dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , different hypotheses on the location of EZ) to build the generative Virtual Epileptic Patient (VEP) brain model. The PLOS COMPUTATIONAL BIOLOGY later simulations allow to mimic outputs of experimental modalities such as electro-and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). Following the virtual brain reconstruction, the BVEP is constructed by embedding the generative VEP brain model within a probabilistic programming language (PPL) tool such as Stan  [68]  or PyMC3  [39]  to fit and validate the simulations against the patient-specific data. Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation which can be run in popular computing environments (see https://mc-stan. org). PyMC3 provides several MCMC algorithms for model specification directly in Python code (see https://docs.
Structural brain network model The structural connectome was built with the TVB-specific reconstruction pipeline using generally available neuroimaging software  [28, 63, 67]. First, the command recon-all from the Freesurfer package  [71]  in version v6. 0. 0 was used to reconstruct and parcellate the brain anatomy from T1-weighted images. Then, the T1-weighted images were coregistered with the diffusion weighted images by the linear registration tool flirt  [72]  from the FSL package in version 6. 0 using the correlation ratio cost function with 12 degrees of freedom. The MRtrix package in version 0. 3. 15 was then used for the tractography. The fibre orientation distributions were estimated from DWI using spherical deconvolution  [73]  by the dwi2fod tool with the response function estimated by the dwi2response tool using the tournier algorithm  [74].
Synthetic data and model inversion The Virtual Brain (TVB  [59] ) is an open-access neuroinformatics platform written in Python to stimulate large-scale brain network models based on individual subject data. In this study, the VEP model simulation was performed within TVB using a Heun integration scheme (with the time step of 0. 04 ms). A zero-mean white Gaussian noise with a variance of 0. 0025 was added to the system state variables. Moreover, the initial conditions were selected randomly in the interval (-2. 0, 5. 0) for each state variable. To simulate the seizure activity for a virtual patient, we selected two brain regions as EZ and three regions as PZ, defined at the nodes EZ idx 2 {7, 35}, and PZ idx 2 {6, 12, 28}, respectively. The excitability value was chosen as η ez = -1. 6 corresponding to regions in EZ, and η pz = -2.
4 for the regions in PZ, whereas all the other regions were defined as HZ with η hz = -3. 6. Finally, Bayesian inference is performed by Stan using its command line interface  [99]  to invert the BVEP model for the simulated data sets. Simulations were run on the JURECA booster in the Ju ¨lich Supercomputing Centre  [66]. The JURECA booster has a peak computing capacity of 5 petaflop/s, with 1,640 compute nodes using Intel Xeon Phi processors with 68 cores each. This multicore architecture allows the simulation of multiple parallel HMC chains, each on a separate processor. Typical simulations were deployed on 2040 cores and took in average 10 hours to execute. The code for simulations and posterior-based analysis was implemented in Python. The main source code repository is available at https://github.
Following the virtual brain simulations, the BVEP model is constructed by embedding the generative VEP brain model within a probabilistic programming language (PPL) tool such as Stan  [68]  or PyMC3  [39]  to fit and validate the simulations against the patient-specific data (Fig  1G ). In the present work, for inversion of the BVEP model, we used recently developed No-U-Turn Sampler (NUTS  [19, 38] ) algorithm, a self-tuning variant of Hamiltonian Monte Carlo (HMC  [69, 70] ), while High Performance Computing (HPC) allows us to run parallel MCMC for Bayesian inference. Finally, the cross-validation is performed by WAIC or approximate LOO from the existing samples of log-likelihood to assess the model's ability in new data prediction (Fig  1H ). HPC on the JURECA booster in the Ju ¨lich Supercomputing Centre can operate in parallel the brain tractography, simulations, and model validation.
