20,904 research outputs found
Initial-boundary value problems for conservation laws with source terms and the Degasperis-Procesi equation
We consider conservation laws with source terms in a bounded domain with
Dirichlet boundary conditions. We first prove the existence of a strong trace
at the boundary in order to provide a simple formulation of the entropy
boundary condition. Equipped with this formulation, we go on to establish the
well-posedness of entropy solutions to the initial-boundary value problem. The
proof utilizes the kinetic formulation and the compensated compactness method.
Finally, we make use of these results to demonstrate the well-posedness in a
class of discontinuous solutions to the initial-boundary value problem for the
Degasperis-Procesi shallow water equation, which is a third order nonlinear
dispersive equation that can be rewritten in the form of a nonlinear
conservation law with a nonlocal source term.Comment: 24 page
Towards Distributed Convoy Pattern Mining
Mining movement data to reveal interesting behavioral patterns has gained
attention in recent years. One such pattern is the convoy pattern which
consists of at least m objects moving together for at least k consecutive time
instants where m and k are user-defined parameters. Existing algorithms for
detecting convoy patterns, however do not scale to real-life dataset sizes.
Therefore a distributed algorithm for convoy mining is inevitable. In this
paper, we discuss the problem of convoy mining and analyze different data
partitioning strategies to pave the way for a generic distributed convoy
pattern mining algorithm.Comment: SIGSPATIAL'15 November 03-06, 2015, Bellevue, WA, US
Relation between Kitaev magnetism and structure in -RuCl
Raman scattering has been employed to investigate lattice and magnetic
excitations of the honeycomb Kitaev material -RuCl and its
Heisenberg counterpart CrCl. Our phonon Raman spectra give evidence for a
first-order structural transition from a monoclinic to a rhombohedral structure
for both compounds. Significantly, only -RuCl features a large
thermal hysteresis, consistent with the formation of a wide phase of
coexistence. In the related temperature interval of K, we observe a
hysteretic behavior of magnetic excitations as well. The stronger magnetic
response in the rhombohedral compared to the monoclinic phase evidences a
coupling between the crystallographic structure and low-energy magnetic
response. Our results demonstrate that the Kitaev magnetism concomitant with
fractionalized excitations is susceptible to small variations of bonding
geometry.Comment: 9 pages, 8 figures, To appear in PR
GOLF SWING MOTION ANALYSIS: CHALLENGES AND SOLUTIONS
With advanced motion capture technologies available, the scope and depth of motion analysis now is mainly limited by the capability of the analysis software. Golf swing analysis requires advanced motion analysis methods such as detection of the true impact instant, computation of the impact conditions, definition and use of local reference frames (body and club) in various ways, and determination and use of the functional swing plane. The purpose of this paper was to identify and present solutions for the unique challenges encountered in golf swing motion analysis and to demonstrate the application of such solutions by using Kwon3D, a comprehensive motion analysis program
Electronic structures of doped anatase : (M=Co, Mn, Fe, Ni)
We have investigated electronic structures of a room temperature diluted
magnetic semiconductor : Co-doped anatase . We have obtained the
half-metallic ground state in the local-spin-density approximation(LSDA) but
the insulating ground state in the LSDA++SO incorporating the spin-orbit
interaction. In the stoichiometric case, the low spin state of Co is realized
with the substantially large orbital moment. However, in the presence of oxygen
vacancies near Co, the spin state of Co becomes intermediate. The
ferromagnetisms in the metallic and insulating phases are accounted for by the
double-exchange-like and the superexchange mechanism, respectively. Further,
the magnetic ground states are obtained for Mn and Fe doped ,
while the paramagnetic ground state for Ni-doped .Comment: 5 pages, 4 figure
Nonlinear Response of Cylindrical Shells to Underwater Explosion: Testings and Numerical Prediction Using USA/DYNA3D / June 1, 1991 - March 1, 1992
The views expressed are those of the authors and do not reflect the offical policy or position of DoD or US Government.Nonlinear 3-D Dynamic Analysis Code (VEC/DYNA3D) has been interfaced with Underwater Shock Analysis Code (USA) and capabilities were developed to perform numerical analysis of submerged and semi-submerged marine structures subjected to underwater explosion. A series of numerical analysis were performed to determine the elastic and elasto-plastic responses of cylindrica shell type structures. The results were favorably compared with those of underwater explosion testings. The coupled code USA/DYNA3D makes possible to predict shock-induced damage response of naval structure. In addition, numerical sensitivity analyses were undertaken to determine the importance of various physical and numerical modeling factors. This study showed clearly three types of response modes of cylinder subjected to a side-on explosion: accordion mode, breathing mode and whipping mode.This report was prepared for and funded by both Defense Nuclear Agency,
Alexandria, VA 20311 and Naval Postgraduate School, Monterey, CA 93943.Approved for public release; distribution is unlimited
Astronomy in the Cloud: Using MapReduce for Image Coaddition
In the coming decade, astronomical surveys of the sky will generate tens of
terabytes of images and detect hundreds of millions of sources every night. The
study of these sources will involve computation challenges such as anomaly
detection and classification, and moving object tracking. Since such studies
benefit from the highest quality data, methods such as image coaddition
(stacking) will be a critical preprocessing step prior to scientific
investigation. With a requirement that these images be analyzed on a nightly
basis to identify moving sources or transient objects, these data streams
present many computational challenges. Given the quantity of data involved, the
computational load of these problems can only be addressed by distributing the
workload over a large number of nodes. However, the high data throughput
demanded by these applications may present scalability challenges for certain
storage architectures. One scalable data-processing method that has emerged in
recent years is MapReduce, and in this paper we focus on its popular
open-source implementation called Hadoop. In the Hadoop framework, the data is
partitioned among storage attached directly to worker nodes, and the processing
workload is scheduled in parallel on the nodes that contain the required input
data. A further motivation for using Hadoop is that it allows us to exploit
cloud computing resources, e.g., Amazon's EC2. We report on our experience
implementing a scalable image-processing pipeline for the SDSS imaging database
using Hadoop. This multi-terabyte imaging dataset provides a good testbed for
algorithm development since its scope and structure approximate future surveys.
First, we describe MapReduce and how we adapted image coaddition to the
MapReduce framework. Then we describe a number of optimizations to our basic
approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table
Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution
Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images
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