dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, we do classification by k-medoids. We choose the size of 1024 × 1024 as it results in the highest values of all the measures, i. e. , SE , SP, RI, ARI, F , and P compared with the other resolutions. In addition, Figure  7  shows the impact of SNN on the pairwise correlation matrices of the datasets D O L and D O V. Comparing the subfigures (c) and (d) with (a) and (b), it can be seen that the average of intra-camera correlations, i. e. , the diagonal parts, has increased while the average of the inter-camera correlations has decreased. This improvement in the correlations between RNs produces better results for k-medoids. The value of κ in SNN for each dataset was experimentally determined. Different values were tested and κ = 20 and κ = 70 generated the best results in the classification for D O L and D O V , respectively.
Based on Figure  9 , by systematically increasing the number of neurons, the classification results are improved in terms of SE , SP, ARI, F and P. Although the highest values are resulted in the cardinality of 35, up to the cardinality of 50, there are still some fluctuations in the values. For this reason, we set the number of the neurons to 50 in our experiments. The tuning phase of the ANNs can also be used as a benchmark for the capability of the classification in the best case because the "original images" and Google Currents images have the highest resolution in the dataset. The results of social-by-original SI for both datasets D L and D V are shown in Table  6. The social-by-original SI enables identification of smartphones in spite of the fact that the pictures get downgraded during the uploading and downloading process.
In other words, we verify whether two sets of images from different user profiles on different SNs are linked, that is inter-layer UPL. The strengths of our method include the possibility to exploit images from different SNs, not only the "original images", but also the robustness in spite of the fact that some SNs degrade the resolution of the images more than others. We consider all the different combinations of the selected SNs for each dataset, as shown in Figure  2. The results for all the possible pairs of SNs are presented in Tables  8  and 9. It is worth mentioning that the images in D L used for experiments of inter-layer UPL on different SNs are not from the same scenes, making a more similar real-life situation. Among the results in Table  8 , using Google Currents images to classify the images on the other SN datasets, i.
e. , D W L , D FH L , and D T L produce the highest values of SE , SP, ARI, F , and P, as shown in the first rows of Table  8. For D V , using images in D W V to classify the images in the other datasets, i. e. , D FH L and D FL L concluded the best results. It is interesting that the classification of the images in D FL L in inter-layer UPL compared with the classification of the images in intra-layer UPL generates better results. Given the results, it is proven that the proposed methods are reliable enough to match user profiles on the selected SNs.
Conclusions In this paper, we have compared different classification methods to achieve SI and UPL. The methods can help forensic investigators to find significant information from digital crimes when a set of images captured by a specific number of smartphones and shared on a set of investigated user profiles are provided. We have evaluated our methods on different datasets, i. e. , our dataset and VISON dataset. The obtained results show that with an acceptable error margin, k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. In particular, the results indicate that even in the worst case k-medoids can provide the values of F1-measure 75% and 77%, for SI and UPL tasks, respectively.
Acknowledgements. H. Tavares was supported by the  Portuguese government through FCT -Fundação para a Ciência e a Tecnologia , I. P. , under the projects  PTDC/MAT-PUR/28686/2017 ,  UID/MAT/04561/2013UID/MAT/04561/2013  and  UIDB/MAT/04459/2020. H. Tavares would also like to acknowledge the  Faculty of Sciences of the University of Lisbon  for granting a semestral sabbatical leave, during which part of this work was developed. This work was partially supported by the project  ANR-18-CE40-0013  SHAPO financed by the  French Agence Nationale de la Recherche (ANR).
