Compressive sensing for gait recognition

Compressive Sensing (CS) is a popular signal processing technique, that can exactly reconstruct a signal given a small number of random projections of the original signal, provided that the signal is sufficiently sparse. We demonstrate the applicability of CS in the field of gait recognition as a very effective dimensionality reduction technique, using the gait energy image (GEI) as the feature extraction process. We compare the CS based approach to the principal component analysis (PCA) and show that the proposed method outperforms this baseline, particularly under situations where there are appearance changes in the subject. Applying CS to the gait features also avoids the need to train the models, by using a generalised random projection.

Controls on iron in soils and soil waters of a forested, coastal catchment in subtropical Australia

Soluble organic matter derived from exotic Pinus vegetation forms stronger complexes with iron (Fe) than the soluble organic matter derived from most native Australian species. This has lead to concern about the environmental impacts related to the establishment of extensive exotic Pinus plantations in coastal southeast Queensland, Australia. It has been suggested that the Pinus plantations may enhance the solubility of Fe in soils by increasing the amount of organically complexed Fe. While this remains inconclusive, the environmental impacts of an increased flux of dissolved, organically complexed Fe from soils to the fluvial system and then to sensitive coastal ecosystems are potentially damaging. Previous work investigated a small number of samples, was largely laboratory based and had limited application to field conditions. These assessments lacked field-based studies, including the comparison of the soil water chemistry of sites associated with Pinus vegetation and undisturbed native vegetation. In addition, the main controls on the distribution and mobilisation of Fe in soils of this subtropical coastal region have not been determined. This information is required in order to better understand the relative significance of any Pinus enhanced solubility of Fe. The main aim of this thesis is to determine the controls on Fe distribution and mobilisation in soils and soil waters of a representative coastal catchment in southeast Queensland (Poona Creek catchment, Fraser Coast) and to test the effect of Pinus vegetation on the solubility and speciation of Fe. The thesis is structured around three individual papers. The first paper identifies the main processes responsible for the distribution and mobilisation of labile Fe in the study area and takes a catchment scale approach. Physicochemical attributes of 120 soil samples distributed throughout the catchment are analysed, and a new multivariate data analysis approach (Kohonen’s self organising maps) is used to identify the conditions associated with high labile Fe. The second paper establishes whether Fe nodules play a major role as an iron source in the catchment, by determining the genetic mechanism responsible for their formation. The nodules are a major pool of Fe in much of the region and previous studies have implied that they may be involved in redox-controlled mobilisation and redistribution of Fe. This is achieved by combining a detailed study of a ferric soil profile (morphology, mineralogy and micromorphology) with the distribution of Fe nodules on a catchment scale. The third component of the thesis tests whether the concentration and speciation of Fe in soil solutions from Pinus plantations differs significantly from native vegetation soil solutions. Microlysimeters are employed to collect unaltered, in situ soil water samples. The redox speciation of Fe is determined spectrophotometrically and the interaction between Fe and dissolved organic matter (DOM) is modelled with the Stockholm Humic Model. The thesis provides a better understanding of the controls on the distribution, concentration and speciation of Fe in the soils and soil waters of southeast Queensland. Reductive dissolution is the main mechanism by which mobilisation of Fe occurs in the study area. Labile Fe concentrations are low overall, particularly in the sandy soils of the coastal plain. However, high labile Fe is common in seasonally waterlogged and clay-rich soils which are exposed to fluctuating redox conditions and in organic-rich soils adjacent to streams. Clay-rich soils are most common in the upper parts of the catchment. Fe nodules were shown to have a negligible role in the redistribution of dissolved iron in the catchment. They are formed by the erosion, colluvial transport and chemical weathering of iron-rich sandstones. The ferric horizons, in which nodules are commonly concentrated, subsequently form through differential biological mixing of the soil. Whereas dissolution/ reprecipitation of the Fe cements is an important component of nodule formation, mobilised Fe reprecipitates locally. Dissolved Fe in the soil waters is almost entirely in the ferrous form. Vegetation type does not affect the concentration and speciation of Fe in soil waters, although Pinus DOM has greater acidic functional group site densities than DOM from native vegetation. Iron concentrations are highest in the high DOM soil waters collected from sandy podosols, where they are controlled by redox potential. Iron concentrations are low in soil solutions from clay and iron oxide rich soils, in spite of similar redox potentials. This is related to stronger sorption to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for microbial metabolisation and reductive dissolution of Fe. Modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxidising conditions. Thus, inputs of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides and increase the flux of dissolved iron out of the catchment. Such inputs are most likely from the lower catchment, where podosols planted with Pinus are most widely distributed. Significant outcomes other than the main aims were also achieved. It is shown that mobilisation of Fe in podosols can occur as dissolved Fe(II) rather than as Fe(III)-organic complexes. This has implications for the large body of work which assumes that Fe(II) plays a minor role. Also, the first paper demonstrates that a data analysis approach based on Kohonen’s self organising maps can facilitate the interpretation of complex datasets and can help identify geochemical processes operating on a catchment scale.

