dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The world is undergoing major changes or technological developments with the advent of computer "things", first on the Internet and then in Cloud Computing (CC). Computer networks are no longer isolated, they are opening up to the great world of Internet, which now connects not only computers but also a number of smart and miniaturized objects of our daily life such as embedded electronic chips, sensors, actuators, and smart software, for providing pervasive information access. These objects can be mobile, wirelessly connected and dynamically reconfigurable. Moreover, the considered smart devices are capable in collecting, storing, transferring and processing data from the environment, without discontinuity between the virtual and the physical worlds  [1] [2] [3]. This is known as the Internet of Things (IoT), which has been defined by the International Telecommunication Union (ITU) as a network that enables connectivity in any place and any time for any connected whatever devices.
Hence, the need to merge the Cloud and IoT technologies emerge. As a result, the concept of CoT was born  [29, 30]. The emergence of the new concepts of CoT appears in recent years: Sensing-as-a-Service, Video-Monitoring-as-a-Service, Database-as-aservice, Identity and policy management as a service, Big Data Analytics-as-a-Service, Dataas-a-Service, Sensor-as-a-Service, etc.
Architecture of CoT The CoT is an opportunity that represents the ongoing trend for the next generation applications of IoT smart services  [30]. The IoT objects generate a big amount of data that will be processed and analyzed in the Cloud in order to produce important information. That information can be very sensitive, they are used by many smart services and/or applications. Therefore, to achieve the objective of efficiently managing the large amount of generated data, the existing cloud architecture needs to be reinforced. This enhancement is necessary to be more efficient and practical for the IoT based real-time services in terms of energy consumption, security, privacy, and end-to-end delays. To overcome these two last problems, cloud architectures are migrating to distributed architectures closer to the network edge like it is the case for Cloudlets, micro-cloud and fog nodes.
With CoT, the data transfer from the real world towards the Cloud is made possible. One particularly important issue that has not yet been addressed is how to provide appropriate authorization policies and rules while ensuring that only authorized users have access to the sensitive data. 2. Heterogeneity. The wide heterogeneity of devices as well as operating systems, cloud platforms and their available services, are important challenge in CoT. Due to the fact cloud services are typically embedded with proprietary interfaces, integration of resources becomes a not easy task given the customization and specificity of CoT service providers. This issue can become worse when services depend on a number of different providers. 3. Big data. With the huge amount of upcoming connected devices that are estimated to 50 billion by 2020, more specific attention must be paid to data transfer, storage, processing and access of the enormous quantity of produced data.
Therefore, IoT is among the main sources to big data, and the CC has the role of processing and storing these data. The need for scalable computing cloud platforms is real because mobile devices are ubiquitousness and sensor are pervasive and every day 2. 5 quintillion bytes of data are generated  [10, 56]. Moreover, data integrity is an important aspect because of its impact on the QoS and also because of outsourced data privacy and security  [57]. 4. Performance. CoT applications require specific performances and QoS at many levels like for data transfer, communication, computation, and storage aspects. Particularly, obtaining good network performance towards the Cloud is a great challenge because bandwidth increase does not follow computation and storage evolution  [39]. 5. Legal and social aspects.
Conventional and IoT security In this context, IoT refers to the interconnection of large heterogeneous networks of things in different communication models including human-to-human (H2H), human-to-machine (H2M), and object-to-object or machine-to-machine (M2M). Its specific end goal is to provide advanced and intelligent services to users. Connected objects such as sensors, actuators and mobile devices, monitor the environment and collect all kinds of data in real time. In an IoT model, devices equipped with sensors know how to deliver lightweight data gathered around them, allowing cloud resources to extract data from actuators, which improve communication between the nodes. As a major gathering platform for the development of constrained devices in IoT, WSNs have been widely studied a number of methods and algorithms for securing WSNs have been proposed in the literature.
