A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Age of acquisition: Its neural and computational mechanisms.

The acquisition of new skills over a life span is a remarkable human ability. This ability, however, is constrained by age of acquisition (AoA); that is, the age at which learning occurs significantly affects the outcome. This is most clearly reflected in domains such as language, music, and athletics. This article provides a perspective on the neural and computational mechanisms underlying AoA in language acquisition. The authors show how AoA modulates both monolingual lexical processing and bilingual language acquisition. They consider the conditions under which syntactic processing and semantic processing may be differentially sensitive to AoA effects in second-language acquisition. The authors conclude that AoA effects are pervasive and that the neural and computational mechanisms underlying learning and sensorimotor integration provide a general account of these effects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measuring consistency in TTL-based caches

We propose a general modeling framework to evaluate the performance of cache consistency algorithms. In addition to the usual hit rate, we introduce the hit^* rate as a consistency measure, which captures the fraction of non-stale downloads from the cache. We apply these ideas to the analysis of the fixed TTL consistency algorithm in the presence of network delays. The hit and hit^* rates are evaluated when requests and updates are modeled by renewal processes. Classical results on the renewal function lead to various bounds.     

Modeling for local dynamic behaviors of phenol biodegradation in bubble columns

A coupled three-dimensional (3-D) model, combining hydrodynamics with biochemical reactions, was developed to simulate the local transient flow patterns and the dynamic behaviors of cell growth and phenol biodegradation by yeast Candida tropicalis in the bubble-column bioreactor, using the computational fluid dynamic (CFD) method. In order to validate this proposed model effectively, the validation of the local hydrodynamic characteristics of the gas-mineral salt solution (gas-liquid) two-phase system, with the phenol concentration of 1200 mg/L, and with the absence of cells, was performed in a square-sectioned bubble column bioreactor using the LDA system and conductivity probe. Furthermore, the validation of phenol biodegradation behaviors by yeast Candida tropicalis at different initial concentrations of phenol and cell was also carried out in the above bubble-column bioreactor. The results indicated that the model simulations had a satisfying agreement with the experimental data. Finally, the local instantaneous flow and phenol biodegradation features including gas holdup, gas velocity, liquid velocity, cell concentration and phenol concentration inside the bioreactor were successfully predicted in different-scale bubble columns by the proposed model. © 2006 American Institute of Chemical Engineers AIChE J, 2006     

Optimal design of gating systems by gradient search methods

A numerical optimization technique based on gradient-search is applied to obtain an optimal design of a typical gating system used for the gravity process to produce aluminum parts. This represents a novel application of coupling nonlinear optimization techniques with a foundry process simulator, and it is motivated by the fact that a scientifically guided search for better designs based on techniques that take into account the mathematical structure of the problem is preferred to commonly found trial-and-error approaches. The simulator applies the finite volume method and the VOF algorithm for CFD analysis. The direct gradient optimization algorithm, sequential quadratic programming (SQP), was used to solve both a 2D and a 3D gating system design problems using two design variables. The results clearly show the effectiveness of the proposed approach for finding high quality castings when compared with current industry practices.     

