Axiomatising timed automata
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: Unique Fixpoint Induction for Mobile Processes
abstract: Complete proof systems for bisimulation equivalences in the π-calculus with recursion are presented. The key inference rule dealing with recursion is unique fixpoint induction which generalises that used in [Mil84] for regular pure-CCS. It is shown that the proof systems are sound, and also complete when restricted to the subset of π-calculus where recursions are guarded and the parallel composition is disallowed. These results on the one hand extend the proof systems for recursion-free π-calculus in [Lin94, Lin95] to infinite processes, on the other hand extend the inference system for guarded regular pure-CCS of [Mil84] to π-calculus.

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: Computing bisimulations for finite-control π-calculus
abstract: Symbolic bisimulation avoids the infinite branching problem caused by instantiating input names with all names in the standard definition of bisimulation in π-calculus. However, it does not automatically lead to an efficient algorithm, because symbolic bisimulation is indexed by conditions on names, and directly manipulating such conditions can be computationally costly. In this paper a new notion of bisimulation is introduced, in which the manipulation of maximally consistent conditions is replaced with a systematic employment of schematic names. It is shown that the new notion captures symbolic bisimulation in a precise sense. Based on the new definition an efficient algorithm, which instantiates input names “on-the-fly”, is presented to check bisimulations for finite-control π-calculus.

title: Symbolic Bisimulations

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: 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: 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: 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: 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.

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.