33,526 research outputs found
The Dynamic Phase Transition for Decoding Algorithms
The state-of-the-art error correcting codes are based on large random
constructions (random graphs, random permutations, ...) and are decoded by
linear-time iterative algorithms. Because of these features, they are
remarkable examples of diluted mean-field spin glasses, both from the static
and from the dynamic points of view. We analyze the behavior of decoding
algorithms using the mapping onto statistical-physics models. This allows to
understand the intrinsic (i.e. algorithm independent) features of this
behavior.Comment: 40 pages, 29 eps figure
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Plan Selection in Medicare Part D: Evidence from administrative Data
We study the Medicare Part D prescription drug insurance program as a bellwether for designs of private, non-mandatory health insurance markets, focusing on the ability of consumers to evaluate and optimize their choices of plans. Our analysis of administrative data on medical claims in Medicare Part D suggests that less than 10 percent of individuals enroll in plans that are ex post optimal with respect to total cost (premiums and co-payments). Relative to the benchmark of a static decision rule, similar to the Plan Finder provided by the Medicare administration, that conditions next year’s plan choice only on the drugs consumed in the current year, enrollees lost on average about $300 per year. These numbers are hard to reconcile with decision costs alone; it appears that unless a sizeable fraction of consumers value plan features other than cost, they are not optimizing effectively
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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