914 research outputs found
Abelian Magnetic Monopoles and Topologically Massive Vector Bosons in Scalar-Tensor Gravity with Torsion Potential
A Lagrangian formulation describing the electromagnetic interaction -
mediated by topologically massive vector bosons - between charged, spin-(1/2)
fermions with an abelian magnetic monopole in a curved spacetime with
non-minimal coupling and torsion potential is presented. The covariant field
equations are obtained. The issue of coexistence of massive photons and
magnetic monopoles is addressed in the present framework. It is found that
despite the topological nature of photon mass generation in curved spacetime
with isotropic dilaton field, the classical field theory describing the
nonrelativistic electromagnetic interaction between a point-like electric
charge and magnetic monopole is inconsistent.Comment: 18 pages, no figure
Engineering study to determine feasible methods of simulating planetary albedo and radiation effects upon the thermal balance of spacecraft Final report
Planetary radiation and albedo effects on thermal balance of spacecraft orbiting Mars and Venu
Fast online computation of the Qn estimator with applications to the detection of outliers in data streams
We present FQN (Fast Qn), a novel algorithm for online computation of the Qn scale estimator. The algorithm works in the sliding window model, cleverly computing the Qn scale estimator in the current window. We thoroughly compare our algorithm for online Qn with the state of the art competing algorithm by Nunkesser et al., and show that FQN (i) is faster, requiring only O(s) time in the worst case where s is the length of the window (ii) its computational complexity does not depend on the input distribution and (iii) it requires less space. To the best of our knowledge, our algorithm is the first that allows online computation of the Qn scale estimator in worst case time linear in the size of the window. As an example of a possible application, besides its use as a robust measure of statistical dispersion, we show how to use the Qn estimator for fast detection of outliers in data streams. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach
One Table to Count Them All: Parallel Frequency Estimation on Single-Board Computers
Sketches are probabilistic data structures that can provide approximate
results within mathematically proven error bounds while using orders of
magnitude less memory than traditional approaches. They are tailored for
streaming data analysis on architectures even with limited memory such as
single-board computers that are widely exploited for IoT and edge computing.
Since these devices offer multiple cores, with efficient parallel sketching
schemes, they are able to manage high volumes of data streams. However, since
their caches are relatively small, a careful parallelization is required. In
this work, we focus on the frequency estimation problem and evaluate the
performance of a high-end server, a 4-core Raspberry Pi and an 8-core Odroid.
As a sketch, we employed the widely used Count-Min Sketch. To hash the stream
in parallel and in a cache-friendly way, we applied a novel tabulation approach
and rearranged the auxiliary tables into a single one. To parallelize the
process with performance, we modified the workflow and applied a form of
buffering between hash computations and sketch updates. Today, many
single-board computers have heterogeneous processors in which slow and fast
cores are equipped together. To utilize all these cores to their full
potential, we proposed a dynamic load-balancing mechanism which significantly
increased the performance of frequency estimation.Comment: 12 pages, 4 figures, 3 algorithms, 1 table, submitted to EuroPar'1
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