The continuing use of complementary and alternative medicine in South Australia: costs and beliefs in 2004

Objective: To survey the use, cost, beliefs and quality of life of users of complementary and alternative medicine (CAM). Design: A representative population survey conducted in 2004 with longitudinal comparison to similar 1993 and 2000 surveys. Participants: 3015 South Australian respondents over the age of 15 years (71.7% participation). Results: In 2004, CAMs were used by 52.2% of the population. Greatest use was in women aged 25-34 years, with higher income and education levels. CAM therapists had been visited by 26.5% of the population. In those with children, 29.9% administered CAMs to them and 17.5% of the children had visited CAM therapists. The total extrapolated cost in Australia of CAMs and CAM therapists in 2004 was AUD$1.8 billion, which was a decrease from AUD$2.3 billion in 2000. CAMs were used mostly to maintain general health. The users of CAM had lower quality-of-life scores than non-users. Among CAM users, 49.7% used conventional medicines on the same day and 57.2% did not report the use of CAMs to their doctor. About half of the respondents assumed that CAMs were independently tested by a government agency; of these, 74.8% believed they were tested for quality and safety, 21.8% for what they claimed, and 17.9% for efficacy. Conclusions: Australians continue to use high levels of CAMs and CAM therapists. The public is often unaware that CAMs are not tested by the Therapeutic Goods Administration for efficacy or safety.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

How does globalisation interact with higher education? The continuing lack of consensus:The continuing lack of consensus

This essay attempts to ascertain whether a particular meaning of globalisation, and view on its effects and the appropriate response to it, are becoming standardised across academia. To do so, it content-analyses a representative sample of new scholarship, mapping the various approaches of current researchers towards globalisation. The essay shows how globalisation remains a contested concept within studies of higher education, as in many other fields. Rather than globalisation being taken to refer unambiguously to global flows, pressures or trends, its meaning continues to depend on the particular perspective adopted by contemporary researchers. The same conflict is apparent concerning the impacts which are reputed to globalisation and with regard to the appropriate response to globalisation amongst academics and higher education institutions (HEIs) more generally. Perhaps the only apparent point of consensus amongst contemporary researchers is the claim that globalisation affects HEIs, rather than HEIs themselves being implicated in the promotion of globalisation. This position underplays the often important role of HEIs in encouraging cross-border flows and pressures, and global trends such as marketisation.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

The use of open learning in the training of neurotechnologists:a regional case study

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The education and training of technicians in the construction industry

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The effectiveness of an organisations' equal opportunities policy for the role and training of women, particularly in the fire service

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The continuing relevance of Germany's engine for CEE andthe EU

The article looks first into the nature of the relations between Germany and the CEE countries a decade since the accession of the CEE countries to the EU. The relations are characterized as normalised and intensive with diverse levels of closeness and co-operation reflecting of the conceptual and ideological compatibility/differences. Next, the article focuses on the German attitude to the euro zone crisis. Germany has become a hegemon in the rescue effort aimed at stabilisation and economic invigoration of the euro zone. However, German hegemony has developed by default, not by design: her leading position is linked with considerable political and financial costs. Germany moved central stage and took the position of a reluctant hegemon. However, German role is contested internationally (it has not the support of the French government in key areas) as well as internally (particularly by the Federal Constitutional Court and the Bundesbank).The article argues that the new situation makes the German-CEE relations increasingly relevant for both sides. The German leadership of the EU increasing split along the north-south divide requires backing by the Northern group countries to which the CEE in general belongs. Given a number of reasons the CEE countries implement three distinctive strategies of co-operation with Germany in European politics. Also military co-operation, which remained rather limited so far, may receive new impulses, given the financial austerity. © 2013 The Regents of the University of California.

Special Features of Musical Education in Distant Learning (Training of Intonation Thinking)

The problem of using modern technologies in distant learning of intonation thinking is described in this article. An importance of intonation learning for musician students and the possibilities, provided by World Wide Web and multimedia technologies are the main point of this article.