Capacitive nanoelectromechanical switch based on suspended carbon nanotube array

We present the fabrication and high frequency characterization of a capacitive nanoelectromechanical system (NEMS) switch using a dense array of horizontally aligned single-wall carbon nanotubes (CNTs). The nanotubes are directly grown onto metal layers with prepatterned catalysts with horizontal alignment in the gas flow direction. Subsequent wetting-induced compaction by isopropanol increases the nanotube density by one order of magnitude. The actuation voltage of 6 V is low for a NEMS device, and corresponds to CNT arrays with an equivalent Young's modulus of 4.5-8.5 GPa, and resistivity of under 0.0077 Ω·cm. The high frequency characterization shows an isolation of -10 dB at 5 GHz. © 2010 American Institute of Physics.

Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.