Analysis, detection and classification of signals using scalar and vector sparse matrix transforms

Several pattern recognition problems require accurate modeling of signals with high dimensionality, p, often from a limited number of samples, n. We present high-dimensional signal analysis techniques based on the Sparse Matrix Transform (SMT). The recently proposed SMT successfully models high-dimensional signals in various application domains when n is small, including the case with n \u3c p. The resulting decorrelating transform is sparse, full rank, and inexpensive to apply, typically requiring only O(p) computation. Our main contribution is the vector SMT, a novel method for sparse matrix transform computation in distributed environments such as in wireless sensor networks (WSNs). We envision a scenario where each sensor generates a vector output. Together, all sensor outputs form a p-dimensional aggregated vector, x. The vector SMT algorithm then performs distributed decorrelation of x by applying pair-wise transforms to pairs of sensor outputs (i.e., subvectors of x) until x is fully decorrelated. Simulations with multi-view camera networks show that the vector SMT effectively decorrelates the multiple camera views with low total communication between sensors. Because our method enables joint processing of multiple views, we observe significant improvements to anomaly detection accuracy in artificial and real data sets compared to when the views are processed independently. Another important contribution is the graphical-SMT algorithm, a new, fast design method for sparse matrix transforms, suited for signals with underlying graphical structure such as images and networks. Finally, we develop an SMT-based, sparse framework for hypotheses testing and apply it to classification and anomaly detection using human faces and hyperspectral image data sets

Evaluating and improving local hyperspectral anomaly detectors

Abstract—This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure of the data in local detection methods, such as the RX detector, when the number of available training pixels n is not much larger than (and may even be smaller than) the data dimensionality p. Our first contribution is to formulate and employ a mean-log-volume approach for evaluating local anomaly detectors. Traditionally, the evaluation of a detector’s accuracy has been problematic. Anomalies are loosely defined as pixels that are unusual with respect to the other pixels in a local or global context. This loose definition makes it easy to develop anomaly detection algorithms – and many have been proposed – but more difficult to evaluate or compare them. Our mean-log-volume approach allows for an effective evaluation of a detector’s accuracy without requiring labeled testing data or an overly-specific definition of an anomaly. The second contribution is to investigate the use of the Sparse Matrix Transform (SMT) to model the local covariance structure of hyperspectral images. The SMT has been previously shown to provide full rank estimates of large covariance matrices even in the n < p scenario. Traditionally, the number of training pixels needed for good estimates of the covariance needs to be at least as large as the data dimensionality (and preferably it should be several times larger). Therefore, when one deploys the RX detector in a sliding window, the choices to select small window sizes are limited because of the n> p restriction associated to the covariance estimation. Our results suggest that RX-style detectors using the SMT covariance estimates perform favorably compared to other methods even (indeed, especially) in the regime of very small window sizes. I

Distributed Signal Decorrelation in Wireless Sensor Networks Using the Sparse Matrix Transform

ABSTRACT In this paper, we propose the vector SMT, a new decorrelating transform suitable for performing distributed anomaly detection in wireless sensor networks (WSN). Here, we assume that each sensor in the network performs vector measurements, instead of a scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor measurements, until the vectors from all sensors are completely decorrelated. We perform simulations with a network of cameras, where each camera records an image of the monitored environment from its particular viewpoint. Results show that the proposed transform effectively decorrelates image measurements from the multiple cameras in the network. Because it enables joint processing of the multiple images, our method provides significant improvements to anomaly detection accuracy when compared to the baseline case when we process the images independently

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

International audienceHybrid 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

A high-performance simd floating point unit for bluegene/l: Architecture, compilation, and algorithm design

We describe the design, implementation, and evaluation of a dual-issue SIMD-like extension of the PowerPC 440 floating-point unit (FPU) core. This extended FPU is targeted at both IBM’s massively parallel Blue-Gene/L machine as well as more pervasive embedded platforms. It has several novel features, such as a computational crossbar and cross-load/store instructions, which enhance the performance of numerical codes. We further discuss the hardware-software co-design that was essential to fully realize the performance benefits of the FPU when constrained by the memory bandwidth limitations and high penalties for misaligned data access imposed by the memory hierarchy on a BlueGene/L node. We describe several novel compiler and algorithmic techniques to take advantage of this architecture. Using both hand-optimized and compiled code for key linear algebraic kernels, we validate the architectural design choices, evaluate the success of the compiler, and quantify the effectiveness of the novel algorithm design techniques. Preliminary performance data shows that the algorithm-compiler-hardware combination delivers a significant fraction of peak floating-point performance for compute-bound kernels such as matrix multiplication, and delivers a significant fraction of peak memory bandwidth for memory-bound kernels such as daxpy, while being largely insensitive to data alignment

Deco<em></em>mposition of <em>Eucalyptus grandis</em> and <em>Acacia mangium</em> leaves and fine roots in tropical conditions did not meet the home field Advantage hypothesis

International audienceUnlike Eucalyptus monocultures, nitrogen fixing trees are likely to improve the soil nutrient status through the decomposition of N-enriched litter. The Home Field Advantage (HFA) hypothesis states that plants can create conditions that increase the decomposition rates of their own litter. However, there may not be any HFA when most of the decomposers are generalists. A reciprocal transplant decomposition experiment of fine roots and leaves of Acacia mangium and Eucalyptus grandis was undertaken in monocultures of these two species to test the HFA hypothesis using a complete randomized design with three blocks. Three litterbags containing leaf or fine root residues of each species were collected every 3 months from each plot over 12 months for fine roots and 24 months for leaves. The litter mass and C, N and P concentrations were measured at each sampling date. The concentrations of C-compounds were measured 0, 12 and 24 months from the start of the experiment. There was no evidence of HFA for either the leaves or the fine roots of either species. The decomposition rates were slower for Acacia litter than for Eucalyptus litter even though initial N concentrations were 1.9-2.9 times higher and P concentrations were 1.5-3.3 times higher in the Acacia residues. N:P ratios were greater than 20-30 for the residues of both species, with the highest values for Acacia. Litter decomposition depended partly on the C quality of the litter, primarily in terms of water soluble compounds and lignin content. As shown recently in tropical rainforests, these results suggest that the activity of decomposers is limited by energy starvation in tropical planted forests. Decomposer activity may also have been limited by P availability which may not have been directly related to the P concentrations or C:P ratios in the residues

Abstract

The BlueGene/L supercomputer is expected to deliver new levels of application performance by providing a combination of good single-node computational performance and high scalability. To achieve good single-node performance, the BlueGene/L design includes a special dual floating-point unit on each processor and the ability to use two processors per node. BlueGene/L also includes both a torus and a tree network to achieve high scalability. We demonstrate how benchmarks and applications can take advantage of these architectural features to get the most out of BlueGene/L. 1