dataesr/scientific-paragraphs-categorization huggingface dataset:
Note II, Suppl. Fig. 14 ) to test if an ensemble of predictions yield improvements. This approach also reveals the extent of complementarity between participants. Overall, the ensemble method of "yangsuhui" (0. 6654) and detection enhanced segmentation of "swtnb" (0. 6348) achieved the best s-scores. Notably, both methods try to better capture the underlying data variation by combining feature maps obtained from different backbones. Spatial pyramid pooling of features by "michaelqiyao" in PSPNet  25  to better capture the varying sizes of individual region-of-interest (ROIs) of artefact classes (Suppl. Fig. 3c, d ) in the EAD2019 dataset was also an effective strategy for single stage neural networks. Alternative strategies such as cascaded mask R-CNN and mask aided R-CNN appear to fare worse.
Acknowledgements The research was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the Department of Health. Parts of this work was also supported by  MedIAN network  ( EPSRC EP/N026993/1 ) and  Cancer Research UK. SA, BB, AB and JEE is supported by  NIHR BRC , FYZ by  Ludwig Institute for Cancer Research (LICR)  and JR by  LICR  and  EPSRC   Seebibyte Programme Grant  ( EP/M013774/1 ). We would also like to acknowledge the annotators and our technical report reviewers.
Material Characterization The crystalline phase was identified by XRD using a Philips X'Pert diffractometer (PANalytical, Orsay, France) equipped with CuKα radiation source (λ = 1. 5406 Å). The diffractograms were recorded at room temperature in the 2θ-range 10-80°. The experimental patterns were refined by the Rietveld method with FULLPROF software program. The variance of the relative amounts of atomic components in the samples were analyzed by inductively coupled plasma-atomic emission spectrometry (ICP-AES, OPTIMA 7000DV, Perkin Elmer Co. , Ltd. , UK). The morphology and composition were studied by SEM (ZEISS model ULTRA 55) coupled with an energy-dispersive X-ray attached analyzer (EDX). HRTEM images were obtained using an electronic microscope JEOL model JEM-2010. Raman spectra were taken using a micro-Raman spectrophotometer (Horiba, Longjumeau, France) equipped with an optical microscope (×100 objective) and a 633 nm He-Ne laser excitation line.
Data were recorded in steps of 1. 6 cm -1 with an acquisition time of 30 s. The wavenumber calibration was regularly verified by using the Raman peak at 520 cm -1 of a silicon crystal as a reference. BET specific surface area was measured by nitrogen adsorption/desorption at 77 K in a relative pressure P/P0 = 0. 0-1. 0, with P and P0 being the equilibrium and saturation pressure, respectively, using a Quantachrome NOVA automated gas sorption analyzer (Anton Paar GmbH, Blankenfelde-Mahlow, Germany). The pore size distribution and pore volume were estimated using the Barrett-Joyner-Halenda (BJH) method  [46]. Electrochemical tests were conducted on CR2025-type coin cells assembled in a glove box (moisture and oxygen content ≤5 ppm) under argon atmosphere. The procedure to fabricate the cathode was as follows: 85 wt.
Material Characterization The crystalline phase was identified by XRD using a Philips X'Pert diffractometer (PANalytical, Orsay, France) equipped with CuKα radiation source (λ = 1. 5406 Å). The diffractograms were recorded at room temperature in the 2θ-range 10-80 •. The experimental patterns were refined by the Rietveld method with FULLPROF software program. The variance of the relative amounts of atomic components in the samples were analyzed by inductively coupled plasma-atomic emission spectrometry (ICP-AES, OPTIMA 7000DV, Perkin Elmer Co. , Ltd. , UK). The morphology and composition were studied by SEM (ZEISS model ULTRA 55) coupled with an energy-dispersive X-ray attached analyzer (EDX). HRTEM images were obtained using an electronic microscope JEOL model JEM-2010. Raman spectra were taken using a micro-Raman spectrophotometer (Horiba, Longjumeau, France) equipped with an optical microscope (×100 objective) and a 633 nm He-Ne laser excitation line.
