Chernoff Index for Cox Test of Separate Parametric Families

The asymptotic efficiency of a generalized likelihood ratio test proposed by Cox is studied under the large deviations framework for error probabilities developed by Chernoff. In particular, two separate parametric families of hypotheses are considered [Cox, 1961, 1962]. The significance level is set such that the maximal type I and type II error probabilities for the generalized likelihood ratio test decay exponentially fast with the same rate. We derive the analytic form of such a rate that is also known as the Chernoff index [Chernoff, 1952], a relative efficiency measure when there is no preference between the null and the alternative hypotheses. We further extend the analysis to approximate error probabilities when the two families are not completely separated. Discussions are provided concerning the implications of the present result on model selection

On the Identifiability of Diagnostic Classification Models

This paper establishes fundamental results for statistical inference of diagnostic classification models (DCM). The results are developed at a high level of generality, applicable to essentially all diagnostic classification models. In particular, we establish identifiability results of various modeling parameters, notably item response probabilities, attribute distribution, and Q-matrix-induced partial information structure. Consistent estimators are constructed. Simulation results show that these estimators perform well under various modeling settings. We also use a real example to illustrate the new method. The results are stated under the setting of general latent class models. For DCM with a specific parameterization, the conditions may be adapted accordingly

A Fused Latent and Graphical Model for Multivariate Binary Data

We consider modeling, inference, and computation for analyzing multivariate binary data. We propose a new model that consists of a low dimensional latent variable component and a sparse graphical component. Our study is motivated by analysis of item response data in cognitive assessment and has applications to many disciplines where item response data are collected. Standard approaches to item response data in cognitive assessment adopt the multidimensional item response theory (IRT) models. However, human cognition is typically a complicated process and thus may not be adequately described by just a few factors. Consequently, a low-dimensional latent factor model, such as the multidimensional IRT models, is often insufficient to capture the structure of the data. The proposed model adds a sparse graphical component that captures the remaining ad hoc dependence. It reduces to a multidimensional IRT model when the graphical component becomes degenerate. Model selection and parameter estimation are carried out simultaneously through construction of a pseudo-likelihood function and properly chosen penalty terms. The convexity of the pseudo-likelihood function allows us to develop an efficient algorithm, while the penalty terms generate a low-dimensional latent component and a sparse graphical structure. Desirable theoretical properties are established under suitable regularity conditions. The method is applied to the revised Eysenck's personality questionnaire, revealing its usefulness in item analysis. Simulation results are reported that show the new method works well in practical situations.Comment: 49 pages, 6 figures, and 5 table

Guaranteed-cost consensus for multiagent networks with Lipschitz nonlinear dynamics and switching topologies

Guaranteed-cost consensus for high-order nonlinear multi-agent networks with switching topologies is investigated. By constructing a time-varying nonsingular matrix with a specific structure, the whole dynamics of multi-agent networks is decomposed into the consensus and disagreement parts with nonlinear terms, which is the key challenge to be dealt with. An explicit expression of the consensus dynamics, which contains the nonlinear term, is given and its initial state is determined. Furthermore, by the structure property of the time-varying nonsingular transformation matrix and the Lipschitz condition, the impacts of the nonlinear term on the disagreement dynamics are linearized and the gain matrix of the consensus protocol is determined on the basis of the Riccati equation. Moreover, an approach to minimize the guaranteed cost is given in terms of linear matrix inequalities. Finally, the numerical simulation is shown to demonstrate the effectiveness of theoretical results.Comment: 16 page

Control of Surface Wettability via Strain Engineering

Reversible control of surface wettability has wide applications in lab-on-chip systems, tunable optical lenses, and microfluidic tools. Using a graphene sheet as a sample material and molecular dynamic (MD) simulations, we demonstrate that strain engineering can serve as an effective way to control the surface wettability. The contact angles $\theta$ of water droplets on a graphene vary from 72.5$^\circ$ to 106$^\circ$ under biaxial strains ranging from -10% to 10% that are applied on the graphene layer. For an intrinsic hydrophilic surface (at zero strain), the variation of $\theta$ upon the applied strains is more sensitive, i.e., from 0$^\circ$ to 74.8$^\circ$. Overall the cosines of the contact angles exhibit a linear relation with respect to the strains. In light of the inherent dependence of the contact angle on liquid-solid interfacial energy, we develop an analytic model to show the $\cos \theta$ as a linear function of the adsorption energy $E_{ads}$ of a single water molecule over the substrate surface. This model agrees with our MD results very well. Together with the linear dependence of $E_{ads}$ on biaxial strains, we can thus understand the effect of strains on the surface wettability. Thanks to the ease of reversibly applying mechanical strains in micro/nano-electromechanical systems (MEMS/NEMS), we believe that strain engineering can be a promising means to achieve the reversibly control of surface wettability.Comment: Submitted to Physical Review E on September 17, 2012, manuscript ID: EW1084

