Mechanical constraint and loading on ferroelectric memory capacitors

The influence of mechanical constraint imposed by device geometry upon the switching response of a ferroelectric thin film memory capacitor is investigated. The memory capacitor was represented by two-dimensional ferroelectric islands of different aspect ratio, mechanically constrained by surrounding materials. Its ferroelectric non-linear behaviour was modeled by a crystal plasticity constitutive law and calculated using the finite element method. The switching response of the device, in terms of remnant charge storage, was determined as a function of geometry and constraint. The switching response under applied in-plane tensile stress and hydrostatic pressure was also studied experimentally. Our results showed that (1) the capacitor's aspect ratio could significantly affect the clamping behaviour and thus the remnant polarization, (2) it was possible to maximise the switching charge through the optimisation of the device geometry, and (3) it is possible to find a critical switching stress at zero electric field and a critical coercive field at zero residual stress. © 2009 Materials Research Society.

The misbehavior of value and the discipline of the will.

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

POMDP-based statistical spoken dialog systems: A review

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems. © 1963-2012 IEEE.