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 $s$ of a stationary process $X_1,...,X_n$. We suppose that the process is either $\beta$ or $\tau$-mixing. We provide a model selection procedure based on a generalization of Mallows' $C_p$ 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