Developing knowledge precincts in regional towns: opportunities and challenges

While extensive literature exists on knowledge-based urban development (KBUD) focusing on large metropolitan cities, there is a paucity of literature looking into similar developments in small regional towns. The major aim of the paper is to examine the nature and potential for building knowledge precincts in regional towns. Through a review of extant literature on knowledge precincts, five key value elements and principles for development are identified. These principles are then tested and applied to a case study of the small town of Cooroy in Noosa, Australia. The Cooroy Lower Mill Site and its surroundings are the designated location for what may be called a community-based creative knowledge precinct. The opportunities and challenges for setting up a creative knowledge precinct in Cooroy were examined. The study showed that there is a potential to develop Cooroy with the provision of cultural and learning facilities, partnerships with government, business and educational institutions, and networking with other creative and knowledge precincts in the region. However, there are also specific challenges relating to the development of a knowledge precinct within the regional town and these relate to critical mass, competition and governance.

Brisbane Airport Atelier : Team Delta

This architectural and urban design project was conducted as part of the Brisbane Airport Corporations master-planning Atelier, run in conjunction with City Lab. This creation and innovation event brought together approximately 80 designers, associated professionals, and both local and state government representatives to research concepts for future development and planning of the Brisbane airport site. The Team Delta research project explored the development of a new precinct cluster around the existing international terminal building; with a view of reinforcing the sense of place and arrival. The development zone explores the options of developing a subtropical character through landscape elements such as open plazas, tourist attractions, links to existing adjacent waterways, and localised rapid transport options. The proposal tests the possibilities of developing a cultural hub in conjunction with transport infrastructure and the airport terminal(s).

Film review : Citizen Murdoch?

It hasn’t been a good year for media barons. Actually, it’s not been a great century. In 2007 Baron Conrad Black was sent to jail in the US for embezzling his shareholders. Silvio Berlusconi’s grip on the Italian media hasn’t prevented a steady flow of allegations of sleaze and scandal since 2009, which have reduced him to a global laughing stock. And since July 2011, we have seen the dizzying fall of Rupert Murdoch and his son, James, from their positions of unquestioned (and unquestionable) authority at the helm of the world’s most powerful media empire.

RcppArmadillo : accelerating R with high-performance C++ linear algebra

The R statistical environment and language has demonstrated particular strengths for interactive development of statistical algorithms, as well as data modelling and visualisation. Its current implementation has an interpreter at its core which may result in a performance penalty in comparison to directly executing user algorithms in the native machine code of the host CPU. In contrast, the C++ language has no built-in visualisation capabilities, handling of linear algebra or even basic statistical algorithms; however, user programs are converted to high-performance machine code, ahead of execution. A new method avoids possible speed penalties in R by using the Rcpp extension package in conjunction with the Armadillo C++ matrix library. In addition to the inherent performance advantages of compiled code, Armadillo provides an easy-to-use template-based meta-programming framework, allowing the automatic pooling of several linear algebra operations into one, which in turn can lead to further speedups. With the aid of Rcpp and Armadillo, conversion of linear algebra centered algorithms from R to C++ becomes straightforward. The algorithms retains the overall structure as well as readability, all while maintaining a bidirectional link with the host R environment. Empirical timing comparisons of R and C++ implementations of a Kalman filtering algorithm indicate a speedup of several orders of magnitude.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.

Tracing the influence of non-narrative film and expanded cinema sound on experimental music

This study surveys and interrogates key conceptual frameworks and artistic practises that flow through the distinct but interconnected traditions of non-narrative film and experimental music, and examines how these are articulated in my own creative sound practise.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

Weekly cisplatin concurrently with radiotherapy in head and neck squamous cell cancer : a retrospective analysis of a tertiary institute experience

Radiotherapy combined with three weekly 100 mg/m2 of cisplatin is the accepted standard of care in head and neck squamous cell carcinoma. However, this regimen is associated with severe toxicities with devastating effects on patients. Alternative protocols like weekly 40 mg/m2 have been used in an attempt to reduce toxicities. The main objective of the present study is to identify the dose intensities and toxicities of weekly cisplatin in patients treated in a tertiary centre over a 12 month period. Included patients had squamous cell carcinoma arising in the oral cavity, oropharynx, larynx, or hypopharynx. Patients were excluded if they had nasopharyngeal squamous cell carcinoma, distant metastasis or if they had prior treatment for head and neck cancer excluding neck dissection. During the study period, 52 patients met the inclusion criteria and their data were retrospectively obtained from the patients' database of St James hospital, Dublin. The median age of the study cohort was 54 years (range 33-73). Of the patients, 40 (76.9 %) were male and 12 (20.1 %) were female. The primary tumour sites were as follows: oral cavity and oropharynx in 38 (73 %), larynx in 10 (19 %), and hypopharynx in 4 (8 %). In total, 33 (63.5 %) patients had stage IV disease, while 19 (36.5 %) had stage III disease. Treatment was definitive in 35 (67 %) patients and adjuvant in 17 (35 %). Full-dose radiotherapy was achieved in 50 (96 %) patients. Only 22 (42.3 %) patients completed the intended six cycles of chemotherapy. Cumulative dose of 200 mg/m2 or more was reached in 37 (71 %) patients. The acute adverse effects included grades 3 and 4 mucositis, which occurred in 22 (43.3 %) and 6 patients (12 %), respectively. Grade 3 and 4 neutropenia occurred in six (11.5 %) and three (5.7 %) patients, respectively. The only other haematological toxicity was grade 3 anaemia in 20 (38.4 %) patients. There was no grade 3 or 4 renal toxicity among the study cohort, although grade 2 was observed in six (11.5 %) patients. Death occurred in one patient due to neutropenic septicaemia. In conclusion, weekly cisplatin is associated with moderate to severe toxicities and might lead to suboptimal chemotherapy delivery. More prospective clinical studies are required to determine the optimal chemoradiation regimen in head and neck squamous cell carcinoma.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Summarisation of short-term and long-term videos using texture and colour

We present a novel approach to video summarisation that makes use of a Bag-of-visual-Textures (BoT) approach. Two systems are proposed, one based solely on the BoT approach and another which exploits both colour information and BoT features. On 50 short-term videos from the Open Video Project we show that our BoT and fusion systems both achieve state-of-the-art performance, obtaining an average F-measure of 0.83 and 0.86 respectively, a relative improvement of 9% and 13% when compared to the previous state-of-the-art. When applied to a new underwater surveillance dataset containing 33 long-term videos, the proposed system reduces the amount of footage by a factor of 27, with only minor degradation in the information content. This order of magnitude reduction in video data represents significant savings in terms of time and potential labour cost when manually reviewing such footage.