5,606 research outputs found
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, , is a probability distribution over probability
distributions , where each such is a probability distribution over . When we sample from , we do not observe
directly, but only indirectly and in very noisy fashion, by sampling from
repeatedly, independently times from the distribution . The problem is
to infer 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
. We bound the quality of the solution as a function of the size of
the samples 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 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 resonance entails a reminiscence to
perturbative 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 in viscous QGP
We have investigated the effect of viscosity on the gluonic dissociation of
in an equilibrating plasma. Suppression of 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 increases for a viscous plasma.
Low 's are found to be more suppressed due to viscosity than the
high 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
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