However, finding a set of parameters that yields the best possible model fit to the recorded data is a challenging task due to the high dimensionality and the nonlinearity involved in the model equations. In our recent study, we proposed a novel probabilistic approach namely Bayesian Virtual Epileptic Patient (BVEP) to establish a link between the probabilistic programming languages (PPLs such as Stan/PyMC3; open-source statistical tools for flexible probabilistic machine learning) and the personalized brain network modeling (e. g. , the VEP brain model generated by TVB  [59] ). Using PPLs along with high-performance computing enables systematic and efficient parameter inference to fit and validate the large-scale brain network models against the patient's data  [4]. Considering a set of competing hypotheses for a model structure, we would need to evaluate the model's predictive power in order to determine the best hypothesis that balance model complexity and predictive accuracy.
This work was funded by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program ,  ANR-17-RHUS-0004 , EPINOV (https://anr. fr)to VJ, FB, and MG, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  and  945539 ,  Human Brain  Project  SGA2  and  SGA3  (https://ec. europa. eu/programmes/horizon2020) to VJ, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant Agreement  and No. 826421 , VirtualBrainCloud (https://ec. europa. eu/programmes/horizon2020) to VJ,  PHRC-I 2013  EPISODIUM (grant number  2014-27 ), the  Fondation pour la Recherche Medicale  ( DIC20161236442 ), and by  SATT Sud-Est ,  827-SA-16-UAM  (https://www.
Introduction Single-molecule localization microscopy (SMLM) is a method of choice for the observation of biological phenomena at nanoscale resolution  [1, 2, 3]. It breaks the diffraction limit of conventional fluorescence microscopy by sequentially activating and localizing a subset of fluorescent molecules. As such, SMLM is a prime example of computational microscopy where suitable acquisitions and algorithmic reconstruction are combined so as to enhance the capabilities of traditional systems. Although SMLM acquisitions are two-dimensional, innovative point-spread functions (PSF) whose shapes vary with depth have been designed to encode the axial position of molecules. These include the popular astigmatism  [4]  or double-helix  [5]  PSFs. Therefore, in addition to efficient localization algorithms, well-calibrated models of these PSFs are essential to reach the promised nanoscale resolution  [6].
To our knowledge, such a reconstruction of both RI and fluorescence density from the same fluorescent dataset (i. e. , without phase measurements) has been investigated only recently by Xue and Waller  [11]. They consider two-layers samples where the bottom layer contains fluorescence-labeled objects and the top layer contains nonlabelled objects. In this context, they demonstrated that the 3D RI map of the non-labelled objects can be reconstructed from defocused fluorescence images that are collected by sequentially stimulating small regions of the fluorescence-labeled layer of the sample. Moreover, they showed that the obtained RI map can be exploited to obtain the scattered PSF and improve the fluorescence signal through deconvolution. This setting differs from the exploitation of the individual emission of fluorophores in SMLM that we propose here in two respects.
First, RI and fluorescence objects are mixed (i. e. , no two-layers samples). Second, fluorescence measurements are recorded at two distinct focal planes (i. e. , biplane SMLM modality). In SMLM, the recovery of the RI has been addressed in  [12, 13]. These two works exploit the fact that SMLM data can be seen as measurements of an optical-diffraction tomography (ODT) system with point-source illuminations inside the sample. In  [12] , the authors assumed that the phase of the measurements was accessible, an assumption which is not met in practice. Moreover, their proposed approach relies on a linear model whose validity is limited to weakly scattering samples  [14]. In our preliminary work  [13] , we introduced a refined RI-reconstruction approach that can handle phaseless measurements under the assumption that the positions of the fluorescent molecules are perfectly known.
Single-Molecule Localization Any SMLM localization software can be used to compute the initial positions {x 0 l } L l=1. However, we found that existing software packages for a biplane modality were not performing well on our simulated dataset. We believe that this is due to the high thickness of the sample together with the small number of acquisitions. Therefore, we adopted a simple yet efficient method. We localize the position of the lth fluorophore based on cross-correlations between the measurements y l and a set {k p } P p=1 of PSF models in ∈ R M. We define them as the the output of the forward model with no scatterer, like in k p = H(0 N , x psf p , 1), (23) where the positions x psf p = (0, 0, p∆z) for p = {-P,. , P } vary along the axial direction. We then initialize x 0 l = ( mh, nh, p∆z) (24) where ( m, n, p) = arg max m,n,p [y l k ∨ p ] m,n , (25) with the 2D discrete convolution and ( • ) ∨ the reflection operator.
