-2013)/ ERC Grant  Agreement No. 308223 ). PhD fellowship grants were supported by  ANR  and  FRM  to D.
Acknowledgements We thank  Prof. Massimo Olivucci  for the inspiring comments during the 2019 GRC conference on articial molecular switches and motors. The support of the  Netherlands Organization for Scientic Research (NWO-CW VIDI  grant  723. 014. 001  to W. S. ) is gratefully acknowleged. M. M. and A. D. L. thank the  Slovak Research and Development Agency  (Project No. SK-FR-19-0002 ), the  Campus France  and the French Embassy in Slovakia in the framework of the Hubert Curien partnerships (PHC Stephanik # 45091SF ) for funding the bilateral cooperation. M. M. acknowledges support from the  Scientic Grant Agency of the Slovak Republic  (Project No. VEGA 1/0562/20 ), the  Ministry of Education, Youth and Sports of the Czech Republic, Operational Programme for Research, Development and Education of the European Regional Development Fund  (Project No.
CZ. 02. 1. 01/0. 0/0. 0/16_019/0000754 ) and  ERC  (project No. 683024  from the  European Union 's  Horizon 2020 ). A. D. L. thanks  Denis Jacquemin  for his advice and the  Agence National de la Recherche  for the funding of the  InducedFluo  project  (Project   No. ANR-19-CE29-0008 ). This research used resources of (i)  High Performance Computing Center of Matej Bel University  supported by the  Research and Development Operational Programme  funded by the  ERDF  (projects ITMS  26230120002  and  26210120002 ) and ( ii) CCIPL  ( Centre de Calcul Intensif des Pays de Loire ). M. D. D. acknowledges  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 871124  Laserlab-Europe. J. F. acknowledges a doctoral fellowship from the  China Scholarship Council  (No.
Introduction The address event representation (AER)  [9, 27, 29]  is a new paradigm in computer vision. Each pixel in an AER camera emits a signal when the change in the pixel value equals a fixed threshold. If the change in value is less than the threshold, then no signal is emitted. The pixels emit their signals asynchronously, i. e. without coordination. There is no concept of an image frame of pixel values all of which are obtained at the same instant in time  [4]. Cameras which supply data in the AER format are referred to as event cameras or as silicon retinas. The latter term arises from an analogy between the AER and the human retina. The advantages of event cameras over conventional cameras are a low power consumption, a very rapid response to changes in the image and a high dynamic range  [6].
Notation for the AER Each pixel emits a measurement when the value recorded by the pixel changes by an amount ±d, where d is a fixed positive threshold. If the change in value is less than d in absolute value, then the pixel in question does not emit any measurement. The measurements emitted by a pixel q form a list (q, s(i), Δ(i)), i = 1, 2, 3,. (1)  where s  (1) , s(2),. is an increasing sequence of times and Δ(i) ∈ {0, 1} is the polarity of event i. The component Δ(i) of the measurement specifies the sign of the change in the pixel value. If Δ(i) = 0, then the value decreases by d and if Δ(i) = 1, then the value increases by d. The full list of measurements obtained from an event camera consists of the union of the lists of measurements (1) over all pixels of the sensor array. Event cameras have lower data rates and lower power consumption than conventional cameras because there is no wasteful output from pixels for which there is only a small or zero change in value.
Experiments with the Fisher-Rao method were carried out on eight datasets. Three of these datasets include the ground truth optical flow obtained by the OptiTrack motion capture system. An additional dataset is obtained from the MVSEC dataset  [38] [39] [40]  which includes the ground truth optical flow 1. 4. Overview Related work is described in section 2. Optical flow estimation using the Fisher-Rao metric is described in section 3. The implementation of the Fisher-Rao method for estimating optical flow is described in section 4. Experiments with the Fisher-Rao method are reported in sections 5, 6 and 7. In particular, an experimental comparison of the Fisher-Rao algorithm with two state of the art algorithms is described in section 7. Section 8 is a conclusion. The OptiTrack system for obtaining ground truth optical flow is described in appendix A.