Performance modeling and evaluation of data/voice services in wireless networks

Application-level performance is a key to the adoption and success of the CDMA 2000. To predict this performance in advance, a detailed end-to-end simulation model of a CDMA network is built to include application traffic characteristics, network architecture, network element details using the proposed simulation methodology. We assess the user-perceived application performance when a RAN and a CN adopt different transport architectures such as ATM and IP. To evaluate the user-perceived quality of voice service, we compare the end-to-end packet delay for different vocoder schemes such as G.711, G.726 (PCM), G.726 (ADPCM), and vocoder bypass scheme. By the simulation results, the vocoder bypass scenario shows 30% performance improvement over the others. We also compare the quality of voice service with and without DPS scheduling scheme. We know that DPS scheme keep the voice delay bound even if the service traffic is high. For data packet performance, HTTP v.1.1 shows better performance than that of HTTP v.1.0 due to the pipelining and TCP persistent connection. We may conclude that IP transport technology is better solution for higher FER environment since the packet overhead of IP is smaller than that of ATM for web browsing data traffic, while it shows opposite effect to the small size voice packet in RAN architecture. We show that the 3G-1X EV-DO system gives much better packet delay performance than 3G-1X RTT. The main conclusion is that end-to-end application-level performance is affected by various elements and layers of the network and thus it must be considered in all phases of the development process. Jae-Hyun Kim He received the B.S., M.S., and Ph.D. degrees, all in computer science and engineering, from Hanyang University, Ansan, Korea, in 1991, 1993, and 1996 respectively. In 1996, he was with the Communication Research Laboratory, Tokyo, Japan, as a Visiting Scholar. From April 1997 to October 1998, he was a post-doctoral fellow at the department of electrical engineering, University of California, Los Angeles. From November 1998 to February 2003, he worked as a member of technical staff in Performance Modeling and QoS management department, Bell laboratories, Lucent Technologies, Holmdel, NJ. He has been with the department of electrical engineering, Ajou University, Suwon, Korea, as an assistant professor since 2003. His research interests include QoS issues and cross layer optimization for high-speed wireless communication. Dr. Kim was the recipient of the LGIC Thesis Prize and Samsung Human-Tech Thesis Prize in 1993 and 1997, respectively. He is a member of the Korean Institute of Communication Sciences (KICS), Korea Institute of Telematics and Electronis (KITE), Korea Information Science Society (KISS), and IEEE. Hyun-Jin Lee received the B.S. degree in electrical engineering from Ajou University, Suwon, Korea, in 2004, and is working toward the M.S. degree and Ph. D. degree in electrical engineering at Ajou University. He has been awarded Samsung Human-Tech Thesis Prize in 2004. His research interests QoS, especially network optimization and wireless packet scheduling. He is a member of the KICS. Sung-Min Oh received the B.S. and M. S. degrees in electrical engineering form Ajou University, Suwon, Korea, in 2004, and is working toward the Ph. D. degree in electrical engineering at Ajou University. His research interests QoS performance analysis and 4G network. He is a member of the KICS. Sung-Hyun Cho received his B.S., M.S., and Ph.D. in computer science and engineering from Hanyang University, Korea, in 1995, 1997, and 2001, respectively. From 2001 to 2005, he has been with Samsung Advanced Institute of Technology, where he has been engaged in the design and standardization of MAC and upper layers of B3G, IEEE 802.16e, and WiBro systems. He is currently a MAC part leader in the telecommunication R&D center of Samsung Electronics. His research interests include 4G air interface design, radio resource management, cross layer design, and handoff in wireless systems.     

General Solution for the Dynamic Modeling of Parallel Robots

In this paper, we present a general method to calculate the inverse and direct dynamic models of parallel robots. The models are expressed in a closed form by a single equation in which all the elements needed are expressed. The solution is given in terms of the dynamic models of the legs, the dynamics of the platform and some Jacobian matrices. The proposed method is applied in this paper on two parallel robots with different structures. Categories (2), (3).     

Formation and electronic structure of interface

In the present work the formation of the interface between polycrystalline silver and thin films of titanium oxide was studied with photoelectron spectroscopy (XPS, UPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS). Titanium oxide was deposited stepwise on 100 nm thick silver films by reactive magnetron sputtering allowing to study the evolution of the interface formation process. The process involves two steps: formation of thin layer of silver oxide and subsequent growth of the TiO₂ film. For better understanding of the silver oxidation process, pure silver films were exposed to a low temperature Ar/O plasma for different time intervals providing a possibility to investigate early stages of the oxide film growth.     

裂缝各向异性介质中P-SV转换波正演模拟

裂缝介质正演是研究裂缝性油藏的各向异性、裂缝检测方法、裂缝AVO反演等的前提。在裂缝方位各向异性(EDA)介质模型的基础上，重点研究了纵波震源激发的地震波所产生P-SV波的有限差分正演模拟方法。从各向异性单斜对称系统的刚性矩阵推导出的波动方程，可以满足EDA介质模型不同方位角入射的二维差分模拟要求。在有限差分模拟中，采用了普通的Kelly差分格式、二阶各向异性吸收边界条件和一般的纵波震源的解析形式，并应用修正的通量传输校(FCT)方法来消除频散。同时，对纵波震源的解析式进行了技术处理，取得了较好的效果。     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.