dataesr/scientific-paragraphs-categorization huggingface dataset:
, we have (a) the filter weights of first convolutional layer of Bondi et al. model [37], (b) those of CNN80 model trained with D 8 0 and (c) those of CNNm trained from the mixed dataset D Mix (c). Those three models have visually very similar filters, in terms of colour or texture. We observe that the filters of Bondi et al. model seem to be slightly smoother than both CNN80 and CNNm models.
that highest accuracies of froze models are very close but under the non-frozen model (Bondi et al. model). For example, on the sub-figure (a), for a QF of [ 1 1 9 _ T D $ D I F F ] 100%, the fully trained model on this QF dataset is equal or higher than other CNN90 fine-tuned models with an accuracy difference below [ 1 2 0 _ T D $ D I F F ] 2%. Similar results may be observed in the sub-figure (b) where the mixed model CNNm has been fine-tuned with D 100.
4. 1. 1 Experiment setup [ 7 4 _ T D $ D I F F ] Datasets description: [ 7 2 _ T D $ D I F F ] -CMI: Dresden dataset [50] is a publicly available dataset suitable for image source attribution problems. Dresden contains more than 13,000 images of 18 different camera models. For each precise scene, several cameras were arranged in approximately the same position and took almost the same picture. This means that each scene is represented by several (or all) existing cameras. The database is composed of color images of very high quality pictures (around 4000 [ 1 0 9 _ T D $ D I F F ] Â 3000 pixels). Note that we selected only natural JPEG photos from camera models with more than one instance. For the purposes of learning, we divided the dataset into separate training, validation, and evaluation sets denoted DT, DV, and DE respectively.
Currently, cartography has made tremendous progress thanks to Geographic Information Systems (GIS) software and satellite or aerial imagery  (Dufour et al. 2013 , Huylenbroeck et al. 2020). In France, the National Forest Inventory carries out a permanent inventory of forest resources and provides a great deal of data, including interactive maps. Data are collected at both the stand and tree levels. At the stand level, the forest cover type is in-terpreted from infrared aerial images at a 50-cm resolution. The final product is a vector layer (BD Foret V2) with polygons of 0. 5 ha minimum attributed to 32 categories of species composition. However, neither AC nor PN has a category of its own, so this data cannot be used to monitor their spatial dynamics. At the tree level, many parameters are recorded on the NFI sample plots in different scientific domains such as dendrometry, botany and pedology  (Hervé et al.
2014). Tree data include diameter, species and position, and height for a sub sample. However, precise plot locations are not publicly available, which prevents cross-analysis with external geographical data. Furthermore, plot density is 1 plot/ 2km 2 with surveys only every 5 years, thus making the data unsuitable for statistical analysis of small areas. Aerial imagery provides only two-dimensional images that do not enable direct measurement of the vertical components of the forest canopy. However, in the beginning of the twenty-first century, technological advances in drones and active remote sensing, in particular through the development of new sensors, opened promising perspectives for accessing forest structures  (Campbell et al. 2017). For instance, UAVs (Unmanned Aerial Vehicles) with Light Detection and Ranging (LiDAR) provide a three-dimensional geo-referenced point cloud corresponding to the surface objects that intercept the laser pulses from a scanner mounted on an aircraft  (Balenović et al.
To ensure proper delineation of the trees, the algorithm's parameters must be carefully chosen in order to correspond to tree allometries, which are often species-and structure-specific. Applying watershed theory to the forest requires some intrinsic parameters for tree architecture, such as the distance to the nearest treetop, crown size, etc. Our main goal was to test the ability of the various parameters in the tree segmentation function of the "lidaRtRee" package  (Monnet 2018)  running on free R software to predict American maple stands in Black poplar island forests. Another objective of this technical paper is to assess the ability of combined LiDAR data and multispectral data acquired by unmanned aerial vehicles (UAVs) to distinguish American maple from Black poplar at the tree level.
