Improved on-line broadcast scheduling with deadlines

We study an on-line broadcast scheduling problem in which requests have deadlines, and the objective is to maximize the weighted throughput, i.e., the weighted total length of the satisfied requests. For the case where all requested pages have the same length, we present an online deterministic algorithm named BAR and prove that it is 4.56-competitive. This improves the previous algorithm of (Kim, J.-H., Chwa, K.-Y. in Theor. Comput. Sci. 325(3):479–488, 2004) which is shown to be 5-competitive by (Chan, W.-T., et al. in Lecture Notes in Computer Science, vol. 3106, pp. 210–218, 2004). In the case that pages may have different lengths, we give a ( )-competitive algorithm where Δ is the ratio of maximum to minimum page lengths. This improves the (4Δ+3)-competitive algorithm of (Chan, W.-T., et al. in Lecture Notes in Computer Science, vol. 3106, pp. 210–218, 2004). We also prove an almost matching lower bound of Ω(Δ/log Δ). Furthermore, for small values of Δ we give better lower bounds. The work described in this paper was fully supported by grants from the Research Grants Council of the Hong Kong SAR, China [CityU 1198/03E, HKU 7142/03E, HKU 5172/03E], an NSF Grant of China [No. 10371094], and a Nuffield Foundation Grant of UK [NAL/01004/G].     

Benchmarking binary classification models on data sets with different degrees of imbalance

In practice, there are many binary classification problems, such as credit risk assessment, medical testing for determining if a patient has a certain disease or not, etc. However, different problems have different characteristics that may lead to different difficulties of the problem. One important characteristic is the degree of imbalance of two classes in data sets. For data sets with different degrees of imbalance, are the commonly used binary classification methods still feasible? In this study, various binary classification models, including traditional statistical methods and newly emerged methods from artificial intelligence, such as linear regression, discriminant analysis, decision tree, neural network, support vector machines, etc., are reviewed, and their performance in terms of the measure of classification accuracy and area under Receiver Operating Characteristic (ROC) curve are tested and compared on fourteen data sets with different imbalance degrees. The results help to select the appropriate methods for problems with different degrees of imbalance.     

Neural network-based mean–variance–skewness model for portfolio selection

In this study, a novel neural network-based mean–variance–skewness model for optimal portfolio selection is proposed integrating different forecasts and trading strategies, as well as investors’ risk preference. Based on the Lagrange multiplier theory in optimization and the radial basis function (RBF) neural network, the model seeks to provide solutions satisfying the trade-off conditions of mean–variance–skewness. The feasibility of the RBF network-based mean–variance–skewness model is verified with a simulation experiment. The experimental results show that, for all examined investor risk preferences and investment assets, the proposed model is a fast and efficient way of solving the trade-off in the mean–variance–skewness portfolio problem. In addition, we also find that the proposed approach can also be used as an alternative tool for evaluating various forecasting models.     

Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management

In this study, we propose a support vector machine (SVM)-based ensemble learning system for customer relationship management (CRM) to help enterprise managers effectively manage customer risks from the risk aversion perspective. This system differs from the classical CRM for retaining and targeting profitable customers; the main focus of the proposed SVM-based ensemble learning system is to identify high-risk customers in CRM for avoiding possible loss. To build an effective SVM-based ensemble learning system, the effects of ensemble members’ diversity, ensemble member selection and different ensemble strategies on the performance of the proposed SVM-based ensemble learning system are each investigated in a practical CRM case. Through experimental analysis, we find that the Bayesian-based SVM ensemble learning system with diverse components and choose from space selection strategy show the best performance over various testing samples.     

On the Handover Performance of a Tri‐threshold Bandwidth Reservation CAC Scheme

A dynamic tri‐threshold call admission control scheme has been developed. This scheme supports voice, data, and multimedia services and it complies with the universal mobile telecommunications system. The performance of the proposed scheme is evaluated under varying handover rates. The QoS performance–including channel utilization, call dropping probability, and blocking probability–is investigated. The performance of the developed scheme is found to be encouraging.     

An intelligent service-based network architecture for wearable robots

We are developing a novel robot concept called the wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, we explore the possible functions of such a robotic network and will present a distributed network architecture based on service components. In order to support the interaction and communication between the components in the wearable robot system, we have developed an intelligent network architecture. This service-based architecture involves three major mechanisms. The first mechanism involves the use of a task coordinator service such that the execution of the services can be managed using a priority queue. The second mechanism enables the system to automatically push the required service proxy to the client intelligently based on certain system-related conditions. In the third mechanism, we allow the system to automatically deliver services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can determine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service, and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture. The architecture is implemented as extensions to the Jini network model.     

