Anthony Wirth

Maximizing quadratic programs: extending Grothendieck's inequality

This paper considers the following type of quadratic programming problem. Given an arbitrary matrix A, whose diagonal elements are zero, find x /spl isin/ {-1, 1}/sup n/ such that x/sup T/Ax is maximized. Our approximation algorithm for this problem uses the canonical semidefinite relaxation and returns a solution whose ratio to the optimum is in /spl Omega/(1/ logn). This quadratic programming problem can be seen as an extension to that of maximizing x/sup T/Ay (where ys components are also /spl plusmn/1). Grothendiecks inequality states that the ratio of the optimum value of the latter problem to the optimum of its canonical semidefinite relaxation is bounded below by a constant. The study of this type of quadratic program arose from a desire to approximate the maximum correlation in correlation clustering. Nothing substantive was known about this problem; we present an /spl Omega/ (1/logn) approximation, based on our quadratic programming algorithm. We can also guarantee that our quadratic programming algorithm returns a solution to the MAXCUT problem that has a significant advantage over a random assignment.

Automatic generation of protein structure cartoons with Pro-origami

SUMMARY Protein topology diagrams are 2D representations of protein structure that are particularly useful in understanding and analysing complex protein folds. Generating such diagrams presents a major problem in graph drawing, with automatic approaches often resulting in errors or uninterpretable results. Here we apply a breakthrough in diagram layout to protein topology cartoons, providing clear, accurate, interactive and editable diagrams, which are also an interface to a structural search method. AVAILABILITY Pro-origami is available via a web server at http://munk.csse.unimelb.edu.au/pro-origami CONTACT a.stivala@pgrad.unimelb.edu.au; pjs@csse.unimelb.edu.au.

Analyzing shared and team mental models

Abstract In recent years, there has been a resurgence of interest in the notion of shared cognition. Subsequent to this interest, two similar yet distinct concepts have emerged: ‘shared mental models’ and ‘team mental models’. A ‘shared mental model’ can be described as the extent to which a dyad of individuals possesses a similar cognitive representation of some situation or phenomenon. The notion of ‘team mental model’, is distinct from that of a shared mental model in that it refers to shared cognition in a team as a collectivity, not shared cognition among dyads of individuals, which the alternative phrase ‘shared mental models’ does allow. While a number of techniques have been developed to measure mental model similarity dyadically, appropriate measures of team mental models have eluded researchers. This issue presents a problem for the evolution of the team mental model concept in psychology and the establishment of its validity, for example, as a predictor of team performance. The primary aim of the current paper was to describe the application of randomization tests as a new method for measuring mental model similarity at the team level, that is the measurement of team mental models. A secondary aim was to apply this technique to examine team mental models (of team functioning) in shop floor teams and shared mental models (of team functioning) among shop floor team members, teamwork ‘experts’ and managers. The advantages and disadvantages of the technique are discussed. Relevance to industry The present project aims to provide authors with a roadmap on how to answer team mental models. The team mental model construct has extreme usefulness to individuals and researchers engaged in attempts to make teams work; to help individuals adapt to teamworking, and to make teams more efficient and productive. Teamwork has expanded exponentially in the last 10–15 years with most workers involved in some form of teamwork. Thus, the present paper is timely in its practical value of helping those in industry and organizations, to understand the dynamics of teamwork, and in particular, the ‘how to’ of measuring team mental models.

Set cover algorithms for very large datasets

The problem of Set Cover - to find the smallest subcollection of sets that covers some universe - is at the heart of many data and analysis tasks. It arises in a wide range of settings, including operations research, machine learning, planning, data quality and data mining. Although finding an optimal solution is NP-hard, the greedy algorithm is widely used, and typically finds solutions that are close to optimal. However, a direct implementation of the greedy approach, which picks the set with the largest number of uncovered items at each step, does not behave well when the input is very large and disk resident. The greedy algorithm must make many random accesses to disk, which are unpredictable and costly in comparison to linear scans. In order to scale Set Cover to large datasets, we provide a new algorithm which finds a solution that is provably close to that of greedy, but which is much more efficient to implement using modern disk technology. Our experiments show a ten-fold improvement in speed on moderately-sized datasets, and an even greater improvement on larger datasets.

Phat-a gene finding program for Plasmodium falciparum

We describe and assess the performance of the gene finding program pretty handy annotation tool (Phat) on sequence from the malaria parasite Plasmodium falciparum. Phat is based on a generalized hidden Markov model (GHMM) similar to the models used in GENSCAN, Genie and HMMgene. In a test set of 44 confirmed gene structures Phat achieves nucleotide-level sensitivity and specificity of greater than 95%, performing as well as the other P. falciparum gene finding programs Hexamer and GlimmerM. Phat is particularly useful for P. falciparum and other eukaryotes for which there are few gene finding programs available as it is distributed with code for retraining it on new organisms. Moreover, the full source code is freely available under the GNU General Public License, allowing for users to further develop and customize it.

Spectral clustering with inconsistent advice

Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.

Fast and accurate protein substructure searching with simulated annealing and GPUs

BackgroundSearching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.ResultsWe developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).ConclusionsThe GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.

Structure-Based Statistical Features and Multivariate Time Series Clustering

We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.

Lock-free parallel dynamic programming

We show a method for parallelizing top down dynamic programs in a straightforward way by a careful choice of a lock-free shared hash table implementation and randomization of the order in which the dynamic program computes its subproblems. This generic approach is applied to dynamic programs for knapsack, shortest paths, and RNA structure alignment, as well as to a state-of-the-art solution for minimizing the maximum number of open stacks. Experimental results are provided on three different modern multicore architectures which show that this parallelization is effective and reasonably scalable. In particular, we obtain over 10 times speedup for 32 threads on the open stacks problem.

Can we do without ranks in Burrows Wheeler transform compression

Compressors based on the Burrows Wheeler transform (1994) convert the transformed text into a string of (move-to-front) ranks. These ranks are then encoded with an Order-0 model, or a hierarchy of such models. Although these rank-based methods perform very well, we believe the transformation to MTF numbers blurs the distinction between individual symbols and is a possible cause of inefficiency. Instead of relying on symbol ranking, we examine the problem of directly encoding the symbols in the BWT text.