PeV-Scale Supersymmetry

Although supersymmetry has not been seen directly by experiment, there are powerful physics reasons to suspect that it should be an ingredient of nature and that superpartner masses should be somewhat near the weak scale. I present an argument that if we dismiss our ordinary intuition of finetuning, and focus entirely on more concrete physics issues, the PeV scale might be the best place for supersymmetry. PeV-scale supersymmetry admits gauge coupling unification, predicts a Higgs mass between 125 GeV and 155 GeV, and generally disallows flavor changing neutral currents and CP violating effects in conflict with current experiment. The PeV scale is motivated independently by dark matter and neutrino mass considerations.Comment: 5 RevTex page

Fast & Confident Probabilistic Categorization

We describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text Mining Workshop 2007. This submission relies on a straightforward implementation of the probabilistic categoriser described in (Gaussier et al., ECIR'02). This categoriser is adapted to handle multiple labelling and a piecewise-linear confidence estimation layer is added to provide an estimate of the labelling confidence. This technique achieves a score of 1.689 on the test data

Contextual anomaly detection in crowded surveillance scenes

AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour