2,118 research outputs found
Asymptotically optimal declustering schemes for 2-dim range queries
AbstractDeclustering techniques have been widely adopted in parallel storage systems (e.g. disk arrays) to speed up bulk retrieval of multidimensional data. A declustering scheme distributes data items among multiple disks, thus enabling parallel data access and reducing query response time. We measure the performance of any declustering scheme as its worst case additive deviation from the ideal scheme. The goal thus is to design declustering schemes with as small an additive error as possible. We describe a number of declustering schemes with additive error O(logM) for 2-dimensional range queries, where M is the number of disks. These are the first results giving O(logM) upper bound for all values of M. Our second result is a lower bound on the additive error. It is known that except for a few stringent cases, additive error of any 2-dimensional declustering scheme is at least one. We strengthen this lower bound to Ω((logM)(d−1/2)) for d-dimensional schemes and to Ω(logM) for 2-dimensional schemes, thus proving that the 2-dimensional schemes described in this paper are (asymptotically) optimal. These results are obtained by establishing a connection to geometric discrepancy. We also present simulation results to evaluate the performance of these schemes in practice
Analysis of Power-aware Buffering Schemes in Wireless Sensor Networks
We study the power-aware buffering problem in battery-powered sensor
networks, focusing on the fixed-size and fixed-interval buffering schemes. The
main motivation is to address the yet poorly understood size variation-induced
effect on power-aware buffering schemes. Our theoretical analysis elucidates
the fundamental differences between the fixed-size and fixed-interval buffering
schemes in the presence of data size variation. It shows that data size
variation has detrimental effects on the power expenditure of the fixed-size
buffering in general, and reveals that the size variation induced effects can
be either mitigated by a positive skewness or promoted by a negative skewness
in size distribution. By contrast, the fixed-interval buffering scheme has an
obvious advantage of being eminently immune to the data-size variation. Hence
the fixed-interval buffering scheme is a risk-averse strategy for its
robustness in a variety of operational environments. In addition, based on the
fixed-interval buffering scheme, we establish the power consumption
relationship between child nodes and parent node in a static data collection
tree, and give an in-depth analysis of the impact of child bandwidth
distribution on parent's power consumption.
This study is of practical significance: it sheds new light on the
relationship among power consumption of buffering schemes, power parameters of
radio module and memory bank, data arrival rate and data size variation,
thereby providing well-informed guidance in determining an optimal buffer size
(interval) to maximize the operational lifespan of sensor networks
Reconstruction of plasma density profiles by measuring spectra of radiation emitted from oscillating plasma dipoles
We suggest a new method for characterising non-uniform density distributions of plasma by measuring the spectra of radiation emitted from a localised plasma dipole oscillator excited by colliding electromagnetic pulses. The density distribution can be determined by scanning the collision point in space. Two-dimensional particle-in-cell simulations demonstrate the reconstruction of linear and nonlinear density profiles corresponding to laser-produced plasma. The method can be applied to a wide range of plasma, including fusion and low temperature plasmas. It overcomes many of the disadvantages of existing methods that only yield average densities along the path of probe pulses, such as interferometry and spectroscopy
Westerbork Ultra-Deep Survey of HI at z=0.2
In this contribution, we present some preliminary observational results from
the completed ultra-deep survey of 21cm emission from neutral hydrogen at
redshifts z=0.164-0.224 with the Westerbork Synthesis Radio Telescope. In two
separate fields, a total of 160 individual galaxies has been detected in
neutral hydrogen, with HI masses varying from 1.1x10^9 to 4.0x10^10 Msun. The
largest galaxies are spatially resolved by the synthesized beam of 23x37
arcsec^2 while the velocity resolution of 19 km/s allowed the HI emission lines
to be well resolved. The large scale structure in the surveyed volume is traced
well in HI, apart from the highest density regions like the cores of galaxy
clusters. All significant HI detections have obvious or plausible optical
counterparts which are usually blue late-type galaxies that are UV-bright. One
of the observed fields contains a massive Butcher-Oemler cluster but none of
the associated blue galaxies has been detected in HI. The data suggest that the
lower-luminosity galaxies at z=0.2 are more gas-rich than galaxies of similar
luminosities at z=0, pending a careful analysis of the completeness near the
detection limit. Optical counterparts of the HI detected galaxies are mostly
located in the 'blue cloud' of the galaxy population although several galaxies
on the 'red sequence' are also detected in HI. These results hold great promise
for future deep 21cm surveys of neutral hydrogen with MeerKAT, APERTIF, ASKAP,
and ultimately the Square Kilometre Array.Comment: 10 pages, 9 figures, Proceedings of ISKAF2010 Science Meeting: A New
Golden Age for Radio Astronomy, June 10-14 2010, Assen, the Netherlands.
