In this paper we present an approach to use prior knowledge in the particle filter framework for 3D tracking, i.e. estimating the state parameters such as joint angles of a 3D object. The probability of the object’s states, including correlations between the state parameters, is learned a priori from training samples. We introduce a framework that integrates this knowledge into the family of pa...

Background: Tramadol is an opioid analgesic indicated for the management of moderate and severe pains. Its side effects, in parts are due to the activation of µ receptors and in parts to increasing central catecholamine and serotonin levels. In the case of long administration, tramadol has a potential to cause dependency, tolerance and also drug abuse. After prohibition of injective diclofenac,...

A critical review was conducted to determine the influence background knowledge has on reading comprehension of primary school-aged children. We identified twenty-three studies that met our criteria and focused links between children in mid late years. Review findings highlight higher levels have a range effects are influenced by nature text, quality situation model required, presence reader mi...

Semantic networks are a popular way of simulating human memory in ACT-R-like cognitive architectures. However, existing implementations fall short in their ability to efficiently work with very large networks required for full-scale simulations of human memories. In this paper, we present SemMemDB, an in-database realization of semantic networks and spreading activation. We describe a relationa...

This paper describes the FACT system for knowledge discovery from text. It discovers associations patterns of co-occurrence amongst keywords labeling the items in a collection of textual documents. In addition, FACT is able to use background knowledge about the keywords labeling the documents in its discovery process. FACT takes a query-centered view of knowledge discovery, in which a discovery...