Pedunculopontine nucleus deep brain stimulation produces sustained improvement in primary progressive freezing of gait

Objective: To assess the efficacy of bilateral pedunculopontine nucleus (PPN) deep brain stimulation (DBS) as a treatment for primary progressive freezing of gait (PPFG). ------ ----- Methods: A patient with PPFG underwent bilateral PPN-DBS and was followed clinically for over 14 months. ------ ----- Results: The PPFG patient exhibited a robust improvement in gait and posture following PPN-DBS. When PPN stimulation was deactivated, postural stability and gait skills declined to pre-DBS levels, and fluoro-2-deoxy-d-glucose positron emission tomography revealed hypoactive cerebellar and brainstem regions, which significantly normalised when PPN stimulation was reactivated. ------ ----- Conclusions: This case demonstrates that the advantages of PPN-DBS may not be limited to addressing freezing of gait (FOG) in idiopathic Parkinson's disease. The PPN may also be an effective DBS target to address other forms of central gait failure.

Copyright future copyright freedom

If copyright law does not liberate us from restrictions on the dissemination of knowledge, if it does not encourage expressive freedom, what is its purpose? This volume offers the thinking and suggestions of some of the finest minds grappling with the future of copyright regulation. The Copyright Future Copyright Freedom conference held in 2009 at Old Parliament House Canberra brought together Lawrence Lessig, Julie Cohen, Leslie Zines, Adrian Sterling, Sam Ricketson, Graham Greenleaf, Anne Fitzgerald, Susy Frankel, John Gilchrist, Michael Kirby and others to share the rich fruits of their experience and analysis. Zines, Sterling and Gilchrist outline their roles in the genesis and early growth of Australian copyright legislation, enriching the knowledge of anyone asking urgent questions about the future of information regulation.

Culture and creative industries in Australia

This paper draws upon the Australian case to argue that the case for support for cultural production and cultural infrastructure has been strengthened overall by its alignment to economic policy goals. In this respect, the rise of creative industries policy discourses is consistent with trends in thinking about cultural policy that have their roots in the Creative Nation strategies of the early 1990s. In terms of the earlier discussion, cultural policy is as much driven by Schumpeterian principals as it is by Keynesian ones. Such an approach is not without attendant risks, and two stand out. The first is the risk of marginalizing the arts, through a policy framework that gives priority to developing the digital content industries, and viewing the creative industries as primarily an innovation platform. The second is that other trends in the economy, such as the strong Australian dollar resulting from the mining boom, undercuts the development of cultural production in the sections of the creative industries where international trade and investment is most significant, such as the film industry and computer games. Nonetheless, after over a decade of vibrant debate, this focus on linking the cultural and economic policy goals of the creative industries has come to be consistent with broader international trends in the field.

Is journalism best located in the creative arts or as a communication discipline?

This paper considers the implications of journalism research being located within the Field of Research associated with the creative arts and writing in the recent Excellence in Research for Australia (ERA) evaluations. While noting that this classification does capture a significant trajectory in Australian journalism research, it also points to some anomalous implications of understanding journalism as an arts discipline, given its historical co-location in universities with communications disciplines, and the mutually reinforcing relationships between the two fields.

Controlling chaos? The value and the challenges of applying complexity theory to project management

The literature abounds with descriptions of failures in high-profile projects and a range of initiatives has been generated to enhance project management practice (e.g., Morris, 2006). Estimating from our own research, there are scores of other project failures that are unrecorded. Many of these failures can be explained using existing project management theory; poor risk management, inaccurate estimating, cultures of optimism dominating decision making, stakeholder mismanagement, inadequate timeframes, and so on. Nevertheless, in spite of extensive discussion and analysis of failures and attention to the presumed causes of failure, projects continue to fail in unexpected ways. In the 1990s, three U.S. state departments of motor vehicles (DMV) cancelled major projects due to time and cost overruns and inability to meet project goals (IT-Cortex, 2010). The California DMV failed to revitalize their drivers’ license and registration application process after spending $45 million. The Oregon DMV cancelled their five year, $50 million project to automate their manual, paper-based operation after three years when the estimates grew to $123 million; its duration stretched to eight years or more and the prototype was a complete failure. In 1997, the Washington state DMV cancelled their license application mitigation project because it would have been too big and obsolete by the time it was estimated to be finished. There are countless similar examples of projects that have been abandoned or that have not delivered the requirements.

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.

Auditory spatial acuity approximates the resolving power of space-specific neurons

The relationship between neuronal acuity and behavioral performance was assessed in the barn owl (Tyto alba), a nocturnal raptor renowned for its ability to localize sounds and for the topographic representation of auditory space found in the midbrain. We measured discrimination of sound-source separation using a newly developed procedure involving the habituation and recovery of the pupillary dilation response. The smallest discriminable change of source location was found to be about two times finer in azimuth than in elevation. Recordings from neurons in its midbrain space map revealed that their spatial tuning, like the spatial discrimination behavior, was also better in azimuth than in elevation by a factor of about two. Because the PDR behavioral assay is mediated by the same circuitry whether discrimination is assessed in azimuth or in elevation, this difference in vertical and horizontal acuity is likely to reflect a true difference in sensory resolution, without additional confounding effects of differences in motor performance in the two dimensions. Our results, therefore, are consistent with the hypothesis that the acuity of the midbrain space map determines auditory spatial discrimination.