Estoppel in the face of consumer protection in land transactions- judicial reasoning or judicial intuition

Nationally, there is much legislation regulating land sale transactions, particularly in relation to seller disclosure of information. The statutes require strict compliance by a seller failing which, in general, a buyer can terminate the contract. In a number of instances, when buyers have sought to exercise these rights, sellers have alleged that buyers have either expressly or by conduct waived their rights to rely upon these statutes. This article examines the nature of these rights in this context, whether they are capable of waiver and, if so, what words or conduct might be sufficient to amount to waiver. The analysis finds that the law is in a very unsatisfactory state, that the operation of those rules that can be identified as having relevance are unevenly applied and concludes that sellers have, in the main, been unsuccessful in defeating buyers' statutory rights as a result of an alleged waiver by those buyers.

Soundness of workflow nets: classification, decidability, and analysis

Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets.We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

The changing face of practice for developing perception : action skill in cricket

The use of bowling machines is common practice in cricket. In an ideal world all batters would face real bowlers in practice sessions, but this is not always possible, for many reasons. The clear advantage of using bowling machines is that they alleviate the workload required from bowlers (Dennis, Finch & Farhart, 2005) and provide relatively consistent and accurate ball delivery which may not be otherwise available to many young batters. Anecdotal evidence suggests that many, if not most of the world’s greatest players use these methods within their training schedules. For example, Australian internationals, Michael Hussey and Matthew Hayden extensively used bowling machines (Hussey & Sygall, 2007). Bowling machines enable batsmen to practice for long periods, developing their endurance and concentration. However, despite these obvious benefits, in recent times the use of bowling machines has been questioned by sport scientists, coaches, ex- players and commentators. For example, Hussey’s batting coach comments “…we never went near a bowling machine in [Michael’s] first couple of years, I think there’s something to that …” (Hussey & Sygall, 2007, p. 119). This chapter will discuss the efficacy of using bowling machines with reference to research findings, before reporting new evidence that provides support for an alternative, innovative and possibly more representative practice design. Finally, the chapter will provide advice for coaches on the implications of this research, including a case study approach to demonstrate the practical use of such a design.

Home literacy education of Taiwanese Australian families : a sociological analysis

This research investigates home literacy education practices of Taiwanese families in Australia. As Taiwanese immigrants represent the largest ¡°Chinese Australian¡± subgroup to have settled in the state of Queensland, teachers in this state often face the challenges of cultural differences between Australian schools and Taiwanese homes. Extensive work by previous researchers suggests that understanding the cultural and linguistic differences that influence how an immigrant child views and interacts with his/her environment is a possible way to minimise the challenges. Cultural practices start from infancy and at home. Therefore, this study is focused on young children who are around the age of four to five. It is a study that examines the form of literacy education that is enacted and valued by Taiwanese parents in Australia. Specifically, this study analyses ¡°what literacy knowledge and skill is taught at home?¡±, ¡°how is it taught?¡± and ¡°why is it taught?¡± The study is framed in Pierre Bourdieu.s theory of social practice that defines literacy from a sociological perspective. The aim is to understand the practices through which literacy is taught in the Taiwanese homes. Practices of literacy education are culturally embedded. Accordingly, the study shows the culturally specialised ways of learning and knowing that are enacted in the study homes. The study entailed four case studies that draw on: observations and recording of the interactions between the study parent and child in their literacy events; interviews and dialogues with the parents involved; and a collection of photographs of the children.s linguistic resources and artefacts. The methodological arguments and design addressed the complexity of home literacy education where Taiwanese parents raise children in their own cultural ways while adapting to a new country in an immigrant context. In other words, the methodology not only involves cultural practices, but also involves change and continuity in home literacy practices. Bernstein.s theory of pedagogic discourse was used to undertake a detailed analysis of parents. selection and organisation of content for home literacy education, and the evaluative criteria they established for the selected literacy knowledge and skill. This analysis showed how parents selected and controlled the interactions in their child.s literacy learning. Bernstein.s theory of pedagogic discourse was used also to analyse change and continuity in home literacy practice, specifically, the concepts of ¡°classification¡± and ¡°framing¡±. The design of this study aimed to gain an understanding of parents. literacy teaching in an immigrant context. The study found that parents tended to value and enact traditional practices, yet most of the parents were also searching for innovative ideas for their adult-structured learning. Home literacy education of Taiwanese families in this study was found to be complex, multi-faceted and influenced in an ongoing way by external factors. Implications for educators and recommendations for future study are provided. The findings of this study offer early childhood teachers in Australia understandings that will help them build knowledge about home literacy education of Taiwanese Australian families.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Convexity, Classification, and Risk Bounds

Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 0–1 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function—that it satisfies a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the case of low noise, and show that in this case, strictly convex loss functions lead to faster rates of convergence of the risk than would be implied by standard uniform convergence arguments. Finally, we present applications of our results to the estimation of convergence rates in function classes that are scaled convex hulls of a finite-dimensional base class, with a variety of commonly used loss functions.