dataesr/scientific-paragraphs-categorization huggingface dataset:
• A conditional variational autoencoder is employed to generate the path parameters given that link state. This work is extensively discussed in  [168] ,  [169] , and a massive ray-tracing-based urban dataset suitable for training purposes as well as the ensuing statistical generation of the 3D spatial channel model is publicly available  [170]. The accuracy of the AI-based model is corroborated in Figs. 23  and 24 for the section of London portrayed in Fig. 8. Fig. 23  compares the actual LoS probabilities in the test data, obtained via ray tracing, with the output of the NN-based link-state predictor. Similarly, Fig. 24  contrasts the CDF of the path loss corresponding to the test data with the CDF of the path loss generated by the trained model.
ACKNOWLEDGMENTS G. Geraci  and  A. Lozano  were supported by  ERC  grant  694974 , by  MINECO 's Project  RTI2018-101040 , by the  Junior Leader Fellowship Program  from  "la Caixa" Banking Foundation , and by the  ICREA Academia program. M. M. Azari  and  S. Chatzinotas  were supported by the  FNR   5G-SKY  project under grant agreement number  13713801 , and the  SMC funding program  through the  Micro5G  project. M. Mezzavilla  and  S. Rangan  were supported by  NSF  grants  1302336 ,  1564142 ,  1547332 , and  1824434 ,  NIST ,  SRC , and the industrial affiliates of NYU WIRELESS. M. Di Renzo  was supported in part by the  European Commission  through the  H2020 ARIADNE project  under grant agreement number  871464  and through the  H2020 RISE-6G  project under grant agreement number  101017011.
A complementary thermomechanical analysis of window failure was performed previously and reported in reference 33. The simulated fire propagation was consistent with observations from the night of the disaster. 6 The simulation allowed the evaluation of local concentrations of effluents and temperature conditions in each room (kitchen, living room and bedroom) of each apartment. In reference 34, an impact model was considered including fire loads from the façade system and from apartment contents, where relevant. This allowed the quantification of the conditions inside the Tower, by an analysis of the toxic and thermal contributions from combustion of both the façade components and each apartment's contents. The analysis of tenability conditions inside Grenfell Tower showed that the same conclusion can be made regardless of the input data for toxic gas yields or the model used, within the limits of the studied dataset and conditions.
m. The simulation takes into account the real geometry of each apartment, lobby and stairs. For each room in each apartment, the effluent concentrations and temperature are fixed by prior analysis of the horizontal fire spread over the Tower. The tenability conditions, in terms of carbon monoxide, hydrogen chloride, hydrogen cyanide and gas temperature can thence be evaluated for each lobby and the stairwell. This investigation sheds light on the impact of toxic and thermal conditions inside the Tower depending on the evolution of the fire spread over the four faces of the Tower. This publication does not assess tenability conditions for individual occupants, for two reasons. First, such analysis requests a lot of behavioural and movement data that is not necessarily available and may be highly speculative, reducing the validity of the analysis.
The contributions from the façade infill panels and the window reveal insulant appeared to be negligible in all scenarios. For the majority of the apartment locations and nine gas yield scenarios investigated, due to the ignition of the apartment contents, oxygen depletion was reached first, or at a similar time to the thermal or toxic FED. This occurred less than 15 min after window failure and fire entry. An example of results for species concentrations in the living room of Flat 196 evaluated in reference 34 is addressed in Figure  5. These evolutions are used as input for each room of each flat in the present research, depending on the flat location. The different yields used in the initial study  34  are reminded in Table  1. The analysis of tenability conditions inside Grenfell Tower showed that, within the limits of the studied dataset and conditions, the same conclusion can be made regardless of the input data for toxic gas yields and the model used.
The overall conclusion was that fires involving apartment contents quickly drive tenability conditions, independently of the dataset and model used, and even if noncombustible insulant is used instead of PIR as façade insulant. Thus, the temperature and toxicity conditions were evaluated numerically in every apartment of the Tower, and the authors have estimated the most severe and the most probable of the nine scenarios that were calculated. In this paper, based on these conclusions, a further analysis of conditions in the uppermost four storeys (floor 20 th to 23 rd ) of the Tower is described. It was on these floors of the Tower where the most fatalities occurred and for which observational data exists on the opening and closing of apartment front doors and the evolution of tenability conditions.