Iron biogeochemistry and associated greenhouse gas evolution in a forested subtropical Australian coastal catchment : Poona Creek, Southeast Queensland

Iron (Fe) is the fourth most abundant element in the Earth’s crust. Excess Fe mobilization from terrestrial into aquatic systems is of concern for deterioration of water quality via biofouling and nuisance algal blooms in coastal and marine systems. Substantial Fe dissolution and transport involve alternate Fe(II) oxidation followed by Fe(III) reduction, with a diversity of Bacteria and Archaea acting as the key catalyst. Microbially-mediated Fe cycling is of global significance with regard to cycles of carbon (C), sulfur (S) and manganese (Mn). However, knowledge regarding microbial Fe cycling in circumneutral-pH habitats that prevail on Earth has been lacking until recently. In particular, little is known regarding microbial function in Fe cycling and associated Fe mobilization and greenhouse (CO2 and CH4, GHG) evolution in subtropical Australian coastal systems where microbial response to ambient variations such as seasonal flooding and land use changes is of concern. Using the plantation-forested Poona Creek catchment on the Fraser Coast of Southeast Queensland (SEQ), this research aimed to 1) study Fe cycling-associated bacterial populations in diverse terrestrial and aquatic habitats of a representative subtropical coastal circumneutral-pH (4–7) ecosystem; and 2) assess potential impacts of Pinus plantation forestry practices on microbially-mediated Fe mobilization, organic C mineralization and associated GHG evolution in coastal SEQ. A combination of wet-chemical extraction, undisturbed core microcosm, laboratory bacterial cultivation, microscopy and 16S rRNA-based molecular phylogenetic techniques were employed. The study area consisted primarily of loamy sands, with low organic C and dissolved nutrients. Total reactive Fe was abundant and evenly distributed within soil 0–30 cm profiles. Organic complexation primarily controlled Fe bioavailability and forms in well-drained plantation soils and water-logged, native riparian soils, whereas tidal flushing exerted a strong “seawater effect” in estuarine locations and formed a large proportion of inorganic Fe(III) complexes. There was a lack of Fe(II) sources across the catchment terrestrial system. Mature, first-rotation plantation clear-felling and second-rotation replanting significantly decreased organic matter and poorly crystalline Fe in well-drained soils, although variations in labile soil organic C fractions (dissolved organic C, DOC; and microbial biomass C, MBC) were minor. Both well-drained plantation soils and water-logged, native-vegetation soils were inhabited by a variety of cultivable, chemotrophic bacterial populations capable of C, Fe, S and Mn metabolism via lithotrophic or heterotrophic, (micro)aerobic or anaerobic pathways. Neutrophilic Fe(III)-reducing bacteria (FeRB) were most abundant, followed by aerobic, heterotrophic bacteria (heterotrophic plate count, HPC). Despite an abundance of FeRB, cultivable Fe(II)-oxidizing bacteria (FeOB) were absent in associated soils. A lack of links between cultivable Fe, S or Mn bacterial densities and relevant chemical measurements (except for HPC correlated