Related work Research on processing techniques suitable for AER data has been prolific in the past few years. AER datasets with ground truth are described by Barranco et al  [5]  and by Zhu et al  [38] [39] [40]. The latter very extensive dataset is used in the experiments described in section 7. Direct conversions of state of the art computer vision algorithms to AER based algorithms are usually achieved by using the intensity information estimated by the local integration of the events (1). This approach is adopted for event correlation applied to stereo matching  [24] , for photoconsistency based estimation of optical flow  [6]  and for machine learning using convolution networks  [31]. However, local integration of events does not preserve the event camera's temporal accuracy. Akolkar et al  [1]  show that the high temporal accuracy of event camera measurements yields up to 70% more information, compared with conventional frame based methods.
The first algorithm searches for moving edges using the time differences between nearby events. The motions of the edges are measured. The next four algorithms are based on the Lucas-Kanade approach in which the optical flow is assumed to be locally constant and linear constraints on the optical flow are obtained using the motion constraint equation together with estimates of the intensity gradient. The remaining four algorithms are based on planes fitted to the measurements. The nine algorithms are evaluated on computer generated data obtained firstly from a translating square and secondly from a rotating bar. The algorithms are then evaluated on three experimental datasets obtained using a rotating camera. In contrast with  [34] , our Fisher-Rao method is a new way of estimating optical flow that does not assume that the optical flow is locally constant and that does not require estimates of the intensity gradient.
In section 6, the Fisher-Rao method is applied to two of the datasets in  [34]. Bardow et al  [3]  estimate the optical flow and the full intensity image from event camera data by minimizing a complicated objective function that penalizes high optical flow gradients, high intensity gradients and large deviations from the motion constraint equation for optical flow. The objective function also includes terms to take into account the fact that only differences in image intensities can be measured. The objective function is minimized using iteration, to yield estimates of optical flow and of the full intensity image. In contrast, our Fisher-Rao method for estimating optical flow does not require the full intensity image. In any case, the task of obtaining the full intensity image is ill-posed in that regularisation is required in order to obtain a unique solution.
The components of the normal velocity are estimated separately by considering first the horizontal motion and then the vertical motion. Experiments are carried out using data from a dynamic and active vision based sensor, DAVIS, which is referred to as the ApsDVS sensor by Berner et al  [8]. The DAVIS sensor provides both AER measurements and complete frames of intensity values. The data from the frames is used to improve the accuracy of the estimates of contour motion. The estimates of the optical flow and the moving contours are checked using AER measurements synthesized from conventional image sequences. In contrast, the Fisher-Rao method does not require frames of intensity values and it does not attempt to identify any contours in the image. The Fisher-Rao method estimates full optical flow vectors, rather than particular components of the flow vectors.
Gherig et al  [18]  describe a general framework for obtaining a grid based representation for event camera measurements. The measurements are initially represented by a weighted sum of Dirac functions. The Dirac functions are convolved with a kernel and the convolved measurements are sampled in space and time to produce a fourth order array, taking the event polarities in (1) into account. A range of different arrays can be produced by varying the weighting of the Dirac functions, varying the kernel or by projection from the fourth order array. Applications to object recognition and optical flow estimation are described. The optical flow is estimated using EV-FlowNet  [39]. Liu and Delbruck  [28]  record events in three time slice memories. The first memory simply accumulates events. The optical flow associated with an incoming event is estimated by matching blocks of data in the second time slice memory with blocks of data in the third time slice memory.
Features are extracted from the spike count arrays using principal components analysis and slow feature analysis. The features are applied to the tracking of cars in a traffic dataset. Algorithms for high speed tracking using event camera measurements are described by Lagorce et al  [26]. The use of local histograms for matching in conventional images is well established  [21, 23, 35]. In this paper the local histograms are normalised to produce probability distributions. Once these distributions are obtained, the optical flow is estimated using powerful methods taken from probability theory, in particular, methods based on the Fisher-Rao metric. The Fisher-Rao metric is described by Amari  [2]  and by Cover and Thomas  [14]. As far as the authors are aware, there is no previous application of the Fisher-Rao metric to the estimation of optical flow using AER measurements.
