Effect of strain on the transport properties of the manganite systems

The effect of strain on the resistivity and thermopower of ferromagnetic manganites has been examined based on the model that incorporates the electron-lattice interaction through the Jahn-Teller effect and an effective hopping determined by nearest neighbour spin-spin correlation of t2g electrons. The metal insulator transition temperature associated with resistivity decreases with increase in strain. In the presence of large strain the system remains in the semiconducting state. Thermopower (S) is positive and increasing function of strain and it exhibits a maximum with temperature. The temperature where maximum of S appears, shifts towards higher (lower) value with in the presence of magnetic field (strain). A large magneto-thermopower that depends on strain is obtained around metal-insulator transition.Comment: 11pages, 4 figure

Differentially Private Model Selection with Penalized and Constrained Likelihood

In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning, and statistics. It provides a rigorous and strong notion of protection for individuals' sensitive information. A fundamental question is how to incorporate differential privacy into traditional statistical inference procedures. In this paper we study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and propose two algorithms to do so. We show that our private procedures are consistent under essentially the same conditions as the corresponding non-private procedures. We also find that under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method using simulation studies and two real data examples

Learning Arbitrary Statistical Mixtures of Discrete Distributions

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.Comment: 23 pages. Preliminary version in the Proceeding of the 47th ACM Symposium on the Theory of Computing (STOC15

Low-Mass Dileptons at the CERN-SpS: Evidence for Chiral Restoration?

Using a rather complete description of the in-medium $\rho$ spectral function - being constrained by various independent experimental information - we calculate pertinent dilepton production rates from hot and dense hadronic matter. The strong broadening of the $\rho$ resonance entails a reminiscence to perturbative $q\bar q$ annihilation rates in the vicinity of the phase boundary. The application to dilepton observables in Pb(158AGeV)+Au collisions - incorporating recent information on the hadro-chemical composition at CERN-SpS energies - essentially supports the broadening scenario. Possible implications for the nature of chiral symmetry restoration are outlined.Comment: 6 pages ReVTeX including 5 eps-figure

Enhancement of gluonic dissociation of J/ψJ/\psi in viscous QGP

We have investigated the effect of viscosity on the gluonic dissociation of $J/\psi$ in an equilibrating plasma. Suppression of $J/\psi$ due to gluonic dissociation depend on the temperature and also on the chemical equilibration rate. In an equilibrating plasma, viscosity affects the temperature evolution and also the chemical equilibration rate, requiring both of them to evolve slowly compared to their ideal counter part. For Au+Au collisions at RHIC and LHC energies, gluonic dissociation of $J/\psi$ increases for a viscous plasma. Low $P_T$ $J/\psi$'s are found to be more suppressed due to viscosity than the high $P_T$ ones. Also the effect is more at LHC energy than at RHIC energy.Comment: 3 pages, 1 figur

Deconfinement and the Hagedorn Transition in String Theory

Superseded and extended in hep-th/0105110 and hep-th/0208112.Comment: Superseded and extended in hep-th/0105110 and hep-th/020811