Singular Effect of Disorder on Electronic Transport in Strong Coupling Electron-Phonon Systems

We solve the disordered Holstein model in three dimensions considering the phonon variables to be classical. After mapping out the phases of the `clean' strong coupling problem, we focus on the effect of disorder at strong electron-phonon (EP) coupling. The presence of even weak disorder (i) enormously enhances the resistivity (\rho) at T=0, simultaneously suppressing the density of states at the Fermi level, (ii) suppresses the temperature dependent increase of \rho, and (iii) leads to a regime with d\rho/dT <0. We locate the origin of these anomalies in the disorder induced tendency towards polaron formation, and the associated suppression in effective carrier density and mobility. These results, explicitly at `metallic' density, are of direct relevance to disordered EP materials like covalent semiconductors, the manganites, and to anomalous transport in the A-15 compounds.Comment: Final versio

Collaborative Training in Sensor Networks: A graphical model approach

Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples

Distributed Kernel Regression: An Algorithm for Training Collaboratively

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.Comment: To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 200

Detecting extra dimensions with gravity wave spectroscopy: the black string brane-world

Using the black string between two branes as a model of a brane-world black hole, we compute the gravity wave perturbations and identify the features arising from the additional polarizations of the graviton. The standard four-dimensional gravitational wave signal acquires late-time oscillations due to massive modes of the graviton. The Fourier transform of these oscillations shows a series of spikes associated with the masses of the Kaluza-Klein modes, providing in principle a spectroscopic signature of extra dimensions.Comment: 4 pages, 5 figures. Comments on the frequency and detectability of the massive mode signal added, and title modified. Version accepted for publication in Phys. Rev. Let