Review of Canadian experience in precise gravimetry

Results of gravity observations made in Canada from 1974 to 1978 reviewed, in order to estimate the true accuracy of present-day gravimetry and thereby assess the potential capability of the method for detecting crustal movements. The standard error of the mean of ties is 15-20 nm/s squared. Inter-instrument comparisons and other tests show, however, that a more realistic estimate of D meter accuracy is 30-40 nm/s squared. This accuracy can only be maintained over the long term where uncertainties in gravimeter calibration curves are minimized by resetting to the same dial reading on the resurveys. A further deterioration in accuracy to 40-50 nm/s squared occurs where reliance is placed on presently available D meter calibration curves. Despite the present accuracy limitations significant time variations in gravity of 100-150 nm/s squared are seen over spatial scales of 10-100 kilometers in Canada over a period of several months

Stochastic partial differential equations with singular terminal condition

In this paper, we first prove existence and uniqueness of the solution of a backward doubly stochastic differential equation (BDSDE) and of the related stochastic partial differential equation (SPDE) under monotonicity assumption on the generator. Then we study the case where the terminal data is singular, in the sense that it can be equal to +$\infty$ on a set of positive measure. In this setting we show that there exists a minimal solution, both for the BDSDE and for the SPDE. Note that solution of the SPDE means weak solution in the Sobolev sense

Graphene: Gas Detector

The resistivity of graphene is sensitive to the presence of gas molecules adsorbed on it. Since graphene is one atom thick, a gas detector made from it might be sensitive to the presence of even single molecules of gas. We developed early stage devices for this purpose. This led us to future directions for research

Quantum Resonances of Weakly Linked, Mesoscopic, Superconducting Dots

We examine quantum properties of mesoscopic, Josephson coupled superconducting dots, in the limit that charging effects and quantization of energy levels within the dots are negligible, but quasi-particle transmission into the weak link is not. We demonstrate that quasi-particle resonances lead to current-phase relations, which deviate markedly from those of weak links connecting macroscopic superconductors. Results for the steady state dc Josephson current of two coupled dots are presented.Comment: Tex, 3 figures available on request to [email protected] (Andy Martin

Non-destructive ultrasonic measurements of case depth

Two ultrasonic methods for nondestructive measurements of the depth of a case-hardened layer in steel are described. One method involves analysis of ultrasonic waves diffused back from the bulk of the workpiece. The other method involves finding the speed of propagation of ultrasonic waves launched on the surface of the work. Procedures followed in the two methods for measuring case depth are described

Modeling Multiple M2's

We investigate the worldvolume theory that describes N coincident M2-branes ending on an M5 brane. We argue that the fields that describe the transverse spacetime coordinates take values in a non-associative algebra. We postulate a set of supersymmetry transformations and find that they close into a novel gauge symmetry. We propose a three-dimensional N=2 supersymmetric action to describe the truncation of the full theory to the scalar and spinor fields, and show how a Basu-Harvey fuzzy funnel arises as the BPS solution to this theory.Comment: Typos corrected, version to appear in PR

Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception.

Beat induction, the means by which humans listen to music and perceive a steady pulse, is achieved via a perceptualand cognitive process. Computationally modelling this phenomenon is an open problem, especially when processing expressive shaping of the music such as tempo change.To meet this challenge we propose Adaptive Frequency Neural Networks (AFNNs), an extension of Gradient Frequency Neural Networks (GFNNs).GFNNs are based on neurodynamic models and have been applied successfully to a range of difficult music perception problems including those with syncopated and polyrhythmic stimuli. AFNNs extend GFNNs by applying a Hebbian learning rule to the oscillator frequencies. Thus the frequencies in an AFNN adapt to the stimulus through an attraction to local areas of resonance, and allow for a great dimensionality reduction in the network.Where previous work with GFNNs has focused on frequency and amplitude responses, we also consider phase information as critical for pulse perception. Evaluating the time-based output, we find significantly improved re-sponses of AFNNs compared to GFNNs to stimuli with both steady and varying pulse frequencies. This leads us to believe that AFNNs could replace the linear filtering methods commonly used in beat tracking and tempo estimationsystems, and lead to more accurate methods