The threats here are related to those different services. 1. Unidentified and unauthorized access. Services in a CoT system must ensure that unidentified and unauthorized users cannot access data, even sensitive or not. A user can also gain more privileges than normally, which allows him to access sensitive data and analysis. This may result from a problem in the authorization infrastructure  [60]. 2. Identity theft. Key pieces of personal information of a valid user can be obtained by the attacker. With this information, he can gain illegal access to restricted services and resources. The victim can lose his data and can be held accountable for the attacker's actions  [76]. 3. Service hijacking. It is a particular kind of network security attack in which the attacker takes control of a communication between two entities and masquerades as one of them.
So, there is a risk of creating a fake profile of that user and then track him without his knowledge. Unforeseen inference. An attacker can comprehensively analyze Data from different sensors using extensive computing power of the cloud. Such analysis can lead up to unforeseen inference about any user. The result of such inference is knowledge which could be exploited to invade the privacy of the user, or even worse, to create inference for future events which were not possible otherwise. Outdated or incorrect personal data Outdate or incorrect personal data have to be rectified. For example, in a medical database, the report of a patient that has been first diagnosed a specific illness, but stated later not to be afflicted. It can be very dangerous if this kind of information are not updated.
Acknowledgments The author thanks  Stephen J. Vincent  of  CASP  who kindly organized a loan of the material consisting of 45 large and 15 regular thin sections. He acknowledges the support of  Phil Salvador  who carefully revised the English text of the latest version of the manuscript. The manuscript also benefited from the constructive comments of  Daria Ivanova ,  Ioan I. Bucur ,  Anton B. Kuznetsov ,  Aral I. Okay , and  Felix Schlagintweit.
This paper presents a new Multiview Stereo Pipeline (MVS), called CARS, dedicated to satellite imagery. This pipeline is intended for massive Digital Surface Model (DSM) production and has therefore been designed to maximize scalability robustness and performance. Those two properties have driven the design of the workflow as well as the choice of algorithms and parameter trends, making our pipeline unique with respect to existing solutions in literature. This paper intends to serve as a reference paper for the pipeline implementation, and therefore provides a detailed description of algorithms and workflow. It also demonstrates the pipeline robustness and stability in several use cases, and compares its accuracy with the state-of-the-art pipelines on a reference dataset.
The Pair Preparation Step is run pair by pair and mainly produces refined epipolar resampling grids and the estimation of the disparity range. The DSM Computation Step processes the output of the Pair Preparation Step for several pairs and computes a unique DSM from them. The remaining of the paper is organized as follows. Section 2 describes algorithms used at each step. Original algorithms are extensively described, while more standard ones are only referenced, the emphasis being on how they are used. Section 3 gives the macroscopic view of the pipeline, e. g. how the algorithms are implemented and how they can be distributed on many computing nodes. Section 4 presents several use cases to demonstrate the stability and efficiency of the pipeline, as well as an accuracy assessment based on the well-known IARPA dataset  (Bosch et al.
Implementation CARS provides a command-line tool based on a Python API which provides functions to perform each step. This API makes use of XArray  (Hoyer, Hamman, 2017)  to model and exchange  data. It also makes use of the full Python stack, along with rasterio  (Gillies et al. , 2013-)  to handle reading and writing of raster files, Fiona and Shapely to read and write vector files, Orfeo ToolBox for image processing  (Grizonnet et al. , 2017) , and VLFeat for SIFT computation  (Vedaldi, Fulkerson, 2008). It is mainly written in Python, with a few parts written in C++. Dask  (Dask Development Team, 2016 ) is used to distribute the worflow on our High Performance Computing infrastructure. It allows to model dependencies as a task graph which is lazily evaluated and dynamically distributed among workers spawned on a HPC or cloud infrastructure.
