Nuclear Modification to Parton Distribution Functions and Parton Saturation

We introduce a generalized definition of parton distribution functions (PDFs) for a more consistent all-order treatment of power corrections. We present a new set of modified DGLAP evolution equations for nuclear PDFs, and show that the resummed $\alpha_s A^{1/3}/Q^2$-type of leading nuclear size enhanced power corrections significantly slow down the growth of gluon density at small-$x$. We discuss the relation between the calculated power corrections and the saturation phenomena.Comment: 4 pages, to appear in the proceedings of QM200

Dynamical properties of a trapped dipolar Fermi gas at finite temperature

We investigate the dynamical properties of a trapped finite-temperature normal Fermi gas with dipole-dipole interaction. For the free expansion dynamics, we show that the expanded gas always becomes stretched along the direction of the dipole moment. In addition, we present the temperature and interaction dependences of the asymptotical aspect ratio. We further study the collapse dynamics of the system by suddenly increasing the dipolar interaction strength. We show that, in contrast to the anisotropic collapse of a dipolar Bose-Einstein condensate, a dipolar Fermi gas always collapses isotropically when the system becomes globally unstable. We also explore the interaction and temperature dependences for the frequencies of the low-lying collective excitations.Comment: 11 pages, 7 figure

Summarisation & Visualisation of Large Volumes of Time-Series Sensor Data

a number of sensors, including an electricity usage sensor supplied by Episensor. This poses our second With the increasing ubiquity of sensor data, challenge, how to summarise an extended period of presenting this data in a meaningful way to electrictiy usage data for a home user. users is a challenge that must be addressed before we can easily deploy real-world sensor network interfaces in the home or workplace. In this paper, we will present one solution to the visualisation of large quantities of sensor data that is easy to understand and yet provides meaningful and intuitive information to a user, even when examining many weeks or months of historical data. We will illustrate this visulalisation technique with two real-world deployments of sensing the person and sensing the home

Automatically detecting important moments from everyday life using a mobile device

This paper proposes a new method to detect important moments in our lives. Our work is motivated by the increase in the quantity of multimedia data, such as videos and photos, which are capturing life experiences into personal archives. Even though such media-rich data suggests visual processing to identify important moments, the oft-mentioned problem of the semantic gap means that users cannot automatically identify or retrieve important moments using visual processing techniques alone. Our approach utilises on-board sensors from mobile devices to automatically identify important moments, as they are happening

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval