dataesr/scientific-paragraphs-categorization huggingface dataset:
BlockManager is a local cache that runs on each node of the Spark cluster. These components are in charge of converting userSpark code into Spark tasks that are executed on the cluster. The vast majority of machines are slave nodes  [29] , which perform all data storage tasks and performing calculations. Each slave has a data node as well as a Task Tracker daemon that communicates with and receives instructions from their master nodes. We used the RDD Spark concept to achieve faster and more efficient HDFS operations for storing and MapReduce operation for the second phase of processing. The RDD is a distributed object collection that is immutable. Each RDD data set is divided into logical partitions that are calculated on different cluster nodes. In order to test this storage method as well as the other methods, we have set up a Hadoop Spark platform which is a distributed computing platform based on the MapReduce and Spark parallel programming model.
It has three prerequisites: • The data is divided into small unitary and independent blocks; • The processes must be placed where there is the data (the provision of a large data storage system and allowing to execute code where the data blocks are); • Spark's fundamental data structure is Resilient Distributed Datasets (RDDs). It is a collection of distributed objects that cannot be changed. Each RDD dataset is divided into logical partitions that are calculated on different cluster nodes. The basic idea behind Spark is the resilient distributed dataset. RDD supports memory computation. This means that it stores the state of memory as an object in jobs, and the object can be shared between jobs. Data in shared memory is ten to one hundred times faster than network and disk. Therefore, the data are distributed between the various HDFS nodes and the generated RDD nodes.
Results In this section, we present some experimental results in our proposed approach for BRSD to present the performance of the framework proposed in this paper. We begin by describing the system specifications of all the distributed local clusters that are used to implement this approach. Then we describe the datasets uses for experimentation and our dataset prepared for the validation task. Table  1  presents the operating system and its characteristics in addition to the frameworks and tools used for our local architecture master/slaves. To validate the proposed approach, we use three famous remote sensing image datasets which are available for the public, they are collected and acquired from multiple sensors like Landsat-8, Spot-4, Spot-5 and many others. The following Table  2  presents the characteristics of these datasets as well as our remote sensing imagery dataset that we prepared and collected from many sensors.
The UC Merced land-use dataset  [34]  is made up of 2100 overhead scene images divided into 21 land-use scene classes. Each class consists of 100 aerial images measuring 256 × 256 pixels in the red-green-blue color space, with a spatial resolution of 0. 3 m per pixel. This dataset was created using aerial ortho imagery obtained from the United States Geological Survey (USGS) National Map; • AID dataset  [35]  was created by collecting sample images from Google Earth imagery. As a result, Google Earth images can be used as aerial images to test scene classification algorithms; • Our dataset is composed of 3000 remote sensing images and 10 classes such as buildings, cars, trees, crops, roads, fastH2O, slowH2O, structure, tracks and trucks. The are many images in RGB and others are multispectral; their spatial resolution is between 0.
• The second metric is the precision: Precision = TP TP + FP (1) • The third metric is the recall: Recall = TP TP + FN (2) • The fourth metric is the F1-score: Figures 6-8 corresponds to the results of the proposed architecture in terms of precision, recall and F-score1 with the VGGNet Model of each label extracted. In addition to an overall comparison between the average error rates for the two models, we compare them on the basis of the classes common to the different datasets. Indeed, in order to have a common benchmark for comparing the efficiency of each model, we present the results of the classification, obtained specifically on the classes in common between the different datasets. The diagrams present the labels in common between the datasets, which are 1-building, 2-cars, 3-crops, 4-roads, 5-structure, 6-tracks, 7-trees, 8-trucks.
Figures 6-8 corresponds to the results of the proposed architecture in terms of precision, recall and F-score1 with VGGNet Model of each label extracted. In addition to an overall comparison between the average error rates for the two models, we compare them on the basis of the classes common to the different datasets. Indeed, in order to have a common benchmark for comparing the efficiency of each model, we present the results of the classification, obtained specifically on the classes in common between the different datasets. The diagrams present the labels in common between the datasets which are 1-building, 2-cars, 3-crops, 4-roads, 5-structure, 6-tracks, 7-trees, 8-trucks. Figures 6-8 corresponds to the results of the proposed architecture in terms of precision, recall and F-score1 with VGGNet Model of each label extracted.
