Regulation of APC/C-Cdh1 and Its Function in Neuronal Survival

This paper presents WebCQ, a prototype of a large-scale Web information monitoring system, WebCQ is designed to discover and detect changes to the World Wide Web (the Web) pages efficiently, and to notify users of interesting changes with a personalized customization. The system consists of four main components: a change detection robot that discovers and detects changes, a proxy cache service that reduces the communication traffics to the original information provider on the remote server, a tool that highlights changes between the web page last seen and the new version of the page, and a change notification service that delivers interesting changes and fresh information to the right users at the right time. A salient feature of our change detection robot is its ability to support various types of web page sentinels for finding and displaying interesting changes to web pages. This paper describes the WebCQ system with an emphasis on general issues in designing and engineering a la..

Properties of a coupled two species atom-heteronuclear molecule condensate

We study the coherent association of a two-species atomic condensate into a condensate of heteronuclear diatomic molecules, using both a semiclassical treatment and a quantum mechanical approach. The differences and connections between the two approaches are examined. We show that, in this coupled nonlinear atom-molecule system, the population difference between the two atomic species plays a significant role in the ground-state stability properties as well as in coherent population oscillation dynamics.Comment: 7 pages, 4 figure

Creating stable molecular condensate using a generalized Raman adiabatic passage scheme

We study the Feshbach resonance assisted stimulated adiabatic passage of an effective coupling field for creating stable molecules from atomic Bose condensate. By exploring the properties of the coherent population trapping state, we show that, contrary to the previous belief, mean-field shifts need not to limit the conversion efficiency as long as one chooses an adiabatic passage route that compensates the collision mean-field phase shifts and avoids the dynamical unstable regime.Comment: 4+\epsilon pages, 3 figure

InfoFilter: Supporting Quality of Service for Fresh Information Delivery

With the explosive growth of the Internet and World Wide Web comes a dramatic increase in the number of users that compete for the shared resources of distributed system environments. Most implementations of application servers and distributed search software do not distinguish among requests to different web pages. This has the implication that the behavior of application servers is quite unpredictable. Applications that require timely delivery of fresh information consequently suffer the most in such competitive environments. This paper presents a model of quality of service (QoS) and the design of a QoS-enabled information delivery system that implements such a QoS modeL The goal of this development is two-fold. On one hand, we want to enable users or applications to specify the desired quality of service requ.irements for their requests so that application-aware QoS adaptation is supported throughout the Web query and search processing. On the other hand, we want to enable an application server to customize how it shou.ld respond to external requests by setting priorities among query requests and allocating server resources using adaptive QoS control mechanisms. We introduce the Infopipe approach as the systems support architecture and underlying technology for building a QoS-enabled distributed system for fresh information delivery

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly