Mathematical methods for anomaly grouping in hyperspectral images

The topological anomaly detection (TAD) algorithm differs from other anomaly detection algorithms in that it does not rely on the data\u27s being normally distributed. We have built on this advantage of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. We have done this by identifying and integrating clusters of anomalous pixels, which we accomplished with a graph-theoretical method that combines spatial and spectral information. By applying our method, the Anomaly Clustering algorithm, to hyperspectral images, we have found that our method integrates small clusters of anomalous pixels, such as those corresponding to rooftops, into single anomalies; this improves visualization and interpretation of objects. We have also performed a local linear embedding (LLE) analysis of the TAD results to illustrate its application as a means of grouping anomalies together. By performing the LLE algorithm on just the anomalies identified by the TAD algorithm, we drastically reduce the amount of computation needed for the computationally-heavy LLE algorithm. We also propose an application of a shifted QR algorithm to improve the speed of the LLE algorithm

Harmonic Analysis Inspired Data Fusion for Applications in Remote Sensing

This thesis will address the fusion of multiple data sources arising in remote sensing, such as hyperspectral and LIDAR. Fusing of multiple data sources provides better data representation and classification results than any of the independent data sources would alone. We begin our investigation with the well-studied Laplacian Eigenmap (LE) algorithm. This algorithm offers a rich template to which fusion concepts can be added. For each phase of the LE algorithm (graph, operator, and feature space) we develop and test different data fusion techniques. We also investigate how partially labeled data and approximate LE preimages can used to achieve data fusion. Lastly, we study several numerical acceleration techniques that can be used to augment the developed algorithms, namely the Nystrom extension, Random Projections, and Approximate Neighborhood constructions. The Nystrom extension is studied in detail and the application of Frame Theory and Sigma-Delta Quantization is proposed to enrich the Nystrom extension

Reproducing Kernel Hilbert Space Pruning for Sparse Hyperspectral Abundance Prediction

Hyperspectral measurements from long range sensors can give a detailed picture of the items, materials, and chemicals in a scene but analysis can be difficult, slow, and expensive due to high spatial and spectral resolutions of state-of-the-art sensors. As such, sparsity is important to enable the future of spectral compression and analytics. It has been observed that environmental and atmospheric effects, including scattering, can produce nonlinear effects posing challenges for existing source separation and compression methods. We present a novel transformation into Hilbert spaces for pruning and constructing sparse representations via non-negative least squares minimization. Then we introduce max likelihood compression vectors to decrease information loss. Our approach is benchmarked against standard pruning and least squares as well as deep learning methods. Our methods are evaluated in terms of overall spectral reconstruction error and compression rate using real and synthetic data. We find that pruning least squares methods converge quickly unlike matching pursuit methods. We find that Hilbert space pruning can reduce error by as much as 40% of the error of standard pruning and also outperform neural network autoencoders

In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in which it is made. In this paper we explore the nature of invariance and equivariance of deep learning models with the goal of better understanding the ways in which they actually capture these concepts on a formal level. We introduce a family of invariance and equivariance metrics that allows us to quantify these properties in a way that disentangles them from other metrics such as loss or accuracy. We use our metrics to better understand the two most popular methods used to build invariance into networks: data augmentation and equivariant layers. We draw a range of conclusions about invariance and equivariance in deep learning models, ranging from whether initializing a model with pretrained weights has an effect on a trained model's invariance, to the extent to which invariance learned via training can generalize to out-of-distribution data.Comment: To appear at NeurIPS 202

EMSL Pore Scale Modeling Challenge/Workshop

Report covers the background for the workshop, objectives, important research directions, necessary capabilities and overall recommendations

Expression of CD40L by the ALVAC-Simian Immunodeficiency Virus Vector Abrogates T Cell Responses in Macaques.

Immunization with recombinant ALVAC/gp120 alum vaccine provided modest protection from human immunodeficiency virus type 1 (HIV-1) and simian immunodeficiency virus (SIV) acquisition in humans and macaques. Vaccine-mediated protection was associated with the elicitation of IgG against the envelope V2 loop and of envelope-specific CD4+ T cell responses. We hypothesized that the simultaneous expression of the costimulatory molecule CD40L (CD154) by the ALVAC-HIV vector could increase both protective humoral and cellular responses. We engineered an ALVAC-SIV coexpressing CD40L with SIVmac251 (ALVAC-SIV/CD40L) gag, pol, and env genes. We compared its immunogenicity in macaques with that of a canonical ALVAC-SIV, with both given as a vector-prime/gp120 in alum boost strategy. The ALVAC-SIV/CD40L was superior to the ALVAC-SIV regimen in inducing binding and tier 1 neutralizing antibodies against the gp120. The increase in humoral responses was associated with the expression of the membrane-bound form of the CD40L by CD4+ T cells in lymph nodes. Unexpectedly, the ALVAC-SIV/CD40L vector had a blunting effect on CD4+ Th1 helper responses and instead favored the induction of myeloid-derived suppressor cells, the immune-suppressive interleukin-10 (IL-10) cytokine, and the down-modulatory tryptophan catabolism. Ultimately, this strategy failed to protect macaques from SIV acquisition. Taken together, these results underlie the importance of balanced vaccine-induced activating versus suppressive immune responses in affording protection from HIV.IMPORTANCE CD40-CD40 ligand (CD40L) interaction is crucial for inducing effective cytotoxic and humoral responses against pathogens. Because of its immunomodulatory function, CD40L has been used to enhance immune responses to vaccines, including candidate vaccines for HIV. The only successful vaccine ever tested in humans utilized a strategy combining canarypox virus-based vector (ALVAC) together with an envelope protein (gp120) adjuvanted in alum. This strategy showed limited efficacy in preventing HIV-1/SIV acquisition in humans and macaques. In both species, protection was associated with vaccine-induced antibodies against the HIV envelope and CD4+ T cell responses, including type 1 antiviral responses. In this study, we tested whether augmenting CD40L expression by coexpressing it with the ALVAC vector could increase the protective immune responses. Although coexpression of CD40L did increase humoral responses, it blunted type 1 CD4+ T cell responses against the SIV envelope protein and failed to protect macaques from viral infection