Image and LiDAR acquisition Because terns were nesting on the banks, we were allowed to fly over the islands only after August 15. The UAV flights were carried out on 20 and 21 August 2017 by "L'Avion Jaune", a French aerial mapping operator using an octorotor FOX-C8 Onyxstar ®. The level of the Loire River was one meter above zero, corresponding to summer low water. Eight successive flights occurred; three with a LiDAR sensor, then five others with a camera. LiDAR data were acquired by a YellowScan Surveyor ® (YellowScan, Montferrier-sur-Lez, France), which included an onboard computer controlling three main components: a Velodyne laser scanner (VLP16), a Global Navigation Satellite System (GNSS) and an Inertial Navigation System (INS) built by Applanix (APX15). The flight height for the Li-DAR was 45 m with a line spacing of 45 m and a sidelap of 60%.
Data pre-processing: LiDAR data Raw data processing was done by the mapping operator. LiDAR data were processed by successively running them through the PosPAC software by Applanix, a Surveyor QGIS plugin by YellowScan, and TerraMatch and TerraScan modules by Ter-raSolid. The classified point cloud was delivered in LAS format. The Digital Terrain Model (DTM) and the Canopy Height Model (CHM) were delivered in Geotif format at 0. 2-m resolution. In order to compare different tree segmentations, we created a flood and non-flood forest mask within the mosaic of islands (Fig. 4. ). Since American maple mainly colonizes river banks  (Gurnell 2014) , we excluded the hardwood forest (oak, elm, ash), located on higher terrain (in the non-flood area on island B), and retained only values between 0 and 2.
Data pre-processing: multispectral imagery and vegetation index Multispectral imagery processing was performed according to scripts developed by L'Avion Jaune and the Correlator3D soft-ware by Simactive. An orthophoto with four spectral bands (blue, green, red and near-infrared) was generated with a Ground Sample Distance (GSD) of 0. 02 m. The orthophoto was resampled to 0. 2 m for consistency with the CHM. Then four simple vegetation indices, excluding the infrared wavelength, were computed: (i) Red / (Green + Blue); (ii) Green / (Red + Blue); (iii) Blue / (Green + Red). We also computed the (iv) Normalized Difference Vegetation Index (NDVI -  Rouse et al. 1973).
Tree crown delineation This step is important because it determines the location and extent of the crowns. Subsequently, the data extracted inside the delineated crowns are used to select the discrimination variables in the classification step. Tree segmentation was performed with the "treeSegmentation" function from the "lidaRtRee" package  (Monnet 2018 ) of the R software (R Development Core Team 2017). Delineation was based on a 3-phase approach, with each phase requiring several values for parametrization. First, we applied filters to remove noise from the CHM file. A "salt-and-pepper" noise generally appears when there are CHM pixels with no corresponding laser points (due to the shading effect of trees, for example), or when there is a low number of pixels inside the canopy. The low and zero values were removed by applying.
Classification model: reference data In order to build a model capable of separating Acer negundo (AC) and Populus nigra (PN), a reference dataset was created by using the 0. 02 cm resolution orthophotos. Very high resolution imagery, such as provided by UAV, are ideal for photointerpretation  (Huylenbroeck et al. 2020 ). The operator selected a total of 316 disks of 1 m diameter and classified them as AC (157) or PN (159). Although the operator is both a botanist and an experienced GIS operator with exhaustive field knowledge of the study site, classification errors might be present in this reference dataset.
Dalponte et al. (2012)  showed that LiDAR height metrics improved the classification of mountainous forests into seven tree species compared to multispectral data only. The variability in findings available in the literature shows that depending on species and forest structures, and probably on acquisition period, there are variations in the importance of metrics of different types for species classification. The results obtained here are rather encouraging regarding the potential for mapping AN dynamics on larger areas with simple variables. Meanwhile, we must be aware of limitations as pointed out by  Huylenbroeck et al. (2020)  regarding upscaling of results. In the case of longer stretch of river, the relevance of spectral indices might decrease because of issues with radiometric calibration of images and greater variability of forest species, structure and phenology.
The use of reference data based only on photointerpretation might also introduce bias or error in the training and validation processes, but it makes also possible to build larger datasets by taking advantage of very high resolution imagery available from UAV. It is clear that this step is highly dependent both on the operator's experience in visual interpretation and his knowledge of the study area, but it is essential to calibrate the predictions as well as possible  (Manfreda et al. 2018 , Demarchi et al. 2020). The trade-off between variability representation thanks to sample size and noise due to visual interpretation still has to be investigated in order to design cost-efficient operational protocols for the acquisition of reference data for species mapping in larger areas. However, there are limitations to our findings, especially for Black poplar.
