The 'nurdle to leg' and other ways of winning cricket matches

The emergence of Twenty20 cricket at the elite level has been marketed on the excitement of the big hitter, where it seems that winning is a result of the muscular batter hitting boundaries at will. This version of the game has captured the imagination of many young players who all want to score runs with “big hits”. However, in junior cricket, boundary hitting is often more difficult due to size limitations of children and games played on outfields where the ball does not travel quickly. As a result, winning is often achieved via a less spectacular route – by scoring more singles than your opponents. However, most standard coaching texts only describe how to play boundary scoring shots (e.g. the drives, pulls, cuts and sweeps) and defensive shots to protect the wicket. Learning to bat appears to have been reduced to extremes of force production, i.e. maximal force production to hit boundaries or minimal force production to stop the ball from hitting the wicket. Initially, this is not a problem because the typical innings of a young player (<12 years) would be based on the concept of “block” or “bash” – they “block” the good balls and “bash” the short balls. This approach works because there are many opportunities to hit boundaries off the numerous inaccurate deliveries of novice bowlers. Most runs are scored behind the wicket by using the pace of the bowler’s delivery to re-direct the ball, because the intrinsic dynamics (i.e. lack of strength) of most children means that they can only create sufficient power by playing shots where the whole body can contribute to force production. This method works well until the novice player comes up against more accurate bowling when they find they have no way of scoring runs. Once batters begin to face “good” bowlers, batters have to learn to score runs via singles. In cricket coaching manuals (e.g. ECB, n.d), running between the wickets is treated as a separate task to batting, and the “basics” of running, such as how to “back- up”, carry the bat, calling and turning and sliding the bat into the crease are “drilled” into players. This task decomposition strategy focussing on techniques is a common approach to skill acquisition in many highly traditional sports, typified in cricket by activities where players hit balls off tees and receive “throw-downs” from coaches. However, the relative usefulness of these approaches in the acquisition of sporting skills is increasingly being questioned (Pinder, Renshaw & Davids, 2009). We will discuss why this is the case in the next section.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Boosting the margin: A new explanation for the effectiveness of voting methods

One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.