PAM: A Process Algebra Manipulator
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 Transition Graph with Assignment
abstract: A new model for message-passing processes is proposed which generalizes the notion of symbolic transition graph as introduced in [HL95], by allowing assignments to be carried in transitions. The main advantage of this generalization is that a wider class of processes can be represented as finite state graphs. Two kinds of operational semantics, ground and symbolic, are given to such graphs. On top of them both ground and symbolic bisimulations are defined and are shown to agree with each other. An algorithm is also presented which computes bisimulation formulae for finite state symbolic transition graphs with assignments, in terms of the greatest solutions of predicate equation systems.

title: An Interactive Proof Tool for Process Algebras
abstract: A proof assistant for process algebras is described. The main novelty of this proof system is that it is parameterised by process calculi: the users can define their own calculi by providing suitable signatures and axioms. Proofs are constructed by directly manipulating process terms, and some forms of induction has been built-in to handle recursion.

title: Complete Inference Systems for Weak Bisimulation Equivalences in the pi-Calculus
abstract: Proof systems for weak bisimulation equivalences in the π-calculus are presented, and their soundness and completeness are shown. The proofs of the completeness results rely on the notion of symbolic bisimulation. Two versions of π-calculus are investigated, one without and the other with the mismatch construction. For each version of the calculus proof systems for both late and early weak bisimulation equivalences are studied. Thus there are four proof systems in all. These proof systems are related in a natural way: the proof systems for early and late equivalences differ only in the inference rule for the input prefix, while the proof system for the version of π-calculus with mismatch is obtained by adding a single inference rule for the version without it.

title: Proof systems for message-passing process algebras
abstract: We give sound and complete proof systems for a variety of bisimulation based equivalences over a message-passing process algebra. The process algebra is a generalisation of pureCCS where the actions consist of receiving and sending messages or data on communication channels; the standard prefixing operatora.p is replaced by the two operatorsc?x.p andc!e.p and in addition messages can be tested by a conditional construct. The various proof systems are parameterised on auxiliary proof systems for deciding on equalities or more general boolean identities over the expression language for data. The completeness of these proof systems are thus relative to the completeness of the auxiliary proof systems.

title: Symbolic Bisimulations

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.

title: Improving Generalization by Data Categorization
abstract: In most of the learning algorithms, examples in the training set are treated equally. Some examples, however, carry more reliable or critical information about the target than the others, and some may carry wrong information. According to their intrinsic margin, examples can be grouped into three categories: typical, critical, and noisy. We propose three methods, namely the selection cost, SVM confidence margin, and AdaBoost data weight, to automatically group training examples into these three categories. Experimental results on artificial datasets show that, although the three methods have quite different nature, they give similar and reasonable categorization. Results with real-world datasets further demonstrate that treating the three data categories differently in learning can improve generalization.

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: Navigating inner space: 3-D assistance for minimally invasive surgery
abstract: Since its inception about three decades ago, modern minimally invasive surgery has made huge advances in both technique and technology. However, the minimally invasive surgeon is still faced with daunting challenges in terms of visualization and hand-eye coordination. At the Center for Computer Integrated Surgical Systems and Technology (CISST) we have been developing a set of techniques for assisting surgeons in navigating and manipulating the three-dimensional space within the human body. In order to develop such systems, a variety of challenging visual tracking, reconstruction and registration problems must be solved. In addition, this information must be tied to methods for assistance that improve surgical accuracy and reliability but allow the surgeon to retain ultimate control of the procedure and do not prolong time in the operating room. In this article, we present two problem areas, eye microsurgery and thoracic minimally invasive surgery, where computational vision can play a role. We then describe methods we have developed to process video images for relevant geometric information, and related control algorithms for providing interactive assistance. Finally, we present results from implemented systems.