} is the number of elements in the set {. }. The array A t contains all the spike count arrays L(r + (a 1 , a 2 ), t + a 3 τ ), ( 10 ) such that r is a pixel and (a 1 , a 2 , a 3 ) ≡ a is in {-1, 0, 1} 3. Let ãt (q) be the (m + 2) × (m + 2) × (n + 2) subarray of A t defined by ãt (q) = A t q 1 -(m + 3)/2 : q 1 + (m + 3)/2, q 2 -(m + 3)/2 : q 2 + (m + 3)/2 , ( 11 ) where : is the MATLAB notation for a range of array entries. The sub-array ãt (q) is referred to as a block centred at (q, t). It contains the 27 sub-arrays obtained by setting r = q in (10). Let n q be the number of non-zero entries in ãt (q). A list C t is made of the pixels q for which n q (m + 2) 2 (n + 2)f ( 12 ) where f is a fixed parameter taking a value in [0, 1]. The optical flow is estimated only for the pixels contained in C t.
Experiments with five datasets This section describes experiments to test the Fisher-Rao method for estimating optical flow using five new datasets, namely data 1, data 2, data 3, data 4 and data 5. The datasets data 1, data 2, data 3 and data 4 were obtained using the asynchronous time-based image sensor (ATIS)  [32]  made by Prophesee. Data 5 was obtained using the next generation sensor, H-VGA. Ground truth optical flow was obtained for data 3, data 4 and data 5 using the OptiTrack motion capture system  [30]. Further information about OptiTrack is given in appendix A. The relevant properties of the proposed datasets are summarized in table 2. The sixth dataset, MV/SEC, is described in section 7. 1 below. The parameter values used to test the Fisher-Rao algorithm on these datasets are summarised in table  3.
Data 1 The data were obtained in a laboratory using an ATIS event camera which was rotated about a fixed axis while viewing two flat pages. The camera was initially at rest, then it was rotated about the axis and finally brought to rest at the end of the motion. The first measurement was obtained at time t min = 8513 875 μs and the last measurement was obtained at time t max = 11 791 554 μs. The estimated optical flow is shown in figure  1  for four consecutive time intervals, each one of length 400 000 μs, with the parameter τ in sections 3. 3 and 3. 4 given by τ = 400 000/(n + 2)μs. The size of the pixel array was 240 × 304. A pixel in figure  1  is blue if the events with Δ(i) = 0 predominate. A pixel is yellow if the events with Δ(i) = 1 predominate. If the number of events with Δ(i) = 0 is equal to the number of events with Δ(i) = 1, then the pixel is white.
Data 2 The data consist of measurements obtained by an ATIS event camera placed in a city street. The camera was mounted on a tripod and directed towards the road traffic. The pixel array is of size 240 × 304. The first measurement was obtained at t min = 13 μs and the last was obtained at t max = 5. 06 × 10 7 μs. The four images shown in figure  2  were obtained from consecutive time intervals, each one of length 10 5 μs. The parameter τ was given by τ = 10 5 /(n + 2) μs. As in figure  1 , each optical flow vector is scaled up by a factor of 10 and some flow vectors are not shown in order to improve the visibility of the remaining vectors. The parameters m, n, f, β 1 , β 2 , σ, maxFlow and have the same values as in section 5. 1 for data 1. Grey level image frames are available for data 2.
Comparison with ground truth for data 3 and data 4 The ATIS datasets data 3 and data 4 are used to test the Fisher-Rao method by comparing the estimated optical flow with a ground truth optical flow provided by a motion capture system. The position and orientation of a moving camera relative to a planar set of points are measured over time. The ground truth optical flow is obtained by projecting the points into the camera. The motions of the camera and the motions of the planar set of points are measured using the motion capture system OptiTrack [30], which consists of 8 Flex13 cameras. The motion capture has a sub-millimeter spatial resolution and an acquisition frequency of 120 Hz. All technical specifications, including information about accuracy, can be found on the web page  [30]. Further information about the calculations used in OptiTrack is given in appendix A.