(2016)  parametrisation of the LCDM halo mass-concentration relation at redshift  = 0. As listed in Table  3 , the resulting NFW profile has  max = 39. 6 km s -1 and  max = 8. 0 kpc, or, in terms of virial mass and concentration,  200 = 1. 04×10 10 M and  = 12. 5. This initial virial mass is roughly a factor of two lower than the average halo mass inferred for galaxies which host as many GCs as Fornax, but well within the scatter of that relation (see; e. g. ,  Forbes et al. 2018; Shao et al. 2020). The N-body realisation of the halo model is constructed with 10 6 particles (10 7 in some cases, as specified in section 3. 3) using the Zeno 6 software package developed by Joshua Barnes at the Uni-6 http://www. ifa. hawaii. edu/faculty/barnes/zeno/ versity of Hawaii. This software provides routines for Monte Carlo sampling a given distribution function whereby a system in virial equilibrium may be generated.
Simulation code We use the N-body code GADGET-2  (Springel 2005)  to evolve of our N-body models. This code implements a hierarchical tree algorithm to compute gravitational interactions. Forces between particles are smoothed with a Plummer-equivalent softening length of  P = 40 pc for  = 10 6 and  P = 13 pc for  = 10 7 particles. For each of the orbits described in section 2. 2, the -body model is evolved on the respective orbit in the potential of Section 2. 1 for ∼ 10 Gyr. We have also evolved the same N-body models in isolation to identify the smallest radius for which our mass profiles are numerically converged. Defining this as the innermost radius where circular velocities deviate by less than ∼ 1% from the target NFW profile, we consider our results converged outside  conv = 450 pc and  conv = 140 pc for our 10 6 or 10 7 -particle runs, respectively.
ACKNOWLEDGEMENTS This work used the DiRAC@Durham facility managed by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). The equipment was funded by  BEIS  capital funding via STFC capital grants  ST/K00042X/1 ,  ST/P002293/1 ,  ST/R002371/1  and  ST/S002502/1 ,  Durham University  and  STFC  operations grant  ST/R000832/1. DiRAC is part of the  National e-Infrastructure. RE acknowledges support provided by a  CITA National Fellowship  and by funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement No. 834148 ). AF is supported by a  UKRI   Future Leaders Fellowship  [grant number  MR/T042362/1 ] and  Leverhulme Trust. ISS is supported by the  Arthur B.
For the EBSD mode, the sample were mechanically polished up to 1 μm. For some cross section samples, it was possible to carry out an additional ionic polishing with a PIPS ILLION 2 with accelerating voltage 4 kV to obtain a clean sample surface state very close to the surface edge. The observations were performed with different microscopes: a FEG Zeiss Supra 55VP equipped with an Oxford EBSD Symmetry detector with an accelerating voltage set to 12 to 15 kV, a FEI Quanta 600 with an accelerating voltage of 20 kV, and a FEG Zeiss Σigma 600 with an accelerating voltage of 25 kV. The EBSD data treatment was realized using Atex software  [34].
Acknowledgments The authors would like to thank the  RATP (Paris Public Transports) (X. Quost S. Simon  and  P. Boutet ) and  British Steel  ( F. Fau  and  F. Cristofari ) for providing the steel samples and founding this study. We also would like to thank  E Vittecoq  ( Hepia Geneva ) for the design of the test bench,  P. Veuillet ,  A. Pâquet  and  D. Leveque  ( INSA-Lyon, LaMCoS ) for the technical support during the mounting of the bench and the realization of the tests.
Materials and Methods: A retrospective analysis of data collected from all patients with mRCC between 2007 (the time of TKI was a standard of care) and 2019 was carried out at the Paoli-Calmettes Institute (Marseille, France). Computer research software using artificial intelligence (ConSoRe ® ) was used to identify patients and assess their characteristics. Results: Between January 2007 and December 2019, 11 out of 660 (1. 6%) mRCC patients had metastases of the gastrointestinal tract. The median age was 62 years. Of the 11 patients, 81. 8% experienced digestive bleeding or anemia. Only 2 patients were asymptomatic. The metastases were mainly duodenal (50%) and gastric (41. 6%). The median time from cancer diagnosis and from metastatic disease to gastrointestinal metastasis was 4. 3 years (3 months-19.
