ForewordThe Pierre Auger Observatory has begun a major Upgrade of its already impressive capabilities, with an emphasis on improved mass composition determination using the surface detectors of the Observatory. Known as AugerPrime, the upgrade will include new 4 m 2 plastic scintillator detectors on top of all 1660 water-Cherenkov detectors, updated and more flexible surface detector electronics, a large array of buried muon detectors, and an extended duty cycle for operations of the fluorescence detectors.This Preliminary Design Report was produced by the Collaboration in April 2015 as an internal document and information for funding agencies. It outlines the scientific and technical case for AugerPrime 1 . We now release it to the public via the arXiv server. We invite you to review the large number of fundamental results already achieved by the Observatory and our plans for the future.The Pierre Auger Collaboration 1 As a result of continuing R&D, slight changes have been implemented in the baseline design since this Report was written. These changes will be documented in a forthcoming Technical Design Report. ix x Executive Summary Present Results from the Pierre Auger ObservatoryMeasurements of the Auger Observatory have dramatically advanced our understanding of ultra-high energy cosmic rays. The suppression of the flux around 5×10 19 eV is now confirmed without any doubt. Strong limits have been placed on the photon and neutrino components of the flux indicating that "top-down" source processes, such as the decay of superheavy particles, cannot account for a significant part of the observed particle flux. A largescale dipole anisotropy of ∼7% amplitude has been found for energies above 8×10 18 eV. In addition there is also an indication of the presence of a large scale anisotropy below the ankle. Particularly exciting is the observed behavior of the depth of shower maximum with energy, which changes in an unexpected, non-trivial way. Around 3×10 18 eV it shows a distinct change of slope with energy, and the shower-to-shower variance decreases. Interpreted with the leading LHC-tuned shower models, this implies a gradual shift to a heavier composition. A number of fundamentally different astrophysical model scenarios have been developed to describe this evolution. The high degree of isotropy observed in numerous tests of the small-scale angular distribution of UHECR above 4×10 19 eV is remarkable, challenging original expectations that assumed only a few cosmic ray sources with a light composition at the highest energies. Interestingly, the largest departures from isotropy are observed for cosmic rays with E > 5.8×10 19 eV in ∼20 • sky-windows. Due to a duty cycle of ∼15% of the fluorescence telescopes, the data on the depth of shower maximum extend only up to the flux suppression region, i.e. 4×10 19 eV. Obtaining more information on the composition of cosmic rays at higher energies will provide crucial means to discriminate between the model classes and to understand the origin of the observed flux suppre...

No abstract

Abstract. Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

he Pierre Auger Observatory, located on a vast, high plain in western\ud Argentina, is the world's largest cosmic ray observatory. The objectives\ud of the Observatory are to probe the origin and characteristics of cosmic\ud rays above 10(17) eV and to study the interactions of these, the most\ud energetic particles observed in nature. The Auger design features an\ud array of 1660 water Cherenkov particle detector stations spread over\ud 3000 km(2) overlooked by 24 air fluorescence telescopes. In addition,\ud three high elevation fluorescence telescopes overlook a 23.5 km(2),\ud 61-detector infilled array with 750 in spacing. The Observatory has been\ud in successful operation since completion in 2008 and has recorded data\ud from an exposure exceeding 40,000 km(2) sr yr. This paper describes the\ud design and performance of the detectors, related subsystems and\ud infrastructure that make up the Observatory

Laser scanners are increasingly used to create semantically rich 3D models of buildings for civil engineering applications such as planning renovations, space usage planning, and building maintenance. Currently these models are created manually -a time-consuming and error-prone process. This paper presents a method to automatically convert the raw 3D point data from a laser scanner positioned at multiple locations throughout a building into a compact, semantically rich model. Our algorithm is capable of identifying and modeling the main structural components of an indoor environment (walls, floors, ceilings, windows, and doorways) despite the presence of significant clutter and occlusion, which occur frequently in natural indoor environments. Our method begins by extracting planar patches from a voxelized version of the input point cloud. We use a conditional random field model to learn contextual relationships between patches and use this knowledge to automatically label patches as walls, ceilings, or floors. Then, we perform a detailed analysis of the recognized surfaces to locate windows and doorways. This process uses visibility reasoning to fuse measurements from different scan locations and to identify occluded regions and holes in the surface. Next, we use a learning algorithm to intelligently estimate the shape of window and doorway openings even when partially occluded. Finally, occluded regions on the surfaces are filled in using a 3D inpainting algorithm. We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms yielding promising results.