On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ���desirable��� as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds ��� for hypotheses and for data ��� is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Economic growth, inequality and technology adoption in transitional economies

The stylized facts that motivate this thesis include the diversity in growth patterns that are observed across countries during the process of economic development, and the divergence over time in income distributions both within and across countries. This thesis constructs a dynamic general equilibrium model in which technology adoption is costly and agents are heterogeneous in their initial holdings of resources. Given the households��� resource level, this study examines how adoption costs influence the evolution of household income over time and the timing of transition to more productive technologies. The analytical results of the model constructed here characterize three growth outcomes associated with the technology adoption process depending on productivity differences between the technologies. These are appropriately labeled as ���poverty trap���, ���dual economy��� and ���balanced growth���. The model is then capable of explaining the observed diversity in growth patterns across countries, as well as divergence of incomes over time. Numerical simulations of the model furthermore illustrate features of this transition. They suggest that that differences in adoption costs account for the timing of households��� decision to switch technology which leads to a disparity in incomes across households in the technology adoption process. Since this determines the timing of complete adoption of the technology within a country, the implications for cross-country income differences are obvious. Moreover, the timing of technology adoption appears to be impacts on patterns of growth of households, which are different across various income groups. The findings also show that, in the presence of costs associated with the adoption of more productive technologies, inequalities of income and wealth may increase over time tending to delay the convergence in income levels. Initial levels of inequalities in the resources also have an impact on the date of complete adoption of more productive technologies. The issue of increasing income inequality in the process of technology adoption opens up another direction for research. Specifically increasing inequality implies that distributive conflicts may emerge during the transitional process with political- economy consequences. The model is therefore extended to include such issues. Without any political considerations, taxes would leads to a reduction in inequality and convergence of incomes across agents. However this process is delayed if politico-economic influences are taken into account. Moreover, the political outcome is sub optimal. This is essentially due to the fact that there is a resistance associated with the complete adoption of the advanced technology.

Cerebellar augmented joint control for a humanoid robot

The joints of a humanoid robot experience disturbances of markedly different magnitudes during the course of a walking gait. Consequently, simple feedback control techniques poorly track desired joint trajectories. This paper explores the addition of a control system inspired by the architecture of the cerebellum to improve system response. This system learns to compensate the changes in load that occur during a cycle of motion. The joint compensation scheme, called Trajectory Error Learning, augments the existing feedback control loop on a humanoid robot. The results from tests on the GuRoo platform show an improvement in system response for the system when augmented with the cerebellar compensator.

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant���s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time ��� minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

Predicting effects of monotony on driver���s vigilance

Monotony has been identified as a contributing factor to road crashes. Drivers��� ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks, such as driving on Australian rural roads, many of which are monotonous by nature. Highway design in particular attempts to reduce the driver���s task to a merely lane-keeping one. Such a task provides little stimulation and is monotonous, thus affecting the driver���s attention which is no longer directed towards the road. Inattention contributes to crashes, especially for professional drivers. Monotony has been studied mainly from the endogenous perspective (for instance through sleep deprivation) without taking into account the influence of the task itself (repetitiveness) or the surrounding environment. The aim and novelty of this thesis is to develop a methodology (mathematical framework) able to predict driver lapses of vigilance under monotonous environments in real time, using endogenous and exogenous data collected from the driver, the vehicle and the environment. Existing approaches have tended to neglect the specificity of task monotony, leaving the question of the existence of a ���monotonous state��� unanswered. Furthermore the issue of detecting vigilance decrement before it occurs (predictions) has not been investigated in the literature, let alone in real time. A multidisciplinary approach is necessary to explain how vigilance evolves in monotonous conditions. Such an approach needs to draw on psychology, physiology, road safety, computer science and mathematics. The systemic approach proposed in this study is unique with its predictive dimension and allows us to define, in real time, the impacts of monotony on the driver���s ability to drive. Such methodology is based on mathematical models integrating data available in vehicles to the vigilance state of the driver during a monotonous driving task in various environments. The model integrates different data measuring driver���s endogenous and exogenous factors (related to the driver, the vehicle and the surrounding environment). Electroencephalography (EEG) is used to measure driver vigilance since it has been shown to be the most reliable and real time methodology to assess vigilance level. There are a variety of mathematical models suitable to provide a framework for predictions however, to find the most accurate model, a collection of mathematical models were trained in this thesis and the most reliable was found. The methodology developed in this research is first applied to a theoretically sound measure of sustained attention called Sustained Attention Response to Task (SART) as adapted by Michael (2010), Michael and Meuter (2006, 2007). This experiment induced impairments due to monotony during a vigilance task. Analyses performed in this thesis confirm and extend findings from Michael (2010) that monotony leads to an important vigilance impairment independent of fatigue. This thesis is also the first to show that monotony changes the dynamics of vigilance evolution and tends to create a ���monotonous state��� characterised by reduced vigilance. Personality traits such as being a low sensation seeker can mitigate this vigilance decrement. It is also evident that lapses in vigilance can be predicted accurately with Bayesian modelling and Neural Networks. This framework was then applied to the driving task by designing a simulated monotonous driving task. The design of such task requires multidisciplinary knowledge and involved psychologist Rebecca Michael. Monotony was varied through both the road design and the road environment variables. This experiment demonstrated that road monotony can lead to driving impairment. Particularly monotonous road scenery was shown to have the most impact compared to monotonous road design. Next, this study identified a variety of surrogate measures that are correlated with vigilance levels obtained from the EEG. Such vigilance states can be predicted with these surrogate measures. This means that vigilance decrement can be detected in a car without the use of an EEG device. Amongst the different mathematical models tested in this thesis, only Neural Networks predicted the vigilance levels accurately. The results of both these experiments provide valuable information about the methodology to predict vigilance decrement. Such an issue is quite complex and requires modelling that can adapt to highly inter-individual differences. Only Neural Networks proved accurate in both studies, suggesting that these models are the most likely to be accurate when used on real roads or for further research on vigilance modelling. This research provides a better understanding of the driving task under monotonous conditions. Results demonstrate that mathematical modelling can be used to determine the driver���s vigilance state when driving using surrogate measures identified during this study. This research has opened up avenues for future research and could result in the development of an in-vehicle device predicting driver vigilance decrement. Such a device could contribute to a reduction in crashes and therefore improve road safety.