In order to track the event-based camera, markers are put on its casing so that when the camera moves the trajectory of the casing can be updated in real time. The camera observes a planar printed pattern which contains a set of points that provide the data for the Fisher-Rao algorithm. The planar pattern also contains markers from which the ground truth optical flow is calculated. The Fisher-Rao algorithm estimates the optical flow using time slices of 400 ms (2. 5 Hz) in data 3 and slices of 100 ms (10 Hz) in data 4. The ground truth image velocities are computed from the sequences of positions of the tracked points obtained at times between two consecutive Fisher-Rao estimates of the optical flow. The camera motion to obtain data 3 is similar to that used for data 1: the camera is placed on a tripod and rotated about a vertical axis, first clockwise and then counterclockwise, while the planar pattern is held stationary.
The parameters are m = 11 pixels, n = 11 pixels, f = 1/30, σ = 4 pixels, β 1 = 10, β 2 = 4, maxFlow = 5 pixels τ -1 and = 0. 025. The value of τ is τ = 100 000/(n + 2)μs. Figure  6  shows the distribution of the directions errors and the mean amplitude errors, calculated as for data 3. The errors are higher than for data 3 because the pattern was moved manually with a velocity varying in amplitude and direction, as required to retain the pattern within the field of view. The estimated mean and standard deviation of the directions errors are respectively -7. 5 × 10 -2 rad and 4 × 10 -2 rad. The lower performance of the Fisher-Rao flow in this experiment is likely to be due to the varying velocity of the pattern. The matrices for the Fisher-Rao metric are estimated over spatiotemporal volumes whose dimensions have to fit the motion.
Data 5 Data 5 was obtained using a new generation H-VGA of the ATIS sensor. H-VGA and ATIS are compared in table  1. The H-VGA sensor has an increased spatial resolution, a higher signal to noise ratio, sharper images and better performance in low light, as compared with the ATIS sensor. As a result, more accurate estimates of the optical flow can be obtained from H-VGA measurements. An example of the optical flow obtained from data 5 is shown in figure  7. Some flow vectors are omitted in order to make the flow field clearer. The flow vectors are also scaled up by a factor of 10. The ground truth optical flow is obtained from OptiTrack using markers attached to a plane surface held by the experimenter, as shown in figure  8. The results of the Fisher-Rao algorithm are compared with ground truth only for the optical flow arising from the moving plane.
Translating square The data set translSquare is computer generated. It shows a textureless square of size 40 × 40 pixels 2 translating with a constant velocity of  (20, 20)   It is not possible to obtain the full optical flow vectors along the sides of the square because of the aperture problem. The normal components of the optical flow vectors are obtained using  (9). The parameters used by the Fisher-Rao algorithm are m = 11 pixels, n = 11 pixels, f = 1/100, β 1 = 5, σ = 2 pixels, maxFlow = 2 pixels Δs -1. The condition λ 2 β 2 λ 3 in (  14 ) is discarded because the eigenvalues λ 2 , λ 3 of the Fisher-Rao matrix both tend to be small compared with λ 1. It was found that very few spatiotemporal volumes contain enough measurements with Δ(i) = 0 and with Δ(i) = 1 to enable the calculation of both of the matrices J (0), J (0) in  (8).
1, the normal component of the optical flow is obtained using  (9). In this particular case the normal optical flow coincides with the full optical flow. The parameters used by the Fisher-Rao algorithm are the same as for translSquare. The condition λ 2 β 2 λ 3 in (  14 ) is discarded. As in the case of the translating square, if only one of J (0), J (0) in (  8 ) is available, then J(0) is set equal to that matrix. The estimated optical flows for the second subinterval and the eleventh subinterval are shown in figures 9(b) and (c) respectively. The flow vectors are scaled up by a factor of 10 and some flow vectors are removed in order to improve the visibility of the remaining vectors. The flow vectors in the remaining subintervals are similar. The flow vector data have the form (q, (u, v)), such that (u, v) is the normal component of the flow vector and q is the base point.
Comparison with EV -FlowNet 2R and EV -FlowNet 4R The data for the comparison between the Fisher-Rao algorithm and EV -FlowNet 2R were obtained from the multi vehicle stereo event camera (MVSEC) dataset  [38] [39] [40]. The dataset contains stereo event camera measurements from a car, motorcycle, hexacopter and hand held camera. The data were obtained from both indoor and outdoor environments. The Fisher-Rao algorithm was applied to the indoor hexacopter measurements. Zhu et al  [39]  obtained the ground truth optical flow using the Vicon motion capture system with 20 cameras to observe the hexacopter. The camera was a DAVIS m346B with a resolution of 346 × 260 pixels 2. The list of events was obtained from the file indoor_flying1_data. hdf5. The ground truth optical flow and a list of time stamps were obtained from the MVSEC file indoor_flying1_gt_flow_dist.