with DOC) was likely due to complex biogeochemical interactions. Neither did variations in cultivable bacterial densities correlate with plantation forestry practices, despite total cultivable bacterial densities being significantly lower in estuarine soils when compared with well-drained plantation soils and water-logged, riparian native-vegetation soils. Given that bacterial Fe(III) reduction is the primary mechanism of Fe oxide dissolution in soils upon saturation, associated Fe mobilization involved several abiotic and biological processes. Abiotic oxidation of dissolved Fe(II) by Mn appeared to control Fe transport and inhibit Fe dissolution from mature, first-rotation plantation soils post-saturation. Such an effect was not observed in clear-felled and replanted soils associated with low SOM and potentially low Mn reactivity. Associated GHG evolution post-saturation mainly involved variable CO2 emissions, with low, but consistently increasing CH4 effluxes in mature, first-rotation plantation soil only. In comparison, water-logged soils in the riparian native-vegetation buffer zone functioned as an important GHG source, with high potentials for Fe mobilization and GHG, particularly CH4 emissions in riparian loam soils associated with high clay and crystalline Fe fractions. Active Fe–C cycling was unlikely to occur in lower-catchment estuarine soils associated with low cultivable bacterial densities and GHG effluxes. As a key component of bacterial Fe cycling, neutrophilic FeOB widely occurred in diverse aquatic, but not terrestrial, habitats of the catchment study area. Stalked and sheathed FeOB resembling Gallionella and Leptothrix were limited to microbial mat material deposited in surface fresh waters associated with a circumneutral-pH seep, and clay-rich soil within riparian buffer zones. Unicellular, Sideroxydans-related FeOB (96% sequence identity) were ubiquitous in surface and subsurface freshwater environments, with highest abundance in estuary-adjacent shallow coastal groundwater water associated with redox transition. The abundance of dissolved C and Fe in the groundwater-dependent system was associated with high numbers of cultivable anaerobic, heterotrophic FeRB, microaerophilic, putatively lithotrophic FeOB and aerobic, heterotrophic bacteria. This research represents the first study of microbial Fe cycling in diverse circumneutral-pH environments (terrestrial–aquatic, freshwater–estuarine, surface–subsurface) of a subtropical coastal ecosystem. It also represents the first study of its kind in the southern hemisphere. This work highlights the significance of bacterial Fe(III) reduction in terrestrial, and bacterial Fe(II) oxidation in aquatic catchment Fe cycling. Results indicate the risk of promotion of Fe mobilization due to plantation clear-felling and replanting, and GHG emissions associated with seasonal water-logging. Additional significant outcomes were also achieved. The first direct evidence for multiple biomineralization patterns of neutrophilic, microaerophilic, unicellular FeOB was presented. A putatively pure culture, which represents the first cultivable neutrophilic FeOB from the southern hemisphere, was obtained as representative FeOB ubiquitous in diverse catchment aquatic habitats.

