Re-imagining static Utopias : unraveling the bat/human problem

The making of the modern world has long been fuelled by utopian images that are blind to ecological reality. Botanical gardens are but one example – who typically portray themselves as miniature, isolated 'edens on earth'. Whilst respected, heritage-laden institutions such as the Royal Botanical Gardens in Sydney, Australia promote such an idealised image they are now self-evidently also the vital ‘lungs’ of a crowded city as well as a critical habitats for threatened biodiversity (in this case notably flying foxes). In 2010 the 'Remnant Emergency Artlab' set out to alleviate this utopian hangover through a creative provocation called the 'Botanical Gardens ‘X-Tension’ - an imagined city-wide, distributed, network of 'ecological gardens' - in order to ask, what now needs to be better understood, connected and therefore ultimately conserved?

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Supportive-expressive group therapy for women with metastatic breast cancer: Improving access for Australian women through use of teleconference

While supportive-expressive group therapy (SEGT) has been found to be effective in significantly reducing distress associated with life-threatening illness, the challenge in Australia is to develop a means of providing supportive interventions to rural women who may be isolated both by the experience of illness and by geographical location. In this study an adaptation of SEGT was provided to women with metastatic breast cancer (n =21), who attended face-to-face or by telephone conference call. Participants showed significant gains on standardised measures of well-being, including a reduction in negative affect and an increase in positive affect, over a 12-month period. A reduction in intrusive and avoidant stress symptoms was also observed over 12 months; however, this difference was not significant. These outcomes suggest that SEGT, delivered in an innovative way within a community setting, may be an effective means of moderating the adverse effects of a diagnosis of metastatic breast cancer while improving access to supportive care for rural women. These results are considered exploratory, as the study did not include a matched control group.

Intermittent access to 20% ethanol induces high ethanol consumption in Long-Evans and Wistar rats

BACKGROUND: There has been some difficulty getting standard laboratory rats to voluntarily consume large amounts of ethanol without the use of initiation procedures. It has previously been shown that standard laboratory rats will voluntarily consume high levels of ethanol if given intermittent-access to 20% ethanol in a 2-bottle-choice setting [Wise, Psychopharmacologia 29 (1973), 203]. In this study, we have further characterized this drinking model. METHODS: Ethanol-naïve Long-Evans rats were given intermittent-access to 20% ethanol (three 24-hour sessions per week). No sucrose fading was needed and water was always available ad libitum. Ethanol consumption, preference, and long-term drinking behaviors were investigated. Furthermore, to pharmacologically validate the intermittent-access 20% ethanol drinking paradigm, the efficacy of acamprosate and naltrexone in decreasing ethanol consumption were compared with those of groups given continuous-access to 10 or 20% ethanol, respectively. Additionally, ethanol consumption was investigated in Wistar and out-bred alcohol preferring (P) rats following intermittent-access to 20% ethanol. RESULTS: The intermittent-access 20% ethanol 2-bottle-choice drinking paradigm led standard laboratory rats to escalate their ethanol intake over the first 5 to 6 drinking sessions, reaching stable baseline consumption of high amounts of ethanol (Long-Evans: 5.1 +/- 0.6; Wistar: 5.8 +/- 0.8 g/kg/24 h, respectively). Furthermore, the cycles of excessive drinking and abstinence led to an increase in ethanol preference and increased efficacy of both acamprosate and naltrexone in Long-Evans rats. P-rats initiate drinking at a higher level than both Long-Evans and Wistar rats using the intermittent-access 20% ethanol paradigm and showed a trend toward a further escalation in ethanol intake over time (mean ethanol intake: 6.3 +/- 0.8 g/kg/24 h). CONCLUSION: Standard laboratory rats will voluntarily consume ethanol using the intermittent-access 20% ethanol drinking paradigm without the use of any initiation procedures. This model promises to be a valuable tool in the alcohol research field.

Sponsorship-linked marketing: The role of articulation in memory

Corporate sponsorship of events contributes significantly to marketing aims, including brand awareness as measured by recall and recognition of sponsor‐event pairings. Unfortunately, resultant advantages accrue disproportionately to brands having a natural or congruent fit with the available sponsorship properties. In three cued‐recall experiments, the effect of articulation of sponsorship fit on memory for sponsor‐event pairings is examined. While congruent sponsors have a natural memory advantage, results demonstrate that memory improvements via articulation are possible for incongruent sponsor‐event pairings. These improvements are, however, affected by the presence of competitor brands and the way in which memory is accessed.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Compilation of somatic mutations of the CDKN2 gene in human cancers : non-random distribution of base substitutions

The CDKN2 gene, encoding the cyclin-dependent kinase inhibitor p16, is a tumour suppressor gene that maps to chromosome band 9p21-p22. The most common mechanism of inactivation of this gene in human cancers is through homozygous deletion; however, in a smaller proportion of tumours and tumour cell lines intragenic mutations occur. In this study we have compiled a database of over 120 published point mutations in the CDKN2 gene from a wide variety of tumour types. A further 50 deletions, insertions, and splice mutations in CDKN2 have also been compiled. Furthermore, we have standardised the numbering of all mutations according to the full-length 156 amino acid form of p16. From this study we are able to define several hot spots, some of which occur at conserved residues within the ankyrin domains of p16. While many of the hotspots are shared by a number of cancers, the relative importance of each position varies, possibly reflecting the role of different carcinogens in the development of certain tumours. As reported previously, the mutational spectrum of CDKN2 in melanomas differs from that of internal malignancies and supports the involvement of UV in melanoma tumorigenesis. Notably, 52% of all substitutions in melanoma-derived samples occurred at just six nucleotide positions. Nonsense mutations comprise a comparatively high proportion of mutations present in the CDKN2 gene, and possible explanations for this are discussed.