Generation of maximally entangled mixed states of two atoms via on-resonance asymmetric atom-cavity couplings

A scheme for generating the maximally entangled mixed state of two atoms on-resonance asymmetrically coupled to a single mode optical cavity field is presented. The part frontier of both maximally entangled mixed states and maximal Bell violating mixed states can be approximately reached by the evolving reduced density matrix of two atoms if the ratio of coupling strengths of two atoms is appropriately controlled. It is also shown that exchange symmetry of global maximal concurrence is broken if and only if coupling strength ratio lies between $\frac{\sqrt{3}}{3}$ and $\sqrt{3}$ for the case of one-particle excitation and asymmetric coupling, while this partial symmetry-breaking can not be verified by detecting maximal Bell violation.Comment: 5 pages, 5 figure

Squeezing induced transition of long-time decay rate

We investigate the nonclassicality of several kinds of nonclassical optical fields such as the pure or mixed single photon-added coherent states and the cat states in the photon-loss or the dephasing channels by exploring the entanglement potential as the measure. It is shown that the long-time decay of entanglement potentials of these states in photon loss channel is dependent of their initial quadrature squeezing properties. In the case of photon-loss, transition of long-time decay rate emerges at the boundary between the squeezing and non-squeezing initial non-gaussian states if log-negativity is adopted as the measure of entanglement potential. However, the transition behavior disappears if the concurrence is adopted as the measure of entanglement potential. For the case of the dephasing, distinct decay behaviors of the nonclassicality are also revealed.Comment: 7 pages, 7 figures, RevTex

Competition of different evaluation schemes in the continuous variable game

An asymmetric generalization of classical Cournot's duopoly game was introduced and the simulation scheme of its quantized version was analyzed. In this scheme, the player assigned by a 'classical' measurement scheme always wins the player assigned by a quantum measurement scheme. It was shown that the fluctuation causes the disadvantage game rule of the 'quantum' player.Comment: 5 pages, 4 figures, RevTex4, some references are adde

High-Dimensional Bayesian Inference in Nonparametric Additive Models

A fully Bayesian approach is proposed for ultrahigh-dimensional nonparametric additive models in which the number of additive components may be larger than the sample size, though ideally the true model is believed to include only a small number of components. Bayesian approaches can conduct stochastic model search and fulfill flexible parameter estimation by stochastic draws. The theory shows that the proposed model selection method has satisfactory properties. For instance, when the hyperparameter associated with the model prior is correctly specified, the true model has posterior probability approaching one as the sample size goes to infinity; when this hyperparameter is incorrectly specified, the selected model is still acceptable since asymptotically it is proved to be nested in the true model. To enhance model flexibility, two new $g$-priors are proposed and their theoretical performance is examined. We also propose an efficient MCMC algorithm to handle the computational issues. Several simulation examples are provided to demonstrate the computational advantages of our method

Bayesian Ultrahigh-Dimensional Screening Via MCMC

We explore the theoretical and numerical property of a fully Bayesian model selection method in sparse ultrahigh-dimensional settings, i.e., $p\gg n$, where $p$ is the number of covariates and $n$ is the sample size. Our method consists of (1) a hierarchical Bayesian model with a novel prior placed over the model space which includes a hyperparameter $t_n$ controlling the model size, and (2) an efficient MCMC algorithm for automatic and stochastic search of the models. Our theory shows that, when specifying $t_n$ correctly, the proposed method yields selection consistency, i.e., the posterior probability of the true model asymptotically approaches one; when $t_n$ is misspecified, the selected model is still asymptotically nested in the true model. The theory also reveals insensitivity of the selection result with respect to the choice of $t_n$. In implementations, a reasonable prior is further assumed on $t_n$ which allows us to draw its samples stochastically. Our approach conducts selection, estimation and even inference in a unified framework. No additional prescreening or dimension reduction step is needed. Two novel $g$-priors are proposed to make our approach more flexible. A simulation study is given to display the numerical advantage of our method

Quasi-periodic solutions for differential equations with an elliptic-type degenerate equilibrium point under small perturbations

