An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

The effects of introducing ester linking groups on the flexoelectro-optic properties of symmetric bimesogens

In the chiral nematic phase, flexoelectricity can give rise to an interesting electrooptic switching effect, known as flexoelectro-optic switching. Flexoelectro-optic switching gives a fast v-shaped switching regime. Previous studies show that symmetric bimesogens are particularly suited for flexoelectro-optic switching. By introducing two ester linking groups into the molecular structure of a symmetric bimesogen, it was hypothesised that the flexoelectric properties will be enhanced significantly because of the resulting increase in the dipole moment of the molecules. This was found to be the correct; however, the inclusion of ester linking groups reduced the liquid crystallinity of the material.

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.