Formation mechanisms of secondary organic aerosols in relation to laser printer emissions

Due to their large surface area, complex chemical composition and high alveolar deposition rate, ultrafine particles (UFPs) (< 0.1 ìm) pose a significant risk to human health and their toxicological effects have been acknowledged by the World Health Organisation. Since people spend most of their time indoors, there is a growing concern about the UFPs present in some indoor environments. Recent studies have shown that office machines, in particular laser printers, are a significant indoor source of UFPs. The majority of printer-generated UFPs are organic carbon and it is unlikely that these particles are emitted directly from the printer or its supplies (such as paper and toner powder). Thus, it was hypothesised that these UFPs are secondary organic aerosols (SOA). Considering the widespread use of printers and human exposure to these particles, understanding the processes involved in particle formation is of critical importance. However, few studies have investigated the nature (e.g. volatility, hygroscopicity, composition, size distribution and mixing state) and formation mechanisms of these particles. In order to address this gap in scientific knowledge, a comprehensive study including state-of-art instrumental methods was conducted to characterise the real-time emissions from modern commercial laser printers, including particles, volatile organic compounds (VOCs) and ozone (O3). The morphology, elemental composition, volatility and hygroscopicity of generated particles were also examined. The large set of experimental results was analysed and interpreted to provide insight into: (1) Emissions profiles of laser printers: The results showed that UFPs dominated the number concentrations of generated particles, with a quasi unimodal size distribution observed for all tests. These particles were volatile, non-hygroscopic and mixed both externally and internally. Particle microanalysis indicated that semi-volatile organic compounds occupied the dominant fraction of these particles, with only trace quantities of particles containing Ca and Fe. Furthermore, almost all laser printers tested in this study emitted measurable concentrations of VOCs and O3. A positive correlation between submicron particles and O3 concentrations, as well as a contrasting negative correlation between submicron particles and total VOC concentrations were observed during printing for all tests. These results proved that UFPs generated from laser printers are mainly SOAs. (2) Sources and precursors of generated particles: In order to identify the possible particle sources, particle formation potentials of both the printer components (e.g. fuser roller and lubricant oil) and supplies (e.g. paper and toner powder) were investigated using furnace tests. The VOCs emitted during the experiments were sampled and identified to provide information about particle precursors. The results suggested that all of the tested materials had the potential to generate particles upon heating. Nine unsaturated VOCs were identified from the emissions produced by paper and toner, which may contribute to the formation of UFPs through oxidation reactions with ozone. (3) Factors influencing the particle emission: The factors influencing particle emissions were also investigated by comparing two popular laser printers, one showing particle emissions three orders of magnitude higher than the other. The effects of toner coverage, printing history, type of paper and toner, and working temperature of the fuser roller on particle number emissions were examined. The results showed that the temperature of the fuser roller was a key factor driving the emission of particles. Based on the results for 30 different types of laser printers, a systematic positive correlation was observed between temperature and particle number emissions for printers that used the same heating technology and had a similar structure and fuser material. It was also found that temperature fluctuations were associated with intense bursts of particles and therefore, they may have impact on the particle emissions. Furthermore, the results indicated that the type of paper and toner powder contributed to particle emissions, while no apparent relationship was observed between toner coverage and levels of submicron particles. (4) Mechanisms of SOA formation, growth and ageing: The overall hypothesis that UFPs are formed by reactions with the VOCs and O3 emitted from laser printers was examined. The results proved this hypothesis and suggested that O3 may also play a role in particle ageing. In addition, knowledge about the mixing state of generated particles was utilised to explore the detailed processes of particle formation for different printing scenarios, including warm-up, normal printing, and printing without toner. The results indicated that polymerisation may have occurred on the surface of the generated particles to produce thermoplastic polymers, which may account for the expandable characteristics of some particles. Furthermore, toner and other particle residues on the idling belt from previous print jobs were a very clear contributing factor in the formation of laser printer-emitted particles. In summary, this study not only improves scientific understanding of the nature of printer-generated particles, but also provides significant insight into the formation and ageing mechanisms of SOAs in the indoor environment. The outcomes will also be beneficial to governments, industry and individuals.

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

Attractive subfamilies of BLS curves for implementing high-security pairings

Barreto-Lynn-Scott (BLS) curves are a stand-out candidate for implementing high-security pairings. This paper shows that particular choices of the pairing-friendly search parameter give rise to four subfami- lies of BLS curves, all of which offer highly efficient and implementation- friendly pairing instantiations. Curves from these particular subfamilies are defined over prime fields that support very efficient towering options for the full extension field. The coefficients for a specific curve and its correct twist are automat-ically determined without any computational effort. The choice of an extremely sparse search parameter is immediately reflected by a highly efficient optimal ate Miller loop and final exponentiation. As a resource for implementors, we give a list with examples of implementation-friendly BLS curves through several high-security levels.

