Model checking value-passing processes
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: 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: Symbolic Bisimulations

title: A Proof System for Timed Automata
abstract: A proof system for timed automata is presented, based on a CCS-style 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 Φ ▹ t = u where Φ is a clock constraint and t, u are terms denoting timed automata. It is proved that the proof system is complete for timed bisimulation over the recursion-free subset of the language. The completeness proof relies on the notion of symbolic timed bisimulation. The axiomatisation is also extended to handle an important variation of timed automata where each node is associated with an invariant constraint.

title: A Note on the Decomposition Methods for Support Vector Regression
abstract: The dual formulation of support vector regression involves two closely related sets of variables. When the decomposition method is used, many existing approaches use pairs of indices from these two sets as the working set. Basically, they select a base set first and then expand it so all indices are pairs. This makes the implementation different from that for support vector classification. In addition, a larger optimization subproblem has to be solved in each iteration. We provide theoretical proofs and conduct experiments to show that using the base set as the working set leads to similar convergence (number of iterations). Therefore, by using a smaller working set while keeping a similar number of iterations, the program can be simpler and more efficient.

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: 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: Automatic Detection and Segmentation of Robot-Assisted Surgical Motions
abstract: Robotic surgical systems such as Intuitive Surgical's da Vinci system provide a rich source of motion and video data from surgical procedures. In principle, this data can be used to evaluate surgical skill, provide surgical training feedback, or document essential aspects of a procedure. If processed online, the data can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. This paper examines our progress at developing techniques for "parsing" raw motion data from a surgical task into a labelled sequence of surgical gestures. Our current techniques have achieved >90% fully automated recognition rates on 15 datasets.

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.