Cases study In order to demonstrate that our pipeline is robust and generic, we applied it to a representative set of data, including 3 different VHR sensors, different landscapes, and pairs as well as triplets: • Pair of Worldview3 images over Buenos Aires, Argentina, of size 43 008 × 36 864 acquired on 2015. 12. 18 (from the IARPA dataset  (Bosch et al. , 2016) ). The resolution of output DSM has been set to 0. 5 meters for Pleiades cases, 0. 3 meters for the Worldview3 case and 2 meters for the SPOT7. The spatial reference system is the local UTM zone. Cases involving triplet of images have been processed by forming two pairs sharing the middle view. Those pairs were independently processed by the pair preparation step, and then merged during the DSM computation step. For all experiments we used SRTM at 30 meters resolution as the initial low resolution DTM, except from the SPOT7 Alps cases, were we used SRTM at 90 meters because of the poor quality of the 30 meters version over the area.
Accuracy assessment on IARPA challenge data subset In order to assess the performance of the proposed pipeline, we selected a subset of the well-known IARPA dataset. We selected the six Worldview3 images acquired on 18th December 2015 on the same track. It is noteworthy that since the proposed pipeline does not perform any bundle adjustment, we used an in-house tool to bundle adjust the images beforehand, without ground control points. We then paired one of the center view (4th image in the sequence) with the five remaining ones, forming five pairs of images, and ran the pair preparation step at full image scale for each pair. Note that pair (4, 3) has been discarded since it exhibits strong oscillations of line of sights, along with a narrow viewing angle (α = 2. 49m/p). Finally, we ran the compute DSM step with the four pairs simultaneously processed and raterised.
CONCLUSION While still in an early and active development state at CNES, CARS already achieves most of the goals it has been designed for: distributed computing on many nodes, stability and robustness enforced by the anticipation of error-prone steps in the pair preparation phase, and ability to produce full scale collections of images from the latest VHR sensors. In this paper, we also detailed our original algorithm for epipolar geometry estimation and refinement, which includes an initial low resolution DTM and allows to greatly reduce the disparity range to explore. Finally, we demonstrated that accuracy is comparable to other state-of-the-art pipelines on using images from the IARPA dataset. Future work include missing features, such as outliers rejection before rasterisation, smarter rasterisation strategies and point cloud regularisation.
Acknowledgments This research has been conducted as part of the project  Labex MME-DII  ( ANR11-LBX-0023-01 ). The authors also acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-17-CE26-0001  (project  BREAKRISK ). We thank  William Kengne  for helpful discussions, and the editor as well as two anonymous referees whose suggestions greatly helped improve the readability of this paper.
Recently, the ATLAS Collaboration made public a measurement of the effective leptonic weak mixing angle using √ s = 8 TeV data collected in 2012 with the ATLAS detector  [1] , corresponding to an integrated luminosity of 20. 2 fb -1  [2]. The measurement of sin 2 θ l eff in ATLAS is inferred from the A 4 angular coefficient, one of the eight coefficients, which together with the unpolarised cross section, describe entirely the production dynamics of the Z boson in perturbative quantum chromodynamics (QCD). The measurement of the angular coefficients relies on the reconstruction of the angular distributions of the leptons in the rest frame of the Z boson. A fit to reference angular distributions is then performed and angular coefficients are extracted in the full phase space. The measurement is performed in three orthogonal channels that are analysed independently, and then combined, after verifying their compatibility: two central electrons (eeCC), two central muons (µ µCC), one central and one forward electron (eeCF).
These data show a weaker suppression of strangeness compared to the u-, d-quark sea densities in the PDF, which motivates the choice of the CT10nnlo PDF set for the baseline model. The other PDF sets which show a reasonable agreement with the data, MMHT14nnlo and CT14nnlo, are used to assess the uncertainties. The modelling of the A i coefficients is validated by comparing with the 8 TeV measurement of the angular coefficients in Z-boson decays  [20]. The accuracy of the Z data is propagated as an uncertainty in the W -boson mass. Good agreement between the measurements and the NNLO predictions is observed for the relevant angular coefficients, except for A 2 , for which the full discrepancy is taken as an uncertainty. The W -boson transverse momentum at a given rapidity is modelled with the Pythia 8 PS generator.