Data were recorded in steps of 1. 6 cm -1 with an acquisition time of 30 s. The wavenumber calibration was regularly verified by using the Raman peak at 520 cm -1 of a silicon crystal as a reference. BET specific surface area was measured by nitrogen adsorption/desorption at 77 K in a relative pressure P/P 0 = 0. 0-1. 0, with P and P 0 being the equilibrium and saturation pressure, respectively, using a Quantachrome NOVA automated gas sorption analyzer (Anton Paar GmbH, Blankenfelde-Mahlow, Germany). The pore size distribution and pore volume were estimated using the Barrett-Joyner-Halenda (BJH) method  [46]. Electrochemical tests were conducted on CR2025-type coin cells assembled in a glove box (moisture and oxygen content ≤5 ppm) under argon atmosphere. The procedure to fabricate the Energies 2020, 13, 3487 5 of 22 cathode was as follows: 85 wt.
INTRODUCTION In the last decade, taking benefit from large improvement of geophysical datasets and methods, researchers have much deepened the knowledge of the structure and dynamics of the Calabrian Arc subduction zone (Figures  1  and 2 ), the most active area of Italy from the seismic point of view. Neri et al. (2009 Neri et al. ( , 2012) )  have found that southern Calabria is the only place in the Calabrian Arc subduction zone where the subduction slab is still continuous over depth, while detachment of the deepest portion of subducting lithosphere has already occurred beneath the Arc edges (northern Calabria and northeastern Sicily, respectively; Figure  1 ). Other investigators (see, e. g. ,  Presti et al. , 2013; Totaro et al. , 2016)  have distinguished the main compressional domain west of Calabrian Arc due to NNWtrending Africa-Europe plate convergence from the subduction domain where plate convergence and gravity-induced rollback of the Ionian subducting slab coexist (Figure  1 ).
We have located the hypocenters of these earthquakes by the Simulps standard linearized method  (Evans et al. , 1994)  using the P and S arrival times available from the INGV bulletin database (http://terremoti. ingv. it/) and the 3D local velocity model estimated by  Neri et al. (2012)  by integration of crustal  (Orecchio et al. , 2011)  and sub-crustal  (Neri et al. , 2009)  seismotomographic models of the Tyrrhenian region. The linearized procedure of location based on time residual minimization has led to location error estimates of the order of 1 km (ERH) and 2 km (ERZ) for earthquakes located in the Calabrian Arc. Slightly larger errors of the order of 2 km (ERH) and 3 km (ERZ) were obtained in the same area by  Presti et al. (2017)  using the Bayloc Bayesian non-linear location algorithm.
using global positioning system (GPS) data collected between 1994 and 2013. Palano's dataset included 379 continuous GPS stations and 80 episodic GPS sites located in Sicily and the Northern Apennines in the framework of specific Projects. Palano (2015) aligned estimated GPS velocities to a fixed Eurasian reference frame, derived a continuous velocity gradient tensor on a regular 0. 35 °× 0. 35 °grid and then computed the average 2-D strain-rate tensor reported in Figure 5 (see Palano, 2015 for further details). The figure evidences clear difference between low values of 0-15 nanostrain/yr of extensional horizontal strain in southern Calabria and quite larger values of 35-55 nanostrain/yr along the rest of the Apennine extensional belt in the Italian peninsula from northern Calabria to central Italy.
Eight recently collected and fixed in 96% ethanol specimens of M. czernjawskii and nine specimens of Macropodia tenuirostris  (Leach, 1814)  were used for the molecular genetic barcode study of the sequence of subunit I of the mitochondrial cytochrome oxidase gene (COI). Additional material was obtained from the GenBank (Table  1 ). DNA extraction followed the modified silica-based method  (Ivanova et al. 2008 ). The Magnetic Beads, coated with SiO 2 , were used. To amplify the COI, specific fragments of the Folmer primers  (Folmer et al. 1994)  were used. If the amplification with the Folmer primer pair failed, then one of the CrustF1 or CrustF2 was used instead of LCO1490 as a forward primer  (Costa et al. 2007 ). The amplifications were carried out in a T100 Thermal Cycler (Bio-Rad) thermal cycler, using a pre-made Polymerase Chain Reaction (PCR) mix (ScreenMix) from Evrogene.
