On the annealed VC entropy for margin classifiers: A statistical mechanics study

Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.

Advances in large margin classifiers

We apply methods of Statistical Mechanics to study the generalization performance of Support vector Machines in large data spaces.

Lobe interactions within the thallus margin and the maintenance of symmetry in the lichen Parmelia conspersa (Ehrh. ex Ach.)Ach.

This study determined whether the radial growth of lobes of the foliose lichen Parmelia conspersa (Ehrh. ex Ach.)Ach. was influenced by the radial growth and morphology of their closest neighbours and whether such interactions influence thallus symmetry. The radial growth and morphology of a sample of adjacent lobes from six thalli was measured. Positive correlations were observed between radial growth and lobe width in three thalli and with the degree of bifurcation of the lobe in two thalli. Negative correlations between the radial growth of adjacent lobes were observed in four thalli suggesting that faster growing lobes may inhibit the growth of their neighbours.Lobes glued next to individual lobes had no signifiacnt effect on the radial growth of wide or narrow lobes. Lobes glued 1-2 mm in front of their neighbours exhibited an intital phase of increased radial growth and then a phase of slower growth. Radial growth decreased when the lobes were glued 2 mm behind their neighbours and these lobes were essentially eliminated by the growth of the adjacent lobes. The data suggest that lobe interactions may incresae lobe growth variation within a thallus. However, the decrease in radial growth of lobes which protrude from the margin and the elimination of slower growing lobes may help to maintain thallus symmetry.

Normal dispersion fibre-enhanced nonlinear optical loop mirror for 2R regeneration and phase margin improvement

A novel all-optical regeneration technique using loop-mirror intensity-filtering and nonlinear broadening in normal-dispersion fibre is described. The device offers 2R-regeneration function and phase margin improvement. The technique is applied to 40Gbit/s return-to-zero optical data streams.

Normal dispersion fibre-enhanced nonlinear optical loop mirror for 2R regeneration and phase margin improvement

A novel all-optical regeneration technique using loop-mirror intensity-filtering and nonlinear broadening in normal-dispersion fibre is described. The device offers 2R-regeneration function and phase margin improvement. The technique is applied to 40Gbit/s return-to-zero optical data streams.

Optimising market share and profit margin:SMDP-based tariff pricing under the smart grid paradigm

Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.