Brightening single-photon emitters by combining an ultrathin metallic antenna and a silicon quasi-BIC antenna

Bright single-photon emitters(SPEs)are fundamental components in many quantum applications.However,it is difficult to simultaneously get large Purcell enhancements and quantum yields in metallic nanostructures because of the huge losses in the metallic nanostructures.Herein,we propose to combine an ultrathin metallic bowtie antenna with a silicon antenna above a metallic substrate to simultaneously get large Purcell enhancements,quantum yields,and collection efficiencies.As a result,the brightness of SPEs in the hybrid nanostructure is greatly increased.Due to the deep subwavelength field confinement(mode size<10 nm)of surface plasmons in the ultrathin metallic film(thickness<4 nm),the Purcell enhancement of the metallic bowtie antenna improves by more than 25 times when the metal thickness decreases from 20 nm to 2 nm.In the hybrid nanostructures by combining an ultrathin metallic bowtie antenna with a silicon antenna,the Purcell enhancement(Fp≈2.6×10⁶)in the hybrid nanostructures is 63 times greater than those(≤4.1×10⁴)in the previous metallic and hybrid nanostructures.Because of the reduced ratio of electromagnetic fields in the ultrathin metallic bowtie antenna when the high-index silicon antenna is under the quasi-BIC state,a high quantum yield(QY≈0.70)is obtained.Moreover,the good radiation directivity of the quasi-BIC(bound state in the continuum)mode of the silicon antenna and the reflection of the metallic substrate result in a high collection efficiency(CE≈0.71).Consequently,the overall enhancement factor of brightness of a SPE in the hybrid nanostructure is EF?≈Fp×QY×CE≈1.3×10⁶,which is 5.6×10² times greater than those(EF?≤2.2×103)in the previous metallic and hybrid nanostructures.     

实证研究中预测模型的选择:从逐步回归到信息标准

本文首先对显著性变量同变量显著性之间的关系予以讨论并区分,进而评价逐步回归模型选择法的缺陷性。在此基础上,我们对以AIC和B IC为代表的各种基于信息标准的模型选择法予以介绍和评论。同逐步回归法相比,信息标准模型选择法有着坚实的统计理论基础及清晰而优良的统计性质。本文通过基于近十年中国股市数据的实证检验说明,信息标准同逐步回归相比往往能产生具有更强预测能力的计量模型,因此值得在未来的实证研究中注意并推广。     

对苯二酚在碳纳米管修饰玻碳电极的电化学行为及电化学动力学研究

碳纳米管(Carbon Nanotubes,CNT)自1991年发现以来,因其结构所具有的高比表面,高电导率,稳定的化学性质与超常的机械强度已成为世界范围内的研究热点,并应用于催化、气体储藏和电极材料等领域。用CNT修饰的电极具有良好的电化学性能并且已经取得了很好的实验结果[1],因此研究碳     

Spline Estimation of Discontinuous Regression Functions

Abstract

This article deals with regression function estimation when the regression function is smooth at all but a finite number of points. An important question is: How can one produce discontinuous output without knowledge of the location of discontinuity points? Unlike most commonly used smoothers that tend to blur discontinuity in the data, we need to find a smoother that can detect such discontinuity. In this article, linear splines are used to estimate discontinuous regression functions. A procedure of knot-merging is introduced for the estimation of regression functions near discontinuous points. The basic idea is to use multiple knots for spline estimates. We use an automatic procedure involving the least squares method, stepwise knot addition, stepwise basis deletion, knot-merging, and the Bayes information criterion to select the final model. The proposed method can produce discontinuous outputs. Numerical examples using both simulated and real data are given to illustrate the performance of the proposed method.     

Causal analysis of futures sugar prices in Zhengzhou

In this paper, we are interested in investigating the causal relationships among futures sugar prices in the Zhengzhou futures exchange market (ZF), the spot sugar prices in Zhengzhou (ZS) and the futures sugar prices in New York futures exchange market (NF). A useful tool called Bayesian network is introduced to analyze the problem. Since there are only three variables in our Bayesian network, the algorithm is straightforward: we display all the 25 possible network structures and adopt certain scoring metrics to evaluate them. We applied five different scoring metrics in total. Firstly, for each metric, we obtained 24 scores, each calculated from one of the 24 possible structures i.e. a Directed Acyclic Graph (DAG). Then we eliminated the network structure which represents the independence of the three variables according to our prior knowledge concerning the futures sugar market. After that, the optimal network structure which implies the causal relationships was selected according to the corresponding scoring metric. Finally, after comparing the results from different scoring metrics, we obtained the relatively affirmative conclusion that ZS causes ZF from both the Bayesian Dirichlet (BD) metric, Bayesian Dirichlet-Akaike Information Criterion (BD-AIC) metric, Bayesian Dirichlet-Bayesian Information Criterion (BD-BIC) metric and Bayesian Information Criterion (BIC) metric. The conclusions that NF causes ZF and ZF causes ZS from the Akaike Information Criterion (AIC) metric and ZF causes ZS from the BIC metric were useful and significant to our investigation.     

An Alternating Direction Method of Multipliers for MCP-penalized Regression with High-dimensional Data

The minimax concave penalty (MCP) has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. In this paper, we develop an efficient alternating direction method of multipliers (ADMM) with continuation algorithm for solving the MCP-penalized least squares problem in high dimensions. Under some mild conditions, we study the convergence properties and the Karush–Kuhn–Tucker (KKT) optimality conditions of the proposed method. A high-dimensional BIC is developed to select the optimal tuning parameters. Simulations and a real data example are presented to illustrate the efficiency and accuracy of the proposed method.     

Output-only identification and dynamic analysis of time-varying mechanical structures under random excitation: A comparative assessment of parametric methods

This article addresses the problem of parametric time-domain identification and dynamic analysis for time-varying (TV) mechanical structures under unobservable random excitation. The methods presented are based on time-dependent autoregressive moving average (TARMA) models, and are classified according to the mathematical structure imposed on the TV parameter evolution as unstructured parameter evolution, stochastic parameter evolution, and deterministic parameter evolution. The features and relative merits of each class are outlined. A representative method from each is then assessed through its application to the identification and dynamic analysis of a laboratory TV structure consisting of a beam with a mass moving on it. The results are mutually compared and contrasted to those obtained through “frozen-configuration” (multiple experiment) baseline identification.     

Modelling subset multivariate ARCH model via the AIC principle

In this paper we consider the problem of identifying a parsimonious subset multivariate ARCH model based on the AIC principle. The proposed approach can reduce the number of parameters in the final ARCH specification and allows for non-constant correlations between the components. Some simulation results illustrate the viability of the proposed procedure.     

发散维数SICA惩罚Cox回归模型的一种修正BIC调节参数选择器

本文研究了发散维数SICA惩罚Cox回归模型的调节参数选择问题,提出了一种修正的BIC调节参数选择器.在一定的正则条件下,证明了方法的模型选择相合性.数值结果表明提出的方法表现要优于GCV准则.