In addition to an overall comparison between the average error rates for the two models, we compare them on the basis of the classes common to the different datasets. Indeed, in order to have a common benchmark for comparing the efficiency of each model, we present the results of the classification, obtained specifically on the classes in common between the different datasets. The diagrams present the labels in common between the datasets which are 1-building, 2-cars, 3-crops, 4-roads, 5-structure, 6-tracks, 7-trees, 8-trucks. The obtained results illustrated in the Figure  9  presented the obtained results of our proposed appproch with VGGNet on the three datasets and prove that our approach with VGGNet model outperforms with our databases and Siri-Whu dataset more than AID dataset. The obtained results illustrated in Figure  9  presented the obtained results of our proposed approach with VGGNet on the three datasets and prove that our approach with the VGGNet model outperforms with our databases and Siri-Whu dataset more than AID dataset.
The obtained results illustrated in the Figure  9  presented the obtained results of our proposed appproch with VGGNet on the three datasets and prove that our approach with VGGNet model outperforms with our databases and Siri-Whu dataset more than AID dataset. Now we will presents results with the second deep learning model used which is the UNet model. Figure  10  corresponds to the results of the proposed architecture on the three datasets and proves that our approach with the UNet model outperforms our databases more than the AID dataset and Siri-Whu dataset. Figure  14  corresponds to the results of the proposed architecture with the UNet Model of each label extracted on our datasets. The obtained results illustrated in the Figure  9  presented the obtained results of our proposed appproch with VGGNet on the three datasets and prove that our approach with VGGNet model outperforms with our databases and Siri-Whu dataset more than AID dataset.
The obtained results after the application of UNET and VGGNet on the SIRI-WHU, AID databases, our database and the multispectral images database. The results that we obtained after the application of the UNet model on the images the bases with 3 bands in term of precision are 84 %, 73 % and 61 %. Applying the UNet model to the 16-band images gave us 94 % accuracy. On the other hand, the application on the 3-band images gave us an accuracy of 84 % at most. The increase in the accuracy rate is due to the nature of the images used for this model which gives additional information on the areas to be predicted. Some images of our database are shown to illustrate our results. Figure  12      The obtained results after the application of UNET and VGGNet on the SIRI-WHU, were the AID databases, our database and the multispectral images database.
The results that we obtained after the application of the UNet model on the images the bases with three bands in term of precision are 84 %, 73 % and 61 %. Applying the UNet model to the 16-band images gave us 94 % accuracy. On the other hand, the application on the three-band images gave us an accuracy of 84 % at most. The increase in the accuracy rate is due to the nature of the images used for this model which gives additional information on the areas to be predicted. Some images of our database are shown to illustrate our results. Figure  12  represents the three-band image results where image (a) is the real RGB image and (b) is the result of our work. We denote that the objects detected in this type of image are impervious surfaces, low vegetation, buildings, cars, trees and clutter.
Another type of remote sensing data from our multiple source databases is proposed to validate the proposed approach where we use five experiments' data which come from SPOT-5 satellite and acquired in 2015 and located in Reunion island. The size of each image is 1. 2 GB and each image has been cut into N images in TIFF format (Tagged Image File Format), seven bands and its size is about 60 MB. Results are presented in Figure  14. Then we applied the different tasks indicated in the proposed framework which aims to classify our dataset using DL on Spark and Tensorflow by distributing the data distributed and executed parallel processing. As can be seen from Table  5 , our approach takes full advantage of the entire configuration resources and it makes good results by comparing it to the Google inception V3 model which archives 78.
