Sans Faute

Sans Faute (without fail) considers how technology has influenced Chinese society, culture and practice. The exhibition seeks to reveal a shared language between Western and Chinese contexts that exists through employing technology as a universal creative meeting point. The pervasive impact of the current digital landscape has been widely recognised, embraced and continues to influence many aspects of Chinese lives. The significance of these shifting contexts on contemporary art is explored in Sans Faute. Through the presentation of a range of video installations, the exhibition aims to initiate rigorous and critical engagement with these provocative works that challenge traditional ideologies to discover emergent ideas. Featuring works by eight artists from mainland China, Sans Faute is an exploration of different attitudes collected together to provide a complete picture of how these artists build a new iconic imagery. "The works of these artists present a younger generation's thinking of the reality of contemporary China through their own emotional and individual life experience," says Stephen Danzig, IDAprojects director. "We are currently witnessing an amazing re-identification in so many elements that make up China's social fabric - none more so than what's happening in the arts currently. You only need to see what's happening in Beijing to realise the pace of change." Sans Faute has been supported by QUT Confucius Institute.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

Identifying product families using data mining techniques in manufacturing paradigm

Identifying product families has been considered as an effective way to accommodate the increasing product varieties across the diverse market niches. In this paper, we propose a novel framework to identifying product families by using a similarity measure for a common product design data BOM (Bill of Materials) based on data mining techniques such as frequent mining and clus-tering. For calculating the similarity between BOMs, a novel Extended Augmented Adjacency Matrix (EAAM) representation is introduced that consists of information not only of the content and topology but also of the fre-quent structural dependency among the various parts of a product design. These EAAM representations of BOMs are compared to calculate the similarity between products and used as a clustering input to group the product fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.

A case study of utilising concept knowledge in a topic specific document collection

The use of ‘topic’ concepts has shown improved search performance, given a query, by bringing together relevant documents which use different terms to describe a higher level concept. In this paper, we propose a method for discovering and utilizing concepts in indexing and search for a domain specific document collection being utilized in industry. This approach differs from others in that we only collect focused concepts to build the concept space and that instead of turning a user’s query into a concept based query, we experiment with different techniques of combining the original query with a concept query. We apply the proposed approach to a real-world document collection and the results show that in this scenario the use of concept knowledge at index and search can improve the relevancy of results.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

In-plane graphene/boron-nitride heterostructure as efficient metal-free electrocatalyst for the oxygen reduction reaction

Exploiting metal-free catalysts for the oxygen reduction reaction (ORR) and understanding their catalytic mechanisms are vital for the development of fuel cells (FCs). Our study has demonstrated that in-plane heterostructures of graphene and boron nitride (G/BN) can serve as an efficient metal-free catalyst for the ORR, in which the C-N interfaces of G/BN heterostructures act as reactive sites. The formation of water at the heterointerface is both energetically and kinetically favorable via a fourelectron pathway. Moreover, the water formed can be easily released from the heterointerface, and the catalytically active sites can be regenerated for the next reaction. Since G/BN heterostructures with controlled domain sizes have been successfully synthesized in recent reports (e.g. Nat. Nanotechnol., 2013, 8, 119), our results highlight the great potential of such heterostructures as a promising metal-free catalyst for ORR in FCs.

The unequal distribution of road trauma in China : understanding risk factors to improve public health

The World Health Organization identifies road trauma as a major public health issue in every country; most notably among low-to-middle income countries. More than 90% of all road fatalities occur in these countries, although they have only 48% of all registered vehicles [1]. Unprecedented focus has been placed on reducing the global road trauma burden through the United Nations Decade of Action for Road Safety (2011-2020). China is rapidly transitioning from a nation of bicycle riders and pedestrians to one where car ownership and use is increasing. This transition presents important public health, mobility, and safety challenges. Rapid motorisation has resulted in an increased road trauma burden, shouldered disproportionately among the population. Vulnerable road users (bicyclists, pedestrians, and motorcyclists) are of particular concern, representing 70% of all road-related fatalities [1]. Furthermore, those at greatest risk of sustaining a crash-related disability are: male, older, less educated, and earning a lower income [2] and residing in urban areas [3], with higher fatality rates in north-western poorer provinces [3]. Speeding is a key factor in road crashes in China [1, 4] and is one of two risk factors targeted in the Bloomberg Philanthropies-funded Global Road Safety Program operating in two Chinese cities over five year [5] to which the first author has provided expert advice. However, little evidence exists to help understand the factors underpinning speeding behaviour. Previous research conducted by the authors in Beijing and Hangzhou explored personal, social, and legal factors relating to speeding to assist in better understanding the motivations for non-compliance with speed limits. Qualitative and quantitative research findings indicated that speeding is relatively common, including self-reported travel speeds of greater than 30 km/hour above posted speed limits [6], and that the road safety laws and enforcement practices may, in some circumstances, contribute to this [7]. Normative factors were also evident; the role of friends, family members and driving instructors were influential. Additionally, using social networks to attempt to avoid detection and penalty was reported, thereby potentially reinforcing community perceptions that speeding is acceptable [8, 9]. The authors established strong collaborative links with the Chinese Academy of Sciences and Zhejiang Police College to conduct this research. The first author has worked in both institutions for extended time periods and recognises that research must include an understanding of culturally-relevant issues if road safety is to improve in China. Future collaborations to assist in enhancing our understanding of such issues are welcomed. References [1] World Health Organization. (2009). Global status report on road safety: Time for action; Geneva. [2] Chen, H., Du, W., & Li, N. (2013). The socioeconomic inequality in traffic-related disability among Chinese adults: the application of concentration index. Accident Analysis & Prevention, 55(101-106). [3] Wang, S. Y., Li, Y. H., Chi, G. B., Xiao, S. Y., Ozanne-Smith, J., Stevenson, M., & Phillips, M. (2008). Injury-related fatalities in China: an under-recognised public-health problem. The Lancet (British edition), 372(9651), 1765-1773. [4] He, J., King, M. J., Watson, B., Rakotonirainy, A., & Fleiter, J. J. (2013). Speed enforcement in China: National, provincial and city initiatives and their success. Accident Analysis & Prevention, 50, 282-288. [5] Bhalla, K., Li, Q., Duan, L., Wang, Y., Bishai, D., & Hyder, A. A. (2013). The prevalence of speeding and drink driving in two cities in China: a mid project evaluation of ongoing road safety interventions. Injury, 44, 49-56. doi:10.1016/S0020-1383(13)70213-4. [6] Fleiter, J. J., Watson, B., & Lennon, A. (2013). Awareness of risky behaviour among Chinese drivers. Peer-reviewed paper presented at 23rd Canadian Multidisciplinary Road Safety Conference, Montréal, Québec. [7] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2009). Speeding in Australia and China: A comparison of the influence of legal sanctions and enforcement practices on car drivers. Peer-reviewd paper presented at Australasian Road Safety Research Policing Education Conference, Sydney. [8] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2011). Social influences on drivers in China. Journal of the Australasian College of Road Safety, 22(2), 29-36. [9] Fleiter, J. J., Watson, B., Guan, M. Q., Ding, J. Y., & Xu, C. (2013). Characteristics of Chinese Drivers Attending a Mandatory Training Course Following Licence Suspension. Peer-reviewed paper presented at Road Safety on Four Continents, Beijing, China.