Max Dupain and the photography of Australian architecture

This thesis considers Max Dupain (1911-1992) and his contribution to the development of architectural photography in Australia. Through his continuous and prolific output over six decades of professional photography Dupain greatly stimulated awareness of and interest in Australian architecture. Before Dupain began specialising in the field, little consistent professional architectural photography had been practised in Australia. He and some of his close associates subsequently developed architectural photography as both a specialised branch of photography and - perhaps more significantly - as a necessary adjunct to architectural practice. In achieving these dual accomplishments, Dupain and like-minded practitioners succeeded in elevating architectural photography to the status of a discipline in its own right. They also gave Australians generally a deeper understanding of the heritage represented by the nation's built environment. At the same time, some of the photographic images he created became firmly fixed in the public imagination as historical icons within the development of a distinctive Australian tradition in the visual arts. Within his chosen field Dupain was the dominant Australian figure of his time. He was instrumental in breaking the link with Pictorialism by bringing Modernist and Documentary perspectives to Australian architectural photography. He was an innovator in the earlier decades of his professional career, however, his photographic techniques and practice did not develop beyond that. By the end of the 1980s he had largely lost touch with the technology and techniques of contemporary practice. Dupain's reputation, which has continued growing since his death in 1992, therefore arises from reasons other than his photographic images alone. It reflects his accomplishment in raising his fellow citizens' awareness of a worthwhile home-grown artistic tradition.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Mary and Max

Synopsis and critique of Australian film in animation, comedy, and drama genres.

Preparation of reticulated MAX-phase support with morphology-controllable nanostructured ceria coating for gas exhaust catalyst devices

Reticulated porous Ti3AlC2 ceramic, a member of the MAX-phase family (Mn+1AXn phases, where M is an early transition metal, A is an A-group element, and X is carbon and/or nitrogen), was prepared from the highly dispersed aqueous suspension by a replica template method. Through a cathodic electrogeneration method, nanocrystalline catalytic CeO2 coatings were deposited on the conductive porous Ti 3AlC2 supports. By adjusting the pH value and cathodic deposition current, coatings exhibiting nanocellar, nanosheets-like, or bubble-free morphologies can be obtained. This work expects to introduce a novel practically feasible material system and a catalytic coating preparation technique for gas exhaust catalyst devices.