In one week of running to collect 200 pb -1 , i. e about 2 × 10 6 W -boson candidates at √ s = 14 TeV and about 3 × factor of two is obtained with the HL-LHC PDF sets. A 2 MeV PDF uncertainty is reached with LHeC PDF sets, a further factor of ∼ 4 improvement. A total (stat+PDF) uncertainty of about 11 MeV is obtained with 200 pb -1 of data with the current PDF sets.
For PLS computation, the function plsr() from the R-package pls has been applied. 25 Autoscaled x-data have been used because of a better model performance than with using sum 100 normalized data. The rdCV parameters were three segments in the outer CV loop (test set split), five segments in the inner CV loop (optimization of A, the number of PLS components), and 100 repetitions. The resulting optimum model complexity, A OPT , is the most frequent value obtained for A. A final PLS model with A OPT components using all objects has been calculated, and the resulting regression coefficients (standardized because of autoscaled x-data) used as a measure for the variable importance. K-nearest neighbor classification was performed with sum 100 normalized x-data, using the Euclidean distance and applying the function knn() from the R-package class.
KNN was combined with rdCV  24  for a separate estimation of the optimum number of neighbors, k OPT , and of the model performance, together with their variabilities for varying random splits into calibration and test sets. The rdCV parameters were the same as used for DPLS. RF classification is based on repeatedly created decision trees (the forest) applying the method CART (classification and regression trees) and using randomly selected subsets of the variables. RF calculations were done with the function randomForest() from the R-package randomForest,  26  using sum 100 normalized x-data. A rCV was applied with three segments (two for the training sets and one for the test sets), 100 repetitions, and 500 trees per training. The function used for RF provides a measure for the variable importance for class separation, namely, the mean decreasing accuracy (MDA).
Acknowledgements We acknowledge the financial support of the  Cross-Disciplinary Program on Instrumentation and Detection of CEA , the  French Alternative Energies and Atomic Energy Commission. This work has been partially funded by the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework of " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  French National Research Agency (ANR). I. Ch. Avetissov  was supported by the project of the  Ministry of Science and High Education of the Russian Federation  (the grant  RFMEFI57418X0186 ). The work of  F. A. Danevich  and  V. I. Tretyak  was supported in part by the  IDEATE International Associated Laboratory (LIA)  and by the program of the  National Academy of Sciences of Ukraine Fundamental  research on high-energy and nuclear physics (International cooperation).
Accounting for the 1-halo trispectrum term on top of SSC, as has been done in some current analyses, is not sufficient for capturing the full non-Gaussian impact: braiding and the rest of in-survey covariance have to be accounted for. Finally, I discuss why the inclusion of non-Gaussianity generally eases up parameter degeneracies, making cosmological constraints more robust for astrophysical uncertainties. I release publicly the data and a Python notebook reproducing the results and plots of the article at https://github. com/fabienlacasa/BraidingArticle.
4. 2) and for halo occupation distribution (HOD, Sect. 4. 3). Finally, I discuss the results in Sect. 5 and, in particular, I consider how parameter degeneracies are generally eased up by the inclusion of non-Gaussianity. At https://github. com/fabienlacasa/ BraidingArticle the data and a Python notebook that allows to reproduce all plots and results of the article are available, along with a bit more information.
ACKNOWLEDGEMENTS This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958. AS is supported by the  ERC  through a  CoG grant. AL acknowledges support by the  Programme National des Hautes Energies (France). AP acknowledges support by the  Centre National d'Etudes Spatiales. The authors acknowledge the LISA Data Challenge Team that developed the core part of the code used for parameter estimations.
m. ) frame is determined event by event by fully reconstructing the other B meson. The branching fractions for the two-body decays B AE → X cc K AE can thus be measured independently of any a priori knowledge of the X cc decay properties. We use a data sample with an integrated luminosity of 424 fb -1  [14] , collected with the BABAR detector at the PEP-II storage ring, at a c. m. energy corresponding to the ϒð4SÞ mass. Charged tracks are reconstructed with a fivelayer silicon vertex tracker (SVT) and a 40-layer drift chamber (DCH), located in a 1. 5 T magnetic field generated by a superconducting solenoidal magnet. The energies of photons and electrons are measured with a CsI(Tl) electromagnetic calorimeter. Charged hadron identification is performed using ionization measurements in the SVT and DCH and using a ring-imaging Čerenkov detector.
