Conceptualization in reference production:Probabilistic modeling and experimental testing

In psycholinguistics, there has been relatively little work investigating conceptualization-how speakers decide which concepts to express. This contrasts with work in natural language generation (NLG), a subfield of artificial intelligence, where much research has explored content determination during the generation of referring expressions. Existing NLG algorithms for conceptualization during reference production do not fully explain previous psycholinguistic results, so we developed new models that we tested in three language production experiments. In our experiments, participants described target objects to another participant. In Experiment 1, either size, color, or both distinguished the target from all distractor objects; in Experiment 2, either color, type, or both color and type distinguished it from all distractors; In Experiment 3, color, size, or the border around the object distinguished the target. We tested how well the different models fit the distribution of description types (e.g., "small candle," "gray candle," "small gray candle") that participants produced. Across these experiments, the probabilistic referential overspecification model (PRO) provided the best fit. In this model, speakers first choose a property that rules out all distractors. If there is more than one such property, then they probabilistically choose one on the basis of a preference for that property. Next, they sometimes add another property, with the probability again determined by its preference and speakers' eagerness to overspecify

Efficient nonparametric identification for high-precision motion systems: A practical comparison based on a medical X-ray system

The need for accurate knowledge of complex dynamical behavior for high-performance mechatronic systems led to the development of a vast amount of nonparametric system identification approaches over the recent years. The aim of this paper is to compare several proposed methods based on experiments on a physical complex mechanical system to bridge the gap between identification theory and practical applications in industry where basic identification approaches are often the norm. Typical practical implications such as operation under closed-loop control, multivariable coupled behavior and nonlinear effects are included in the analysis. Finally, a possible approach for fast and reliable identification is illustrated based on measurement results of an interventional medical X-ray system