Who will do general surgery?

The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included.Advantages to patients of a single anaesthetic for more than one operation are obvious; attracting generalist surgeons, training them and ensuring they have adequate credentials remain hurdles.Martin H Bruening and Guy J Madder

Distant Vehicle Detection Using Radar and Vision

For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using convolutional neural networks exhibit excellent performance on existing datasets such as KITTI. However, the performance of these networks falls when detecting small (distant) objects. We demonstrate that incorporating radar data can boost performance in these difficult situations. We also introduce an efficient automated method for training data generation using cameras of different focal lengths

Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer

We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation.Comment: Accepted at ICRA201

Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018. Video summary: http://youtu.be/ebIrBn_nc-

Spectres of migration and the ghosts of Ellis Island

This article is based on in-depth interviews carried out with producers involved in the restoration of Ellis Island Immigration Station, New York and those responsible for turning it into a successful national heritage site which opened to the public in 1990. The buildings on Ellis Island operated as an Immigration Station between approximately 1892 and 1924 during which time they processed over 16 million migrants of predominantly European origin. An analysis of interviews conducted as well as readings of Ellis Island taken from archives, folklore and US popular culture suggest that the site is imbued with the spectropolitics of its politically emotive immigrant processing past. Rather than dismissing the spectrality associated with Ellis Island as folkloric or irrational, the article attempts to untangle the different meanings attributed to the `ghosts' that circulate through the buildings and material objects that inhabit the island. It suggests that a number of `tropes' of ghostliness can be associated with the island; uncanny ghosts which defy the sanitizing force of the restoration; conjured ghosts, which are deliberately invoked by producers for various political and economic purposes, and the ghosts of deconstruction which make any meta-narrative of immigration history at Ellis Island a precarious if not troubling achievement

An Investigation Into the Application of Data Mining Techniques to Characterize Agricultural Soil Profiles

The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data; this has led to new methods and techniques such as data mining that can bridge the knowledge gap. The research aims to use these new data mining techniques and apply them to a soil science database to establish if meaningful relationships can be found. A data set extracted from the WA Department of Agriculture and Food (DAFW A) soils database has been used to conduct this research. The database contains measurements of soil profile data from various locations throughout the south west agricultural region of Western Australia. The research established that meaningful relationships can be found in the soil profile data at different locations. In addition, comparison was made between current cluster techniques and statistical methods to establish the most effective method. The research compared two data mining algorithms against a benchmark that was established using standard statistical analysis in use at the DAFW A. The EM and FarthestFirst data mining algorithms were tested in five case studies and it was found that FarthestFirst was more accurate at clustering instances than EM in all cases when tested against actual known clusters groups. The known groups were two traits EC and Clay within two soil types. It was concluded that data mining had a number of advantages over current statistical methods but the methods research can not completely replace them at this stage. The outcome of the research may have many benefits: to agriculture in general, to soil management and to environmental management. The research has been collaboration between the Edith Cowan University and the DAFW A, with the results and outcomes to be shared between the two organizations

Low water-soluble superphosphate fertiliser for pasture production in south-western Australia

Single superphosphate is derived from chemically treating rock phosphate into relative proportions of monobasic, dibasic and tribasic calcium phosphate to produce a commonly used source of phosphorus fertiliser for pasture systems. The leaching of phosphorus on susceptible soil types contributes to eutrophication and environmental damage. By modifying the chemistry of single superphosphate to match a soils phosphorus binding index and rainfall conditions, pasture dry matter yield can be maintained and leaching of phosphorus significantly reduced

Towards robust night and day place recognition using visible and thermal imaging

The chief challenge facing persistent robotic navigation using vision sensors is the recognition of previously visited locations under different lighting and illumination conditions. The majority of successful approaches to outdoor robot navigation use active sensors such as LIDAR, but the associated weight and power draw of these systems makes them unsuitable for widespread deployment on mobile robots. In this paper we investigate methods to combine representations for visible and long-wave infrared (LWIR) thermal images with time information to combat the time-of-day-based limitations of each sensing modality. We calculate appearance-based match likelihoods using the state-of-the-art FAB-MAP [1] algorithm to analyse loop closure detection reliability across different times of day. We present preliminary results on a dataset of 10 successive traverses of a combined urban-parkland environment, recorded in 2-hour intervals from before dawn to after dusk. Improved location recognition throughout an entire day is demonstrated using the combined system compared with methods which use visible or thermal sensing alone

Receiving Bad News: A Thematic Analysis of Stroke Survivor Experiences.

Background: Breaking bad news to patients may be required in service provision to stroke survivors. While challenging, it may be critical to the retention of optimism and participation in rehabilitation. Objectives: To explore the experience of stroke survivors when receiving bad news (RBN) from medical practitioners. Methods: Data were obtained via 1:1 interviews conducted at stroke support groups with survivors at least 12 months into recovery and subsequently transcribed for thematic analysis and coded using NVivo. Results: Eight of 10 participants experienced RBN, and 2 participants did not. The themes of being "lucky to be alive" and waiting for "delayed information" were expressed by all participants early in the interviews. Three sub-themes emerged and were labelled alliance, dissent, and dissatisfaction, each with a further 3 contextual themes. The perception of RBN was marked amongst the dissent and dissatisfaction groups, with the latter reporting negative implications for their rehabilitation as well as negative emotions, such as anger and anxiety. The perception of a poor-quality relationship with medical practitioners was said to impede rehabilitation and recovery processes. The dissent group was characterized by initial disbelief after RBN and consequently poorer long-term outcomes, whilst the Alliance group experienced very good quality of care due to existing personal knowledge and therefore did not perceive RBN during their early medical meetings. Conclusions: In the period soon after their stroke, survivors required their medical practitioners to not only communicate knowledge and information, but also needed validation of their hopes and fears for the future from an empathically attuned clinician