Background Estimation To estimate {b l } L l=1 , we apply a simple algorithm suitable for a background that slowly varies in space and time. In SMLM, this is a common assumption  [42]. Our procedure proceeds in two steps. 1. For each measurement y l , we mask an area around the estimated position x l and inpaint it using the function regionfill of Matlab 1  to obtain ȳl. 2. We apply a spatio-temporal (3D) median filter along the stack of masked and inpainted measurements Ȳ = [ȳ 1 • • • ȳL ] to take advantage of the spatio-temporal smoothness of the background.
z z z Figure  4 : Observable region of the sample. Illustration of the region of the sample that is "illuminated" by a fluorophore far from (left) and close to (middle) the detection system. The right scheme illustrates the fact that SMLM data carry more information about RI regions that are close to the detection system (positive z). frame (from the activation of one molecule) carries information about the part of sample that lies between the activated molecule and the optical system (see Figure  4 ). As such, there are more SMLM frames that carry information about z-planes with positive z than frames that carry information about z-planes with negative z. Moreover, waves produced by fluorophores with negative z-positions propagate through a larger layer of the sample, inducing more scattering.
A new detection and visualization tool to inspect raw LiDAR data for archaeological prospection is introduced in this paper. It allows the supervised extraction of linear structures (ridge or hollow) from the 3D ground points, for on-line detailed analysis of their cross and longitudinal profiles. Using raw data provides a richer information than an interpolated digital terrain model. In particular, the extraction process is made aware of point repartition irregularities caused by dense canopies in forested environments. The tool is based on a recent curvilinear structure extraction framework with fast execution time, that ensures a good interaction. Additional performance is achieved through the detection of the local terrain trend around the structure, that allows finer characterizations of the extracted structure.
INTRODUCTION Airborne laser scanning, also called LiDAR for Light Detection And Ranging, is a 3D data acquisition technique based on the emission of a laser beam swept over the measured scene and on the reception of echoes on hit obstacles. The measure of the signal travelling time provides a detailed survey of the overflight landscape  (Wehr, Lohr, 1999). In forested environment, the received signal is composed of multiple echoes that correspond to the successive hit obstacles, from the forest canopy, down to lower vegetation levels and, finally, to the ground itself. A point classification is performed to separate ground points from low, medium, or high vegetation, and from other kind of specific features, such as water surfaces, buildings or wire lines. A surface is fit to ground points using optimization techniques to produce a digital terrain model (DTM)  (Axelsson, 2000).
RELATED WORKS Airborne LiDAR is more and more commonly used for archaeological prospection tasks. In particular in forested areas, visual inspections of DTM views often reveal small morphological structures, that are occluded by the vegetation during field surveys  (Devereux et al. , 2005 , Georges-Leroy et al. , 2011). They can be delineated in the DTM and measured using geographical information system (GIS) post-processing  (Sittler, 2004). A full-waveform LiDAR sensor may provide much better data  (Doneus et al. , 2008 , Lasaponara et al. , 2011). This technique registers a sampling of the complete laser signal rather than main echoes only. In forested areas, it provides a denser point cloud and better range determination  (Mallet, Bretar, 2009 ), but at the price of a much larger amount of data storage and additional pre-processing steps.
Direct processing of dense cloud of 3D points has also been studied. In particular, methods based on profile line analysis are usually effective and fast, but at our knowledge, most of them address structured environments, such as industrial scenes  (Jiang, Bunke, 1994)  or urban landscapes  (Sithole, Vosselman, 2006) , with more stable morphology and less surface perturbations than rural landscapes. Then, point density variations are much stronger in forested sites. The goal of the present work is to develop a generic tool to extract linear structures from raw LiDAR data and to allow a detailed visualization of their cross and longitudinal profiles. In order to get rid of DTM interpolation errors, only raw ground points are processed. No attempt is made to interpret the found structures. This process is left under control of specialists.