Discussion The most common challenge of remote sensing applications are: • ROI (Region of Interest) segmentation and classification; • Few available datasets with labeled images; • Segmentation is a hard task and dependent on image resolution; • Image sizes are higher and higher. The purpose of our work is to deal with these challenges, and thus to analyze and classify heterogeneous multi-source remote sensing image and to use a parallel distributed architecture coupling two CNN models for feature extraction and labeling. We used a pre-trained DL: VGGNet and UNet for classification and segmentation, they are used as a feature extractor. The use of FCN without a dense layer for that reason can accept images of any size. We were faced with our approach to standard limitations such as: complex preprocessing which dependent on image scaling and size and complex extracted features, dependent on pre-processing hyperparameters.
To obtain further insights into the nature of the changes that accompany the molecular interactions, Salamon et al. reported on a simple procedure to disentangle the contribution of the mass and structural changes that accompany the molecular interactions  [18]. This procedure has been applied to decipher the nature of peptide interactions with lipid membranes (few examples  [19] [20] [21] ). This graphical method is an important development towards a broad quantitative analysis. One further step towards a rigorous quantitative analysis has been made by the team of Wolfgang Knoll, who developed a user friendly software (WINSPALL, version 3. 02, developed by Worm J. at the Max Planck Institute for Polymer Research, Mainz, Germany; reflectivity simulation program solving Fresnel equations) based on Fresnel equations and the matrices formalism.
g. , 1 s). In this configuration, we are able to record the total PWR signal in less than 10 s. One additional feature of this new generation regards data acquisition and real-time analysis. In the previous software version included in the instrument and developed by Aviv and Proterion, only the acquired spectra were visualized by the user during the experiment. That means that, in order to directly analyze the experiment, the user needed to estimate or measure for each spectra the resonance minimum position and spectral depth for both polarizations as well as to monitor for any changes in TIR. This was quite time-consuming and unpractical. Therefore, with this in mind, a new program written in Labview that allows to parameterize, control, and monitor the experiment. The data are analyzed in real time, meaning that the position of the TIR and the resonance minimum angle position in pand sresonances are extracted on the go.
Moreover, the spectral depth is also monitored. Kinetics data are obtained and the correlation between the different parameters can also be directly visualized in the form of graphs to inform users respectively on the equilibrium, the orientation/anisotropy (ratios of p and s resonance shifts) and the homogeneity of the system (which can lead to changes in the spectral depth or even the appearance of additional fringes within the same resonance, (as reported in Section 2  [16, 24, 30, 31] ). This is of particular importance during multi-step experiments such as the monitoring of the ligand binding and induced receptor conformational changes, in which it is essential to ensure that the system is at equilibrium at each point in the ligand titration in order to determine a valid dissociation constant.
In that case, spectral analysis of the data gives one access to a full set of information, allowing one to distinguish between the contributions of both partners  [22]. It makes it possible to follow structural changes, monitored by means of optical dichroïsm, occurring in either lipid or protein molecules, and thus to distinguish between changes occurring in these two phases. We can also refer to molecular polarizability and molecular shape within ordered thin films  [44]. Indeed, Tollin and collaborators have applied this protocol, using 1-10 mol% of an acyl chain chromophore-labeled PC incorporated into a solid-supported PC bilayer deposited onto a hydrated silica surface. PWR measurements were made at two exciting light wavelengths, one of which was in the absorbance spectral region of the chromophore.
CLEM-based 3D reconstruction enabled the identification of dendritic spines that were not visible in LM or EM alone. In LM, the limited axial resolution prevents the identification of axially oriented spines, which are easily detected in 3D-EM  [49]  (Fig 1E  and 1F ; see also corresponding movie at https://www. opendata. bio. ens. psl. eu/3DCLEM-Spines/S1_Movie. zip; login: guest, password: EnsData0811). On the other hand, laterally oriented spines with the longest and thinnest necks are conspicuous in LM stacks but can be difficult to find in 3D-EM datasets without the cues provided by LM. The proportion of spines recovered with CLEM versus LM alone could amount to up to 30% per ROI, and 5% per ROI versus EM alone, highlighting the advantage of CLEM over unimodal microscopy approaches.
