Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

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.

Microfabrication and application of reservoir pins for liquid transfer in biotechnology

Microarraying involves laying down genetic elements onto a solid substrate for DNA analysis on a massively parallel scale. Microarrays are prepared using a pin-based robotic platform to transfer liquid samples from microtitre plates to an array pattern of dots of different liquids on the surface of glass slides where they dry to form spots diameter < 200 μm. This paper presents the design, materials selection, micromachining technology and performance of reservoir pins for microarraying. A conical pin is produced by (i) conventional machining of stainless steel or wet etching of tungsten wire, followed by (ii) micromachining with a focused laser to produce a microreservoir and a capillary channel structure leading from the tip. The pin has a flat end diameter < 100 μm from which a 500 μm long capillary channel < 15 μm wide leads up the pin to a reservoir. Scanning electron micrographs of the metal surface show roughness on the scale of 10 μm, but the pins nevertheless give consistent and reproducible spotting performance. The pin capacity is 80 nanolitres of fluid containing DNA, and at least 50 spots can be printed before replenishing the reservoir. A typical robot holds can hold up to 64 pins. This paper discusses the fabrication technology, the performance and spotting uniformity for reservoir pins, the possible limits to miniaturization of pins using this approach, and the future prospects for contact and non-contact arraying technology.

Fabrication and electrical characteristics of carbon nanotube-based microcathodes for use in a parallel electron-beam lithography system

The development of the Nanolith parallel electron-beam writing head was discussed. The fabrication and electrical characteristics of carbon nanotube-based microcathodes for use in the lithographic system were described. The microcathode exhibited a peak current of 10.5 μA at 48 V when operated with a duty cycle of 0.5 percent.