Radio signal attenuation and path losses depend on the environment and have been recognized to be difficult to calculate and predict. Past studies of the signal propagation, in an indoor environment have used several models with varying degrees of success and complexity. The aim of this paper is, by a precise description of the analytic model for an indoor environment, and uses it for determini...

Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between st...

Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to dev...

Abstract. We consider systems of hyperbolic balance laws governing flows of an arbitrary number of components equipped with general equations of state. The components are assumed to be immiscible. We compare two such models; one in which thermal equilibrium is attained trough a relaxation procedure, and a fully relaxed model in which equal temperatures are instantaneously imposed. We describe h...

An important issue in software maintenance and evolution is change propagation: given a primary change that is made in order to meet a new or changed requirement, what additional, secondary, changes are needed? We have previously developed techniques for effectively supporting change propagation within design models of intelligent agent systems. In this paper, we propose how this approach is ap...

While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps. We investigate whether Target Propagation (TPROP) style approaches can address these shortcomings. Unfortunately, extensive expe...

This is a discussion document for the IEEE document of the IEEE 802.15.4a channel modeling subgroup. It gives the current status of the generic channel model for UWB that has been discussed. Feedback from all participants is requested.

The study of premixed laminar flames is a central question in combustion theory. Here the differential equations describing steady flame propagation are investigated mathematically. The emphasis is on analytic results, hence a simple model, a onestep exothermic reaction with linear or radiative heat loss is studied. The equations governing our model can be considered as travelling wave equation...

Constraint propagation algorithms present inherent parallelism. Each constraint behaves as a concurrent process triggered by changes in the store of variables, updating the store in its turn. There is an inherent sequentiality, as well, since a constraint must be executed only as the consequence of a previous execution of another constraint. We have developed di erent parallel execution models ...

Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contributio...