The belief noisy-or model applied to network reliability analysis

One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quan-tification step where the Conditional Probability Tables (CPTs) are determined. The number of parameters included in CPTs increases exponentially with the number of parent variables. The most common solution is the application of the so-called canonical gates. The Noisy-OR (NOR) gate, which takes advantage of the independence of causal interactions, provides a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both aleatory and epistemic uncertainty of the network. Compared with NOR, more rich information which is of great value for making decisions can be got when the available knowledge is uncertain. Specially, when there is no epistemic uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR are presented in this paper in order to meet various needs of engineers. The application of BNOR model on the reliability evaluation problem of networked systems demonstrates its effectiveness

Quantum Teleportation of Optical Quantum Gates

We show that a universal set of gates for quantum computation with optics can be quantum teleported through the use of EPR entangled states, homodyne detection, and linear optics and squeezing operations conditioned on measurement outcomes. This scheme may be used for fault-tolerant quantum computation in any optical scheme (qubit or continuous variable). The teleportation of nondeterministic nonlinear gates employed in linear optics quantum computation is discussed.Comment: 4 pages, 1 figure, published versio

Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces

Imprecise probability models for inference in exponential families

When considering sampling models described by a distribution from an exponential family, it is possible to create two types of imprecise probability models. One is based on the corresponding conjugate distribution and the other on the corresponding predictive distribution. In this paper, we show how these types of models can be constructed for any (regular, linear, canonical) exponential family, such as the centered normal distribution. To illustrate the possible use of such models, we take a look at credal classification. We show that they are very natural and potentially promising candidates for describing the attributes of a credal classifier, also in the case of continuous attributes