Distributed Large Scale Network Utility Maximization

Recent work by Zymnis et al. proposes an efficient primal-dual interior-point method, using a truncated Newton method, for solving the network utility maximization (NUM) problem. This method has shown superior performance relative to the traditional dual-decomposition approach. Other recent work by Bickson et al. shows how to compute efficiently and distributively the Newton step, which is the main computational bottleneck of the Newton method, utilizing the Gaussian belief propagation algorithm. In the current work, we combine both approaches to create an efficient distributed algorithm for solving the NUM problem. Unlike the work of Zymnis, which uses a centralized approach, our new algorithm is easily distributed. Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods. As an additional contribution, this is the first work that evaluates the performance of the Gaussian belief propagation algorithm vs. the preconditioned conjugate gradient method, for a large scale problem.Comment: In the International Symposium on Information Theory (ISIT) 200

System combination using auxiliary information for speaker verification

Recent studies in speaker recognition have shown that score-level combination of subsystems can yield significant performance gains over individual subsystems. We explore the use of auxiliary information to aid the combination procedure. We propose a modi-fied linear logistic regression procedure that conditions combination weights on the auxiliary information. A regularization procedure is used to control the complexity of the extended model. Several auxiliary features are explored. Results are presented for data from the 2006 NIST speaker recognition evaluation (SRE). When an es-timated degree of nonnativeness for the speaker is used as auxiliary information, the proposed combination results in a 15 % relative re-duction in equal error rate over methods based on standard linear logistic regression, support vector machines, and neural networks

DT-MRI data visualisation using the dual tree complex wavelet transform

In this paper a novel visualisation method for diffusion tensor MRI datasets is introduced. This is based on the use of Complex Wavelets in order to produce “stripy ” textures which depict the anisotropic component of the diffusion tensors. Grey-scale pixel intensity is used to show the isotropic component. This paper also discusses enhancements of the technique for 3D visualisation. 1

Combining Network Utility Maximization and Adaptive Modulation

Abstract — We combine network utility maximization with adaptive modulation and examine adaptive rate and adaptive transmission powers schemes that maximize average utility, subject to constraints on average transmission power and BER. A broad class of parameterized utility functions is considered as are instantaneous and average BER. Closed form optimal strategies are found for several commonly used classes of modulation. In many cases these functions adapt to changing channel conditions differently than functions designed to maximize spectral efficiency. I