17,732 research outputs found
Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
We propose an adaptive diffusion mechanism to optimize a global cost function
in a distributed manner over a network of nodes. The cost function is assumed
to consist of a collection of individual components. Diffusion adaptation
allows the nodes to cooperate and diffuse information in real-time; it also
helps alleviate the effects of stochastic gradient noise and measurement noise
through a continuous learning process. We analyze the mean-square-error
performance of the algorithm in some detail, including its transient and
steady-state behavior. We also apply the diffusion algorithm to two problems:
distributed estimation with sparse parameters and distributed localization.
Compared to well-studied incremental methods, diffusion methods do not require
the use of a cyclic path over the nodes and are robust to node and link
failure. Diffusion methods also endow networks with adaptation abilities that
enable the individual nodes to continue learning even when the cost function
changes with time. Examples involving such dynamic cost functions with moving
targets are common in the context of biological networks.Comment: 34 pages, 6 figures, to appear in IEEE Transactions on Signal
Processing, 201
Search algorithms as a framework for the optimization of drug combinations
Combination therapies are often needed for effective clinical outcomes in the
management of complex diseases, but presently they are generally based on
empirical clinical experience. Here we suggest a novel application of search
algorithms, originally developed for digital communication, modified to
optimize combinations of therapeutic interventions. In biological experiments
measuring the restoration of the decline with age in heart function and
exercise capacity in Drosophila melanogaster, we found that search algorithms
correctly identified optimal combinations of four drugs with only one third of
the tests performed in a fully factorial search. In experiments identifying
combinations of three doses of up to six drugs for selective killing of human
cancer cells, search algorithms resulted in a highly significant enrichment of
selective combinations compared with random searches. In simulations using a
network model of cell death, we found that the search algorithms identified the
optimal combinations of 6-9 interventions in 80-90% of tests, compared with
15-30% for an equivalent random search. These findings suggest that modified
search algorithms from information theory have the potential to enhance the
discovery of novel therapeutic drug combinations. This report also helps to
frame a biomedical problem that will benefit from an interdisciplinary effort
and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Intersystem soft handover for converged DVB-H and UMTS networks
Digital video broadcasting for handhelds (DVB-H) is the standard for broadcasting Internet Protocol (IP) data services to mobile portable devices. To provide interactive services for DVB-H, the Universal Mobile Telecommunications System (UMTS) can be used as a terrestrial interaction channel for the unidirectional DVB-H network. The converged DVB-H and UMTS network can be used to address the congestion problems due to the limited multimedia channel accesses of the UMTS network. In the converged network, intersystem soft handover between DVB-H and UMTS is needed for an optimum radio resource allocation, which reduces network operation cost while providing the required quality of service. This paper deals with the intersystem soft handover between DVB-H and UMTS in such a converged network. The converged network structure is presented. A novel soft handover scheme is proposed and evaluated. After considering the network operation cost, the performance tradeoff between the network quality of service and the network operation cost for the intersystem soft handover in the converged network is modeled using a stochastic tree and analyzed using a numerical simulation. The results show that the proposed algorithm is feasible and has the potential to be used for implementation in the real environment
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