159,793 research outputs found
Quantum-disordered slave-boson theory of underdoped cuprates
We study the stability of the spin gap phase in the U(1) slave-boson theory
of the t-J model in connection to the underdoped cuprates. We approach the spin
gap phase from the superconducting state and consider the quantum phase
transition of the slave-bosons at zero temperature by introducing vortices in
the boson superfluid. At finite temperatures, the properties of the bosons are
different from those of the strange metal phase and lead to modified gauge
field fluctuations. As a result, the spin gap phase can be stabilized in the
quantum critical and quantum disordered regime of the boson system. We also
show that the regime of quantum disordered bosons with the paired fermions can
be regarded as the strong coupling version of the recently proposed nodal
liquid theory.Comment: 5 pages, Replaced by the published versio
Dominant mobility modulation by the electric field effect at the LaAlO_3 / SrTiO_3 interface
Caviglia et al. [Nature (London) 456, 624 (2008)] have found that the
superconducting LaAlO_3 / SrTiO_3 interface can be gate modulated. A central
issue is to determine the principal effect of the applied electric field. Using
magnetotransport studies of a gated structure, we find that the mobility
variation is almost five times as large as the sheet carrier density.
Furthermore, superconductivity can be suppressed at both positive and negative
gate bias. These results indicate that the relative disorder strength strongly
increases across the superconductor-insulator transition.Comment: 4 pages, 4 figure
20 K superconductivity in heavily electron doped surface layer of FeSe bulk crystal
A superconducting transition temperature Tc as high as 100 K was recently
discovered in 1 monolayer (1ML) FeSe grown on SrTiO3 (STO). The discovery
immediately ignited efforts to identify the mechanism for the dramatically
enhanced Tc from its bulk value of 7 K. Currently, there are two main views on
the origin of the enhanced Tc; in the first view, the enhancement comes from an
interfacial effect while in the other it is from excess electrons with strong
correlation strength. The issue is controversial and there are evidences that
support each view. Finding the origin of the Tc enhancement could be the key to
achieving even higher Tc and to identifying the microscopic mechanism for the
superconductivity in iron-based materials. Here, we report the observation of
20 K superconductivity in the electron doped surface layer of FeSe. The
electronic state of the surface layer possesses all the key spectroscopic
aspects of the 1ML FeSe on STO. Without any interface effect, the surface layer
state is found to have a moderate Tc of 20 K with a smaller gap opening of 4
meV. Our results clearly show that excess electrons with strong correlation
strength alone cannot induce the maximum Tc, which in turn strongly suggests
need for an interfacial effect to reach the enhanced Tc found in 1ML FeSe/STO.Comment: 5 pages, 4 figure
Computationally Efficient Nonparametric Importance Sampling
The variance reduction established by importance sampling strongly depends on
the choice of the importance sampling distribution. A good choice is often hard
to achieve especially for high-dimensional integration problems. Nonparametric
estimation of the optimal importance sampling distribution (known as
nonparametric importance sampling) is a reasonable alternative to parametric
approaches.In this article nonparametric variants of both the self-normalized
and the unnormalized importance sampling estimator are proposed and
investigated. A common critique on nonparametric importance sampling is the
increased computational burden compared to parametric methods. We solve this
problem to a large degree by utilizing the linear blend frequency polygon
estimator instead of a kernel estimator. Mean square error convergence
properties are investigated leading to recommendations for the efficient
application of nonparametric importance sampling. Particularly, we show that
nonparametric importance sampling asymptotically attains optimal importance
sampling variance. The efficiency of nonparametric importance sampling
algorithms heavily relies on the computational efficiency of the employed
nonparametric estimator. The linear blend frequency polygon outperforms kernel
estimators in terms of certain criteria such as efficient sampling and
evaluation. Furthermore, it is compatible with the inversion method for sample
generation. This allows to combine our algorithms with other variance reduction
techniques such as stratified sampling. Empirical evidence for the usefulness
of the suggested algorithms is obtained by means of three benchmark integration
problems. As an application we estimate the distribution of the queue length of
a spam filter queueing system based on real data.Comment: 29 pages, 7 figure
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