1 and above 1. 9 GeV=c. Because the use of only these two regions leads to large uncertainty in the background parameters, we add data points in the two regions 1. 34-1. 36 and 1. 53-1. 57 GeV=c, where there is no peak, as indicated in Fig. 2. Figure  2  also shows the fit to the simulated signal K AE momentum spectrum for all charmonia peaks in the simulation. A good description is obtained when using, for each peak, a narrow Gaussian, whose width depends on momentum varying from 13 MeV=c for the J=ψ to 9 MeV=c for the ψ 0 , and a two-piece Gaussian, 100 MeV=c wide on the left and 60 MeV=c wide on the right. A similar fit is performed for the Xð3872Þ with a dedicated Monte Carlo (MC) sample (Fig. 3 ). The narrow Gaussian width is measured to be 7 MeV=c and the wide Gaussian tails are 47 MeV=c on each side.
2019/10151-2 , No. 2010/07359-6  and No. 1999/05404-3 ;  Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC) ;  Czech Republic -Grant  No. MSMT CR LTT18004 ,  LM2015038 ,  LM2018102 ,  CZ. 02. 1. 01/0. 0/0. 0/16013/0001402 ,  CZ. 02. 1. 01/0. 0/0. 0/18046/0016010  and  CZ. 02. 1. 01/0. 0/0. 0/17049/0008422 ;  France -Centre de Calcul IN2P3/CNRS ;  Centre National de la Recherche Scientifique (CNRS) ;  Conseil Régional Ile-de-France ;  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS );  Département Sciences de l'Univers (SDU-INSU/CNRS) ;  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63  within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ;  Deutsche Forschungsgemeinschaft (DFG) ;  Finanzministerium Baden-Württemberg ;  Helmholtz Alliance for Astroparticle Physics (HAP) ;  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ;  Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen ;  Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg; Italy -Istituto Nazionale di Fisica Nucleare (INFN) ;  Istituto Nazionale di Astrofisica (INAF) ;  Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ;  CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE) ;  México -Consejo Nacional de Ciencia y Tecnología (CONACYT)  No.
Signal process The double parton scattering process producing two positively charged W bosons decaying in the muon channel is obtained using the generator Herwig 7. 1. 2 [77, 78] , which is fully capable of generating double W events. We utilized the possibility of accessing the information about the outgoing leptons directly from the matrix element (i. e. at PL) before corrections are applied on their momenta. Our method for preparing the hadron level event datasets is based on a re-weighting procedure, as shortly explained below. As a result, we obtain a perfect correspondence between the theoretical PL results of Section 4 and the PL results generated by the reweighted Herwig for all the correlation scenarios. (a) 1 η 3 - 2 - 1 - 0 1 2 3 [fb] The ratio R min-corr shows the comparison of different models to the min-corr scenario.
Acknowledgements We thank  Piet J. Mulders  and  Jonathan Gaunt  for useful discussions. SC acknowledges support from he  Agence Nationale de la Recherche  under the Projects No. ANR-18-ERC1-0002  and No. ANR-16-CE31-0019  and the  European Research Council (ERC)  under the program QWORK (contract no. 320389 ). TK acknowledges support from the  Alexander von Humboldt Foundation. MM acknowledges support from the grant  LTC17038  of the INTER- EXCELLENCE program  at the  Ministry of Education, Youth and Sports of the Czech Republic.
Acknowledgements. This work was partially funded by the  IPEV  project on the high latitude polarization of the auroral emissions  POLARLIS  (project number  1026 ). It was also funded by the  CNRS   National solar-terrestrial physics program  (PNST). We thank  Patrick Guio  for helpfull discussions on this topics. We also thank  Pr. Mourad Djebli  for having made this collaboration possible. The editor thanks  Joseph Lemaire  and an anonymous reviewer for their assistance in evaluating this paper.