1,438 research outputs found
Origins of Extragalactic Cosmic Ray Nuclei by Contracting Alignment Patterns induced in the Galactic Magnetic Field
We present a novel approach to search for origins of ultra-high energy cosmic
rays. These particles are likely nuclei that initiate extensive air showers in
the Earth's atmosphere. In large-area observatories, the particle arrival
directions are measured together with their energies and the atmospheric depth
at which their showers maximize. The depths provide rough measures of the
nuclear charges. In a simultaneous fit to all observed cosmic rays we use the
galactic magnetic field as a mass spectrometer and adapt the nuclear charges
such that their extragalactic arrival directions are concentrated in as few
directions as possible. Using different simulated examples we show that, with
the measurements on Earth, reconstruction of extragalactic source directions is
possible. In particular, we show in an astrophysical scenario that source
directions can be reconstructed even within a substantial isotropic background.Comment: 14 pages, 15 figure
Learning 3D Shape Completion under Weak Supervision
We address the problem of 3D shape completion from sparse and noisy point
clouds, a fundamental problem in computer vision and robotics. Recent
approaches are either data-driven or learning-based: Data-driven approaches
rely on a shape model whose parameters are optimized to fit the observations;
Learning-based approaches, in contrast, avoid the expensive optimization step
by learning to directly predict complete shapes from incomplete observations in
a fully-supervised setting. However, full supervision is often not available in
practice. In this work, we propose a weakly-supervised learning-based approach
to 3D shape completion which neither requires slow optimization nor direct
supervision. While we also learn a shape prior on synthetic data, we amortize,
i.e., learn, maximum likelihood fitting using deep neural networks resulting in
efficient shape completion without sacrificing accuracy. On synthetic
benchmarks based on ShapeNet and ModelNet as well as on real robotics data from
KITTI and Kinect, we demonstrate that the proposed amortized maximum likelihood
approach is able to compete with recent fully supervised baselines and
outperforms data-driven approaches, while requiring less supervision and being
significantly faster
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