Carbon monoxide, hydrogen chloride and hydrogen cyanide concentrations, and gas temperature can thus be evaluated in the lobbies and stairs. The numerical model of the conditions inside the lobbies and stairs was based on two key input datasets. First, the thermal and toxic conditions inside the apartments, as derived from previous modelling. 34 cond, the status of doors, as derived from a detailed review and analysis of the expert report's to references 2-5, and the final Phase 1 report of, the Grenfell Tower Inquiry,  1  which allowed the definition of a general scenario for resident evacuation at different times during the disaster. The results of the simulation were validated by comparison with data on local conditions in the Tower over time, from an analysis of resident testimony during 999 calls recorded during the event.
Examples of temperatures and CO concentrations predicted at a height of 1. 5 m above floor level (corresponding to typical mean nose and face height), on the 13 th floor are shown in Figures  7  and 8  respectively. The thermal and toxicity conditions, in terms of gas temperature and effluent concentrations, were evaluated for the stairwell at the 13th floor. These data were then used as boundary conditions for the stairwell at the base of the 20th floor, to simulate the effluent migration from lower floors. It is assumed that there was no contribution from the lobbies of the 14 th to 19 th floors because no occupants left these floors after 01:50 a. m. (Table  2 ). The simulated conditions in the lower (10 th to 13 rd ) floors indicates severe toxicity and thermal conditions in lobbies and the stairwell.
| DNA extraction, amplification and sequencing Whole genomic DNA was extracted from fin tissue preserved in 96% EtOH. DNA extraction was performed following the HotSHOT method  (Truett et al. , 2000) , using 50 mM NaOH and 1 M Tris-HCl. For each specimen, the authors amplified a 652 bp fragment of the mitochondrial gene coding for cytochrome C oxidase subunit I (COI). The primers F2 and R2 designed by  Ward et al. (2005)  were used. Fragments were amplified using PCR protocols as described by Wil-   (Thompson et al. , 1994)  and edited using GENEIOUS 9. 0. 5 [http://www. geneious. com, (Kearse et al. , 2012) ]. All generated sequences were deposited in GenBank (Accession numbers: OL413590-OL413655).
| Genetic distances and sequence-based species delimitation analysis To visualize the relationships between haplotypes of Scartichthys spp. among the different sampling localities, the authors first constructed a haplotype network using the haplonet function of the package "pegas"  (Paradis, 2010)  in the R statistical environment (R Core Team, 2017). They then implemented Neighbour-Joining (NJ), using the software package MEGA 6  (Tamura et al. , 2013). Confidence in topology was evaluated by bootstrap analysis with 1000 replicates  (Felsenstein, 1985). Maximum likelihood (ML) and Bayesian inference methods were used to perform sequence-based species delimitation analysis. The authors used sequences of Ophioblennius macclurei (KF930203), Cirripectes variolosus (MH707881), Cirripectes polyzona (HQ168554) and Exallias brevis (MF409572) downloaded from GenBank to root the trees in all analyses.
As each sequence-based species delimitation method is susceptible to pitfalls, the authors used a 50% consensus among the five different methods they implemented as a robust delimitation scheme  (Hubert & Hanner, 2015; Kekkonen et al. , 2015; Kekkonen & Hebert, 2014 ): (a) Automatic Barcode Gap Discovery (ABGD;  Puillandre et al. , 2012)  available at https://bioinfo. mnhn. fr/abi/public/abgd/, (b) single (sPTP) and (c) multiple rate version (mPTP) of the Poisson Tree Process (PTP,  Zhang et al. , 2013)  available at https://mptp. h-its. org/#/tree and (d) single (sGMYC) and (e) multiple rate version (mGMYC) of General Mixed Yule-Coalescent (GMYC) as implemented in the R package Splits 1. 0-19  (Fujisawa & Barraclough, 2013). The ABGD analysis requires a DNA alignment as the input; the PTP analyses require an ML tree as the input, and GMYC analyses require an ultrametric tree as the input.