In order to make the comparison with EV -FlowNet 2R and EV -FlowNet 4R in table 2 it is necessary to scale the errors to give the values obtained after t seconds of the flow. It is apparent from tables 2 and 4 that the errors for the Fisher-Rao algorithm are near to the errors for EV -FlowNet 2R and EV -FlowNet 4R. The advantage of the Fisher-Rao algorithm is that it is much simpler to initialize. Grey level images and ground truth optical flow are not required. It is not necessary to train a complicated deep neural network. It is only necessary to tune the values of the nine parameters listed in table 3. The experimental results show that the Fisher-Rao algorithm is stable, in that only minor changes in the values of the parameters are required if the dataset is changed (table 4).
Although several pre-clinical studies have demonstrated that EPCs potentiate neurogenesis and angiogenesis in mouse models of stroke  [8] [9] [10] , the clinical translation of cell-based therapies still raises safety concerns due to their potential adverse side effects  [11]. For this reason, clinical trials investigating the potential benefits of cell therapies for ischemic stroke are scarce  [12]. In particular, one randomized phase I/IIa clinical trial (NCT01468064) testing the intravenous injection of autologous EPCs for acute stroke  [13]  did not show serious adverse effects or allergic reactions related to the treatment. In this context, previous studies have reported that the administration of EPC-conditioned medium (EPC-secretome) promotes a beneficial response in pre-clinical models of cerebral ischemia and hypoperfusion  [14, 15].
Wound healing assay CD34 + -ECs (2 × 10 5 cells/well) were seeded in 1% gelatin-coated 24-well plates. After 24 h, CD34 + -ECs were serum-starved in ECM with 0. 1% BSA for 6 h. Then, a wound was created by scratching the cell monolayer with a 200-μL tip. Cells were rinsed with DMEM to remove cell debris and treated with EPC-secretome (scHD-CM, scSP-CM, or scCM) or scEBM. Images of the wound were taken immediately after the scratch (time 0) and 16 h after the treatments using a phasecontrast microscope (Nikon). The wound healing of EPC-secretome-treated cells was calculated by measuring the difference between the initial (time 0) and final (time 16 h) wound area using the ImageJ software. The migration of EPC-secretome-treated CD34 + -ECs was calculated as the relative percentage compared to the scEBM-treated cells.
Capillary-like tubulogenesis assay Angiogenesis μ-slides (IBIDI, Germany) were coated with Matrigel ™ (BD Biosciences-10 μL/well) and incubated at 37 °C for 1 h, as previously described  [21]. After serumstarvation (ECM + 0. 1% BSA) for 16 h, CD34 + -ECs were detached and seeded (12 × 10 3 cells/well) on the surface of polymerized Matrigel ™ and treated with EPCsecretome (scSP-CM, scHD-CM, scCM) or scEBM. After 6-h incubation, pictures were taken using a phase-contrast microscope (Nikon) with a 5 × magnification objective. The number of tubular structures was determined using the Wimasis ® Image Analysis software , and the tubulogenesis of EPC-secretome-treated CD34 + -ECs was calculated as the relative percentage versus the scEBMtreated cells.
qPCR The mRNA from cells was extracted using the Nucle-oSpin ® RNA/protein kit (Macherey-Nagel, Germany). Fig. 2  Identification of proteins in scCM. Detection of pro-angiogenesis factors in two batches of scCM was performed by Angiogenesis Proteome Profiler (a). Two different batches of scCM were analysed by MS. Over 1200 proteins were identified and the two batches of scCM shared 1041 proteins (b). The set of proteins shared between two batches of scCM (1041) were converted in gene identifiers and then submitted to an enrichment analysis using the GeneOntology/Panther software. The proteins were classified according to protein class (c), molecular (d), or biological function (e). Among the Panther pathways that were over-represented in the enrichment analysis of scCM samples, several were related to cell metabolism (f) (See figure on next page.