Spine ultrastructure along the basal dendrites of L2/3 cortical pyramidal neurons We used our CLEM workflow to quantify the full extent of the ultrastructural diversity of dendritic spines along the basal dendrites of L2/3 PNs of the SSC of 3 adult mice. We exhaustively segmented 254 μm of the basal dendritic arborization of 4 neurons, and we reconstructed a total of 390 individual spines (S1 Data). As spine distance to the soma spanned from 20 to 140 μm, with basal dendrites extending up to 150 μm  [72] [73] [74] [75] , our dataset can be considered representative of the whole spine population on these dendrites. The average linear density of dendritic spines was 1. 5 ± 0. 3 spine. μm -. We then quantified the following parameters for each spine: neck length, neck diameter, head volume, head longitudinal diameter (referred to as "head length"), head orthogonal diameter (referred to as "head diameter"), number of PSDs, and PSD area (Fig 2A, S1 Data).
Fluorescence microscopy of fixed tissue Slices containing electroporated neurons were trimmed to small (5 to 10 mm 2 ) pieces centered on a relatively isolated fluorescent neuron, then mounted in a custom-made chamber on #1. 5 glass coverslips. The mounting procedure consisted in enclosing the slices between the glass coverslip and the bottom of a cell culture insert  (Falcon, ref. 353095)  adapted to the flat surface with a silicon O-ring gasket (Leica, Wetzlar, Germany) and fixed with fast-curing silicon glue (see panel A in S1 Fig). Volumes of GFP and tdTomato signals were acquired in 12 bits mode (1024 × 1024 pixels) with z-steps of 400 nm using an inverted Leica TCS SP8 confocal laser scanning microscope equipped with a tunable white laser and hybrid detectors and controlled by the LAF AS software.
Image segmentation Dendrites were segmented from SBEM stacks using the software Microscopy Image Browser (MIB)  [156]. 3D reconstruction was performed with the software IMOD  [157]  (http://bio3d. colorado. edu/imod/). Individual PSDs were manually outlined based on the apposition with a presynaptic terminal and differences in membrane contrast and thickness between postsynaptic and nonsynaptic membranes on individual SBEM images (see example movie at https:// www. opendata. bio. ens. psl. eu/3DCLEM-Spines/S1_Movie. zip; login: guest, password: EnsData0811). 3D spine models were imported in the software Blender (www. blender. org) for subsampling, and the quantification of spine section areas along their main axis was done with in-house python scripts. Other measurements were performed using IMOD and in-house python scripts.
Measurement of shrinkage correction factors Ultrathin sections of both HPF-frozen tissues and chemically fixed tissues were observed using a Philips TECNAI 12 electron microscope (Thermo Fisher Scientific). Cellular compartments contacted by a presynaptic bouton containing synaptic vesicles and exhibiting a visible electron-dense PSD at the contact site, but no mitochondrion within their cytosol, were identified as dendritic spine heads. Cross-section areas of random spine heads and the curvilinear lengths of their PSD were quantified in both conditions using the softwares MIB and IMOD. N = 277 spine head sections were segmented in HPF-frozen cortical slices from 2 female mice, and N = 371 spine head sections were segmented in chemically fixed cortical slices originating from the same 2 mice.
Multicompartment electrical model All simulations were implemented in Python using NEURON libraries  [158]  and in-house scripts. Ordinary differential equations were solved with NEURON-default backward Euler method, with Δt = 0. 05 ms. Scripts and model definition files are available in a GitHub repository: https:// github. com/p-serna/SpineModel. Biophysical constants were taken from the literature as follows: membrane capacitance C m = 1 μF/cm 2  [38] ; cytosolic resistivity ρ = 300 O. cm  [84, 159] ; synaptic conductivities were modeled as biexponential functions g(t) = A g max (e -t / t 2 -e -t / t 1 ) where A is a normalizing constant and (t 1 , t 2 ) define the kinetics of the synapses: GABAergic conductance (t 1 , t 2 ) = (0. 5, 15) ms, AMPAR-dependent conductance (t 1 , t 2 ) = (0.