Ordering of Ge islands on Si(001) substrates patterned by nanoindentation

Spatial organization of Ge islands, grown by physical vapor deposition, on prepatterned Si(001) substrates has been investigated. The substrates were patterned prior to Ge deposition by nanoindentation. Characterization of Ge dots is performed by atomic force microscopy and scanning electron microscopy. The nanoindents act as trapping sites, allowing ripening of Ge islands at those locations during subsequent deposition and diffusion of Ge on the surface. The results show that island ordering is intrinsically linked to the nucleation and growth at indented sites and it strongly depends on pattern parameters.

A functional screen identifies lateral transfer of beta-glucuronidase (gus) from bacteria to fungi

Lateral gene transfer (LGT) from prokaryotes to microbial eukaryotes is usually detected by chance through genome-sequencing projects. Here, we explore a different, hypothesis-driven approach. We show that the fitness advantage associated with the transferred gene, typically invoked only in retrospect, can be used to design a functional screen capable of identifying postulated LGT cases. We hypothesized that beta-glucuronidase (gus) genes may be prone to LGT from bacteria to fungi (thought to lack gus) because this would enable fungi to utilize glucuronides in vertebrate urine as a carbon source. Using an enrichment procedure based on a glucose-releasing glucuronide analog (cellobiouronic acid), we isolated two gus(+) ascomycete fungi from soils (Penicillium canescens and Scopulariopsis sp.). A phylogenetic analysis suggested that their gus genes, as well as the gus genes identified in genomic sequences of the ascomycetes Aspergillus nidulans and Gibberella zeae, had been introgressed laterally from high-GC gram(+) bacteria. Two such bacteria (Arthrobacter spp.), isolated together with the gus(+) fungi, appeared to be the descendants of a bacterial donor organism from which gus had been transferred to fungi. This scenario was independently supported by similar substrate affinities of the encoded beta-glucuronidases, the absence of introns from fungal gus genes, and the similarity between the signal peptide-encoding 5' extensions of some fungal gus genes and the Arthrobacter sequences upstream of gus. Differences in the sequences of the fungal 5' extensions suggested at least two separate introgression events after the divergence of the two main Euascomycete classes. We suggest that deposition of glucuronides on soils as a result of the colonization of land by vertebrates may have favored LGT of gus from bacteria to fungi in soils.

Fabrication using a rapid prototyping system and in vitro characterization of PEG-PCL-PLA scaffolds for tissue engineering

n the field of tissue engineering new polymers are needed to fabricate scaffolds with specific properties depending on the targeted tissue. This work aimed at designing and developing a 3D scaffold with variable mechanical strength, fully interconnected porous network, controllable hydrophilicity and degradability. For this, a desktop-robot-based melt-extrusion rapid prototyping technique was applied to a novel tri-block co-polymer, namely poly(ethylene glycol)-block-poly(epsi-caprolactone)-block-poly(DL-lactide), PEG-PCL-P(DL)LA. This co-polymer was melted by electrical heating and directly extruded out using computer-controlled rapid prototyping by means of compressed purified air to build porous scaffolds. Various lay-down patterns (0/30/60/90/120/150��, 0/45/90/135��, 0/60/120�� and 0/90��) were produced by using appropriate positioning of the robotic control system. Scanning electron microscopy and micro-computed tomography were used to show that 3D scaffold architectures were honeycomb-like with completely interconnected and controlled channel characteristics. Compression tests were performed and the data obtained agreed well with the typical behavior of a porous material undergoing deformation. Preliminary cell response to the as-fabricated scaffolds has been studied with primary human fibroblasts. The results demonstrated the suitability of the process and the cell biocompatibility of the polymer, two important properties among the many required for effective clinical use and efficient tissue-engineering scaffolding.

