Stability and noise in biochemical switches

Many processes in biology, from the regulation of gene expression in bacteria to memory in the brain, involve switches constructed from networks of biochemical reactions. Crucial molecules are present in small numbers, raising questions about noise and stability. Analysis of noise in simple reaction schemes indicates that switches stable for years and switchable in milliseconds can be built from fewer than one hundred molecules. Prospects for direct tests of this prediction, as well as implications, are discussed

Recent blackouts in US and continental Europe: is liberalisation to blame?

The paper starts with a detailed technical overview of recent blackouts in the US, Sweden/Denmark and Italy in order to analyse common threads and lessons to be learnt. The blackouts have exposed a number of challenges facing utilities worldwide. Increased liberalisation of electricity supply industry has resulted in a significant increase in inter-area (or cross-border) trades which often are not properly accounted for when assessing system security. The traditional decentralised way of operating systems by TSOs, with each TSO looking after its own control area and little information exchange, resulted in inadequate and slow response to contingencies. A new mode of coordinated operation for real-time security assessment and control is needed in order to maintain system security. This new mode of operation requires overcoming a number of organisational, psychological, legal and technical challenges but the alternative is either to risk another blackout or run the interconnected system very conservatively, maintaining large security margin at a high cost to everyone. The paper also includes technical appendices explaining engineering power system concepts to non-engineering audience.electricity, USA, Sweden, Denmark

Statistical Mechanics and Visual Signal Processing

The nervous system solves a wide variety of problems in signal processing. In many cases the performance of the nervous system is so good that it apporaches fundamental physical limits, such as the limits imposed by diffraction and photon shot noise in vision. In this paper we show how to use the language of statistical field theory to address and solve problems in signal processing, that is problems in which one must estimate some aspect of the environment from the data in an array of sensors. In the field theory formulation the optimal estimator can be written as an expectation value in an ensemble where the input data act as external field. Problems at low signal-to-noise ratio can be solved in perturbation theory, while high signal-to-noise ratios are treated with a saddle-point approximation. These ideas are illustrated in detail by an example of visual motion estimation which is chosen to model a problem solved by the fly's brain. In this problem the optimal estimator has a rich structure, adapting to various parameters of the environment such as the mean-square contrast and the correlation time of contrast fluctuations. This structure is in qualitative accord with existing measurements on motion sensitive neurons in the fly's brain, and we argue that the adaptive properties of the optimal estimator may help resolve conlficts among different interpretations of these data. Finally we propose some crucial direct tests of the adaptive behavior.Comment: 34pp, LaTeX, PUPT-143

Occam factors and model-independent Bayesian learning of continuous distributions

Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the integration over the model space determine the free parameter of the theory ("smoothness scale") self-consistently. This persists even for distributions that are atypical in the prior and is a step towards a model-independent theory for learning continuous distributions. Finally, we point out that a wrong parameterization of a model family may sometimes be advantageous for small data sets.Comment: publication revisions: extended introduction, new references, other minor corrections; 6 pages, 6 figures, revte

Electricity Network Investment and Regulation for a Low Carbon Future

The requirement for significantly higher electricity network investment in the UK seems certain as the capacity of distributed generation and large scale renewables increases on the system. In this paper, which forms a chapter in the forthcoming Book “Delivering a Low Carbon Electricity System: Technologies, Economics and Policy”, the authors make a number of significant suggestions for improvement to the current system of network regulation. First, they suggest that the RPI-X system needs to be overhauled in favour of a simpler yardstick based system and which allows for more merchant transmission investments. Second, future regulation should involve more negotiated regulation involving agreements between network owners and purchasers of network services. This would be particularly advantageous for decisions on new network investments. Third, more extensive use needs to be made of locational pricing within the transmission and distribution system in order to facilitate the least cost expansion of low carbon generation, including micropower. Fourth, consideration needs to be given to ownership unbundling of distribution networks from retail supply. This would better facilitate the entry of distributed generation and the development of appropriate competition between grid and off-grid generation supply and demand side management. Finally, there needs to be a significant increase in R&D expenditure in electricity networks supported by customer levies

Ambiguous model learning made unambiguous with 1/f priors

What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the answer depends on the prior expectations of the dynamics of the model parameter that is to be inferred from the data. Local time constraints on the priors are insufficient to pick one interpretation over another. On the other hand, nonlocal time constraints, induced by a $1/f$ noise spectrum of the priors, is shown to permit learning of a specific model parameter even when there are infinitely many equally plausible interpretations of the data. This transition is inferred by a remarkable mapping of the model estimation problem to a dissipative physical system, allowing the use of powerful statistical mechanical methods to uncover the transition from indeterminate to determinate model learning.Comment: 8 pages, 2 figure