ILSD: AN INTERACTIVE TOOL TO EXTRACT AND ANALYZE LINEAR STRUCTURES Based on the extraction framework algorithms proposed in  (Even, Ngo, 2020) , a new tool called ILSD (for Interactive Linear Structure Detector), dedicated to archaeological prospection purposes, has been developed and is available as an open source software. It allows to extract and analyze linear structures (ridge or hollow) by detecting and tracking their cross profiles in the 3D ground point cloud of the LiDAR data set. A ridge structure corresponds to an elongated structure above the local terrain trend, for instance a wall or a dike, whereas a hollow structure extends similarly below the local trend, for instance a ditch or a trench. The detection process is left under control by the user, who decides where it should take place by manually drawing an initial stroke across relevant details on the DTM view.
ILSD implementation For larger accessibility and improved reusability, the development is based on light easily integrated cross-platform libraries: GLFW 1  to handle windows and render context, Glad 2  to load OpenGL 3D tools on Windows (GLEW 3  on Mac OS), ImGui 4  for the user interface, STB 5  for image format handling, and ShapeLib 6  to export detected structures in shape format accepted by most GIS. Sources and executable code of ILSD are available in a public GitHub repository: https://github. com/evenp/ILSD. git.
CONCLUSION AND PERSPECTIVES This paper introduced an interactive tool, called ILSD, to extract linear ridge and hollow structures from LiDAR raw data and immediately visualize and measure their cross and longitudinal profiles. Processing raw data rather than the interpolated DTM makes it aware of irregular point repartitions due to LiDAR signal occlusion by a dense vegetation cover. This may contribute to reduce the risk of misinterpretation caused by the heterogeneous ground point arrangement, especially in forested environments. Based on a recent curvilinear structure extraction framework using efficient digital geometry algorithms, fast execution time achieved ensures a good level of interaction to extract several hundreds of meters long structures. The original ridge model was enriched with the detection of surrounding local trend to provide more accurate bounds to localize the structure and allow geometrical properties estimations.
ACKNOWLEDGEMENTS This work was realized in the scope of the  SolHoM project (Interactions hommes-milieux et évolution des sols au cours des deux derniers millénaires dans le massif du Fossard , dir. Anne Poszwa), an interdisciplinary project of  Université de Lorraine. The LiDAR acquisition, realized in the scope of PCR  AGER  project ( Projet collectif de recherche -Archéologie et GEoarchéologie du premier Remiremont et de ses abords , dir. Charles Kraemer), was made available for the present work.
Inelastic scattering The first Medipix2-based X-ray detector commissioned at ESRF was the focal plane detector of the diced crystal analyzer on ID16 inelastic scattering beamline  (Huotari 2006). This detector consisted of a Medi-pix2 chip with 500 μm thick silicon sensor coupled to the parallel port of a PRIAM  (Ponchut et al. , 2007)  readout interface, and an ESRF Linux acquisition workstation. Photon-counting detection was desirable for efficient detection of the low available X-ray signal at the detector input. By exploiting the energy-dispersive property of the analyzer and the small pixel size of Medipix2, the effective energy resolution of the instrument could be improved from 190 meV to 27 meV  (Huotari et al. , 2006) , with a detector contribution of only 7 meV thereby leaving room for further improvement.
Later, the Medipix2 module was replaced by a Timepix module. Successful use of Timepix in this application led the ESRF to retain MAXIPIX/Timepix as the detector platform for the new inelastic scattering beamline ID20, consisting of two end stations as detailed below: The ID20 Raman scattering end station consists of a gantry embedding six multimirror analyzers, the sample stage and an X-ray focusing optics as shown in Fig. 2. Each analyzer is fitted with a custom detector module implementing one single Timepix chip with a 500 μm thick silicon sensor  (Huotari et al. , 2017). As the instrument operates in near-backscattering geometry, a compact detector module with a small distance between the pixel matrix and the physical edge of the module had to be designed. All detector modules are connected to MAXIPIX controllers with 2 m long VHDCI cables in order to enable independent motion of each analyzer in a wide angular range.
