Atomic scale simulation of deformation in magnesium single crystals

The deformation behaviour of magnesium single crystals under plane strain conditions has been examined using molecular dynamics modelling. The simulations were based on an existing atomic potential for magnesium taken from the literature. A strain of 10% was applied at rates of 3x10⁹s^-1 and 3x10⁷s^-1. The simulations predicted the formation of mechanical twins that accommodated extension in the c-axis direction of the hexagonal unit cell. However, the predicted twin is not of the same kind found in magnesium, but is that commonly observed in titanium. It is believed that further analysis of the physical properties predicted by this interatomic potential will shed more light on the atomic processes controlling twinning in Magnesium alloys. It also highlights the need for improvements to the interatomic potential such that more accurate deformation behaviour can be attained.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.