Once again on the importance of propagation effects for jet noise modelling

Several turbulent jet noise models starting from the classical Lighthill acoustic analogy to state-of-the art models are considered. No attempt is made to present any complete overview of jet noise theories. Instead, the aim is to emphasise the importance of sound generation and meanflow effects for the understanding and prediction of jet noise. For a recent acoustic analogy model, the consequences of jet flow simplification on the predicted sound spectra shape and the effective noise source location in the jet are discussed. © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager

Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue statemust encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches. © 2011 ACM.