Explaining Rising Returns to Education in Urban China in the 1990s

Although theory predicts that international trade will decrease the relative demand for skilled workers in relatively skill-deficit countries, in recent decades many developing countries have experienced rising wage premiums for skilled workers. We examines this puzzle by quantifying the relative importance of different supply and demand factors in explaining the rapid increase in the returns to education experienced by China during the 1990s. Analyzing Chinese urban household survey and census data for six provinces, we find that although changes in the structure of demand did reduce the demand for skilled workers, consistent with trade theory, the magnitude of the effect was modest and more than offset by institutional reforms and technological changes that increased the relative demand for skill.education, earnings, inequality, China

A new algorithm for finding the k shortest transport paths in dynamic stochastic networks

The static K shortest paths (KSP) problem has been resolved. In reality, however, most of the networks are actually dynamic stochastic networks. The state of the arcs and nodes are not only uncertain in dynamic stochastic networks but also interrelated. Furthermore, the cost of the arcs and nodes are subject to a certain probability distribution. The KSP problem is generally regarded as a dynamic stochastic optimization problem. The dynamic stochastic characteristics of the network and the relationships between the arcs and nodes of the network are analyzed in this paper, and the probabilistic shortest path concept is defined. The mathematical optimization model of the dynamic stochastic KSP and a genetic algorithm for solving the dynamic stochastic KSP problem are proposed. A heuristic population initialization algorithm is designed to avoid loops and dead points due to the topological characteristics of the network. The reasonable crossover and mutation operators are designed to avoid the illegal individuals according to the sparsity characteristic of the network. Results show that the proposed model and algorithm can effectively solve the dynamic stochastic KSP problem. The proposed model can also solve the network flow stochastic optimization problems in transportation, communication networks, and other networks

Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from optimal. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called \emph{decentralized stick-breaking policy representation} (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods

Drosophila arc Encodes a Novel Adherens Junction-Associated PDZ Domain Protein Required for Wing and Eye Development

AbstractLoss of arc function results in a downwardly curved wing and smaller eyes with a reduced number of ommatidia. Consistent with this phenotype, molecular analysis shows that arc mRNA and protein are expressed in the wing imaginal disc and in clusters of cells in the morphogenetic furrow of the eye imaginal disc. The 36-kb arc transcription unit contains 10 exons that are spliced to form a 5.5-kb mRNA. The encoded Arc protein is 143,000 Da and contains two PDZ (PSD-95, Discs large, ZO-1) domains; there is no close structural similarity to other PDZ proteins. In addition to its expression in imaginal discs, arc is expressed during embryogenesis in epithelia undergoing morphogenesis, including the invaginating posterior midgut, evaginating Malpighian tubule buds, elongating hindgut, invaginating salivary glands, intersegmental grooves, and developing tracheae. Arc protein colocalizes with Armadillo (β-catenin) to the apical (luminal) surface of these developing epithelia, indicating that it is associated with adherens junctions. Genes that are required for patterning of embryonic epithelia (e.g., tailless, Krüppel, fork head, and brachyenteron) or for progression of the morphogenetic furrow (i. e., hedgehog) are required to establish or maintain the regional expression of arc. Misexpression of arc in the eye imaginal discs results in rough and larger eyes with fused ommatidia. We propose that arc affects eye development by modulating adherens junctions of the developing ommatidium

Detecting Differential Expression from RNA-seq Data with Expression Measurement Uncertainty

High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detecting differentially expressed (DE) genes from RNA-seq are based on statistics that compare normalized read counts between conditions. However, there are few methods considering the expression measurement uncertainty into DE detection. Moreover, most methods are only capable of detecting DE genes, and few methods are available for detecting DE isoforms. In this paper, a Bayesian framework (BDSeq) is proposed to detect DE genes and isoforms with consideration of expression measurement uncertainty. This expression measurement uncertainty provides useful information which can help to improve the performance of DE detection. Three real RAN-seq data sets are used to evaluate the performance of BDSeq and results show that the inclusion of expression measurement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users, which is freely available at the website http://parnec.nuaa.edu.cn/liux/GSBD/GamSeq-BDSeq.html.Comment: 20 pages, 9 figure

Building complex language processors in VoiceXML.

VoiceXML was accepted by the World Wide Web Consortium (W3C) as a standard XML-based markup language for distributed Web-based voice services. It was designed to provide a way for web developers to use a familiar markup style to deliver voice content to the Internet. Grammars are used to specify the words and patterns of words that a user can say at any particular point in a dialog. However, in most current applications, VoiceXML is used as a simple language processor used to convert speech to text. In this thesis, we investigate the expressive power of pure VoiceXML and the expressive power of Java Speech Grammar Format (JSGF) tagging mechanism, ECMAScript and server-side processing. In order to build complex language processors in VoiceXML, we give a combination of VoiceXML JSGF tagging, ECMAScript and server-side processing. The thesis work is concerned with the ability to use VoiceXML to define semantics as well as syntax. A prototype has been implemented to demonstrate the efficiency of different approaches.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L58. Source: Masters Abstracts International, Volume: 41-04, page: 1112. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2002