The ASICs are read out by 6 data acquisition cards. Each card reads a row of 8 ASICs of a sensor and sends the data to a Linux PC server that in turn sends the data to the data storage. The data coming from each data acquisition card therefore are a portion of the overall image (a strip of 8 ASICs in a sensor) and the full image has to be reconstructed before it is sent to the data storage. This turned out to be a major challenge. A precision mechanical structure provides the accurate relative positions of the three sensors and acts as a heat sink to remove the heat generated by the 48 ASICs. Synchronization and power electronics, diagnostics of temperature and humidity, and a chiller to provide the flow of cooling water complete the detector system.
Controls and data acquisition channel The data lines of each row of 8 ASICs are linked through a 300 way connector and a flexi rigid adapter to an FPGA data acquisition card, known as Front End Module (FEM), developed by STFC as data acquisition electronics for the Large Pixel Detector project of the European XFEL  (Coughlan 2011). Each FEM card has 3 Spartan3 FPGAs and a Virtex5 FPGA. Two of the Spartan3 FPGAs are the interface of the board to the Medipix3 ASICs and are used to distribute and multiplex the signals. The Virtex5 FPGA is the main engine of the card and it is used for sequencing, data decoding and data buffering. The Virtex5 FPGA has dual PowerPC440 embedded processors used to manage the DDR2 memory controller. The third Spartan3 FPGA is used as configuration controller and boot device.
The ASICs are driven with a clock frequency of 200 MHz and since the eight LVDS data lines of each Medipix3 ASIC of the row are read out in parallel, a set of registers of the entire detector (over 3 million 12 bit registers) is downloaded in 492 μs. The data are then sent from each FEM card to a Linux PC cluster via a 10 Gbit/s fiber optic link. A 1 Gbit/s copper Ethernet link of each card is used as control channel and to stream the diagnostic data. In the first version of Excalibur the data acquisition and control architecture of the detector system consisted of 6 parallel read-out channels each of those controlling and receiving data from a row of 8 ASICs. A data acquisition channel was made out of a FEM card, a 10 Gbit/s fiber optic link and a Linux PC. A cluster of 6 Linux PCs was necessary to drive the detector.
The data were sent from the PC cluster to the remote storage and recorded in HDF5 format. Each node opened the same file on the file system and wrote its portion of data in the correct location of the file so as the full image could be reconstructed  (Thompson et al. , 2012). After a couple of upgrades of the infrastructure (new storage file system and faster data link connecting the beam line to the storage) the parallel writer data acquisition system managed to be able to deliver a sustained frame rate of up to 275 frames per second. The frame rate was only limited by some bottlenecks in the FEM cards and not by the data acquisition system. However, in order to make the data acquisition system still more flexible it was finally upgraded to a different hardware and software architecture. In the new data acquisition architecture the 10 Gbit/s data links from the 6 FEM cards are now sent to a deep buffer switch instead of being connected directly to the Linux PCs.
The data acquisition cards that send the data through the various links send the UDP packets related to a specific image frame to the same address. Consequently, the deep buffer switch routes the packets related to the same frame, but coming from different links, to a single link downstream the switch that is connected to a Linux PC. Every frame is addressed by the data acquisition cards to a different Linux PC in a round robin fashion. In this way each Linux PC operates on the full frame. The Linux PC grabs the frame and writes it to the data storage independently on the other servers by using the HDF5 Virtual Data Set libraries. The HDF5 VDS present the data to the final user as if they were a single data set. This architecture is called ODIN-DAQ  (Yendell et al. , 2018). It is possible to operate Excalibur in burst mode where a number of image frames is acquired at a frame rate up to 2000 frames per second, stored in the local RAM on the FEM cards, and then streamed to disk after the acquisition.