2,478 research outputs found
A Possible Extension of a Trial State in the TDHF Theory with Canonical Form in the Lipkin Model
With the aim of the extension of the TDHF theory in the canonical form in the
Lipkin model, the trial state for the variation is constructed, which is an
extension of the Slater determinant. The canonicity condition is imposed to
formulate the variational approach in the canonical form. A possible solution
of the canonicity condition is given and the zero-point fluctuation induced by
the uncertainty principle is investigated. As an application, the ground state
energy is evaluated.Comment: 15 pages, 1 figure, using PTPTeX styl
Utility of su(1,1)-Algebra in a Schematic Nuclear su(2)-Model
The su(2)-algebraic model interacting with an environment is investigated
from a viewpoint of treating the dissipative system. By using the
time-dependent variational approach with a coherent state and with the help of
the canonicity condition, the time-evolution of this quantum many-body system
is described in terms of the canonical equations of motion in the classical
mechanics. Then, it is shown that the su(1,1)-algebra plays an essential role
to deal with this model. An exact solution with appropriate initial conditions
is obtained by means of Jacobi's elliptic function. The implication to the
dissipative process is discussed.Comment: 14 pages using PTPTeX.st
Adaptive density estimation for stationary processes
We propose an algorithm to estimate the common density of a stationary
process . We suppose that the process is either or
-mixing. We provide a model selection procedure based on a generalization
of Mallows' and we prove oracle inequalities for the selected estimator
under a few prior assumptions on the collection of models and on the mixing
coefficients. We prove that our estimator is adaptive over a class of Besov
spaces, namely, we prove that it achieves the same rates of convergence as in
the i.i.d framework
Dynamic fluctuations in the superconductivity of NbN films from microwave conductivity measurements
We have measured the frequency and temperature dependences of complex ac
conductivity, \sigma(\omega)=\sigma_1(\omega)-i\sigma_2(\omega), of NbN films
in zero magnetic field between 0.1 to 10 GHz using a microwave broadband
technique. In the vicinity of superconducting critical temperature, Tc, both
\sigma_1(\omega) and \sigma_2(\omega) showed a rapid increase in the low
frequency limit owing to the fluctuation effect of superconductivity. For the
films thinner than 300 nm, frequency and temperature dependences of fluctuation
conductivity, \sigma(\omega,T), were successfully scaled onto one scaling
function, which was consistent with the Aslamazov and Larkin model for two
dimensional (2D) cases. For thicker films, \sigma(\omega,T) data could not be
scaled, but indicated that the dimensional crossover from three dimensions (3D)
to 2D occurred as the temperature approached Tc from above. This provides a
good reference of ac fluctuation conductivity for more exotic superconductors
of current interest.Comment: 8 pages, 7 Figures, 1 Table, Accepted for publication in PR
Stable ferromagnetism in p-type carbon-doped ZnO nanoneedles
Author name used in this publication: C. S. Wei2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Model selection in High-Dimensions: A Quadratic-risk based approach
In this article we propose a general class of risk measures which can be used
for data based evaluation of parametric models. The loss function is defined as
generalized quadratic distance between the true density and the proposed model.
These distances are characterized by a simple quadratic form structure that is
adaptable through the choice of a nonnegative definite kernel and a bandwidth
parameter. Using asymptotic results for the quadratic distances we build a
quick-to-compute approximation for the risk function. Its derivation is
analogous to the Akaike Information Criterion (AIC), but unlike AIC, the
quadratic risk is a global comparison tool. The method does not require
resampling, a great advantage when point estimators are expensive to compute.
The method is illustrated using the problem of selecting the number of
components in a mixture model, where it is shown that, by using an appropriate
kernel, the method is computationally straightforward in arbitrarily high data
dimensions. In this same context it is shown that the method has some clear
advantages over AIC and BIC.Comment: Updated with reviewer suggestion
Analyzing the House Fly's Exploratory Behavior with Autoregression Methods
This paper presents a detailed characterization of the trajectory of a single
housefly with free range of a square cage. The trajectory of the fly was
recorded and transformed into a time series, which was fully analyzed using an
autoregressive model, which describes a stationary time series by a linear
regression of prior state values with the white noise. The main discovery was
that the fly switched styles of motion from a low dimensional regular pattern
to a higher dimensional disordered pattern. This discovered exploratory
behavior is, irrespective of the presence of food, characterized by anomalous
diffusion.Comment: 20 pages, 9 figures, 1 table, full pape
The reliability of the AIC method in Cosmological Model Selection
The Akaike information criterion (AIC) has been used as a statistical
criterion to compare the appropriateness of different dark energy candidate
models underlying a particular data set. Under suitable conditions, the AIC is
an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate
model A with respect to the truth T. Thus, a dark energy model with a smaller
AIC is ranked as a better model, since it has a smaller Kullback-Leibler
discrepancy with T. In this paper, we explore the impact of statistical errors
in estimating the AIC during model comparison. Using a parametric bootstrap
technique, we study the distribution of AIC differences between a set of
candidate models due to different realizations of noise in the data and show
that the shape and spread of this distribution can be quite varied. We also
study the rate of success of the AIC procedure for different values of a
threshold parameter popularly used in the literature. For plausible choices of
true dark energy models, our studies suggest that investigating such
distributions of AIC differences in addition to the threshold is useful in
correctly interpreting comparisons of dark energy models using the AIC
technique.Comment: Figures and further discussions of the results were added, and the
version matches the version published in MNRA
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