The Master mix consisted of 1× ScreenMixc plus 0. 6 μM of each primer and 1 μl of DNA template and complet-   (Kimura 1980). Statistical calculations were performed using PaST package  (Hammer 2013 ). The dataset with records of Macropodia czernjawskii in the Black Sea, data on their morphometry, clutch size and epibiosis are provided as Suppl. material 1, Table  S1  and uploaded to GBIF  (Spiridonov et al. 2020). Macropodia czerniavskii (misspelling)  -Forest 1964: 348, 351-354; Forest and Zariquiey Álvarez 1964: 230, figs 4, 9, 10, 15; Zariquiey Álvarez 1968: 478 (key), 479, figs 161a, 162d; Noël 1992: 136 (key). The lot of ZIN-RAS 1609 was identified as containing type specimens of the species  (Spiridonov and Petryashov 2011) : one male (all pereopods detached, right cheliped present), one separated pereopod of male, one female ovigerous (both chelipeds undetached) in a separate jar; and three female ov (all pereopods detached, one pleon detached) in another jar.
0 mm) had a clutch at the latest stage V in June 2013. The latter clutch was also the largest one of those observed and consisted of 1239 developing embryos while the other females carried from 351 to 986 eggs (see dataset in  Spiridonov et al. 2020). There was a weak and statistically non-significant positive correlation between the female size (CW) and the number of eggs at embryonic development stages I-II (Fig. 9b ). Egg diameter at embryonic development stages I and II ranged from 0. 371 to 0. 537 mm (mean 0. 45 + 0. 03 mm; n = 6) showed a weak negative correlation with the female size (r = -0. 385, t = -0. 834, p = 0. 451). The volume of eggs at these stage averaged 0. 051 + 0. 0093 mm 3. Colouration. Mimicking substrate and algae: carapace with whitish pattern on greenish background, legs greyish, with irregular whitish transverse bands (Fig.
10c ). Practically all crabs examined shortly after the collection and preservation in 2008-2018 had significant epibiosis. In some females, i. e. (ZMMU Ma 3538 and 3546), organisms of epibiosis covered 100% of the dorsal carapace surface and most of their legs. Males were seeming-  ly less decorated, with significant area of the carapace and pereopods without epibiosis. The bulk of epibiosis consisted of algae and cyanobacteria. In twelve carefully examined specimens, 25 autotrophic eukaryote taxa were found, identified to the lowest possible level (see dataset in  Spiridonov et al. 2020). Green algae Cladophora sp. and calcareous Corallinales gen. sp. were most commonly recorded (in 50% of specimens). They were followed by phaeophytes Pilayella cf. littoralis, Sphacelaria cirrosa (Roth) C.
Conclusions Macropodia czernjawskii has been re-described here on the basis of the type and the topotypic material and provided with molecular barcode (COI gene) data from the topotypic specimens. It is a distinct species, described from the Black Sea, although it was missing in the regional faunal accounts for more than a century due to the interplay of historical circumstances and personal research attitudes. The present analysis is based on historical and new collections and the review of published regional records of Macropodia spp. These indicate that M. czernjawskii is very likely the only species of the genus that occurs in the Black Sea. The extended dataset of the molecular barcodes has provided a better understanding of the status and the relationships of M. czernjawskii and some of its congeners.
Original and other available material of COI sequences of Macropodia spp. and outgroup taxa (Inachus aguiarii de Brito Capello, 1876 and Hyas araneus(Linnaeus, 1758)) used in the molecular-genetic analysis in the present study. BoLD -Barcoding of Life Data -http://www. boldsystems. org; GenBank -https://www. ncbi. nlm. nih. gov.
Acknowledgements We are deeply obliged to  Dr. Victor V. Petryashov  (1956Petryashov ( -2018)), who was the head of the  Malacostraca division of ZIN-RAS  and constantly helped our work with the ZIN-RAS collection and regard this paper as a small tribute in his memory. We thank  Drs William Santana  and  Cédric d'Udekem d'Acoz  for a comprehensive review of the manuscript and useful comments and suggestions. Fieldwork in 2008-2016 was supported by the funding for the  Black Sea Expedition of IO RAS. Fieldwork of  Vitaly Timofeev  was conducted in the framework of the state assignment on the theme №  0828-2018-0002  (state registration №  АААА-А18-118020890074-2 ). This study is part of the  Russian Foundation for Basic Research (RFBR)  project  16-04-01526  А " Scale and patterns of endemism of the Crustacea Decapoda in the seas of Russia ".
