Steiner Variations on Random Surfaces

Ambartzumian et.al. suggested that the modified Steiner action functional had desirable properties for a random surface action. However, Durhuus and Jonsson pointed out that such an action led to an ill-defined grand-canonical partition function and suggested that the addition of an area term might improve matters. In this paper we investigate this and other related actions numerically for dynamically triangulated random surfaces and compare the results with the gaussian plus extrinsic curvature actions that have been used previously.Comment: 8 page

The Wrong Kind of Gravity

The KPZ formula shows that coupling central charge less than one spin models to 2D quantum gravity dresses the conformal weights to get new critical exponents, where the relation between the original and dressed weights depends only on the central charge. At the discrete level the coupling to 2D gravity is effected by putting the spin models on annealed ensembles of planar random graphs or their dual triangulations, where the connectivity fluctuates on the same time-scale as the spins. Since the sole determining factor in the dressing is the central charge, one could contemplate putting a spin model on a quenched ensemble of 2D gravity graphs with the ``wrong'' central charge. We might then expect to see the critical exponents appropriate to the central charge used in generating the graphs. In such cases the KPZ formula could be interpreted as giving a continuous line of critical exponents which depend on this central charge. We note that rational exponents other than the KPZ values can be generated using this procedure for the Ising, tricritical Ising and 3-state Potts models.Comment: 8 pages, no figure

Editorial

No abstract available

George Baillie on peptide array, a technique that transformed research on phosphodiesterases

George Baillie speaks to Francesca Lake (Managing Editor, Future Science OA). George Baillie is a Professor and PI within the Institute of Cardiovascular and Medical Sciences at the University of Glasgow (Glasgow, UK). His research over the last 15 years has examined many aspects of the cAMP signaling pathway in disease and he has published over 140 papers on the subject. His major discovery was that phosphodiesterases are ‘compartmentalized’, and it is their location within cells that direct their function. The Baillie/Houslay laboratory was the first to discover a specific function for a single isoform of PDE4 (namely PDE4D5 with β-arrestin desensitizes the β2-adrenergic receptor). His laboratory has since gone on to ascribe functions to several other PDE4 isoforms. He is a founder and director of Sannox Therapeutics, a spin-out venture within University of Glasgow. He is also a member of the Editorial Board of Future Science OA and Co-Editor of Cellular Signalling

Smooth Random Surfaces from Tight Immersions?

We investigate actions for dynamically triangulated random surfaces that consist of a gaussian or area term plus the {\it modulus} of the gaussian curvature and compare their behavior with both gaussian plus extrinsic curvature and ``Steiner'' actions.Comment: 7 page

An audio-based sports video segmentation and event detection algorithm

In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques