Angular dependent magnetothermopower of alpha-(ET)2KHg(SCN)4

The magnetic field and angular dependencies of the thermopower and Nernst effect of the quasi-two-dimensional organic conductor alpha-(ET)2KHg(SCN)4 are experimentally measured at temperatures below (4 K) and above (9 K) the transition temperature to fields of In addition, a theoretical model which involves a magnetic breakdown effect between the q1D and q2D bands is proposed in order to simulate the data. Analysis of the background components of the thermopower and Nernst effect imply that at low temperatures, in the CDW state, the properties of alpha-(ET)2KHg(SCN)4 are determined mostly by the orbits on the new open Fermi sheets. Quantum oscillations observed in the both thermoelectric effects, at fields above 8 T, originate only from the alpha orbit.Comment: 25 pages, 18 figure

Minimax lower bounds for function estimation on graphs

We study minimax lower bounds for function estimation problems on large graph when the target function is smoothly varying over the graph. We derive minimax rates in the context of regression and classification problems on graphs that satisfy an asymptotic shape assumption and with a smoothness condition on the target function, both formulated in terms of the graph Laplacian

Optimality of Poisson processes intensity learning with Gaussian processes

In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a $d$-dimensional domain. This method was proposed by Adams, Murray and MacKay (ICML, 2009), who developed a tractable computational approach and showed in simulation and real data experiments that it can work quite satisfactorily. The results presented in the present paper provide theoretical underpinning of the method. In particular, we show how to tune the priors on the hyper parameters of the model in order for the procedure to automatically adapt to the degree of smoothness of the unknown intensity and to achieve optimal convergence rates