University of Bristol

A central challenge in socio-physics is understanding how groups of self-interested agents make collective decisions. For humans many insights in the underlying opinion formation process have been gained from network models, which represent agents as nodes and social contacts as links. Over the past decade these models have been expanded

to include the feedback of the opinions held by agents on the structure of the network. While a verification of these adaptive models in humans is still difficult, evidence is now starting to appear in opinion formation experiments with animals, where the choice that is being made concerns the direction of movement. In this talk I show how analytical insights can be gained from adaptive networks models and how predictions from these models can be verified in experiments with swarming animals. The results of this work point to a similarity between swarming and human opinion formation and reveal insights in the dynamics of the opinion formation process. In particular I show that in a population that is under control of a strongly opinionated minority a democratic consensus can be restored by the addition of

uninformed individuals.

Support Vector Machines are a new and very promising approach to

machine learning. They can be applied to a wide range of tasks such as

classification, regression, novelty detection, density estimation,

etc. The approach is motivated by statistical learning theory and the

algorithms have performed well in practice on important applications

such as handwritten character recognition (where they currently give

state-of-the-art performance), bioinformatics and machine vision. The

learning task typically involves optimisation theory (linear, quadratic

and general nonlinear programming, depending on the algorithm used).

In fact, the approach has stimulated new questions in optimisation

theory, principally concerned with the issue of how to handle problems

with a large numbers of variables. In the first part of the talk I will

overview this subject, in the second part I will describe some of the

speaker's contributions to this subject (principally, novelty

detection, query learning and new algorithms) and in the third part I

will outline future directions and new questions stimulated by this

research.

We address the effect of extreme geometry on a non-convex variational problem motivated by recent investigations of magnetic domain walls trapped by sharp thin necks. We prove the existence of local minimizers representing geometrically constrained walls under suitable symmetry assumptions on the domains and provide an asymptotic characterization of the wall profile. The asymptotic behavior, which depends critically on the scaling of length and width of the neck, turns out to be qualitatively different from the higher-dimensional case and a richer variety of regimes is shown to exist.

he study of polycrystals of shape-memory alloys and rigid-perfectly plastic materials gives rise to problems of nonlinear homogenization involving degenerate energies. We present a characterisation of the strain and stress fields for some classes of problems in plane strain and also for some three-dimensional situations. Consequences for shape-memory alloys and rigid-perfectly plastic materials are discussed through model problems. In particular we explore connections to previous conjectures characterizing those shape-memory polycrystals with non-trivial recoverable strain.

Meeting to mark Sir Roger Penrose's 75th Birthday