A Note on the Decomposition Methods for Support Vector Regression
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: 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: 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: 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: 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: 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: "On-the-fly Instantiation" of Value-passing Processes
abstract: The traditional approach to automatic verification of value-passing processes is to translate them into pure processes, by instantiating input variables over all possible values, then submit the resulting pure processes to verification tools. The main disadvantage of this approach is that problems with infinite value domain can not be dealt with, even if these values are never used by the program, as in the case of communication protocols. In this paper we propose an algorithm which works directly on value-passing processes, represented as symbolic transition graphs with assignment. The key idea is to instantiate input variables “on-the-fly”, while checking bisimulation, making it possible not to store the entire concrete transition system either before or during the verification process. As a consequence problems with large, even infinite, state spaces can be handled. The algorithm has been implemented and tested on some typical verification problems.

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.