Rational design of serine protease inhibitors

Proteases regulate a spectrum of diverse physiological processes, and dysregulation of proteolytic activity drives a plethora of pathological conditions. Understanding protease function is essential to appreciating many aspects of normal physiology and progression of disease. Consequently, development of potent and specific inhibitors of proteolytic enzymes is vital to provide tools for the dissection of protease function in biological systems and for the treatment of diseases linked to aberrant proteolytic activity. The studies in this thesis describe the rational design of potent inhibitors of three proteases that are implicated in disease development. Additionally, key features of the interaction of proteases and their cognate inhibitors or substrates are analysed and a series of rational inhibitor design principles are expounded and tested. Rational design of protease inhibitors relies on a comprehensive understanding of protease structure and biochemistry. Analysis of known protease cleavage sites in proteins and peptides is a commonly used source of such information. However, model peptide substrate and protein sequences have widely differing levels of backbone constraint and hence can adopt highly divergent structures when binding to a protease’s active site. This may result in identical sequences in peptides and proteins having different conformations and diverse spatial distribution of amino acid functionalities. Regardless of this, protein and peptide cleavage sites are often regarded as being equivalent. One of the key findings in the following studies is a definitive demonstration of the lack of equivalence between these two classes of substrate and invalidation of the common practice of using the sequences of model peptide substrates to predict cleavage of proteins in vivo. Another important feature for protease substrate recognition is subsite cooperativity. This type of cooperativity is commonly referred to as protease or substrate binding subsite cooperativity and is distinct from allosteric cooperativity, where binding of a molecule distant from the protease active site affects the binding affinity of a substrate. Subsite cooperativity may be intramolecular where neighbouring residues in substrates are interacting, affecting the scissile bond’s susceptibility to protease cleavage. Subsite cooperativity can also be intermolecular where a particular residue’s contribution to binding affinity changes depending on the identity of neighbouring amino acids. Although numerous studies have identified subsite cooperativity effects, these findings are frequently ignored in investigations probing subsite selectivity by screening against diverse combinatorial libraries of peptides (positional scanning synthetic combinatorial library; PS-SCL). This strategy for determining cleavage specificity relies on the averaged rates of hydrolysis for an uncharacterised ensemble of peptide sequences, as opposed to the defined rate of hydrolysis of a known specific substrate. Further, since PS-SCL screens probe the preference of the various protease subsites independently, this method is inherently unable to detect subsite cooperativity. However, mean hydrolysis rates from PS-SCL screens are often interpreted as being comparable to those produced by single peptide cleavages. Before this study no large systematic evaluation had been made to determine the level of correlation between protease selectivity as predicted by screening against a library of combinatorial peptides and cleavage of individual peptides. This subject is specifically explored in the studies described here. In order to establish whether PS-SCL screens could accurately determine the substrate preferences of proteases, a systematic comparison of data from PS-SCLs with libraries containing individually synthesised peptides (sparse matrix library; SML) was carried out. These SML libraries were designed to include all possible sequence combinations of the residues that were suggested to be preferred by a protease using the PS-SCL method. SML screening against the three serine proteases kallikrein 4 (KLK4), kallikrein 14 (KLK14) and plasmin revealed highly preferred peptide substrates that could not have been deduced by PS-SCL screening alone. Comparing protease subsite preference profiles from screens of the two types of peptide libraries showed that the most