Clustering user data for user modelling in the GUIDE multi-modal set-top box

Chapter 20 Clustering User Data for User Modelling in the GUIDE Multi-modal Set- top Box PM Langdon and P. Biswas 20.1 ... It utilises advanced user modelling and simulation in conjunction with a single layer interface that permits a ...

Kinetic control of catalytic CVD for high-quality graphene at low temperatures.

Low-temperature (∼600 °C), scalable chemical vapor deposition of high-quality, uniform monolayer graphene is demonstrated with a mapped Raman 2D/G ratio of >3.2, D/G ratio ≤0.08, and carrier mobilities of ≥3000 cm(2) V(-1) s(-1) on SiO(2) support. A kinetic growth model for graphene CVD based on flux balances is established, which is well supported by a systematic study of Ni-based polycrystalline catalysts. A finite carbon solubility of the catalyst is thereby a key advantage, as it allows the catalyst bulk to act as a mediating carbon sink while optimized graphene growth occurs by only locally saturating the catalyst surface with carbon. This also enables a route to the controlled formation of Bernal stacked bi- and few-layered graphene. The model is relevant to all catalyst materials and can readily serve as a general process rationale for optimized graphene CVD.

The unidirectional emptying box

A theoretical description of the turbulent mixing within and the draining of a dense fluid layer from a box connected to a uniform density, quiescent environment through openings in the top and the base of the box is presented in this paper. This is an extension of the draining model developed by Linden et al. (Annu. Rev. Fluid Mech. vol. 31, 1990, pp. 201-238) and includes terms that describe localized mixing within the emptying box at the density interface. Mixing is induced by a turbulent flow of replacement fluid into the box and as a consequence we predict, and observe in complementary experiments, the development of a three-layer stratification. Based on the data collated from previous researchers, three distinct formulations for entrainment fluxes across density interfaces are used to account for this localized mixing. The model was then solved numerically for the three mixing formulations. Analytical solutions were developed for one formulation directly and for a second on assuming that localized mixing is relatively weak though still significant in redistributing buoyancy on the timescale of the draining process. Comparisons between our theoretical predictions and the experimental data, which we have collected on the developing layer depths and their densities show good agreement. The differences in predictions between the three mixing formulations suggest that the normalized flux turbulently entrained across a density interface tends to a constant value for large values of a Froude number FrT, based on conditions of the inflow through the top of the box, and scales as the cube of FrT for small values of FrT. The upper limit on the rate of entrainment into the mixed layer results in a minimum time (tD) to remove the original dense layer. Using our analytical solutions, we bound this time and show that 0.2tE ≈tD tE, i.e. the original dense layer may be depleted up to five times more rapidly than when there is no internal mixing and the box empties in a time tE. © 2010 Cambridge University Press.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.