Edited by J. van Leeuwen. Movies of rendered rotating data cubes are
available at http://www.astro.rug.nl/~verheyen/BUDHIES/index.htm
Parallel symbolic state-space exploration is difficult, but what is the alternative?
State-space exploration is an essential step in many modeling and analysis
problems. Its goal is to find the states reachable from the initial state of a
discrete-state model described. The state space can used to answer important
questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a
starting point for sophisticated investigations expressed in temporal logic.
Unfortunately, the state space is often so large that ordinary explicit data
structures and sequential algorithms cannot cope, prompting the exploration of
(1) parallel approaches using multiple processors, from simple workstation
networks to shared-memory supercomputers, to satisfy large memory and runtime
requirements and (2) symbolic approaches using decision diagrams to encode the
large structured sets and relations manipulated during state-space generation.
Both approaches have merits and limitations. Parallel explicit state-space
generation is challenging, but almost linear speedup can be achieved; however,
the analysis is ultimately limited by the memory and processors available.
Symbolic methods are a heuristic that can efficiently encode many, but not all,
functions over a structured and exponentially large domain; here the pitfalls
are subtler: their performance varies widely depending on the class of decision
diagram chosen, the state variable order, and obscure algorithmic parameters.
As symbolic approaches are often much more efficient than explicit ones for
many practical models, we argue for the need to parallelize symbolic
state-space generation algorithms, so that we can realize the advantage of both
approaches. This is a challenging endeavor, as the most efficient symbolic
algorithm, Saturation, is inherently sequential. We conclude by discussing
challenges, efforts, and promising directions toward this goal
Toward Rigorous Telecoupling Causal Attribution: A Systematic Review and Typology
Telecoupled flows of people, organisms, goods, information, and energy are expanding across the globe. Causes are integral components of the telecoupling framework, yet the rigor with which they have been identified and evaluated to date is unknown. We address this knowledge gap by systematically reviewing causal attribution in the telecoupling literature (n = 89 studies) and developing a standardized causal terminology and typology for consistent use in telecoupling research. Causes are defined based on six criteria: sector (e.g., environmental, economic), system of origin (i.e., sending, receiving, spillover), agent, distance, response time (i.e., time lapse between cause and effect), and direction (i.e., producing positive or negative effects). Using case studies from the telecoupling literature, we demonstrate the need to enhance the rigor of telecoupling causal attribution by combining qualitative and quantitative methods via process-tracing, counterfactual analysis, and related approaches. Rigorous qualitative-quantitative causal attribution is critical for accurately assessing the social-ecological causes and consequences of telecouplings and thereby identifying leverage points for informed management and governance of telecoupled systems.ISSN:2071-105
Replica theory for learning curves for Gaussian processes on random graphs
Statistical physics approaches can be used to derive accurate predictions for
the performance of inference methods learning from potentially noisy data, as
quantified by the learning curve defined as the average error versus number of
training examples. We analyse a challenging problem in the area of
non-parametric inference where an effectively infinite number of parameters has
to be learned, specifically Gaussian process regression. When the inputs are
vertices on a random graph and the outputs noisy function values, we show that
replica techniques can be used to obtain exact performance predictions in the
limit of large graphs. The covariance of the Gaussian process prior is defined
by a random walk kernel, the discrete analogue of squared exponential kernels
on continuous spaces. Conventionally this kernel is normalised only globally,
so that the prior variance can differ between vertices; as a more principled
alternative we consider local normalisation, where the prior variance is
uniform
- …