AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present AGWAN (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the AGWAN model to real-world graphs and for generating random graphs from the model. Using the Enron “who communicates with whom” social graph, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to the structure of real-world graphs.

England's Citizenship Education Experiment - Active Citizenship or Service?

This chapter explores the history of active citizenship education in English schools in relation to the more established tradition of service learning in the US.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

Video Event Recognition by Dempster-Shafer Theory

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

A table-top laser-based source of short, collimated, ultra-relativistic positron beams

An ultra-relativistic electron beam passing through a thick, high-Z solid target triggers an electromagnetic cascade, whereby a large number of high energy photons and electron-positron pairs are produced. By exploiting this physical process, we present here the first experimental evidence of the generation of ultra-short, highly collimated and ultra-relativistic positron beams following the interaction of a laser-wake field accelerated electron beam with high-Z solid targets. Clear evidence has also been obtained of the generation of GeV electron-positron jets with variable composition depending on the solid target material and thickness. The percentage of positrons in the overall leptonic beam has been observed to vary from a few per cent up to almost fifty per cent, implying a quasi-neutral electron-positron beam. We anticipate that these beams will be of direct relevance to the laboratory study of astrophysical leptonic jets and their interaction with the interstellar medium.

High resolution X-ray spectroscopy in fast electron transport studies

A detailed knowledge of the physical phenomena underlying the generation and the transport of fast electrons generated in high-intensity laser-matter interactions is of fundamental importance for the fast ignition scheme for inertial confinement fusion.

Here we report on an experiment carried out with the VULCAN Petawatt beam and aimed at investigating the role of collisional return currents in the dynamics of the fast electron beam. To that scope, in the experiment counter-propagating electron beams were generated by double-sided irradiation of layered target foils containing a Ti layer. The experimental results were obtained for different time delays between the two laser beams as well as for single-sided irradiation of the target foils. The main diagnostics consisted of two bent mica crystal spectrometers placed at either side of the target foil. High-resolution X-ray spectra of the Ti emission lines in the range from the Ly alpha to the K alpha line were recorded. In addition, 2D X-ray images with spectral resolution were obtained by means of a novel diagnostic technique, the energy-encoded pin-hole camera, based on the use of a pin-hole array equipped with a CCD detector working in single-photon regime. The spectroscopic measurements suggest a higher target temperature for well-aligned laser beams and a precise timing between the two beams. The experimental results are presented and compared to simulation results.