Gene transfer to plants by diverse species of bacteria

Agrobacterium is widely considered to be the only bacterial genus capable of transferring genes to plants. When suitably modified, Agrobacterium has become the most effective vector for gene transfer in plant biotechnology1. However, the complexity of the patent landscape2 has created both real and perceived obstacles to the effective use of this technology for agricultural improvements by many public and private organizations worldwide. Here we show that several species of bacteria outside the Agrobacterium genus can be modified to mediate gene transfer to a number of diverse plants. These plant-associated symbiotic bacteria were made competent for gene transfer by acquisition of both a disarmed Ti plasmid and a suitable binary vector. This alternative to Agrobacterium-mediated technology for crop improvement, in addition to affording a versatile ���open source��� platform for plant biotechnology, may lead to new uses of natural bacteria��� plant interactions to achieve plant transformation.

The p38 mitogen-activated protein kinase regulates interleukin-1beta-induced IL-8 expression via an effect on the IL-8 promoter in intestinal epithelial cells

Several lines of evidence implicate the p38 mitogen-activated protein kinase (p38 MAPK) in the proinflammatory response to bacterial agents and cytokines. Equally, the transcription factor, nuclear factor (NF)-kappaB, is recognized to be a critical determinant of the inflammatory response in intestinal epithelial cells (IECs). However, the precise inter-relationship between the activation of p38 MAPK and activation of the transcription factor NF-kappaB in the intestinal epithelial cell (IEC) system, remains unknown. Here we show that interleukin (IL)-1beta activates all three MAPKs in Caco-2 cells. The production of IL-8 and monocyte chemotactic protein 1 (MCP-1) was attenuated by 50% when these cells were preincubated with the p38 MAPK inhibitor, SB 203580. Further investigation of the NF-kappaB signalling system revealed that the inhibitory effect was independent of the phosphorylation and degradation of IkappaBalpha, the binding partner of NF-kappaB. This effect was also independent of the DNA binding of the p65 Rel A subunit, as well as transactivation, determined by an NF-kappaB luciferase construct, using both SB 203580 and dominant-negative p38 MAPK. Evaluation of IL-8 and MCP-1 RNA messages by reverse transcription-polymerase chain reaction (RT-PCR) revealed that the inhibitory effect of SB 203580 was associated with a reduction in this parameter. Using an IL-8-luciferase promoter construct, an effect of p38 upon its activation by both pharmacological and dominant-negative p38 construct co-transfection was demonstrated. It is concluded that p38 MAPK influences the expression of chemokines in intestinal epithelial cells, through an effect upon the activation of the chemokine promoter, and does not directly involve the activation of the transcription factor NF-kappaB

Moving Forward: Students and teachers against racism

Racism in education is one of the key issues facing schools, communities and the nation. Racism is about the exercise of power by individuals, groups and communities against each other. Whatever form it takes, racism has great potential to hurt and to harm. This book tells a series of stories from 11 very different government and non-government schools in Queensland. These stories show the positive measures that are being taken in schools to promote harmony, respect, understanding and fairness between school members, and with people in the community. The stories offer a simple lesson: solutions to racism must be local solutions. They must be culturally appropriate and relevant to specific communities. There is no single solution. However, this book shows that, through a mixture of strategies, students, teachers, schools and communities can make a difference, influencing the school and community culture and improving the educational outcomes and life chances of students of diverse backgrounds.

Shadowlands (Directed by Caroline Heim)

The controversial love affair of CS Lewis, Oxford scholar and writer of the Narnia Chronicles, is set in a constellation of music, sculpture and mime. CS Lewis���s intriguing relationship with poet Joy Davidman moves, inspires and confronts us with the big questions. Beauty contrasts with the ephemeral land of the shadows. Crossbow���s adaption of William Nicholson���s soulful and witty play explores the joy and the grief of ���experience: that most brutal of teachers.��� Showcasing the abilities of Brisbane and Sydney actors, the company that brought you The Miracle Worker and Anne of the Thousand Days, will quicken your senses and stir your heart with Shadowlands. All performances have a tactile tour of the stage 20 minutes before the start time of the show. Special signed performance for hearing impaired patrons Thur 5 Aug 2pm

Video-stimulated accounts: Young children accounting for interactional matters in front of peers

Research in the early years places increasing importance on participatory methods to engage children. The playback of video-recording to stimulate conversation is a research method that enables children���s accounts to be heard and attends to a participatory view. During video-stimulated sessions, participants watch an extract of video-recording of a specific event in which they were involved, and then account for their participation in that event. Using an interactional perspective, this paper draws distinctions between video-stimulated accounts and a similar research method, popular in education, that of video-stimulated recall. Reporting upon a study of young children���s interactions in a playground, video-stimulated accounts are explicated to show how the participants worked toward the construction of events in the video-stimulated session. This paper discusses how the children account for complex matters within their social worlds, and manage the accounting of others in the video-stimulated session. When viewed from an interactional perspective and used alongside fine grained analytic approaches, video-stimulated accounts are an effective method to provide the standpoint of the children involved and further the competent child paradigm.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics, most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainly to accompany observations of the environment. This paper describes how uncertainly can be characterised for a vision system that locates coloured landmark in a typical laboratory environment. The paper describes a model of the uncertainly in segmentation, the internal camera model and the mounting of the camera on the robot. It =plains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainly model,