Trapped charge dynamics in a sol-gel based TiO(2) high- k gate dielectric silicon metal-oxide-semiconductor field effect transistor.

We have studied the response of a sol-gel based TiO(2), high k dielectric field effect transistor structure to microwave radiation. Under fixed bias conditions the transistor shows frequency dependent current fluctuations when exposed to continuous wave microwave radiation. Some of these fluctuations take the form of high Q resonances. The time dependent characteristics of these responses were studied by modulating the microwaves with a pulse signal. The measurements show that there is a shift in the centre frequency of these high Q resonances when the pulse time is varied. The measured lifetime of these resonances is high enough to be useful for non-classical information processing.

Deliberation in the motor system: reflex gains track evolving evidence leading to a decision.

Both decision making and sensorimotor control require real-time processing of noisy information streams. Historically these processes were thought to operate sequentially: cognitive processing leads to a decision, and the outcome is passed to the motor system to be converted into action. Recently, it has been suggested that the decision process may provide a continuous flow of information to the motor system, allowing it to prepare in a graded fashion for the probable outcome. Such continuous flow is supported by electrophysiology in nonhuman primates. Here we provide direct evidence for the continuous flow of an evolving decision variable to the motor system in humans. Subjects viewed a dynamic random dot display and were asked to indicate their decision about direction by moving a handle to one of two targets. We probed the state of the motor system by perturbing the arm at random times during decision formation. Reflex gains were modulated by the strength and duration of motion, reflecting the accumulated evidence in support of the evolving decision. The magnitude and variance of these gains tracked a decision variable that explained the subject's decision accuracy. The findings support a continuous process linking the evolving computations associated with decision making and sensorimotor control.

Modelling and Fragility Analysis of Non-Ductile Reinforced Concrete Buildings in Low-to-Moderate Seismic Zones

Reinforced concrete buildings in low-to-moderate seismic zones are often designed only for gravity loads in accordance with the non-seismic detailing provisions. Deficient detailing of columns and beam-column joints can lead to unpredictable brittle failures even under moderate earthquakes. Therefore, a reliable estimate of structural response is required for the seismic evaluation of these structures. For this purpose, analytical models for both interior and exterior slab-beam-column subassemblages and for a 1/3 scale model frame were implemented into the nonlinear finite element platform OpenSees. Comparison between the analytical results and experimental data available in the literature is carried out using nonlinear pushover analyses and nonlinear time history analysis for the subassemblages and the model frame, respectively. Furthermore, the seismic fragility assessment of reinforced concrete buildings is performed on a set of non-ductile frames using nonlinear time history analyses. The fragility curves, which are developed for various damage states for the maximum interstory drift ratio are characterized in terms of peak ground acceleration and spectral acceleration using a suite of ground motions representative of the seismic hazard in the region.

BOLTZMANN MACHINES FOR SPEECH RECOGNITION.

Boltzmann machines offer a new and exciting approach to automatic speech recognition, and provide a rigorous mathematical formalism for parallel computing arrays. In this paper we briefly summarize Boltzmann machine theory, and present results showing their ability to recognize both static and time-varying speech patterns. A machine with 2000 units was able to distinguish between the 11 steady-state vowels in English with an accuracy of 85%. The stability of the learning algorithm and methods of preprocessing and coding speech data before feeding it to the machine are also discussed. A new type of unit called a carry input unit, which involves a type of state-feedback, was developed for the processing of time-varying patterns and this was tested on a few short sentences. Use is made of the implications of recent work into associative memory, and the modelling of neural arrays to suggest a good configuration of Boltzmann machines for this sort of pattern recognition.

Cryogenic single-shot spectroscopy of a floating poly-silicon gate transistor

Trapped electrons, located close to the channel of a transistor, are promising as data storage elements in non-classical information processing. Cryogenic microwave spectroscopy has shown that these electrons give rise to high quality factor resonances in the drain current and a post excitation dynamic behaviour that is related to the system lifetime. Using a floating poly-silicon gate transistor, single shot spectroscopy is performed to characterise the dynamic behaviour during excitation. This behaviour is seen to be dominated by the decay of the transient component, which gives rise to oscillations around the high quality factor resonance. © 2012 American Institute of Physics.

Polychromatic single-shot spectroscopy on SOI-FET trapped electrons

The behavior of trapped electrons, in a dielectric close to the channel of a silicon SOI-FET, is studied by cryogenic microwave spectroscopy. On-resonance microwave excitation causes one of these trapped electrons to undergo spatial Rabi oscillations between widely separated trap sites. This charge displacement causes a change in the drain current of the transistor, resulting in high quality factor resonances in continuous wave spectroscopy. The potential of this effect for non-classical information processing is investigated through polychromatic single-shot spectroscopy, using on-resonance and difference frequencies. Interaction between different trapped electrons is seen in the post excitation behavior and the possibilities of quantum gate operations are discussed. © The Electrochemical Society.

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

We develop methods for performing filtering and smoothing in non-linear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.