Conclusion American maple (Acer negundo L. ) is an introduced species, which competes with the endemic Black poplar (Populus nigra L. ). We used an airborne LiDAR scanner and imagery-derived vegetation index data to discriminate American maple from Black poplar with 90% accuracy on a complex of four islands inside the National Nature Reserve of Saint-Mesmin in the Loire River (France), based on a training and validation dataset obtained by photointerpretation. Mapping the American maple is all the more important since beavers (reintroduced in the 1970s) do not consume it, though it consumes Black poplar. We found that tree crown delineation is optimized by the "treeSegmentation" function from the "lidaRtRee" package  (Monnet 2018)  running under free R software (R Development Core Team 2017).
Acknowledgements This study is part of the  BioMareau  project, funded within the framework of the  Loire River Interregional Program 2014-2020 ". Funding institutions are the  Centre-Val de Loire Region  (no. 2016-00108348 ),  INRA ,  IRSTEA  and the  European Union  (Europe being engaged in the Loire basin through the  EU operational program  of the  European Regional Development Fund , no. 2016-EX000604 ). The  BioMareau  project has been approved by the  CNRS Loire LTER "Zone Atelier Loire " and the regional  MIDI (Habitat and Diversity) research network. Michel Chantereau  and  Damien Hémeray  from the  National Natural Reserve of Saint-Mesmin  are acknowledged for their interest and their support for science at the Mareau-aux-prés Islands. We also thank  Vicki Moore  for her relevant editorial remarks.
The cerebral vasculature is a dense network of arteries, capillaries and veins. Quantifying variations of the vascular organization across individuals, brain regions or disease models is challenging. We used immunolabeling and tissue clearing to image the vascular network of adult mouse brains and developed a pipeline to segment terabyte-sized multichannel images from light sheet microscopy enabling the construction, analysis and visualization of vascular graphs comprised of over 100 million vessel segments. We generated datasets from over 20 mouse brains, with labeled arteries, veins and capillaries according to their anatomical regions and characterized the organization of the vascular network across brain regions, highlighting local adaptations and functional correlates and proposing a classification of cortical regions based on vascular topological features.
, 2013; Kleinfeld et al. , 2011; Quintana et al. , 2019; Tsai et al. , 2009). More recently, such injections have been combined with light sheet microscopy in optically cleared samples  (Breckwoldt et al. , 2016; Di Giovanna et al. , 2018; Lagerweij et al. , 2017; Liebmann et al. , 2016; Lugo-Hernandez et al. , 2017; Todorov et al. , 2019; Zhang et al. , 2018). While vessel-filling allows for a bright labeling that facilitates imaging and downstream analysis, the nature of the reconstructed vessels is unknown, and can to this day only be retrieved manually on highly resolved datasets  (Blinder et al. , 2013). Additionally, reproducibility is challenging with intracardiac perfusions  (Quintana et al. , 2019). Alternative labeling methods have been used, such as tail-vein lectin injections (d  'Esposito et al.
In addition, there is also a lack of tools designed to process data from 3D fluorescent microscopy on terabyte-sized image stacks. Light sheet imaging and tiling artefacts lead to frequent duplication or fusions of vessels which greatly alter network topologies and compromise large-scale statistical comparisons of vascular datasets. Here we report a method to generate labeled (arteries, capillaries and veins) and region-annotated vascular graphs of the mouse brain. It relies on an immunolabeling cocktail that produces a bright labeling of the vascular endothelial and smooth muscle cells in unsectioned brains using the iDISCO+ technique  (Renier et al. , 2016). To construct vascular graphs, we designed a complete processing pipeline, TubeMap, which addresses issues of vessel duplication and other imaging artifacts as well as the multi-scale nature and hollow tube structure of the vasculature network.
