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Anthony Kuh

Publications and source records attributed to Anthony Kuh.

2 recordsLinked to original sources

Analysis of an evolving email network.

In this paper we study an evolving email network model first introduced by Wang and De Wilde, to the best of our knowledge. The model is analyzed by formulating the network topology as a random process and studying the dynamics of the process. Our analytical results show a number of steady state properties about the email traffic between different nodes and the aggregate networking behavior (i.e., degree distribution, clustering coefficient, average path length, and phase transition), and also confirm the empirical results obtained by Wang and De Wilde. We also conducted simulations confirming the analytical results. Extensive simulations were run to evaluate email traffic behavior at the link and network levels, phase transition phenomena, and also studying the behavior of email traffic in a hierarchical network. The methods established here are also applicable to many other practical networks including sensor networks and social networks.

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Performance Bounds for Single Layer Threshold Networks when Tracking a Drifting Adversary.

This paper finds upper bounds for the generalization error of three tracking algorithms when confronted with a worst case adversary. A system identification model is used where both the target and tracking network are single layer threshold networks, with the target weights changing slowly (the drift problem). Previous work considered random unbiased drifting adversaries. This paper focuses on the analysis of a worst case drifting adversary. For a small drift rate of gamma, we find that upper bounds for the optimal conservative tracker, the perceptron tracker, and the least mean square (LMS) tracker are respectively 2gamma/cos(gammapi),2gamman/, and gamma(2n + 2.5) where n is the number of inputs. Simulation results validate the analysis and also show that the bounds are tight when gamma is small for the perceptron and LMS tracker. The effects of additive noise, correlated inputs and non-Gaussian inputs are also discussed. Copyright 1997 Elsevier Science Ltd.

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