Cultural Policy in Leeds

Bradford raised a few eyebrows with its bid to be City of Culture in 2008, but then demonstrated that its claim could not be so easily dismissed. In recent years, several of the region’s towns and cities have put cultural policies at the heart of their corporate visions and strategies in an effort to unite, develop and promote. This article reports on recent research on cultural policy making in Leeds, a city that clearly has a considerable 'cultural offer', but which has been making rather less of it than several of its northern rivals

Phases of lattice hard core bosons in a periodic superlattice

We study by Quantum Monte Carlo simulations the phase diagram of lattice hard core bosons with nearest-neighbour repulsive interactions, in the presence of a super-lattice of adsorption sites. For a moderate adsorption strength, the system forms crystal phases registered with the adsorption lattice; a "supersolid" phase exists, on both the vacancy and interstitial sides, whereas at commensuration the superfluid density vanishes. The possible relevance of these results to experiments on $^4$He films adsorbed on graphite is discussed.Comment: 5 pages, 5 figure

Plan stability: replanning versus plan repair

The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the {em stable} repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning

Partial Transfer Learning with Selective Adversarial Networks

Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains. Existing domain adversarial networks assume fully shared label space across domains. In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space. Previous methods typically match the whole source domain to the target domain, which are prone to negative transfer for the partial transfer problem. We present Selective Adversarial Network (SAN), which simultaneously circumvents negative transfer by selecting out the outlier source classes and promotes positive transfer by maximally matching the data distributions in the shared label space. Experiments demonstrate that our models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets