Distributed Signal Decorrelation and Detection in Multi View Camera Networks Using the Vector Sparse Matrix Transform.

This paper introduces the vector sparse matrix transform (vector SMT), a new decorrelating transform suitable for performing distributed processing of high-dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a network of cameras, monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Our results, with both artificial and real data, show that the proposed vector SMT transform effectively decorrelates image measurements from the multiple cameras in the network while maintaining low overall communication energy consumption. Since it enables joint processing of the multiple vector outputs, our method provides significant improvements to anomaly detection accuracy when compared with the baseline case when the images are processed independently.

GTKDynamo: a PyMOL plug-in for QC/MM hybrid potential simulations.

Hybrid quantum chemical/molecular mechanical (QCMM) potentials are very powerful tools for molecular simulation. They are especially useful for studying processes in condensed phase systems, such as chemical reactions that involve a relatively localized change in electronic structure and where the surrounding environment contributes to these changes but can be represented with more computationally efficient functional forms. Despite their utility, however, these potentials are not always straightforward to apply since the extent of significant electronic structure changes occurring in the condensed phase process may not be intuitively obvious. To facilitate their use, we have developed an open-source graphical plug-in, GTKDynamo that links the PyMOL visualization program and the pDynamo QC/MM simulation library. This article describes the implementation of GTKDynamo and its capabilities and illustrates its application to QC/MM simulations.

The sparse matrix transform for covariance estimation and analysis of high dimensional signals.

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition which can be represented as a sparse matrix transform (SMT). The SMT is formed by a product of pairwise coordinate rotations known as Givens rotations. Using this framework, the covariance can be efficiently estimated using greedy optimization of the log-likelihood function, and the number of Givens rotations can be efficiently computed using a cross-validation procedure. The resulting estimator is generally positive definite and well-conditioned, even when the sample size is limited. Experiments on a combination of simulated data, standard hyperspectral data, and face image sets show that the SMT-based covariance estimates are consistently more accurate than both traditional shrinkage estimates and recently proposed graphical lasso estimates for a variety of different classes and sample sizes. An important property of the new covariance estimate is that it naturally yields a fast implementation of the estimated eigen-transformation using the SMT representation. In fact, the SMT can be viewed as a generalization of the classical fast Fourier transform (FFT) in that it uses "butterflies" to represent an orthonormal transform. However, unlike the FFT, the SMT can be used for fast eigen-signal analysis of general non-stationary signals.