| Multilocus time-calibrated phylogeny A time-calibrated phylogeny (BI) of Scartichthys spp. was constructed using the software BEAST2 2. 5. 2  (Bouckaert et al. , 2019). Six molecular markers for each species were included; specifically one mitochondrial and five nuclear regions were used. For the mitochondrial marker, the authors used S. viridis, S. gigas and S. variolatus representative COI sequences produced in this study. In contrast, for these three species they used nuclear sequences (ENC1, myh6, sreb2 and tbr1) produced by  Hundt and Simons (2018)  (Supporting Information -Table  S2 ). The authors did not include S. crapulatus in this analysis as all their previous results indicate that S. crapulatus and S. viridis actually represent the same species (see Results section). Blenniids are relatively rare in the fossil record  (Bannikov, 1998) , but see  Liu et al.
ACKNOWLEDGEMENTS We thank  Alex Gamarra ,  Sebastián Báez ,  Rodrigo Muñoz  and  Italo Fernández  for their help in sampling specimens, and  Catalina Ruz ,  Ignacio Valverde  and  Sarai Morales  for their help in processing samples in the laboratory. We thank  Emily C. Giles  for her technical assistance in the molecular laboratory and help improving the English of the manuscript. Finally, we thank  N. K. Lujan  and two anonymous reviewers for providing constructive reviews of earlier versions of the manuscript. This research was funded by  Fondo Nacional de Desarrollo Científico y Tecnol ogico (FONDECYT)  Grant #  1151094  and  FONDECYT  Grant #  1210216  Proyecto Inserci on Académica (PIA,  2731-010-81  of the P. Universidad Cat olica de Chile  to APM. ) Collections of juvenile specimens in alcohol were funded by  FON-DECYT Grant  #  1150296  grant to MFL.
Materials and methods This study was conducted at the Pprime Institut (UPR 3346, CNRS -Poitiers University, France). To analyze loading distribution and the effect of lower limb alignment, 3D models of the human femur were extracted from medical imaging. Ten intact femurs of ten subjects were imaged in vivo. Data on age and sex, after having removed all personal information as required under the Human Research Ethics protocols, are presented in Table  1. These models are subject to loadings and boundary conditions representing alignment of the lower limb under the effect of varus and valgus deformity.
Segmentation and meshing The methodology used to generate the numerical models was to reconstruct the entire femur from the CT scan acquisition. The Xray scan parameters for the samples were set to 120 kVp, 100 mAs, with 0. 75 mm cross-sections and an image matrix of 512 × 512 pixels with a pixel size of 0. 434 mm. Volume image segmentation and geometric model were performed using 3D SLICER software (Version 4. 11, Kitware, France). The surfaces were then cleaned, healed, closed and filled, the objective being to create two models representing the cortical and the cancellous bones. The fi-nite element models obtained were imported and the analyses were carried out using the ANSYS® WORKBENCH software (Version 2020R2, Ansys Inc, United States) ( Fig. 1  ). Femur bone geometry was discretized using 10-node tetrahedral elements (C3D10) and mesh sensitivity was analyzed versus different element sizes as recommended in the literature  [6] [7] [8].
Post-processing Implicit static FE models were analyzed to evaluate femoral constraints and to estimate fracture force. The same loading magnitude was applied to all configurations so as to explore the effect of the malalignment, which was believed to initiate the fracture by generating stress variations in the weak femoral region. In the current study, mechanical analysis was based on visual exploration and stress data extraction. While the von Mises ( σ vM ) criterion is widely used to predict fracture in finite element biomechanical analyses, it does not consider the difference between compression failure and tensile failure. For this reason, we used the main stress criterion to determine the tensile and compressive strength of the proximal and diaphyseal regions. Fracture load prediction and failure locations were based on the principal stress criterion or the maximum principal stress failure theory.
Because the brighter star is starting to evolve more rapidly than its companion, its characteristics are sensitive functions of age. In Section 2, we discuss the spectroscopic, photometric, and interferometric data used, as well as the measurement of the radial velocities of the stars. In Section 3, we describe the fitting method used to determine the characteristics of the stars, like the masses. In Section 4, we discuss the age measurement for Praesepe and its applications to other areas of stellar astrophysics, like the WD initial-final mass relation (IFMR). 1. 1. Reddening, Distance, and Metallicity  Taylor (2006)  critically examined measurements of Praesepe's reddening and settled on E(B -V ) = 0. 027 ± 0. 004. This is consistent with the reddening derived by Gaia  Collaboration et al.