preferred substrates were not detected by PS SCL screening as a consequence of intermolecular cooperativity being negated by the very nature of PS SCL screening. Sequences that are highly favoured as result of intermolecular cooperativity achieve optimal protease subsite occupancy, and thereby interact with very specific determinants of the protease. Identifying these substrate sequences is important since they may be used to produce potent and selective inhibitors of protolytic enzymes. This study found that highly favoured substrate sequences that relied on intermolecular cooperativity allowed for the production of potent inhibitors of KLK4, KLK14 and plasmin. Peptide aldehydes based on preferred plasmin sequences produced high affinity transition state analogue inhibitors for this protease. The most potent of these maintained specificity over plasma kallikrein (known to have a very similar substrate preference to plasmin). Furthermore, the efficiency of this inhibitor in blocking fibrinolysis in vitro was comparable to aprotinin, which previously saw clinical use to reduce perioperative bleeding. One substrate sequence particularly favoured by KLK4 was substituted into the 14 amino acid, circular sunflower trypsin inhibitor (SFTI). This resulted in a highly potent and selective inhibitor (SFTI-FCQR) which attenuated protease activated receptor signalling by KLK4 in vitro. Moreover, SFTI-FCQR and paclitaxel synergistically reduced growth of ovarian cancer cells in vitro, making this inhibitor a lead compound for further therapeutic development. Similar incorporation of a preferred KLK14 amino acid sequence into the SFTI scaffold produced a potent inhibitor for this protease. However, the conformationally constrained SFTI backbone enforced a different intramolecular cooperativity, which masked a KLK14 specific determinant. As a consequence, the level of selectivity achievable was lower than that found for the KLK4 inhibitor. Standard mechanism inhibitors such as SFTI rely on a stable acyl-enzyme intermediate for high affinity binding. This is achieved by a conformationally constrained canonical binding loop that allows for reformation of the scissile peptide bond after cleavage. Amino acid substitutions within the inhibitor to target a particular protease may compromise structural determinants that support the rigidity of the binding loop and thereby prevent the engineered inhibitor reaching its full potential. An in silico analysis was carried out to examine the potential for further improvements to the potency and selectivity of the SFTI-based KLK4 and KLK14 inhibitors. Molecular dynamics simulations suggested that the substitutions within SFTI required to target KLK4 and KLK14 had compromised the intramolecular hydrogen bond network of the inhibitor and caused a concomitant loss of binding loop stability. Furthermore in silico amino acid substitution revealed a consistent correlation between a higher frequency of formation and the number of internal hydrogen bonds of SFTI-variants and lower inhibition constants. These predictions allowed for the production of second generation inhibitors with enhanced binding affinity toward both targets and highlight the importance of considering intramolecular cooperativity effects when engineering proteins or circular peptides to target proteases. The findings from this study show that although PS-SCLs are a useful tool for high throughput screening of approximate protease preference, later refinement by SML screening is needed to reveal optimal subsite occupancy due to cooperativity in substrate recognition. This investigation has also demonstrated the importance of maintaining structural determinants of backbone constraint and conformation when engineering standard mechanism inhibitors for new targets. Combined these results show that backbone conformation and amino acid cooperativity have more prominent roles than previously appreciated in determining substrate/inhibitor specificity and binding affinity. The three key inhibitors designed during this investigation are now being developed as lead compounds for cancer chemotherapy, control of fibrinolysis and cosmeceutical applications. These compounds form the basis of a portfolio of intellectual property which will be further developed in the coming years.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Gaussian process models for sensor-centric robot localisation