This first part of the pipeline can binarize different types of vascular datasets generated from gel filling or wall labeling methods. However, the labeling of endothelial and arterial walls produces images of both solid (capillaries) and hollow tubes (veins and arteries). The segmentation of hollow vessels hasn't been attempted yet on very large data  (Bates et al. , 2018). We used a machine-learning approach and designed a deep convolutional neural network (CNN) based on an architecture optimized for vessel segmentation on large 3d datasets to fill empty vessels  (Livne et al. , 2019). Our CNN was trained using whole-brain datasets containing only filled vessels and imaged using the binarization pipeline (Figure  2H , Figure  S5A -B and see methods). Tested on an artificial dataset made from a real graph of vessels with radii ranging from 2 to 25 pixels (Figure  S5C ,D), the network had a false positive rate of 0.
1%±0. 06 pixels. We applied this CNN to both the vessels and arterial binary masks to obtain filled tubes throughout the datasets (Figure  2H  and Figure  S5E, F ). The binary image is then used to construct the graph of the vasculature network (Figure  2I ). We built a highly optimized 3d skeletonization algorithm based on a directional parallel thinning method and discrete topology (Figure  2I ). Our skeletonization step traces the center lines of vessels in a typical dataset of 10 billions of foreground pixels in about 1h. Graphs were then created from the centerline image and augmented by information about vessel geometry, identity and brain annotation (Figure  S6  and methods). To determine vessels identity, seed points were placed on clearly identified large veins, based on their radii and low Acta2 expression.
To solve these problems, we developed WobblyStitcher, an open-source software tool for robust and non-rigid stitching of terabyte data sets. WobblyStitcher uses three main steps to stitch the data (Figure  1H ): (i) alignment, (ii) placement, and (iii) stitching. In the alignment step the non-rigid displacements between images are estimated along with additional quality measures of the alignment reliability. In the placement step a combination of tracking and optimization tools is used to robustly position individual tiles within a global image frame. Finally, the stitching step uses 3D interpolation methods in the overlapping regions to compose the final volumetric image. The 3D non-rigid alignment is done in three sub-steps: (a) rigid alignment of the tiles along the axis orthogonal to the light sheet plane (z-axis), (b) placement of the tiles along this axis, followed by (c) non-rigid alignments within each plane (x-y planes).
Final Placement In this second step the tiles are placed non-rigidly considering the validity and quality of the displacement measure. WobblyStitcher first use the quality based global optimal alignment method described above to place the individual x-y planes in each z-slice of the entire data set. For each slice this results in optimal displacements of the connected components. We will refer to each of these connected components in a single plane as cluster for simplicity in the following. In the second placement step, clusters are aligned optimally in the entire image. To achieve this the connectivity structure between the clusters is determined first by constructing a graph with clusters as nodes and edges between two clusters if they are from subsequent z-planes and overlap in the x-y plane.
0 toolbox ClearMap 2. 0 is designed for advanced and fast image processing of large (Terabyte) 3D data sets obtained from tissue clearing. It is a complete redesign of ClearMap  (Renier et al. , 2016). The modular source management allows a unified handling of various data sources (image files, binary files, memory maps, shared memory arrays, numpy arrays, GPU arrays, graph formats) and fast parallel 3D image and graph processing as well as interactive visualization of large 3d images. In the following we provide a list of the ClearMap 2. 0 functionality.
Training To generate a training vascular brain graph for training, we generated datasets comprised only of filled tubes. To obtain such datasets, we designed a preparation of whole head fixation, in order to retain the blood in the tissue (Figure  S5A ), and see the section "blood retention" for the preparation of these brains. After dissection, we immunostained the brain for circulating immunoglobulins  (Liebmann et al. , 2016) , and complemented it with an immunostaining against podocalyxin to insure a better continuity of the labeled capillaries (Figure  S5B ), and we double-stained for Smooth Muscle Actin as before. We cleared and imaged the labeled brains with identical conditions to the endothelial wall-stained samples. Vascular graphs were generated using the same multi-path binarization pipeline (Figure  2A ) and centerline extraction as all other datasets (Figure  S5C ).
