TCP Selective Negative Acknowledgment over IEEE 802.11 Wireless Networks
This paper investigates the performance of the TCP transport protocol over IEEE 802.11 wireless networks. A wireless link is generally characterized by high transmission errors and a varying latency. The erratic packet losses and poor bandwidth utilization which occur as a result limit the TCP's performance, resulting in a curbing of the flow of segments on the TCP connection and a corresponding degradation of the network performance. This paper examines the effects of the 802.11 characteristics on the TCP performance and proposes a scheme to improve the performance of TCP over wireless links. A novel acknowledgement scheme, SNACK, is introduced and a series of ns-2 based simulations are performed to compare its performance against that of other TCP schemes. The simulation results confirm that SNACK provides a better performance than other conventional TCP implementations in 802.11 wireless networks

title: CORNet: A Scalable and Bandwidth-Efficient Optical Burst Switching Ring Architecture for Metro Area Networks
abstract: The rapid evolution of networks has been driven by new advances in enabling technologies and the explosive growth of Internet traffic. optical burst switching (OBS) is not only cost-effective, but also provides high throughput, high bandwidth utilization, and low transmission latency. These benefits make OBS a viable solution for next-generation metro area networks (MANs) in which the traffic models are changed more dramatically than today's networks. Based on wavelength division multiplexing (WDM) technology, this study presents a loss-free optical burst switching metro ring architecture, designated the collision-free optic-burst ring network (CORNet). The proposed architecture ensures a collision-free transmission of data bursts, while retaining the advantage of the statistical multiplexing provided by conventional OBS networks. The simulation results confirm the effectiveness of the proposed CORNet architecture

title: Improving the ramping up behavior of TCP slow start
abstract: This paper proposes an improvement to the TCP ramp up behavior in slow-start. Current implementations of the TCP start-up procedure may result in an exponential growth of the congestion window, which results in the transmission of an excessive number of packets. Consequently, the network becomes congested and the self-clocking mechanism of the TCP fails. By estimating the number of packets that which can be in flight in the network from the viewpoint of the TCP source, this study develops an algorithm to suppress congestion before it becomes significant. The simulation results indicate that the proposed algorithm enhances the TCP performance by establishing a smoother transmission rate at the end of the slow-start phase and reducing the number of dropped packets significantly as a result.

title: A switched delay line based optical switch architecture with a bypass line
abstract: This paper proposes a new switching architecture to be used in all-optical networks. The proposed switch, M-B-Quadro switch, is extended from an original 2/spl times/2 two-stage multi-buffer switched delay line based optical switching node, known as M-Quadro node. By incorporating bypass lines and employing a novel switch control strategy, denoted as LAVS, the switching node can effectively resolve packet contentions, thus reducing the packet deflection probability substantially.

title: Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
abstract: We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thresholds. We derive novel large-margin bounds of common error functions, such as the classification error and the absolute error. In addition to some existing algorithms, we also study two novel boosting approaches for constructing thresholded ensembles. Both our approaches not only are simpler than existing algorithms, but also have a stronger connection to the large-margin bounds. In addition, they have comparable performance to SVM-based algorithms, but enjoy the benefit of faster training. Experimental results on benchmark datasets demonstrate the usefulness of our boosting approaches.

title: Model checking value-passing processes
abstract: An algorithm for model checking value-passing processes is presented. Processes are modeled as symbolic transition graphs with assignments. To specify properties for such processes a graphical predicate mu-calculus is introduced. It allows arbitrary nesting of the least and greatest fixpoints, and contains the propositional mu-calculus as a proper subset. The algorithm instantiates input variables on-the-fly and states are only generated when they are needed for the computation. To handle alternating fix-points properly, a multi-stack is employed and the controlling strategy is such that a state is evaluated without depending on the default values for more deeply nested states. The algorithm is shown correct with respect to the semantics of the predicate mu-calculus. Its complexity is also analysed.

title: Axiomatising timed automata
abstract: Timed automata has been developed as a basic semantic model for real time systems. Its algorithmic aspects for automated analysis have been well studied. But so far there is still no satisfactory algebraic theory to allow the derivation of semantical equivalence of automata by purely syntactical manipulation. The aim of this paper is to provide such a theory. We present an inference system of timed bisimulation equivalence for timed automata based on a CCS-style regular language for describing timed automata. It consists of the standard monoid laws for bisimulation and a set of inference rules. The judgments of the proof system are conditional equations of the form 
$\phi \rhd t = u$ where 
$\phi$ is a clock constraint and t,u are terms denoting timed automata. The inference system is shown to be sound and complete for timed bisimulation. The proof of the completeness result relies on the notion of symbolic timed bisimulation, adapted from the work on value–passing processes.

title: PAM: A Process Algebra Manipulator
abstract: PAM is a general proof tool for process algebras. It allows users to define their own calculi and then perform algebraic style proofs in these calculi by directly manipulating process terms. The logic that PAM implements is equational logic plus recursion, with some features tailored to the particular requirements of process algebras. Equational reasoning is implemented by rewriting, while recursion is dealt with by induction. Proofs are constructed interactively, giving users the freedom to control the proof processes.

title: Power and Type I error rates of goodness-of-fit statistics for binomial generalized estimating equations (GEE) models
abstract: Binary outcomes are very common in medical studies. Logistic regression is typically used to analyze independent binary outcomes while generalized estimating equations regression methods (GEE) are often used to analyze correlated binary data. Several goodness-of-fit (GoF) statistics for the GEE methods have been developed recently. The objective of this study is to compare the power and Type I error rates of existing GEE GoF statistics using simulated data under different conditions. The number of clusters was varied in each condition. Different tested models included discrete, continuous, observation-specific and/or cluster-specific covariates. Two or three observations per cluster were generated with various correlations between observations. No single GEE GoF statistic performed best across all conditions. Generally, the larger the number of clusters, the more powerful the GEE GoF statistics. The GEE GoF statistics with correctly specified working correlation matrices tended to be robust in terms of Type I error rates and more powerful. For data with two observations per cluster, both Evans and Pan's statistics [1998. Goodness of fit in two models for clustered binary data. Ph.D. Dissertation, University of Massachusetts; 2002a. Goodness-of-fit tests for GEE with correlated binary data. Scand. ] and Barnhart-Williamson's statistics [1998. Goodness-of-fit tests for GEE modeling with binary data. Biometrics 54, 720-729.] performed well for detecting the effect of the omitted interaction between two binary covariates. Barnhart-Williamson's statistics were generally the most powerful for detecting other types of interactions in models with at least one continuous covariate. For data with three observations per cluster, Evans and Pan's statistics performed best. fit in two models for clustered binary data. Ph.D. Dissertation, University of Massachusetts; 2002a. Goodness-of-fit tests for GEE with correlated binary data. Scand. ] statistics performed best.