Synchronization in climate dynamics and other extended systems

Synchronization is now well established as representing coherent behaviour between two or more otherwise autonomous nonlinear systems subject to some degree of coupling. Such behaviour has mainly been studied to date, however, in relatively low-dimensional discrete systems or networks. But the possibility of similar kinds of behaviour in continuous or extended spatiotemporal systems has many potential practical implications, especially in various areas of geophysics. We review here a range of cyclically varying phenomena within the Earth's climate system for which there may be some evidence or indication of the possibility of synchronized behaviour, albeit perhaps imperfect or highly intermittent. The exploitation of this approach is still at a relatively early stage within climate science and dynamics, in which the climate system is regarded as a hierarchy of many coupled sub-systems with complex nonlinear feedbacks and forcings. The possibility of synchronization between climate oscillations (global or local) and a predictable external forcing raises important questions of how models of such phenomena can be validated and verified, since the resulting response may be relatively insensitive to the details of the model being synchronized. The use of laboratory analogues may therefore have an important role to play in the study of natural systems that can only be observed and for which controlled experiments are impossible. We go on to demonstrate that synchronization can be observed in the laboratory, even in weakly coupled fluid dynamical systems that may serve as direct analogues of the behaviour of major components of the Earth's climate system. The potential implications and observability of these effects in the long-term climate variability of the Earth is further discussed. © 2010 Springer-Verlag Berlin Heidelberg.

Morphological computation for adaptive behavior and cognition

Traditionally, in robotics, artificial intelligence and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest in the notion of embodiment not only in robotics and artificial intelligence, but also in the neurosciences, psychology and philosophy. In this paper, we introduce the notion of morphological computation, and demonstrate how it can be exploited on the one hand for designing intelligent, adaptive robotic systems, and on the other hand for understanding natural systems. While embodiment has often been used in its trivial meaning, i.e. "intelligence requires a body", the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. Morphological computation is about connecting body, brain and environment. A number of case studies are presented to illustrate the concept. We conclude with some speculations about potential lessons for neuroscience and robotics. © 2006 Elsevier B.V. All rights reserved.

Understanding function and function-to-form evolution

This is a report on a workshop held at Cambridge University Engineering Design Centre, 17-10 June 1992. This workshop was held to discuss the issue of 'function' and 'function-to-form' evolution in mechanical design. The authors organised this workshop as they felt that their understanding of these topics was incomplete and that discussions between researchers might help to clarify some key issues.

The topic chosen for the workshop proved to be a stimulating one. The term 'function' is part of a designer's daily vocabulary, however there is poor agreement about its definition. In order to develop computer systems to support product evolution, a precise definition is required. Further the value of 'function' and 'function-to-form' evolution as a good choice of workshop topic is evident from the lack of firm conclusions that resulted from the sessions. This lack of consensus made for lively discussion and left participants questioning many of their preconceived ideas.

Attendance at the workshop was by invitation only. A list of the participants (not all those invited could attend due to time and financial constraints) is given in Appendix 1.

Bayesian adaptation and adaptively trained systems

As the use of found data increases, more systems are being built using adaptive training. Here transforms are used to represent unwanted acoustic variability, e.g. speaker and acoustic environment changes, allowing a canonical model that models only the "pure" variability of speech to be trained. Adaptive training may be described within a Bayesian framework. By using complexity control approaches to ensure robust parameter estimates, the standard point estimate adaptive training can be justified within this Bayesian framework. However during recognition there is usually no control over the amount of data available. It is therefore preferable to be able to use a full Bayesian approach to applying transforms during recognition rather than the standard point estimates. This paper discusses various approximations to Bayesian approaches including a new variational Bayes approximation. The application of these approaches to state-of-the-art adaptively trained systems using both CAT and MLLR transforms is then described and evaluated on a large vocabulary speech recognition task. © 2005 IEEE.

Dynamics in dye-doped LC systems in presence of trans-cis isomerism

The excitation as well as relaxation dynamics of dye-doped nematic liquid crystal cells has been explored both experimentally and theoretically. Overshoots in the build up of the probe signal diffracted from gratings written onto dye-doped liquid crystal systems have often been observed. The overshoot behaviour makes the accurate measurement of nonlinear optical (NLO) response magnitude difficult and ambiguous. Moreover, it complicates the understanding of the dynamics and the physics of the NLO processes. On the basis of the system with trans-cis isomerisation as a mechanism of the NLO effect the quantitative model has been built to describe the experimental results which we observe. The two unknown material parameters: diffusion coefficient and cis species lifetime are calculated from the relaxation data. A quantitative model of the signal build-up uses these parameters. The calculated dynamic behaviour based on this model correlates very well with the experimental data. The model is used to predict the performance of the system with various dopant diffusion properties.

Synchronization in a pair of thermally coupled rotating baroclinic annuli: understanding atmospheric teleconnections in the laboratory.

Synchronization phenomena in a fluid dynamical analogue of atmospheric circulation is studied experimentally by investigating the dynamics of a pair of thermally coupled, rotating baroclinic annulus systems. The coupling between the systems is in the well-known master-slave configuration in both periodic and chaotic regimes. Synchronization tools such as phase dynamics analysis are used to study the dynamics of the coupled system and demonstrate phase synchronization and imperfect phase synchronization, depending upon the coupling strength and parameter mismatch.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.