The treatment of employee priority in a deed of company arrangement

This article examines one of the changes implemented in the Corporations Amendment (Insolvency) Act 2007 (Cth) . It is argued that the insertion of s 444DA raises some matters that go to the nature of the insolvency process generally and the operation of Pt 5.3A in a particular. The position of employees in insolvency is a matter that is the subject of much comment from a policy perspective. This article does not cover that debate but provides some initial explanation of the need to protect employees. The second part of the article covers the particular background to the voluntary administration system as far as employee rights are concerned as well as the arguments put forward by the government to justify the change in the legislation which inserted s 444DA . It suggests that there was little evidence provided for the need to protect employee priority rights in this particular way. An alternative explanation is given for the change adopted by the government. The third part of the article suggests that the manner in which the legislation seeks to better protect employee creditors is somewhat clumsy in its operation. It raises a number of questions about how the legislation may operate and argues that given the stated aims, some alteration to it would improve its effectiveness.

Re-imagining static Utopias : unraveling the bat/human problem

The making of the modern world has long been fuelled by utopian images that are blind to ecological reality. Botanical gardens are but one example – who typically portray themselves as miniature, isolated 'edens on earth'. Whilst respected, heritage-laden institutions such as the Royal Botanical Gardens in Sydney, Australia promote such an idealised image they are now self-evidently also the vital ‘lungs’ of a crowded city as well as a critical habitats for threatened biodiversity (in this case notably flying foxes). In 2010 the 'Remnant Emergency Artlab' set out to alleviate this utopian hangover through a creative provocation called the 'Botanical Gardens ‘X-Tension’ - an imagined city-wide, distributed, network of 'ecological gardens' - in order to ask, what now needs to be better understood, connected and therefore ultimately conserved?

The constraints-based approach to motor learning : Implications for a non-linear pedagogy in sport and physical education

Recently, a constraints- led approach has been promoted as a framework for understanding how children and adults acquire movement skills for sport and exercise (see Davids, Button & Bennett, 2008; Araújo et al., 2004). The aim of a constraints- led approach is to identify the nature of interacting constraints that influence skill acquisition in learners. In this chapter the main theoretical ideas behind a constraints- led approach are outlined to assist practical applications by sports practitioners and physical educators in a non- linear pedagogy (see Chow et al., 2006, 2007). To achieve this goal, this chapter examines implications for some of the typical challenges facing sport pedagogists and physical educators in the design of learning programmes.

Heidegger's hippies : a dissenting voice on "the problem of the subject"

This is a deliberately contentious paper about the future of the socio-political sphere in the West based on what we know about its past. I argue that the predominant public discourse in Western countries is best characterised as one of selective forgetfulness; a semi-blissful, amnesiacal state of collective dementia that manifests itself in symbolic idealism: informationalism. Informationalism is merely the latest form of idealism. It is a lot like religion insofar as it causally relates abstract concepts with reality and, consequently, becomes confused between the two. Historically, this has proven to be a dangerous state of affairs, especially when elites becomes confused between ideas about how a society should work, and the way it actually does work. Central to the idealism of the information age, at least in intellectual spheres, is the so called "problem of the subject". I argue that the "problem of the subject" is a largely synthetic, destabilising, and ultimately fruitless theoretical abstraction which turns on a synthetically derived, generalised intradiscursive space; existentialist nihilism; and the theoretical baubles of ontological metaphysics. These philosophical aberrations are, in turn, historically concomitant with especially destructive political and social configurations. This paper sketches a theoretical framework for identity formation which rejects the problem of the subject, and proposes potential resources, sources, and strategies with which to engage the idealism that underpins this obfuscating problematic in an age of turbulent social uncertainty. Quite simply, I turn to history as the source of human identity. While informationalism, like religion, is mostly focused on utopian futures, I assert that history, not the future, holds the solutions for substantive problematics concerning individual and social identities. I argue here that history, language, thought, and identity are indissolubly entangled and so should be understood as such: they are the fundamental parts of 'identities in action'. From this perspective, the ‘problem of the subject’ becomes less a substantive intellectual problematic and more a theoretical red herring.

Asymptotic and numerical results for a model of solvent-dependent drug diffusion through polymeric spheres

A model for drug diffusion from a spherical polymeric drug delivery device is considered. The model contains two key features. The first is that solvent diffuses into the polymer, which then transitions from a glassy to a rubbery state. The interface between the two states of polymer is modelled as a moving boundary, whose speed is governed by a kinetic law; the same moving boundary problem arises in the one-phase limit of a Stefan problem with kinetic undercooling. The second feature is that drug diffuses only through the rubbery region, with a nonlinear diffusion coefficient that depends on the concentration of solvent. We analyse the model using both formal asymptotics and numerical computation, the latter by applying a front-fixing scheme with a finite volume method. Previous results are extended and comparisons are made with linear models that work well under certain parameter regimes. Finally, a model for a multi-layered drug delivery device is suggested, which allows for more flexible control of drug release.

A proactive response to the mobbing problem : a guide for HR managers

This paper introduces the concept of workplace mobbing as a destructive organizational behaviour of psychological assaults perpetrated against the target causing them harm and loss of employment. The discussion is drawn from a three year Australian study of 212 self identified targets of workplace mobbing behaviours. The behaviours are typically covert with informal networks and friendship loyalties providing effective mechanisms for emotional abuse, including those arising from human resource management practices. This paper discusses the manipulation of informal sources of power, with the use of gossip, rumour, hearsay, and innuendo to discredit and demonise those targeted. The study explores some of the systemic reasons for these behaviours and identifies some of the contributing risk factors and suggests management practices that can minimise the harm caused.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Optimal-stopping control for airborne collision avoidance and return-to-course flight

This paper considers an aircraft collision avoidance design problem that also incorporates design of the aircraft’s return-to-course flight. This control design problem is formulated as a non-linear optimal-stopping control problem; a formulation that does not require a prior knowledge of time taken to perform the avoidance and return-to-course manoeuvre. A dynamic programming solution to the avoidance and return-to-course problem is presented, before a Markov chain numerical approximation technique is described. Simulation results are presented that illustrate the proposed collision avoidance and return-to-course flight approach.

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.