Pharmacokinetics and safety of single intravenous and oral doses of dolasetron mesylate in healthy women

The protease thrombin seems to play a central role in events following neural injury, whereby the enzyme can act, in concert with other molecules as a hormone or as a growth factor. In cells derived from the nervous system, thrombin induces changes in morphology and proliferation. The signalling mechanisms involved in these thrombin-activated processes are still unclear. In the present study we investigated Ca2+ signals in fura-2 loaded rat astrocytes in primary culture. Brief stimulation of astrocytes with thrombin induced a dose-dependent transient elevation of [Ca2+]i, best fitted by a double-sigmoidal curve giving two EC50 values of 3 pM and 150 pM. Continuous superfusion of cells with thrombin induced Ca2+ responses with three different types of kinetics. In 48% of the cells tested a single transient rise superimposed with fast fluctuations of [Ca2+]i was seen. The following complex long-term changes of [Ca2+]i, dependent on the presence of the agonist thrombin, were observed: i) a biphasic [Ca2+]i elevation, characterized by an initial peak followed by a sustained plateau phase (in 43% of the cells) and ii) oscillations of [Ca2+]i (in 9% of the cells). The observed Ca2+ responses were inhibited by the phospholipase C (PLC) inhibitor U-73122 and the thrombin inhibitor protease nexin-1/glia-derived nexin. The synthetic thrombin receptor activating peptide could mimic the thrombin-induced changes of [Ca2+]i. In astrocytes in Ca2+-free medium, thrombin induced a sharp single transient Ca2+ rise, without superimposed fluctuations. After depletion of intracellular Ca2+ stores with thapsigargin the Ca2+ response to thrombin was diminished or completely suppressed indicating that thrombin induces the release of Ca2+ from intracellular stores. During long-term Ca2+ responses, omission of extracellular Ca2+ resulted in a reversible interruption of the signal. In conclusion our results demonstrate that thrombin by activation of its plasma membrane receptor induces through activation of PLC different types of Ca2+ responses. The complex Ca2+ signals are generated by an interplay of InsP3-mediated Ca2+ release from intracellular stores and Ca2+ entry across the plasma membrane.     

Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network

Facing the complicated non-linear nature of risk evolutions, current risk measurement approaches offer insufficient explanatory power and limited performance. Thus this paper proposes wavelet decomposed non-linear ensemble value at risk (WDNEVaR), a novel semi-parametric paradigm, incorporating both, wavelet analysis and artificial neural network technique to further improve the modeling accuracy and reliability. Wavelet analysis is utilized to capture the multi-scale data characteristics across scales while artificial neural network technique is utilized to reduce estimation biases following non-linear ensemble algorithms. Experiment results in three major markets suggest that the proposed WDNEVaR is superior to more traditional approaches as it provides value at risk (VaR) estimates at higher reliability and accuracy.     

The Determination and Indetermination of Service Times in Manufacturing Systems

The notion of service times is of such fundamental importance in the analysis of queues that it has long been taken for granted. Intuitively, it is used to represent the time interval that a server is capable of completing a dispatched job. However, actual measurements of service times under simple queues in production lines have encountered practical difficulties, in spite of its seemingly deterministic nature. Previous studies have introduced concepts of effective process times to quantify service times. Besides notions of theoretical processing times, raw process times and queueing times, among others, are commonly used in various applications. Their existence causes confusion in the determination of service times and clarification of such terminologies is needed. A simple model is examined to quantify the various concepts and establish their interrelationships. This paper brings out new properties of effective process times with a dynamic dependence on utilization. Discrete event simulations are conducted to verify these properties and explain the phenomenon of indetermination of service times. Both theoretical prediction and simulation results show that unless the system is fully loaded, service time and effective process time are not equivalent and it cannot be measured directly from observations of effective process times.     

Social-cognitive processes as moderators of a couple-focused group intervention for women with early stage breast cancer.

Objective: The purpose was to examine whether social-cognitive variables would moderate the efficacy of a couple-focused group intervention (CG) for women diagnosed with early stage breast cancer. Design: Participants (N = 238) were randomly assigned to 6 sessions of a couple-focused group versus usual care. Intent to treat growth curve modeling analyses indicated that emotional expression and emotional processing moderated CG effects on depression. Main Outcome Measures: The primary outcome measures for this study were psychological distress and psychological well-being. Results: Treatment attrition analyses separating out participants assigned to but not attending CG indicated that emotional expression, emotional processing, and protective buffering moderated the effects of CG among those who attended CG with the most consistent effects noted for emotional processing on indicators of distress and well-being. Conclusion: The CG intervention may be more effective for patients who begin the group experience using emotional approach coping strategies to deal with cancer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)