98,427 research outputs found
Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime
This work examines the close interplay between cooperation and adaptation for
distributed detection schemes over fully decentralized networks. The combined
attributes of cooperation and adaptation are necessary to enable networks of
detectors to continually learn from streaming data and to continually track
drifts in the state of nature when deciding in favor of one hypothesis or
another. The results in the paper establish a fundamental scaling law for the
steady-state probabilities of miss-detection and false-alarm in the slow
adaptation regime, when the agents interact with each other according to
distributed strategies that employ small constant step-sizes. The latter are
critical to enable continuous adaptation and learning. The work establishes
three key results. First, it is shown that the output of the collaborative
process at each agent has a steady-state distribution. Second, it is shown that
this distribution is asymptotically Gaussian in the slow adaptation regime of
small step-sizes. And third, by carrying out a detailed large deviations
analysis, closed-form expressions are derived for the decaying rates of the
false-alarm and miss-detection probabilities. Interesting insights are gained.
In particular, it is verified that as the step-size decreases, the error
probabilities are driven to zero exponentially fast as functions of ,
and that the error exponents increase linearly in the number of agents. It is
also verified that the scaling laws governing errors of detection and errors of
estimation over networks behave very differently, with the former having an
exponential decay proportional to , while the latter scales linearly
with decay proportional to . It is shown that the cooperative strategy
allows each agent to reach the same detection performance, in terms of
detection error exponents, of a centralized stochastic-gradient solution.Comment: The paper will appear in IEEE Trans. Inf. Theor
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
The goal of this paper is to propose novel strategies for adaptive learning
of signals defined over graphs, which are observed over a (randomly
time-varying) subset of vertices. We recast two classical adaptive algorithms
in the graph signal processing framework, namely, the least mean squares (LMS)
and the recursive least squares (RLS) adaptive estimation strategies. For both
methods, a detailed mean-square analysis illustrates the effect of random
sampling on the adaptive reconstruction capability and the steady-state
performance. Then, several probabilistic sampling strategies are proposed to
design the sampling probability at each node in the graph, with the aim of
optimizing the tradeoff between steady-state performance, graph sampling rate,
and convergence rate of the adaptive algorithms. Finally, a distributed RLS
strategy is derived and is shown to be convergent to its centralized
counterpart. Numerical simulations carried out over both synthetic and real
data illustrate the good performance of the proposed sampling and
reconstruction strategies for (possibly distributed) adaptive learning of
signals defined over graphs.Comment: Submitted to IEEE Transactions on Signal Processing, September 201
Optimization and universality of Brownian search in quenched heterogeneous media
The kinetics of a variety of transport-controlled processes can be reduced to
the problem of determining the mean time needed to arrive at a given location
for the first time, the so called mean first passage time (MFPT) problem. The
occurrence of occasional large jumps or intermittent patterns combining various
types of motion are known to outperform the standard random walk with respect
to the MFPT, by reducing oversampling of space. Here we show that a regular but
spatially heterogeneous random walk can significantly and universally enhance
the search in any spatial dimension. In a generic minimal model we consider a
spherically symmetric system comprising two concentric regions with piece-wise
constant diffusivity. The MFPT is analyzed under the constraint of conserved
average dynamics, that is, the spatially averaged diffusivity is kept constant.
Our analytical calculations and extensive numerical simulations demonstrate the
existence of an {\em optimal heterogeneity} minimizing the MFPT to the target.
We prove that the MFPT for a random walk is completely dominated by what we
term direct trajectories towards the target and reveal a remarkable
universality of the spatially heterogeneous search with respect to target size
and system dimensionality. In contrast to intermittent strategies, which are
most profitable in low spatial dimensions, the spatially inhomogeneous search
performs best in higher dimensions. Discussing our results alongside recent
experiments on single particle tracking in living cells we argue that the
observed spatial heterogeneity may be beneficial for cellular signaling
processes.Comment: 19 pages, 11 figures, RevTe
Energy-Efficient Resource Management in Ultra Dense Small Cell Networks: A Mean-Field Approach
In this paper, a novel approach for joint power control and user scheduling
is proposed for optimizing energy efficiency (EE), in terms of bits per unit
power, in ultra dense small cell networks (UDNs). To address this problem, a
dynamic stochastic game (DSG) is formulated between small cell base stations
(SBSs). This game enables to capture the dynamics of both queues and channel
states of the system. To solve this game, assuming a large homogeneous UDN
deployment, the problem is cast as a mean field game (MFG) in which the MFG
equilibrium is analyzed with the aid of two low-complexity tractable partial
differential equations. User scheduling is formulated as a stochastic
optimization problem and solved using the drift plus penalty (DPP) approach in
the framework of Lyapunov optimization. Remarkably, it is shown that by weaving
notions from Lyapunov optimization and mean field theory, the proposed solution
yields an equilibrium control policy per SBS which maximizes the network
utility while ensuring users' quality-of-service. Simulation results show that
the proposed approach achieves up to 18:1% gains in EE and 98.2% reductions in
the network's outage probability compared to a baseline model.Comment: 6 pages, 7 figures, GLOBECOM 2015 (published
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
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