These graphs contain a natural distribution of vessel diameters, which we used to build training cubes of hollow tubes, for which the ground truth is the filled version (Figure  S5D ). We designed and trained the DNN on these data to fill hollow tubes into solid tubes. We trained our network on 100*100*100 pixel wide blocks of artificially generated hollow vessels data. From the skeleton, we generated circular empty tubes as input training data and the filled counterpart as ground truth. To diversify the data, we also generated cubes from both the IgG/Podocalyxin dense capillary channel and from the Acta2 channel alone which has sparse arterial tubes. To diversify the vessel radii, we filtered out capillaries (which are already filled at this resolution) from the graph data to only keep vertices corresponding to vessels with larger radii.
Skeletonization In the first step of the graph construction the binary image is skeletonized. While a large number of skeletonization algorithms exist, they were not suitable for the size of our data sets with running times of over 7 days on our workstation. We thus implemented a fast skeletonization algorithm for terabyte volumetric images that runs in 45min. As skeletonization via thinning is context dependent the processing cannot be easily split into blocks of data. Instead, our algorithm is designed to handle TB sized data as whole. To increase the processing speed, we pre-calculate the thinning actions for all possible local topological configurations into a look-up table and use linear indexing of the image arrays. The algorithm uses 3d parallel thinning with 12 sub-iterations as described in  (Palágyi and Kuba, 1999)  with a series of optimizations.
Graph construction To facilitate the analysis of the vasculature network, the skeletonized binary image is turned into a graph In addition to a large number of graph manipulation and analysis routines, our module also provides graph classes that handle the spatial geometry and other annotational information, as well as the visualization of the graphs in 3d space. In TubeMap, graph construction from the skeletonized image is done as follows: first, a raw graph is constructed by turning each foreground voxel of the skeleton into a vertex that also carries the positional information of the voxel. The vertices are then connected via undirected edges if they are neighbors (27-neighborhood) in the skeleton image. This process can result in local all-to-all connected cliques with more than 2 vertices at branch points of the skeleton.
Vasculature graph atlas annotation TubeMap provides functions to annotate data with labels from reference atlases, such as the Allen Brain Atlas. Annotation is done by acquiring an additional reference image in parallel to the other data channels and align this to the corresponding reference image of the atlas. We used an autofluorescence template from the ABA at 25µm generated from serial 2-photon tomography. To achieve the alignment, TubeMap first resamples the auto-fluorescent image of the sample to the resolution of the atlas reference implemented in TubeMap via iteratively propagating artery or vein labels on the graph topology and at the same time keeping track of the iteration number at each propagation step (i. e. the shortest distance to the newly reached branch points). To consider the different lengths of each branch, the distance transform was computed on the raw graph instead of branch graph in which each branch is expanded to a chain of vertices corresponding to the branch length.
A projection along the overlapping region of the tile is shown with misalignments noticeable in a few planes at the center of the inset. G WobblyStitcher addresses recurrent stitching issues to produce seamless datasets. The same alignment is shown after non-rigid displacement of the planes, correcting the vessel duplications. H Presentation of the stitching pipeline: 1. Tile pairs are first aligned in z, 2. Each plane is aligned individually along x,y. 3. tracking of the alignment quality for a smooth correction of the rigid stitching artefacts. The trajectories of planes are smoothed to prevent jitters 4. Global optimization of all the plane positions. I Raw data resulting from the labeling, imaging and stitching strategy, shown in ventral, dorsal and lateral orientations. A.
Acknowledgements This work was supported by the program " Investissements d'avenir "  ANR-10-IAIHU-06 , the  ERC  starter grant " NeuroRemod" (EU Horizon 2020 research program  under grant agreement  N°758817 ) and the " Paris Emergences" program  awarded to NR. CK was supported by an independent research fellowship from the  Kavli Neural Systems Institute  and the  Center for Physics and Biology at the Rockefeller University. SS is supported by the  Région Ile-de-France DIM Math'Innov. AVP is a  Marie Sklodowska Curie Action fellow (Marie Skłodowska-Curie  grant agreement No  845685 ). NM is supported by grants from the  ANR  as part of the  second Investissements d'Avenir program LIGHT4DEAF  ( ANR-15-RHUS-0001 ) and  LabEx LIFESENSES  ( ANR-10-LABX-65 ),  LHW-Stiftung , and by the  Prix Emergence of the Fondation pour l'Audition.