This work focuses on the existence of quasi-periodic solutions for ordinary and delay differential equations (ODEs and DDEs for short) with an elliptic-type degenerate equilibrium point under quasi-periodic perturbations. We prove that under appropriate hypotheses there exist quasi-periodic solutions for perturbed ODEs and DDEs near the equilibrium point for most parameter values, then apply these results to the delayed van der Pol's oscillator with zero-Hopf singularity.Comment: 32page

Order-2 Asymptotic Optimality of the Fully Distributed Sequential Hypothesis Test

This work analyzes the asymptotic performances of fully distributed sequential hypothesis testing procedures as the type-I and type-II error rates approach zero, in the context of a sensor network without a fusion center. In particular, the sensor network is defined by an undirected graph, where each sensor can observe samples over time, access the information from the adjacent sensors, and perform the sequential test based on its own decision statistic. Different from most literature, the sampling process and the information exchange process in our framework take place simultaneously (or, at least in comparable time-scales), thus cannot be decoupled from one another. Two message-passing schemes are considered, based on which the distributed sequential probability ratio test (DSPRT) is carried out respectively. The first scheme features the dissemination of the raw samples. Although the sample propagation based DSPRT is shown to yield the asymptotically optimal performance at each sensor, it incurs excessive inter-sensor communication overhead due to the exchange of raw samples with index information. The second scheme adopts the consensus algorithm, where the local decision statistic is exchanged between sensors instead of the raw samples, thus significantly lowering the communication requirement compared to the first scheme. In particular, the decision statistic for DSPRT at each sensor is updated by the weighted average of the decision statistics in the neighbourhood at every message-passing step. We show that, under certain regularity conditions, the consensus algorithm based DSPRT also yields the order-2 asymptotically optimal performance at all sensors.Comment: 36 page

Cooperative Change Detection for Online Power Quality Monitoring

This paper considers the real-time power quality monitoring in power grid systems. The goal is to detect the occurrence of disturbances in the nominal sinusoidal voltage/current signal as quickly as possible such that protection measures can be taken in time. Based on an autoregressive (AR) model for the disturbance, we propose a generalized local likelihood ratio (GLLR) detector which processes meter readings sequentially and alarms as soon as the test statistic exceeds a prescribed threshold. The proposed detector not only reacts to a wide range of disturbances, but also achieves lower detection delay compared to the conventional block processing method. Then we further propose to deploy multiple meters to monitor the power signal cooperatively. The distributed meters communicate wirelessly to a central meter, where the data fusion and detection are performed. In light of the limited bandwidth of wireless channels, we develop a level-triggered sampling scheme, where each meter transmits only one-bit each time asynchronously. The proposed multi-meter scheme features substantially low communication overhead, while its performance is close to that of the ideal case where distributed meter readings are perfectly available at the central meter

Signature candidate of quantum chaos far from the semiclassical regime

We numerically investigated the entanglement product in the simplest coupled kicked top model with the spin $j=1$. Different from the dynamical pattern of entanglement in the semiclassical regime, two similar initial states may have discordant entanglement oscillations. A candidate of the quantum signature of this classical chaotic system was proposed. The potential antimonotonic relation between the rank correlation coefficient qualifying the concordant of two entanglement evolutions and the stationary entanglement was preliminarily revealed.Comment: 6 pages, 8 figures, RevTex4, The calculation of the scaled rank correlation coefficient was added. Accepted by Chao

Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection

This work investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints. That is, in a sensor network, the fusion center sequentially acquires samples by selecting one "most informative" sensor at each time until a reliable decision can be made. In particular, the sensor selection is carried out in the online fashion since it depends on all the previous samples at each time. Our goal is to develop the sequential test (i.e., stopping rule and decision function) and sensor selection strategy that minimize the expected sample size subject to the constraints on the error probabilities and sensor usages. To this end, we first recast the usage-constrained formulation into a Bayesian optimal stopping problem with different sampling costs for the usage-contrained sensors. The Bayesian problem is then studied under both finite- and infinite-horizon setups, based on which, the optimal solution to the original usage-constrained problem can be readily established. Moreover, by capitalizing on the structures of the optimal solution, a lower bound is obtained for the optimal expected sample size. In addition, we also propose algorithms to approximately evaluate the parameters in the optimal sequential test so that the sensor usage and error probability constraints are satisfied. Finally, numerical experiments are provided to illustrate the theoretical findings, and compare with the existing methods.Comment: 33 page