Bootstrapping To simulate a large number of spine-spine interactions with limited redundancy, we expanded our distribution of spines using a "smooth" bootstrapping method  [127]. Specifically, the dataset (i. e. , a matrix of dimensions N × N f ) was resampled to generate a new matrix of dimension M × N f , where N is the number of spines, N f is the number of selected features, and M is the final number of synthetic spines. M rows were randomly selected in the original dataset, and zero-centered, feature-dependent Gaussian noise was added to each element of the matrix (excluding absolute quantities, e. g. , number of PSDs or presence of SA). To determine appropriate noise amplitude for each parameter, we generated a synthetic set of M = 500 spines from the original dataset, including Gaussian noise with an arbitrary amplitude σ on 1 parameter at a time.
This new feature distribution was compared to the original distribution using a 2-sample Kolmogorov-Smirnov test (KS test), and this procedure was repeated 1,000 times for each set value of σ. A conservative noise level (σ = 10%) was sufficient to smear parameter distributions while the fraction of synthetic sets that were statistically different from the original set (p < 0. 05, KS test) remained 0 over 1,000 iterations. σ = 10% was valid for all relevant features, and we assumed that such a small noise amplitude would minimally interfere with nonlinear correlations in our dataset. Synthetically generated spines were then used to simulate elementary synaptic signaling using in-house python scripts. We also used bootstrapping to estimate standard deviations in our simulations.
Statistics No statistical methods were used to predetermine sample size. We used a one-way ANOVA on our 4 datasets to test that interneuron and intermice variability were small enough to pool all datasets together (S1 Data). We used KS test to determine that all measured morphological parameters followed a log-normal distribution (S1 Data). We used Mann-Whitney U test for statistical analyses of morphological parameters, except when comparing the probability for SiSs and DiSs to harbor SA, for which we used Pearson χ 2 test. All results in the text are mean ± SD. In Figs  6  and 7 , we plot medians as solid lines, as they better describe where lognormal distributions peak. Shaded areas represent 68% confidence intervals, which span approximately 1 SD on each side of the mean. Distance between spine and soma as a function of all measured morphological parameters of the spines that were segmented.
Acknowledgments We thank members of the  Charrier and Triller laboratories (IBENS, Paris, France) ,  Vincent Hakim  and  Boris Barbour  for insightful discussions. We acknowledge the  ImagoSeine imaging facility  ( Jean-Marc Verbavatz  and  Remi Leborgne ,  Jacques Monod Institute, Paris, France ) for SBEM availability, service, and support,  Liesbeth Hekkings  ( Thermo Fisher Scientific ) for technical support, the  IBENS Imaging Facility , and the  IBENS acute transgenesis facility (Caroline Mailhes-Hamon ).
This work was supported by  INSERM , the  Agence Nationale de la Recherche  (https://anr. fr/)( ANR-13-PDOC-0003  and  ANR-17-ERC3-0009  to C. C. ), the  European Research Council  (https:// erc. europa. eu/)(ERC starting grant  803704  to C. C. ), the  Labex Memolife  (https://www. memolife. biologie. ens. fr/?lang=fr) ( 901/IBENS/LD09  to O. G. ), the company  NIKON France  via the  CIFRE program  (convention n˚2015/1049 to O. G. ) and the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( Human Brain  Project  SGA2  to P. S. ). We are grateful to the  IBENS Imaging Facility (France BioImaging , supported by  ANR-10-INBS-04 ,  ANR-10-LABX-54  MEMO  LIFE , and  ANR-11-IDEX-000-02 PSL � Research University , ''Investments for the future''; NERF 2011-45; FRM DGE 20111123023; and  FRC Rotary International France).
All light and electron microscopy files are available from our institute's database at the following address: https://www. opendata. bio. ens. psl. eu/3DCLEM-Spines (login: guest; password: EnsData0811). All scripts and files defining the computational model are available at the following address: https://github. com/ pabloserna/SpineModel. The numerical data presented in the figures are available here (login: guest; password: EnsData0811): https://www. opendata. bio. ens. psl. eu/3DCLEM-Spines/data/. All other relevant data are within the paper and its Supporting Information files.