005 mas  (Cantat-Gaudin et al. 2018). There are a number of studies indicating that Gaia parallaxes are systematically offset to smaller values (and implying that objects are systematically closer than Gaia indicates;  Lindegren et al. 2018; Zinn et al. 2019) , however. In this paper, we employ the Gaia Early Data Release 3 (EDR3), which may have a smaller parallax offset of -17 μas than DR2  (Lindegren et al. 2021). Based on a simple selection of members within 5°of the cluster center, 6 mas yr -1 of the cluster's proper-motion vector, and parallax within 0. 4 mas of an iteratively determined average, we find w = ¯5. 424 mas. This implies a distance modulus (m -M) 0 = 6. 33 ± 0. 02. Recent spectroscopic examinations of Praesepe stars agree well that the cluster is quite metal-rich, similar to the Hyades.
Abt (1970)  reported eight measurements taken at Mt. Wilson Observatory in 1923, although we only used five of these. The remaining three measurements with phases 0. 1 < f < 0. 3 show clear signs of blending. Finally, there were 10 epochs observed by  Abt & Willmarth (1999)  for which the original spectra have not been recovered. In those cases, the reported velocities were still used in the fitting of a velocity solution. These literature values are reported along with our measurements in Tables  7  and 8. Julian dates reported by  Plaskett et al. (1921)  and UT dates reported by  Abt (1970)  were converted to heliocentric Julian dates. Radial velocity measurements were made using broadening functions (BFs; Rucinski 1992) derived from the spectra. We used the program BF-rvplotter  (Rawls 2016)  to calculate the BFs for each spectrum after modifying the code to fit rotational BFs (rather than Gaussians) to the two star profiles.
Based on the initial fit using a priori measurement errors, we scaled the uncertainties on each velocity data set to return a reduced χ 2 value of approximately 1, and then we refitted. These results are reported in Table  3. The system velocities were in reasonable agreement, but there appeared to be significant offsets that we then corrected for before the joint interferometry-spectroscopy fitting. (The NOT zero-point was used as the reference. ) We find Δγ(KPNO -NOT) = -1. 32 ± 0. 08 km s -1 , Δγ(OHP -NOT) = -0. 48 ± 0. 15 km s -1 , Δγ(MWO -NOT) = -0. 34 ± 1. 09 km s -1 , and Δγ (A&W99 -NOT) = -2. 06 ± 0. 15 km s -1. For the fits to the combined radial velocity and interferometry (visibility amplitudes and closure phases) data set, we developed a program, which we call interfRVorbit. In this case, we use a set of 13 parameters to model the orbits: period P, reference time of a periastron passage t P , orbit velocity amplitudes of both stars K 1 and K 2 , systemic velocities for both stars γ 1 and γ 2 , eccentricity e, argument of periastron ω, argument of the ascending node Ω, inclination i, angular size of the binary semimajor axis a, and brightness ratios in H and K s bands.
We gratefully acknowledge the assistance of  Daryl W. Willmarth  in locating and providing Kitt Peak spectra of ò Cnc from more than 25 yr ago, as well as support from the  National Science Foundation  under grant  AAG 1817217  to  E. L. S. This  research made use of observations from the SIMBAD database, operated at  CDS, Strasbourg, France ; the WEBDA database, operated at the  Institute for Astronomy of the University of Vienna ; and data products from the  AAVSO Photometric All-Sky Survey (APASS) , funded by the  Robert Martin Ayers Sciences Fund  and the  National Science Foundation  (grant  AST-1412587 ). We use observations made with the  Nordic Optical Telescope , owned in collaboration by the  University of Turku and Aarhus University , and operated jointly by  Aarhus University , the  University of Turku , and the  University of Oslo , representing  Denmark, Finland , and  Norway , the  University of Iceland , and  Stockholm University at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrofisica de Canarias.
e. from the same locations, and low correlation for negative pairs. The resulting approach directly learns from coregistered RGB-lidar data without any additional annotation. Additionally, to assess the validity of the proposed method, an experimental setup is designed to relocalize elevation patches in RGB data using the IEEE GRSS Data Fusion Contest 2018 dataset  (Xu et al. , 2019). The dataset contains co-located aerial images and aerial lidar scans, rasterized into elevation models and split into geographically separated training and testing data. Although elevation model is built from aerial lidar (a), it is also closely related to autonomous driving lidar (b) in the sense that elevation maps are commonly produced for automotive trajectory planning and detection. Our contributions are thus as follow: 1.
