Scaling the Indian Buffet Process via Submodular Maximization

Inference for latent feature models is inherently difficult as the inference space grows exponentially with the size of the input data and number of latent features. In this work, we use Kurihara & Welling (2008)'s maximization-expectation framework to perform approximate MAP inference for linear-Gaussian latent feature models with an Indian Buffet Process (IBP) prior. This formulation yields a submodular function of the features that corresponds to a lower bound on the model evidence. By adding a constant to this function, we obtain a nonnegative submodular function that can be maximized via a greedy algorithm that obtains at least a one-third approximation to the optimal solution. Our inference method scales linearly with the size of the input data, and we show the efficacy of our method on the largest datasets currently analyzed using an IBP model.

Improved bounds for subband-adaptive iterative shrinkage/thresholding algorithms.

This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrinkage-thresholding algorithms proposed by Bayram & Selesnick and Vonesch & Unser. The method yields tighter wavelet-domain bounds of the system matrix, thus leading to improved convergence speeds. It is directly applicable to non-redundant wavelet bases, and we also adapt it for cases of redundant frames. It turns out that the simplest and most intuitive setting for the step sizes that ignores subband aliasing is often satisfactory in practice. We show that our methods can be used to advantage with reweighted least squares penalty functions as well as L1 penalties. We emphasize that the algorithms presented here are suitable for performing inverse filtering on very large datasets, including 3D data, since inversions are applied only to diagonal matrices and fast transforms are used to achieve all matrix-vector products.

Weighted least squares with expectation-maximization algorithm for burst detection in U.K. water distribution systems

Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectationmaximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g., water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company's records of pipeline reparation work. © 2014 American Society of Civil Engineers.

Sparse recovery of complex phase-encoded velocity images using iterative thresholding

In this paper we propose a new algorithm for reconstructing phase-encoded velocity images of catalytic reactors from undersampled NMR acquisitions. Previous work on this application has employed total variation and nonlinear conjugate gradients which, although promising, yields unsatisfactory, unphysical visual results. Our approach leverages prior knowledge about the piecewise-smoothness of the phase map and physical constraints imposed by the system under study. We show how iteratively regularizing the real and imaginary parts of the acquired complex image separately in a shift-invariant wavelet domain works to produce a piecewise-smooth velocity map, in general. Using appropriately defined metrics we demonstrate higher fidelity to the ground truth and physical system constraints than previous methods for this specific application. © 2013 IEEE.

The adoption of sustainable remediation behaviour in the US and UK: a cross country comparison and determinant analysis.

The sustainable remediation concept, aimed at maximizing the net environmental, social, and economic benefits in contaminated site remediation, is being increasingly recognized by industry, governments, and academia. However, there is limited understanding of actual sustainable behaviour being adopted and the determinants of such sustainable behaviour. The present study identified 27 sustainable practices in remediation. An online questionnaire survey was used to rank and compare them in the US (n=112) and the UK (n=54). The study also rated ten promoting factors, nine barriers, and 17 types of stakeholders' influences. Subsequently, factor analysis and general linear models were used to determine the effects of internal characteristics (i.e. country, organizational characteristics, professional role, personal experience and belief) and external forces (i.e. promoting factors, barriers, and stakeholder influences). It was found that US and UK practitioners adopted many sustainable practices to similar extents. Both US and UK practitioners perceived the most effectively adopted sustainable practices to be reducing the risk to site workers, protecting groundwater and surface water, and reducing the risk to the local community. Comparing the two countries, we found that the US adopted innovative in-situ remediation more effectively; while the UK adopted reuse, recycling, and minimizing material usage more effectively. As for the overall determinants of sustainable remediation, the country of origin was found not to be a significant determinant. Instead, organizational policy was found to be the most important internal characteristic. It had a significant positive effect on reducing distant environmental impact, sustainable resource usage, and reducing remediation cost and time (p<0.01). Customer competitive pressure was found to be the most extensively significant external force. In comparison, perceived stakeholder influence, especially that of primary stakeholders (site owner, regulator, and primary consultant), did not appear to have as extensive a correlation with the adoption of sustainability as one would expect.

Mass chains with passive interconnection: Complex iterative maps and scalability

This paper introduces the problem of passive control of a chain of N identical masses in which there is an identical passive connection between neighbouring masses and a similar connection to a movable point. The problem arises in the design of multi-storey buildings which are subjected to earthquake disturbances, but applies in other situations, for example vehicle platoons. The paper will study the scalar transfer functions from the disturbance to a given intermass displacement. It will be shown that these transfer functions can be conveniently represented in the form of complex iterative maps and that these maps provide a method to establish boundedness in N of the H ∞-norm of these transfer functions for certain choices of interconnection impedance. © 2013 IEEE.