Optimal Stopping and Worker Selection in Crowdsourcing: an Adaptive Sequential Probability Ratio Test Framework

In this paper, we aim at solving a class of multiple testing problems under the Bayesian sequential decision framework. Our motivating application comes from binary labeling tasks in crowdsourcing, where the requestor needs to simultaneously decide which worker to choose to provide the label and when to stop collecting labels under a certain budget constraint. We start with the binary hypothesis testing problem to determine the true label of a single object, and provide an optimal solution by casting it under the adaptive sequential probability ratio test (Ada-SPRT) framework. We characterize the structure of the optimal solution, i.e., optimal adaptive sequential design, which minimizes the Bayes risk through log-likelihood ratio statistic. We also develop a dynamic programming algorithm that can efficiently approximate the optimal solution. For the multiple testing problem, we further propose to adopt an empirical Bayes approach for estimating class priors and show that our method has an averaged loss that converges to the minimal Bayes risk under the true model. The experiments on both simulated and real data show the robustness of our method and its superiority in labeling accuracy as compared to several other recently proposed approaches

Latent Feature Extraction for Process Data via Multidimensional Scaling

Computer-based interactive items have become prevalent in recent educational assessments. In such items, the entire human-computer interactive process is recorded in a log file and is known as the response process. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory latent variable analysis for process data. Latent variables are extracted through a multidimensional scaling framework and can be empirically proved to contain more information than classic binary responses in terms of out-of-sample prediction of many variables.Comment: 26 pages, 11 figure

The Application of Situational Experiential Teaching in English Reading Teaching in Senior Middle School

The National English Curriculum Standards for General Senior Middle School (2017 ed., 2020 rev.) promotes the establishment of an environment for students to experience English teaching, letting students enter the English world and improve their core literacy to meet teaching expectations. This paper analyzes the connotation of situational experiential teaching mode, then combines teaching examples, thus first creating multi-modal situation, enhancing real sense of students’ experience; secondly basing on the situation task, training students’ problem-solving ability; and thirdly applying situational role and cultivating students’ ability of transfer and innovation, which explores the application of situational experiential teaching method in English reading teaching in senior middle schools from the three aspects.

Analysis on President Xi Jinping’s Expressions in Publicity Translation

In order to make China and Chinese stories better heard, publicity translation has a long way to go and shoulders an arduous task. Although there are some studies about publicity translation, yet only a few have been done on speeches by Chinese leaders. This paper studies several problems as follows: features of President Xi Jinping’s stylistic expressions and analysis on them; the application of publicity translation theories and factors considered while translating, including the translation techniques employed

Tuning of Interlayer Coupling in Large-Area Graphene/WSe2 van der Waals Heterostructure via Ion Irradiation: Optical Evidences and Photonic Applications

Van der Waals (vdW) heterostructures are receiving great attentions due to their intriguing properties and potentials in many research fields. The flow of charge carriers in vdW heterostructures can be efficiently rectified by the inter-layer coupling between neighboring layers, offering a rich collection of functionalities and a mechanism for designing atomically thin devices. Nevertheless, non-uniform contact in larger-area heterostructures reduces the device efficiency. In this work, ion irradiation had been verified as an efficient technique to enhance the contact and interlayer coupling in the newly developed graphene/WSe2 hetero-structure with a large area of 10 mm x 10 mm. During the ion irradiation process, the morphology of monolayer graphene had been modified, promoting the contact with WSe2. Experimental evidences of the tunable interlayer electron transfer are displayed by investigation of photoluminescence and ultrafast absorption of the irradiated heterostructure. Besides, we have found that in graphene/WSe2 heterostructure, graphene serves as a fast channel for the photo-excited carriers to relax in WSe2, and the nonlinear absorption of WSe2 could be effectively tuned by the carrier transfer process in graphene, enabling specific optical absorption of the heterostructure in comparison with separated graphene or WSe2. On the basis of these new findings, by applying the ion beam modified graphene/WSe2 heterostructure as a saturable absorber, Q-switched pulsed lasing with optimized performance has been realized in a Nd:YAG waveguide cavity. This work paves the way towards developing novel devices based on large-area heterostructures by using ion beam irradiation