BoLD accession numbers GenBank accession number Area Voucher specimen museum catalogue number Reference M. czernjawskii BLS565 MT311174 Black Sea, Cis-Caucasian coast ed with milliQ-grade water to make up a total volume of 20 μl. PCR tubes with the mix were preheated (95 °C, 5 min). Annealing temperature was 48 °C (30 sec) during the first 5 cycles and 52 °C (30 sec) during the next 32 cycles. The resulting PCR product was visu- alised in a 2% agarose gel, purified and sequenced us- ing ABI PRISM BigDye Terminator v. 3. 1 on Applied Biosystems DNA Analyzer 3500 ABI. Chromatograms were processed using the Codone Code Aligner. Fas- ta files were aligned using MAFFT v7. 308 (Kazuta- ka 2013). Alignment included 71 sequences (15 were obtained in the current work). The overall length of the aligment was 700 bp.
Applied at a larger scale using Unmanned Aerial Vehicle (UAV) derived ortho-images, the method allows the accurate characterization and high-resolution mapping of the surface coarse sediment size, as it was performed on the two pebble beaches of Etretat (D50 = 5. 99 cm) and Hautot-sur-Mer (D50 = 7. 44 cm) (Normandy, France). Validation results show a very satisfying overall representativity (R 2 = 0. 45 and 0. 75; RMSE = 6. 8 mm and 9. 3 mm at Etretat and Hautot-sur-Mer, respectively), while the method remains fast, easy to apply and low-cost, although the method remains limited by the image resolution (objects need to be longer than 4 cm), and could still be improved in several ways, for instance by adding more manually labeled data to the training dataset, and by considering more accurate methods than the ellipse fitting for measuring the particle sizes.
Data The methodology developed uses photographs taken at human height, which include a quadra structure of known shape and dimensions (0. 84 × 0. 84 m 2 ) placed on the ground. The dataset thus created includes images taken from a bird's eye perspective, between 1. 5 and 2 m high without a tripod (Figure  1a ) and using two different cameras (Nikon D3200 and Apple iPhone 11). These images are corrected for lens distortion effects using a camera calibration model  [33]  and then ortho-rectified by planar transformation within the quadra, with a resolution fixed at 0. 5 mm/pixel (Figure  1b ). This fixed value was set for consistency purposes as the unprocessed image resolution can vary from about 0. 25 mm/pixel for a Nikon D3200 camera to about 0. 5 mm/pixel for an Apple iPhone 11 camera. Each quadra is associated with the geographical coordinates of at least one of its corners, measured in situ using differential GNSS.
Remote Sens. 2020, 12, 3659 3 of 23 The methodology developed uses photographs taken at human height, which include a quadra structure of known shape and dimensions (0. 84 × 0. 84 m²) placed on the ground. The dataset thus created includes images taken from a bird's eye perspective, between 1. 5 and 2 m high without a tripod (Figure  1a ) and using two different cameras (Nikon D3200 and Apple iPhone 11). These images are corrected for lens distortion effects using a camera calibration model  [33]  and then ortho-rectified by planar transformation within the quadra, with a resolution fixed at 0. 5 mm/pixel (Figure  1b ). This fixed value was set for consistency purposes as the unprocessed image resolution can vary from about 0. 25 mm/pixel for a Nikon D3200 camera to about 0. 5 mm/pixel for an Apple iPhone 11 camera.
This is made possible by an ROI classifier which performs object detection (nature and bounding boxes) on the anchors, in order to extract the ROI. ROIs are then scaled to the fixed size of 28 pixels × 28 pixels which corresponds to the dimensions of the convolutional neural network, in order to perform a semantic segmentation on each of them and obtain a mask. Finally, the mask is resized to the initial object scale, and repositioned on the image. The model was trained with a dataset of 46 images and a total of 3598 manually digitized nonoverlapping clasts. This dataset covers as wide a range of environmental conditions as possible with elements of all sizes (from gravels to cobbles), shapes and colors, low to high light, humidity and silting conditions, presence or absence of quadra, ground and Unmanned Aerial Vehicle (UAV) shots, raw or ortho-rectified.
This dataset was artificially augmented in order to improve learning performance  [36]  which means that some images randomly selected in the dataset were copied and transformed (addition of blur/noise, crops, rotations/translations, etc. ) using random parameters, in order to artificially increase the dataset's size. As the training step of a deep learning model works by iterations called epochs subdivided into steps, it is relevant to mention that a total of 100 epochs, each including 100 steps, was required to achieve a satisfactory segmentation capability. After processing an image of clasts, the output of the model is recorded as a series of masks, one per detected element, each of them being associated with a probability value reflecting the certainty of the model to have correctly detected and segmented the object.