We propose a contrastive approach inspired from selfsupervision models for cross-modality patch registration in large images; 2. We define, based on the DFC 2018 dataset, a 3D patch retrieval in RGB aerial images experimental setup; 3. We quantitatively and qualitatively assess that such a learning scheme can learn correlated features for crossmodal localization. The paper is structured as following: Section 2 is dedicated to previous and related work, Section 3 describes the network architecture, Section 4 presents the dataset and Section 5 exposes the experimental results.
, 2011; Chu et al. , 2015; Kim and Walter, 2017; Workman and Jacobs, 2015). It resolves the challenges of viewpoint variations and appearance variations that occur in ground-based imagery  (Kim and Walter, 2017). Lin et al. (2015)  are the first to propose a deep learning method for cross-view geo-localization using Siamese CNN. Their Where-CNN model shows a significant improvement over the handcrafted features. Workman et al. (2015)  match the query ground images against an aerial image database, represented by features extracted from a pre-trained model  (Workman and Jacobs, 2015). Another ground to overhead image matching explores different deep learning approaches for matching and retrieval tasks  (Vo and Hays, 2016). The authors propose a new loss function and incorporate rotation invariance and orientation regression during training.
Localization Workflow Our approach, Cross-Modal Contrastive learning for localization of Earth Observation data (CroCo), uses a two-stage visual place framework. Mapping step. We first create a feature map of the target, large-scale, RGB data. This map will be then used as a prior map for location queries. The features are computed using a convolutional neural network which takes as input RGB patches generated with a sliding window over the original RGB image. Let ϕ RGB be the transfer function of the RGB network and I RGB be the set of patches created with the sliding window, then: Z RGB = z i RGB = ϕ RGB x i RGB x i RGB ∈ I RGB (1) is the set of feature vectors that will be used for localization. Localization step. The second stage aims at localizing the source 3D image patch in the target RGB image.
DATASET To the best of our knowledge, the proposed task, 3D data relocalization in large-scale images, has not been tackled before. Therefore, no dedicated pre-existing dataset could be found. In order to qualitatively and quantitatively assess the performances of CroCo, we repurpose the dataset GRSS DFC 2018 4. As RGB imagery is provided in very high resolution (5cm GSD), we first need to align optical and elevation data. Objective tasks. In order to correctly assess the performance of localization in a realistic scenario, we consider that localization is good if we retrieve the location with a given maximal error. In this approach, we tackle this objective as a classification problem. The RGB images are split in a collection of patches using a sliding window of 2m (resp. 4m). The task is then given a 3D patch, find its corresponding patch in the image collection.
EXPERIMENTS In this section, we evaluate how a contrastive learning framework, CroCo, can generate consistent features from RGB and DEM image pairs. For the two variants of the dataset, 2m and 4m localization, we study the factors that may impact the performance of CroCo: ground sample distance (Sec 5. 1), patch dimension (sec 5. 2) and batch size (sec 5. 3). The first two factors determine the informative detail of the input imagery, while the last is well-known for its impact in SimCLR  (Chen et al. , 2020). All these ablations are done on the validation set, as defined in the dataset section. Finally, given the set of parameters, we evaluate our model on the test set.
Discussion and perspectives Training data. As aforementioned, the size of the training dataset is of importance. We followed the same procedure to generate the training set as we did for generating the evaluation set. We use the NT-Xent loss  (Chen et al. , 2020)  originating from self-supervised literature. It appears that the size of the training set along with the possible data augmentation known as critical for self-supervised learning, shall also be investigated. It is a promising perspective to deeply investigate how to enhance our training set in order to boost the performances. Using time and context in the analysis. We tackle the relocalization problem as a static one patch problem. In practice, acquisitions are often sequential and using the location of the previous images can help enhancing the precision of the localization and discarding the false positives.