Global Fits of the CKM Matrix

We report upon the present status of global fits to Cabibbo-Kobayashi-Maskawa matrix.Comment: 3 pages, 3 figures invited talk presented at EPS conference, Aachen July 17-2

Risk and Business Goal Based Security Requirement and Countermeasure Prioritization

Companies are under pressure to be in control of their assets but at the same time they must operate as efficiently as possible. This means that they aim to implement “good-enough security” but need to be able to justify their security investment plans. Currently companies achieve this by means of checklist-based security assessments, but these methods are a way to achieve consensus without being able to provide justifications of countermeasures in terms of business goals. But such justifications are needed to operate securely and effectively in networked businesses. In this paper, we first compare a Risk-Based Requirements Prioritization method (RiskREP) with some requirements engineering and risk assessment methods based on their requirements elicitation and prioritization properties. RiskREP extends misuse case-based requirements engineering methods with IT architecture-based risk assessment and countermeasure definition and prioritization. Then, we present how RiskREP prioritizes countermeasures by linking business goals to countermeasure specification. Prioritizing countermeasures based on business goals is especially important to provide the stakeholders with structured arguments for choosing a set of countermeasures to implement. We illustrate RiskREP and how it prioritizes the countermeasures it elicits by an application to an action case

Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Outer Approximations of Coherent Lower Probabilities Using Belief Functions

We investigate the problem of outer approximating a coherent lower probability with a more tractable model. In particular, in this work we focus on the outer approximations made by belief functions. We show that they can be obtained by solving a linear programming problem. In addition, we consider the subfamily of necessity measures, and show that in that case we can determine all the undominated outer approximations in a simple manner

Interferon beta in multiple sclerosis: experience in a British specialist multiple sclerosis centre

Background: The efficacy of interferon beta (IFN beta) is well established in relapsing-remitting multiple sclerosis (MS). However, the use of this drug in clinical practice is complex, especially because it is only partially effective, its long term efficacy and side effects are unknown, its efficacy may be abrogated by the development of neutralising antibodies, compliance is variable, and its cost effectiveness is controversial. Objectives and Methods: Analysis of a prospectively followed up series of 101 MS patients treated with IFN beta was undertaken to: (1) monitor the outcome of IFN beta treatment in clinical practice; (2) compare the immunogenicity of the three commercial IFN beta preparations available; (3) assess the proportion of patients fulfilling the current guidelines of the Association of British Neurologists for stopping IFN beta therapy. Results: During a median treatment period of 26 months (range 2–85), the relapse rate decreased by 41%. Although the reduction in the relapse rate was similar for all three commercial products, none of the Avonex treated patients were relapse free, compared with 19% of the Betaferon treated and 27% of the Rebif treated patients (p=0.02). Neutralising antibodies were not detected in Avonex treated patients (0 of 18), compared with 12 of 32 (38%) Betaferon treated and 10 of 23 (44%) Rebif treated patients (p=0.02). Forty of 101 (40%) patients satisfied the current (2001) Association of British Neurologists criteria for stopping IFN beta treatment at some stage during their treatment. Conclusion: IFN beta is effective in reducing the relapse rate in patients with relapsing-remitting MS in routine clinical practice. However, after a median treatment duration of 26 months, 40% of initially relapsing-remitting MS patients seem to have ongoing disease activity, presenting as disabling relapses or insidious progression

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component is the development of efficient search structures to sample possibility distributions based on the DPY transformation as introduced by Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model that allows us to express both possibilistic and probabilistic uncertainty, where the hybrid model is a proper extension of both probabilistic and possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can readily be applied to solve such hybrid models. © Springer International Publishing Switzerland 2016.This work is partially funded by EPSRC PACES project (Ref: EP/J012149/1).Peer Reviewe