This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

On IT and business value in developing countries : A complementarities-based approach

Developing economies accommodate more than three quarters of the world's population. This means understanding their growth and well-being is of critical importance. Information technology (IT) is one resource that has had a profound effect in shaping the global economy. IT is also an important resource for driving growth and development in developing economies. Investments in developing economies, however, have focused on the exploitation of labor and natural resources. Unlike in developed economies, focus on IT investment to improve efficiency and effectiveness of business process in developing economies has been sparse, and mechanisms for deriving better IT-related business value is not well understood. This study develops a complementarities-based business value model for developing economies, and tests the relationship between IT investments, IT-related complementarities, and business process performance. It also considers the relationship between business processes performance and firm-level performance. The results suggest that a coordinated investment in IT and IT-related complementarities related favorably to business process performance. Improvements in process-level performance lead to improvements in firm-level performance. The results also suggest that the IT-related complementarities are not only a source of business value on their own, but also enhance the IT resources' ability to contribute to business process performance. This study demonstrates that a coordinated investment approach is required in developing economies. With this approach, their IT resources and IT-related complementaries would help them significantly in improving their business processes, and eventually their firm-level performances.

Electro-cortical implicit race bias does not vary with participants' race or sex

Earlier research found evidence for electro-cortical race bias towards black target faces in white American participants irrespective of the task relevance of race. The present study investigated whether an implicit race bias generalizes across cultural contexts and racial in- and out-groups. An Australian sample of 56 Chinese and Caucasian males and females completed four oddball tasks that required sex judgements for pictures of male and female Chinese and Caucasian posers. The nature of the background (across task) and of the deviant stimuli (within task) was fully counterbalanced. Event-related potentials (ERPs) to deviant stimuli recorded from three midline sites were quantified in terms of mean amplitude for four components: N1, P2, N2 and a late positive complex (LPC; 350–700 ms). Deviants that differed from the backgrounds in sex or race elicited enhanced LPC activity. These differences were not modulated by participant race or sex. The current results replicate earlier reports of effects of poser race relative to background race on the LPC component of the ERP waveform. In addition, they indicate that an implicit race bias occurs regardless of participant's or poser's race and is not confined to a particular cultural context.

The relationship between self-reported animal fear and ERP modulation : Evidence for enhanced processing and fear of harmless invertebrates in snake- and spider-fearful individuals

The present study used ERPs to compare processing of fear-relevant (FR) animals (snakes and spiders) and non-fear-relevant (NFR) animals similar in appearance (worms and beetles). EEG was recorded from 18 undergraduate participants (10 females) as they completed two animal-viewing tasks that required simple categorization decisions. Participants were divided on a post hoc basis into low snake/spider fear and high snake/spider fear groups. Overall, FR animals were rated higher on fear and elicited a larger LPC. However, individual differences qualified these effects. Participants in the low fear group showed clear differentiation between FR and NFR animals on subjective ratings of fear and LPC modulation. In contrast, participants in the high fear group did not show such differentiation between FR and NFR animals. These findings suggest that the salience of feared-FR animals may generalize on both a behavioural and electro-cortical level to other animals of similar appearance but of a non-harmful nature.

A Bayesian‐Markov process for reliability prediction

Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.

The way

In this wall-mounted sculpture, a car stereo is mounted into a photographic image of a redwood forest. It plays a sparse and evocative guitar soundtrack. The supporting cabinet is finished with timber veneer to resemble a retro home stereo or piece of designer furniture. This work examines how we construct, represent and deploy notions of nature in our contemporary lives. It mixes the languages of furniture design, landscape photography and sculpture. Drawing on Zygmunt Bauman’s theoretical work on “liquid modernity”, this work questions how and where we find space for contemplation and reflection in a contemporary context increasingly defined by temporary social bonds and consumer choices.

Functionalised zinc oxide nanowire gas sensors : enhanced NO2 gas sensor response by chemical modification of nanowire surfaces

Surface coating with an organic self-assembled monolayer (SAM) can enhance surface reactions or the absorption of specific gases and hence improve the response of a metal oxide (MOx) sensor toward particular target gases in the environment. In this study the effect of an adsorbed organic layer on the dynamic response of zinc oxide nanowire gas sensors was investigated. The effect of ZnO surface functionalisation by two different organic molecules, tris(hydroxymethyl)aminomethane (THMA) and dodecanethiol (DT), was studied. The response towards ammonia, nitrous oxide and nitrogen dioxide was investigated for three sensor configurations, namely pure ZnO nanowires, organic-coated ZnO nanowires and ZnO nanowires covered with a sparse layer of organic-coated ZnO nanoparticles. Exposure of the nanowire sensors to the oxidising gas NO2 produced a significant and reproducible response. ZnO and THMA-coated ZnO nanowire sensors both readily detected NO2 down to a concentration in the very low ppm range. Notably, the THMA-coated nanowires consistently displayed a small, enhanced response to NO2 compared to uncoated ZnO nanowire sensors. At the lower concentration levels tested, ZnO nanowire sensors that were coated with THMA-capped ZnO nanoparticles were found to exhibit the greatest enhanced response. ΔR/R was two times greater than that for the as-prepared ZnO nanowire sensors. It is proposed that the ΔR/R enhancement in this case originates from the changes induced in the depletion-layer width of the ZnO nanoparticles that bridge ZnO nanowires resulting from THMA ligand binding to the surface of the particle coating. The heightened response and selectivity to the NO2 target are positive results arising from the coating of these ZnO nanowire sensors with organic-SAM-functionalised ZnO nanoparticles.