Romancing the Dane : reviving (and revising) Ophelia in contemporary YA fiction

Shakespeare’s Ophelia has been circulated in recent times as a figure of the adolescent woman at risk. Mary Pipher’s best-selling and influential Reviving Ophelia (1994) argued that the “story of Ophelia […] shows the destructive forces that affect young women” (20). Without undermining Pipher’s project, this paper reads two contemporary YA romance novels—Lisa Fiedler’s Dating Hamlet (2002) and Lisa Klein’s Ophelia (2006)—in order to demonstrate that not only can Ophelia be appropriated as a figure of empowerment for young women today, but that such appropriations are, seemingly ironically, most powerfully rendered within the genre of romance; a genre long-maligned by feminists as recuperative of patriarchy.--------- These two novels stage interventions both into narratives of female adolescence as a time of being ‘at risk’ or ‘under threat’, and also into narratives of canonical literary patriarchy. Rather than a suicidal Ophelia, subject to the whims of men, these authors imagine Ophelias who take charge of their own destiny; who dictate their own romance and agency; who refuse to be subject to or subjected by, those scripts of cultural authority and heteronormative romance so often perceived as antithetical to female agency. In doing so, they force us to revise our own notions of the romance genre and the functions of canonical literary tradition in contemporary YA culture.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Librarian 2.0 : it’s all in the attitude!

In 2006 Stephen Abram stated that we must “become librarian 2.0 now”. But what is librarian 2.0? This pa- per will present the results of a project that identified the skills, knowledge and attributes required by the successful librarian in the web 2.0 world (and be- yond!). Eighty-one Australian librarians participated in a series of 14 focus groups. Eight themes emerged: technology, communication, team work, user focus, business savvy, evidence based practice, learning, and personal traits.

Experimental validation of an unpowered unmanned aerial system : application to forced landing scenarios

The ability to perform autonomous emergency (forced) landings is one of the key technology enablers identified for UAS. This paper presents the flight test results of forced landings involving a UAS, in a controlled environment, and which was conducted to ascertain the performances of previously developed (and published) path planning and guidance algorithms. These novel 3-D nonlinear algorithms have been designed to control the vehicle in both the lateral and longitudinal planes of motion. These algorithms have hitherto been verified in simulation. A modified Boomerang 60 RC aircraft is used as the flight test platform, with associated onboard and ground support equipment sourced Off-the-Shelf or developed in-house at the Australian Research Centre for Aerospace Automation(ARCAA). HITL simulations were conducted prior to the flight tests and displayed good landing performance, however, due to certain identified interfacing errors, the flight results differed from that obtained in simulation. This paper details the lessons learnt and presents a plausible solution for the way forward.

A spatial collision avoidance strategy for UAVs in a non-cooperative environment

This paper presents a feasible spatial collision avoidance approach for fixed-wing unmanned aerial vehicles (UAVs). The proposed strategy aims to achieve the desired relative bearing in the horizontal plane and relative elevation in the vertical plane so that the host aircraft is able to avoid collision with the intruder aircraft in 3D. The host aircraft will follow a desired trajectory in the collision avoidance course and resume the pre-arranged trajectory after collision is avoided. The approaching stopping condition is determined for the host aircraft to trigger an evasion maneuver to avoid collision in terms of measured heading. A switching controller is designed to achieve the spatial collision avoidance strategy. Simulation results demonstrate that the proposed approach can effectively avoid spatial collision, making it suitable for integration into flight control systems of UAVs.

A gust-attenuation controller for fixed-wing UAVs during collision avoidance course

This paper presents a nonlinear gust-attenuation controller to stabilize velocities, attitudes and angular rates of a fixed-wing unmanned aerial vehicle (UAV) in the presence of wind gusts. The proposed controller aims to achieve a steady-state flight condition such that the host UAV can avoid airspace collision with other UAVs during the cruise flight. Based on the typical UAV model capturing flight aerodynamics, a nonlinear Hinf controller is developed with rapid response property in consideration of actuator constraints. Simulations are conducted for the Shadow UAV to verify performance of the proposed controller. Comparative studies with the proportional-integral derivative (PID) controllers demonstrate that the proposed controller exhibits great performance improvement in a gusty environment, making it suitable for integration into the design of flight control systems for cruise flight with safety guarantees.

UAS See-and-Avoid using two different approaches of Fuzzy Control

This work presents two UAS See and Avoid approaches using Fuzzy Control. We compare the performance of each controller when a Cross-Entropy method is applied to optimase the parameters for one of the controllers. Each controller receive information from an image processing front-end that detect and track targets in the environment. Visual information is then used under a visual servoing approach to perform autonomous avoidance. Experimental flight trials using a small quadrotor were performed to validate and compare the behaviour of both controllers

Ross River virus arthritis

“Epidemics” of a benign disease causing polyarthralgia and rash were first described in Australia in 1927.63 Following the recovery of the causative agent and the advent of serologic tests able to diagnose Ross River virus infection, epidemic polyarthritis has been recognized as endemic in Australia and has occurred as epidemics in numerous Pacific nations. Approximately 4000 cases of epidemic polyarthritis are reported in Australia each year, with a peak of 7800 cases in 1996. Some confusion has been generated recently by use of the term Ross River fever to describe clinical Ross River virus infections because fever does not develop in more than half of those with clinical disease.59 Additional confusion has been generated by efforts to describe any polyarthritis caused by an Australian arbovirus as epidemic polyarthritis. The term epidemic polyarthritis should be used to describe only clinical disease caused by Ross River virus.