Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

Understanding carers' intention for their child to walk to school : further application of the theory of reasoned action

One quarter of Australian children are overweight or obese (ABS, 2010), putting them at increased risk of physical and psychological health problems (Reilly et al., 2003). Overweight and obesity in childhood tends to persist into adulthood and is associated with premature death and morbidity (Reilly & Kelly, 2011). Increases in Australian children’s weight have coincided with declines in active transportation, such as walking, to school (Salmon et al., 2005). Investigating the factors which influence walking to school is therefore important, particularly since walking to school is a low cost and effective means of reducing excess weight (Rosenberg et al., 2006) that can be easily integrated into daily routine (Brophy et al., 2011). While research in this area has expanded (e.g., Brophy et al., 2011; Giles-Corti et al., 2010), it is largely atheoretical (exceptions Napier et al., 2011). This is an important gap from a social marketing perspective given the use of theory lies at the foundation of the framework (NSMC, 2006) and a continued lack of theory use is observed (Luca & Suggs, 2013). The aim of this paper is to empirically examine a widely adopted theory, the deconstructed Theory of Reasoned Action (TRA) (Fishbein & Azjen, 1975), to understand the relative importance of attitude and subjective norms in determining intentions to increase walk to school behaviour.

Understanding caregivers’ intentions for their child to walk to school : further application of the theory of planned behaviour

Increases in childhood obesity have coincided with declines in active transportation to school. This research builds on largely atheoretical extant literature examining factors that influence walk to school behavior through application of the Theory of Planned Behavior (TPB). Understanding caregivers’ decision for their child to walk to/from school is key to developing interventions to promote this cost-effective and accessible health behavior. The results from an online survey of 512 caregivers provide support for the TPB, highlighting the important role of subjective norms. This suggests marketers should nurture caregivers’ perception that important others approve of walking to school.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.

Knowledge gain or system benefit in environmental decision making?

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Interrogating Creative Theory and Creative Work: Inside the Games Studio

The expansion of creative and cultural industries has provided a rich source for theoretical claims and commentary. Much of this reproduces and extends the idea that autonomy is the defining feature of both enterprises and workers. Drawing on evidence from research into Australian development studios in the global digital games industry, the article interrogates claims concerning autonomy and related issues of insecurity and intensity, skill and specialisation, work–play boundaries, identity and attachments. In seeking to reconnect changes in creative labour to the wider production environment and political economy, an argument is advanced that autonomy is deeply contextual and contested as a dimension of the processes of capturing value for firms and workers.

Using a theory of planned behaviour framework to explore hand hygiene beliefs at the ‘5 critical moments’ among Australian hospital-based nurses

Background Improving hand hygiene among health care workers (HCWs) is the single most effective intervention to reduce health care associated infections in hospitals. Understanding the cognitive determinants of hand hygiene decisions for HCWs with the greatest patient contact (nurses) is essential to improve compliance. The aim of this study was to explore hospital-based nurses’ beliefs associated with performing hand hygiene guided by the World Health Organization’s (WHO) 5 critical moments. Using the belief-base framework of the Theory of Planned Behaviour, we examined attitudinal, normative, and control beliefs underpinning nurses’ decisions to perform hand hygiene according to the recently implemented national guidelines. Methods Thematic content analysis of qualitative data from focus group discussions with hospital-based registered nurses from 5 wards across 3 hospitals in Queensland, Australia. Results Important advantages (protection of patient and self), disadvantages (time, hand damage), referents (supportive: patients, colleagues; unsupportive: some doctors), barriers (being too busy, emergency situations), and facilitators (accessibility of sinks/products, training, reminders) were identified. There was some equivocation regarding the relative importance of hand washing following contact with patient surroundings. Conclusions The belief base of the theory of planned behaviour provided a useful framework to explore systematically the underlying beliefs of nurses’ hand hygiene decisions according to the 5 critical moments, allowing comparisons with previous belief studies. A commitment to improve nurses’ hand hygiene practice across the 5 moments should focus on individual strategies to combat distraction from other duties, peer-based initiatives to foster a sense of shared responsibility, and management-driven solutions to tackle staffing and resource issues. Hand hygiene following touching a patient’s surroundings continues to be reported as the most neglected opportunity for compliance.

Career counseling : joint contributions of contextual action theory and the systems theory framework

The influence of constructivism and the ongoing drive for convergence, both of career theories and between theory and practice, have been key drivers in the career development literature for two decades (Patton, International Handbook of Career Guidance, 2008). Both contextual action theory and systems theory are derived from the root metaphor of contextualism, which has been proffered as a worldview to assist scientists and practitioners in organizing day-to-day experiential data. This chapter identifies the theoretical contributions of the Systems Theory Framework (STF) (Patton and McMahon, Career development and systems theory: A new development, 1999, Career psychology in South Africa, 2006) and Contextual Action Theory (Young and Valach, The future of career, 2000, Journal of Vocational Behavior 64:499–514, 2004; Young et al., Career choice and development, 1996, Career choice and development, 2002), each of which has advanced thinking in theory integration and in the integration between theory and practice in the career development and counseling field. Young et al. (Career development in childhood and adolescence, 2007) noted the connections between the Patton and McMahon systems theory approach and the contextual action theory approach and these connections will be highlighted in terms of the application of these theoretical developments to practice in career counseling, with a particular focus on the commonalities between the two approaches and what counselors can learn from each of them. In particular, this chapter will discuss common conceptual understandings and practice dimensions.

A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Understanding the behavior of workshop facilitators in systems analysis and design projects: Developing theory from process modeling projects

Organizational and technological systems analysis and design practices such as process modeling have received much attention in recent years. However, while knowledge about related artifacts such as models, tools, or grammars has substantially matured, little is known about the actual tasks and interaction activities that are conducted as part of analysis and design acts. In particular, key role of the facilitator has not been researched extensively to date. In this paper, we propose a new conceptual framework that can be used to examine facilitation behaviors in process modeling projects. The framework distinguishes four behavioral styles in facilitation (the driving engineer, the driving artist, the catalyzing engineer, and the catalyzing artist) that a facilitator can adopt. To distinguish between the four styles, we provide a set of ten behavioral anchors that underpin facilitation behaviors. We also report on a preliminary empirical exploration of our framework through interviews with experienced analysts in six modeling cases. Our research provides a conceptual foundation for an emerging theory for describing and explaining different behaviors associated with process modeling facilitation, provides first preliminary empirical results about facilitation in modeling projects, and provides a fertile basis for examining facilitation in other conceptual modeling activities.

Folk theory of social change

People have a folk theory of social change (FTSC). A typical Western FTSC stipulates that as a society becomes more industrialized, it undergoes a natural course of social change, in which a communal society marked by communal relationships becomes a qualitatively different, agentic society where market-based exchange relationships prevail. People use this folk theory to predict a society’s future and estimate its past, to understand contemporary cross-cultural differences, and to make decisions about social policies. Nonetheless, the FTSC is not particularly consistent with the existing cross-cultural research on industrialization and cultural differences, and needs to be examined carefully.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.