Transnational business masculinities : media representations of gender and management in global mining corporations

This paper examines Australian media representations of the male managers of two global mining corporations, Rio Tinto and BHP Billiton. These organizations are transnational (or multinational) corporations with assets and/or operations across national boundaries (Dunning and Lundan, 2008), and indeed their respective Chief Executive Officers, Tom Albanese and Marius Kloppers are two of the most economically (and arguably politically) powerful in the world overseeing 37 000 and 39 000 employees internationally. With a 2008 profit of US$15.962 billion and assets of US$ 75.889 Billion BHP Billiton is the world's largest mining company. In terms of its profits and assets Rio Tinto ranks fourth in the world, but with operations in six countries (mainly Canada and Australia) and a 2008 profit of US$10.3 billion it is also emblematic of the transnational in that its ‘budget is larger than that of all but a few nations’ (Giddens, 2003, p. 62).

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Space Station Design Report 2007

Design Proposal for the Blue Lunar Support Hub The conceptual design of a space station is one of the most challenging tasks in aerospace engineering. The history of the space station Mir and the assembly of the International Space Station demonstrate that even within the assembly phase quick solutions have to be found to cope with budget and technical problems or changing objectives. This report is the outcome of the conceptual design of the Space Station Design Workshop (SSDW) 2007, which took place as an international design project from the 16th to the 21st of July 2007 at the Australian Centre for Field Robotics (ACFR), University of Sydney, Australia. The participants were tasked to design a human-tended space station in low lunar orbit (LLO) focusing on supporting future missions to the moon in a programmatic context of space exploration beyond low Earth orbit (LEO). The design included incorporating elements from systems engineering to interior architecture. The customised, intuitive, rapid-turnaround software tools enabled the team to successfully tackle the complex problem of conceptual design of crewed space systems. A strong emphasis was put on improving the integration of the human crew, as it is the major contributor to mission success, while always respecting the boundary conditions imposed by the challenging environment of space. This report documents the methodology, tools and outcomes of the Space Station Design Workshop during the SSDW 2007. The design results produced by Team Blue are presented.