Acceleration of ions up to 20MeV/nucleon in the ultrashort, high-intensity regime

The measurements reported here provide scaling laws for the ion acceleration process in the regime of ultrashort (50 fs), ultrahigh contrast (10) and ultrahigh intensity (> 10W/cm ), never investigated previously. The scaling of the accelerated ion energies was studied by varying a number of parameters such as target thickness (down to 10nm), target material (C and Al) and laser light polar- ization (circular and linear) at 35° and normal laser incidence. A twofold increase in proton energy and an order of magnitude enhancement in ion flux have been observed over the investigated thickness range at 35° angle of incidence. Further- more, at normal laser incidence, measured peak proton energies of about 20 MeV are observed almost independently of the target thickness over a wide range (50nm- 10 µm). 1. © 2012 by Società Italiana di Fisica.

Laser-driven acceleration of ions:State of the art, perspectives and applications

The acceleration of ions with high-power lasers has been a very active field of research during the past 10 years. This paper summarizes the main results obtained in the field, detailing the mechanisms of the acceleration process and the main observed beam characteristics. Perspectives for future development of the field and current and future applications are also discussed. © 2012 by Società Italiana di Fisica.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

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.