Constraints on the Neutrino Parameters from the `Rise-up' in the Boron Neutrino Spectrum at Low Energies

The rise-up in boron neutrino spectrum at low energies has been studied within the framework of `pure LMA' scenario. Indirect bounds on the spectral `upturn' have been obtained from the available solar neutrino data. These bounds have been used to demonstrate the efficacy of the precision measurements of the `upturn' for further constraining the neutrino parameter space allowed by SNO salt phase data. The sterile neutrino flux has been constrained in the light of the recent 766.3 Ty KamLAND spectral data.Comment: Latex 10pages including 3 postscript figure

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

The Many Electron Ground State of the Adiabatic Holstein Model in Two and Three Dimensions

We present the complete ground state phase diagram of the Holstein model in two and three dimension considering the phonon variables to be classical. We first establish the overall structure of the phase diagram by using exact diagonalisation based Monte Carlo (ED-MC) on small lattices and then use a new ``travelling cluster'' approximation (TCA) for annealing the phonon degrees of freedom on large lattices. The phases that emerge include a Fermi liquid (FL), with no lattice distortions, an insulating polaron liquid (PL) at strong coupling, and a charge ordered insulating (COI) phase around half- filling. The COI phase is separated from the Fermi liquid by a regime of phase coexistence whose width grows with increasing electron-phonon coupling. We provide results on the electronic density of states, the COI order parameter, and the spatial organisation of polaronic states, for arbitrary density and electron-phonon coupling. The results highlight the crucial role of spatial correlations in this strong coupling problem.Comment: Final versio

Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections

Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach.Comment: To appear in the Proceedings of the SPIE Conference on Advanced Signal Processing Algorithms, Architectures and Implementations XV, San Diego, CA, July 31 - August 4, 200

Provenance Views for Module Privacy

Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate data items passed between module executions. However, authors/owners of workflows may wish to keep some of this information confidential. In particular, a module may be proprietary, and users should not be able to infer its behavior by seeing mappings between all data inputs and outputs. The problem we address in this paper is the following: Given a workflow, abstractly modeled by a relation R, a privacy requirement \Gamma and costs associated with data. The owner of the workflow decides which data (attributes) to hide, and provides the user with a view R' which is the projection of R over attributes which have not been hidden. The goal is to minimize the cost of hidden data while guaranteeing that individual modules are \Gamma -private. We call this the "secureview" problem. We formally define the problem, study its complexity, and offer algorithmic solutions

A Better Good-Turing Estimator for Sequence Probabilities

We consider the problem of estimating the probability of an observed string drawn i.i.d. from an unknown distribution. The key feature of our study is that the length of the observed string is assumed to be of the same order as the size of the underlying alphabet. In this setting, many letters are unseen and the empirical distribution tends to overestimate the probability of the observed letters. To overcome this problem, the traditional approach to probability estimation is to use the classical Good-Turing estimator. We introduce a natural scaling model and use it to show that the Good-Turing sequence probability estimator is not consistent. We then introduce a novel sequence probability estimator that is indeed consistent under the natural scaling model.Comment: ISIT 2007, to appea