Study Design This was a retrospective monocentric study (Institut Paoli-Calmettes, Marseille, France) enrolling patients over 18 years old with DM in a large cohort of patients with mRCC between January 2007 and December 2019. We chose 2007 as the date because of the marketing authorization of sunitinib, the first TKI indicated for mccRCC. DM could be located throughout the gastrointestinal tract (esophagus, stomach, duodenum, small intestine, colon or rectum). Only endoluminal metastases were considered. The exclusion criteria were pancreatic metastases, including pancreatic metastases with duodenal extension, locoregional digestive extensions of the renal tumor, and invasion of the gastrointestinal tract by peritoneal carcinosis. Computer research software using artificial intelligence (ConSoRe R ), developed in UNICANCER centers, was used to identify patients and assess their characteristics  1.
Statistical Analysis All anonymized patient data were collected from the patient's computer medical file and entered into a spreadsheet. Continuous variables were expressed as medians with standard deviations. Eleven patients met the inclusion criteria for DM of mRCC, with one patient having presented 2 types of DMs (Table  1 -Patient 11) and one patient with duodenal metastasis having been symptomatic 2 times in a 2. 4 year period (Table  1-Patient 9 ). Therefore, we considered 12 metastases for the outcome "characteristics of the DMs" and 13 metastases for the outcome "treatment. ".
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i. e. , height, crown diameter (CD), DBH) and stand-level (i. e. , tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments.
The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R 2 = 0. 29 to R 2 = 0. 61), and prediction of tree DBH (from R 2 = 0. 36 to R 2 = 0. 67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy.
Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7. 4 cm, Bias = 3. 1 cm, and R 2 = 0. 75. Yet, it yielded more accurate estimates of the BA (+3. 5%) and DBH-distribution of the stand than did allometric models -12. 9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.
With the recent accessibility of fully integrated compact LiDAR systems (i. e. , laser scanner unit, GPS receivers, and inertial measurement unit (IMU)) and the continuous progress being made regarding unmanned aerial vehicles (UAVs)  [54, 55] , UAV laser scanning systems (ULS) are becoming increasingly accessible for a wide range of applications  [56] , including forestry. The potential of UAVs for forestry lies in: (i) their flexible navigation systems that allow adapted flying patterns; (ii) their limited logistics and deployment permitting flights as needed in narrow window periods; and (iii) their ability to fly lower and slower than ALS or helicopters with an aerial perspective. In addition, ULS systems have certain advantages over ground-based data collection, for example: (i) they are not affected by the constraints of terrain access and the degradation of the Global Navigation Satellite System (GNSS) signals under the canopy  [57] ; (ii) the flexibility of their flying patterns facilitates multiple views of all points of a scene, which in turn reduces signal occlusion, an issue for all LiDAR systems; and (iii) their point density resolution can easily be adjusted during the flight configuration, permitting the collection of high (50 points/m 2  [58, 59] ), very-high (1500 points/m 2  [60] ), to ultra-high (4000 points/m 2  [61] ) density point clouds, thereby decreasing the boundaries between the ALS and TLS systems  [57, 62].
Most of all, the emergence of high-density ULS point clouds brings new opportunities to characterize forest resources at the tree-level. ULS systems represent an interesting alternative to aerial surveys for small (up to 4 ha) to medium (4-400 ha) size areas. Such areas may be costly for an ALS data collection  [63]  or require too much time for full coverage by ground-based units  [64]. More and more studies have demonstrated that ULS provides accurate data with good repeatability  [58, [65] [66] [67] [68]  and has great potential to support forest inventories (e. g. ,  [57, [66] [67] [68] [69] [70] [71] [72] ). Transferability of ITD to ULS data is now actively being investigated (e. g. ,  [73] [74] [75] [76] [77] ). Despite encouraging results, most recent studies were conducted on softwood stands or on planted forests.
There is still little scientific information regarding the potential of ULS systems on heterogeneous mixedwood or hardwood forests  [59, 78, 79]. The structural complexity of these forest types, which pose additional challenges to the ITD algorithms, also increases the difficulty of validating results at the tree-level  [49]. The main objective of this study was to investigate methodological approaches using ULS data to estimate forest inventory attributes from a naturally grown hardwood stand. This was accomplished by: (i) assessing the transferability of ITD algorithms that were developed for processing ALS and TLS data to ULS data that were collected under various canopy conditions (i. e. , leaf-on and leaf-off); and (ii) comparing automated approaches for the retrieval of forest inventory attributes at the tree-level (i.
e. , height, crown diameter (CD), DBH) and at the stand-level (i. e. , tree count, basal area (BA), DBH-distribution). For both sub-objectives, cross-comparisons between ALS, TLS, and ULS data were performed to provide a comprehensive understanding of the challenges facing various LiDAR data collection systems and processing algorithms. Results are expected to provide guidance in the use of ULS systems in supporting hardwood forest management.
Terrestrial Laser Scanning (TLS) Data The TLS data were collected in May 2017 using a FARO Focus3D S 120 (Faro Technologies Inc. , Lake Mary, FL, USA). For the data acquisition, 13 circular sample plots (11. 28 m radius) were established (Figure  1 ) to obtain laser point coverage of a subsample of trees (n = 258) that were representative of the study site. Each plot was captured in leaf-off conditions from five scan positions to minimize signal occlusion: the plot center and along the north, east, south, and west plot edges, based on compass direction. The center-point of each TLS plot and two scan positions were measured with the RTK equipment used for the field inventory. They were used as ground control points (GCP) to georeference the TLS point cloud and served for co-registration with field inventory and aerial datasets  [82].
The angular resolution between pulses was set to 0. 036 • , which resulted in a point spacing of 6. 3 mm at 10 m distance from the scanner. Eight target spheres (145 mm diameter) were distributed within the sample plots to serve as homologous points between scans and to enable scan co-registration. Mean absolute errors of scan co-registrations that were performed in FARO SCENE 5. 5. 3. 16 software varied between 4. 1 mm and 6. 7 mm, with an average value of 5. 4 mm.
Data Co-Registration The ALS point cloud was set as the reference dataset for data co-registration as suggested by the provincial government of New Brunswick and adopted by most land managers of the province. ULS-R and ULS-V point clouds were preprocessed, geo-referenced, and co-registered against the ALS point cloud by the data provider (RME Geomatic and Phoenix Aerial Systems, respectively). A three-step procedure was applied to finely coregister the TLS point cloud with the field-inventory tree map and ALS and ULS datasets. First, a tree map was created from the TLS point clouds at 1. 3 m AGL following the procedure that was suggested by  [38]  (Section 3. 5. 1). Second, coarse registration between TLS point clouds and the field-inventory tree map was performed by: (i) calculating the 3D roto-translation between the two tree maps using the "Iterative Closest Point (ICP)" tool that is available in CloudCompare  [92] ; and (ii) applying this 3D roto-translation to the TLS point clouds  [93].
Third, a fine co-registration between TLS point clouds and the ULS-V geo-referenced point clouds was performed following the "K-4PCS" method used in  [94]. This involved reducing the density of TLS point clouds (here set to one point/cm 3 using a voxel grid filter) to detect the main 3D key points that were necessary for the fine co-registration process. The latter was performed using the "fine registration (ICP) tool" that is available in CloudCompare. Figure  6  provides an example of the four co-registered datasets stacked together.
SEGMA was also applied to the leaf-off ULS-V point cloud to compare the results that were obtained by the two types of ITD algorithms from the same ULS-V raw point cloud (Figure  5 ). The leaf-on ALS point cloud was also processed using SEGMA to provide an additional comparison between ALS and ULS ITD results. TLS point clouds were processed semi-automatically, using: (i) the SimpleTree algorithm to detect and delineate individual trees; and (ii) CloudCompare software was used to visually inspect SimpleTree results and eventually manually improve tree delineation.
