32,385 research outputs found

    3D-BEVIS: Bird's-Eye-View Instance Segmentation

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    Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.Comment: camera-ready version for GCPR '1

    High energy neutrinos from magnetars

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    Magnetars can accelerate cosmic rays to high energies through the unipolar effect, and are also copious soft photon emitters. We show that young, fast-rotating magnetars whose spin and magnetic moment point in opposite directions emit high energy neutrinos from their polar caps through photomeson interactions. We identify a neutrino cut-off band in the magnetar period-magnetic field strength phase diagram, corresponding to the photomeson interaction threshold. Within uncertainties, we point out four possible neutrino emission candidates among the currently known magnetars, the brightest of which may be detectable for a chance on-beam alignment. Young magnetars in the universe would also contribute to a weak diffuse neutrino background, whose detectability is marginal, depending on the typical neutrino energy.Comment: emulateapj style, 6 pages, 1 figure, ApJ, v595, in press. Important contributions from Dr. Harding added. Major revisions made. More conservative and realistic estimates about the neutrino threshold condition and emission efficiency performed. More realistic typical beaming angle and magnetar birth rate adopte

    Simulating the Initial Stage of Phenolic Resin Carbonization via the ReaxFF Reactive Force Field

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    Pyrolysis of phenolic resins leads to carbon formation. Simulating this resin-to-carbon process atomistically is a daunting task. In this paper, we attempt to model the initial stage of this process by using the ReaxFF reactive force field, which bridges quantum mechanical and molecular mechanical methods. We run molecular dynamics simulations to examine the evolution of small molecules at different temperatures. The main small-molecule products found include H_2O, H_2, CO, and C_2H_2. We find multiple pathways leading to H_2O formation, including a frequent channel via β-H elimination, which has not been proposed before. We determine the reaction barrier for H_2O formation from the reaction rates obtained at different temperatures. We also discuss the relevance of our simulations to previous experimental observations. This work represents a first attempt to model the resin-to-carbon process atomistically

    An exactly solvable phase transition model: generalized statistics and generalized Bose-Einstein condensation

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    In this paper, we present an exactly solvable phase transition model in which the phase transition is purely statistically derived. The phase transition in this model is a generalized Bose-Einstein condensation. The exact expression of the thermodynamic quantity which can simultaneously describe both gas phase and condensed phase is solved with the help of the homogeneous Riemann-Hilbert problem, so one can judge whether there exists a phase transition and determine the phase transition point mathematically rigorously. A generalized statistics in which the maximum occupation numbers of different quantum states can take on different values is introduced, as a generalization of Bose-Einstein and Fermi-Dirac statistics.Comment: 17 pages, 2 figure
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