Finally, the mask is resized to the initial object scale, and repositioned on the image. The model was trained with a dataset of 46 images and a total of 3598 manually digitized non-overlapping clasts. This dataset covers as wide a range of environmental conditions as possible with elements of all sizes (from gravels to cobbles), shapes and colors, low to high light, humidity and silting conditions, presence or absence of quadra, ground and Unmanned Aerial Vehicle (UAV) shots, raw or ortho-rectified. This dataset was artificially augmented in order to improve learning performance  [36]  which means that some images randomly selected in the dataset were copied and transformed (addition of blur/noise, crops, rotations/translations, etc. ) using random parameters, in order to artificially increase the dataset's size.
A total of 46 and 28 quadra measurements were realized at Etretat and Hautot-sur-Mer, respectively, covering the whole surveyed area on both sites. Validation is performed by comparing each quadra distribution (here called "terrestrial samples") to the one being extracted at the same location from the ortho-image, on areas covering 1 × 1 m² so called "UAV samples", considering the terrestrial samples as ground truth. It is important to understand that this validation process only compares distribution indices (quantiles, means, etc. ), as the automatic cross-identification of individuals on both datasets remains a challenging task. On the ortho-image segmentation, smaller objects are expected to be under-represented, due to the lower resolution in comparison to the quadra data (0. 5 mm/pixel) (Figure  9a, b ).
Since the validation results presented in Section 2. 3 showed that measurements by quadra were accurate and reliable, here they were considered as ground truth data for validation purposes. Therefore, some were carried out in parallel with the flight of the UAV to allow cross-validation of the results. A total of 46 and 28 quadra measurements were realized at Etretat and Hautot-sur-Mer, respectively, covering the whole surveyed area on both sites. Validation is performed by comparing each quadra distribution (here called "terrestrial samples") to the one being extracted at the same location from the ortho-image, on areas covering 1 × 1 m 2 so called "UAV samples", considering the terrestrial samples as ground truth. It is important to understand that this validation process only compares distribution indices (quantiles, means, etc.
), as the automatic cross-identification of individuals on both datasets remains a challenging task. On the ortho-image segmentation, smaller objects are expected to be under-represented, due to the lower resolution in comparison to the quadra data (0. 5 mm/pixel) (Figure  9a, b ). Therefore, some were carried out in parallel with the flight of the UAV to allow cross-validation of the results. A total of 46 and 28 quadra measurements were realized at Etretat and Hautot-sur-Mer, respectively, covering the whole surveyed area on both sites. Validation is performed by comparing each quadra distribution (here called "terrestrial samples") to the one being extracted at the same location from the ortho-image, on areas covering 1 × 1 m² so called "UAV samples", considering the terrestrial samples as ground truth.
It is important to understand that this validation process only compares distribution indices (quantiles, means, etc. ), as the automatic cross-identification of individuals on both datasets remains a challenging task. On the ortho-image segmentation, smaller objects are expected to be under-represented, due to the lower resolution in comparison to the quadra data (0. 5 mm/pixel) (Figure  9a, b ). Therefore, the distribution measured on the ortho-images reflects the tail of the distribution measured with quadra (Figure  10a ). In order to compare both types of datasets as relevantly as possible, the clasts detected in the terrestrial samples were filtered. The filtering operation consists of eliminating the pebbles that are smaller than a defined threshold in each terrestrial sample. This threshold is set to be the size of the smallest object detected in the UAV samples of the same location.
When filtered out, the respective ranges of the results and the number of detected pebbles per area unit of the two datasets correspond to what is expected: more small elements are found in the filtered terrestrial samples, and similar amounts are detected for the bigger ones (Figure  10b ). Therefore, the distribution measured on the ortho-images reflects the tail of the distribution measured with quadra (Figure  10a ). In order to compare both types of datasets as relevantly as possible, the clasts detected in the terrestrial samples were filtered. The filtering operation consists of eliminating the pebbles that are smaller than a defined threshold in each terrestrial sample. This threshold is set to be the size of the smallest object detected in the UAV samples of the same location. When filtered out, the respective ranges of the results and the number of detected pebbles per area unit of the two datasets correspond to what is expected: more small elements are found in the filtered terrestrial samples, and similar amounts are detected for the bigger ones (Figure  10b ).