Raster-Based ITD-SEGMA Individual trees were detected and delineated from ALS, ULS-R, and ULS-V rasterized CHM (hereafter referred to as ALS-Raster, ULS-R-Raster, and ULS-V-Raster, respectively) using SEGMA  [30, 97] , which is a raster-based ITD algorithm developed in Python  [98]  and implemented in open-source in the Computree platform  [99]  (Figure  7 ). SEGMA ITD algorithm works on the CHM and involve six main steps: (i) creation of a pit-free CHM; the resolution of 25 cm was chosen based on  [100, 101]  recommendations; (ii) adaptive Gaussian smoothing of the CHM and identification of its local maxima (Figure  7A ); (iii) adaptive filtering of local maxima using exclusion distance criteria to identify tree tops; (iv) watershed delineation of individual tree crowns from the designed tree tops (Figure  7B ); (v) iterative refinement of the delineated tree crowns on the basis of geometric quality criteria that were calculated from the crown shape and 3D features; and (vi) top to bottom delineation of trees in the point cloud from the delineated tree crowns (Figure  7C ).
Tree Height and Crown Diameter (CD) Tree height was computed in SEGMA as the height of the local maximum for each tree that was identified in the ALS-Raster, ULS-R-Raster and ULS-V-Raster datasets (Section 3. 4. 1). Tree CD was also computed in SEGMA from the crown projected area (CD = 2 × Crown projected area/π). SEGMA automatically refines its crown delineation process after computing individual tree attributes. To do so, the algorithm assigns a score to each delineated tree based on simple geometric criteria (e. g. , minimum point density, ratios between tree height and CD), with eventual removal of erroneously detected trees to improve the ITD  [30, 97]. Tree height was computed in the R environment  [105]  for each tree that was delineated by SimpleTree from the ULS-V-Pcloud (Section 3.
4. 2). To do so, the vertical distance is computed between the highest point of the tree and the ground surface elevation directly beneath it. Tree CD was computed for each tree in R following the method that was proposed by  [106]  (Figure  9 ). This method involves five steps for identifying the crownbased height (CBH) of the tree and then computes CD from the projected area of the classified crown points (CD = 2 × Crown projected area/π) by: (i) vertically dividing the tree point cloud into 10-cm height clusters; (ii) fitting convex hull polygons to xy-coordinates of each cluster along the tree bole; (iii) calculating maximum Euclidean distance between the centroid of each convex hull and its vertices, and plotting the results along the z-axis (Figure  9A ); (iv) identifying the CBH of the tree by fitting a segmented (piecewise) regression to the plotted points using the Segmented R package  [107]  (the CBH is defined as the lowest breakpoint (knot) of the segmented regression, which corresponds to the height where the regression slope starts to increase sharply because of the presence of branches) (Figure  9A  "Breakpoint"); and (v) classifying the points above the CBH as belonging to the crown (Figure  9B ), and using them to compute CD.
Diameter at Breast Height (DBH) Three methods were investigated for estimating tree DBH from the delineated trees: 1. Predicting DBH using height and CD allometry (Equation (  1 )) from raster-based ITD trees  [28] ; 2. Predicting DBH using height and CD allometry (Equation (  1 )) from point cloud-based ITD trees  [49] ; 3. Estimating DBH using a cylinder-fitting algorithm onto tree stems  [39, 45, 90]. The choice of the approach differed from one dataset to another, and was guided by the level of detail in the point cloud for a given acquisition configuration (Figure  3 ). Method #2 and method #3 are more restrictive than method #1, as the tree stems need to be recorded in the 3D point cloud for these methods to work. As such, method #2 and method #3 were only assessed on the ULS-V-Pcloud data.