Julia 2010 : the caretaker election

This book provides a comprehensive coverage of one of Australia’s most historic elections, which produced a hung parliament and a carefully crafted minority government that remains a heartbeat away from collapse, as well as Australia’s first elected woman Prime Minister and the Australian Greens’ first lower house Member of Parliament.

The volume considers the key contextual and possibly determining factors, such as: the role of leadership and ideology in the campaign; the importance of state and regional factors (was there evidence of the two or three speed economy at work?); and the role of policy areas and issues, including the environment, immigration, religion, gender and industrial relations. Contributors utilise a wide range of sources and approaches to provide comprehensive insights into the campaign. This volume notably includes the perspectives of the major political groupings, the ALP, the Coalition and the Greens; and the data from the Australian Election Survey. Finally we conclude with a detailed analysis of those 17 days that it took to construct a minority party government.

Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property

Even though the importance of the local monotonicity property for function approximation problems is well established, there are relative few investigations addressing issues related to the fulfillment of the local monotonicity property in Fuzzy Inference System (FIS) modeling. We have previously conducted a preliminary study on the local monotonicity property of FIS models, with the assumption that the extrema point(s) (i.e., the maximum and/or minimum point(s)) is either known precisely or totally unknown. However, in some practical situations, the extrema point(s) can be known imprecisely (as an interval or a fuzzy set). In this paper, the imprecise information is exploited to construct an FIS model that fulfills the local monotonicity property. A procedure to estimate the extrema point(s) of a function is devised. Applicability of the findings to a datadriven modeling problem is further demonstrated.

Face set classification using maximally probable mutual modes

In this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. The main contribution is an algorithm based on the novel concept of maximally probable mutual modes (MMPM). Specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. Recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. The effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individuals