3,647 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

    Leptons from Dark Matter Annihilation in Milky Way Subhalos

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    Numerical simulations of dark matter collapse and structure formation show that in addition to a large halo surrounding the baryonic component of our galaxy, there also exists a significant number of subhalos that extend hundreds of kiloparsecs beyond the edge of the observable Milky Way. We find that for dark matter (DM) annihilation models, galactic subhalos can significantly modify the spectrum of electrons and positrons as measured at our galactic position. Using data from the recent Via Lactea II simulation we include the subhalo contribution of electrons and positrons as boundary source terms for simulations of high energy cosmic ray propagation with a modified version of the publicly available GALPROP code. Focusing on the DM DM -> 4e annihilation channel, we show that including subhalos leads to a better fit to both the Fermi and PAMELA data. The best fit gives a dark matter particle mass of 1.2 TeV, for boost factors of 90 in the main halo and 1950-3800 in the subhalos (depending on assumptions about the background), in contrast to the 0.85 TeV mass that gives the best fit in the main halo-only scenario. These fits suggest that at least a third of the observed electron cosmic rays from DM annihilation could come from subhalos, opening up the possibility of a relaxation of recent stringent constraints from inverse Compton gamma rays originating from the high-energy leptons.Comment: 8 pages, 13 figures; added referenc

    Chimpanzee helping is real, not a byproduct

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    Are focus and givenness prosodically marked in Kinyarwanda and Rwandan English?

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    This paper concentrates on whether systematic variations in pitch, intensity, and duration can be observed as a function of the focused or discourse-given status of a constituent in Kinyarwanda (Guthrie code JD.61), and a relatively recent variety of “New English” in contact with this Bantu language. Kinyarwanda is a tone language, in which the information-structural notion of focus has been reported to be expressed through changes in word order, with focus appearing clause-finally (Kimenyi 1988, Ndayiragije 1999, Ngoboka 2016). In contrast, Standard English is well-known for the prosodic boost associated with narrowly focused words and the prosodic reduction of post-focal items. Crosslinguistically, the prosodic expression of focus and givenness is progressively being considered a marked feature. Zerbian (2015a) predicts that it should not be found in a second language or a contact variety if it is not already present in the first language of a speaker or a group of speakers. Our study finds no evidence that information focus, exhaustive focus, or givenness systematically affect the prosody of Kinyarwanda. We also find no systematic effect of information structure in the variety of English spoken by our Rwandan participants, confirming that this is probably an area of English that is difficult to acquire
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