Compressed Statistical Testing and Application to Radar

We present compressed statistical testing (CST) with an illustrative application to radar target detection. We characterize an optimality condition for a compressed domain test to yield the same result as the corresponding test in the uncompressed domain. We demonstrate by simulation that under high SNR, a likelihood ratio test with compressed samples at 3.3x or even higher compression ratio can achieve detection performance comparable to that with uncompressed data. For example, our compressed domain Sample Matrix Inversion test for radar target detection can achieve constant false alarm rate (CFAR) performance similar to the corresponding test in the raw data domain. By exploiting signal sparsity in the target and interference returns, compressive sensing based CST can incur a much lower processing cost in statistical training and decision making, and can therefore enable a variety of distributed applications such as target detection on resource limited mobile devices.Engineering and Applied Science

Monodisperse, polymeric microspheres produced by irradiation of slowly thawing frozen drops

Monodisperse, polymeric microspheres are formed by injecting uniformly shaped droplets of radiation polymerizable monomers, preferably a biocompatible monomer, having covalent binding sites such as hydroxyethylmethacrylate, into a zone, impressing a like charge on the droplet so that they mutually repel each other, spheroidizing the droplets within the zone and collecting the droplets in a pool of cryogenic liquid. As the droplets enter the liquid, they freeze into solid, glassy microspheres, which vaporizes a portion of the cryogenic liquid to form a layer. The like-charged microspheres, suspended within the layer, move to the edge of the vessel holding the pool, are discharged, fall and are collected. The collected microspheres are irradiated while frozen in the cryogenic liquid to form latent free radicals. The frozen microspheres are then slowly thawed to activate the free radicals which polymerize the monomer to form evenly-sized, evenly-shaped, monodisperse polymeric microspheres

A UV to Mid-IR Study of AGN Selection

We classify the spectral energy distributions (SEDs) of 431,038 sources in the 9 sq. deg Bootes field of the NOAO Deep Wide-Field Survey (NDWFS). There are up to 17 bands of data available per source, including ultraviolet (GALEX), optical (NDWFS), near-IR (NEWFIRM), and mid-infrared (IRAC/MIPS) data, as well as spectroscopic redshifts for ~20,000 objects, primarily from the AGN and Galaxy Evolution Survey (AGES). We fit galaxy, AGN, stellar, and brown dwarf templates to the observed SEDs, which yield spectral classes for the Galactic sources and photometric redshifts and galaxy/AGN luminosities for the extragalactic sources. The photometric redshift precision of the galaxy and AGN samples are sigma/(1+z)=0.040 and sigma/(1+z)=0.169, respectively, with the worst 5% outliers excluded. Based on the reduced chi-squared of the SED fit for each SED model, we are able to distinguish between Galactic and extragalactic sources for sources brighter than I=23.5. We compare the SED fits for a galaxy-only model and a galaxy+AGN model. Using known X-ray and spectroscopic AGN samples, we confirm that SED fitting can be successfully used as a method to identify large populations of AGN, including spatially resolved AGN with significant contributions from the host galaxy and objects with the emission line ratios of "composite" spectra. We also use our results to compare to the X-ray, mid-IR, optical color and emission line ratio selection techniques. For an F-ratio threshold of F>10 we find 16,266 AGN candidates brighter than I=23.5 and a surface density of ~1900 AGN per deg^2.Comment: Submitted to ApJ, 35 pages, 17 figures, 2 table

Distance-based phenotypic association analysis of DNA sequence data

As the cost of sequencing decreases, the demand for association tests that use exhaustive DNA sequence information increases. One such association test is multivariate distance matrix regression (MDMR). We explore some of the features of MDMR using Genetic Analysis Workshop 17 simulated data in search of potential improvements in distance measures. We used genotype data from 697 unrelated individuals, in 200 replications, to test the power of MDMR to detect 13 trait Q2 causative genes based on the Euclidean distance metric. We also estimated the false-positive rate of MDMR using 508 control genes. In addition, we compared MDMR with Mantel’s test and collapsing analysis for rare variants. MDMR performed comparably well even with the Euclidean distance measure

Object Augmentation for the Visually Impaired Using RP

We demonstrate the application of rapid prototyping technology to augment every-day objects for the visually impaired. A freeform fabricator was used to print a tactile alphabet on multiple surfaces including paper, plastic, and metal. We have identified and experimented with multiple non-toxic materials and analyzed the dimensional tolerance, repeatability, and adhesion characteristics on multiple surfaces. Printing time for 1x1cm embossed letters varied from 14 to 52 seconds. More broadly, these experiments open the door to RP applications that involve custom product adaptation to address disabilities.Mechanical Engineerin

Modularity and community detection in bipartite networks

The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.Comment: RevTex 4, 11 pages, 3 figures, 1 table; modest extensions to conten

Carbon Reduction Tools for Municipal Buildings

In an effort to reduce carbon emissions in the UK, the London Borough of Merton began work with Worcester Polytechnic Institute (WPI) researching carbon reduction methods. This project furthers this general goal by developing a replicable model which municipalities can use to map out energy usage of their buildings; as well as constructing a Combined Heat and Power (CHP) financial feasibility toolkit to help municipalities plan and implement District Heat and Power using CHPs

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility. They have also been shown to be effective inverse problem solvers, acting as the prior of the distribution, while the information of the forward model can be granted at the sampling stage. Nonetheless, as the generative process remains in the same high dimensional (i.e. identical to data dimension) space, the models have not been extended to 3D inverse problems due to the extremely high memory and computational cost. In this paper, we combine the ideas from the conventional model-based iterative reconstruction with the modern diffusion models, which leads to a highly effective method for solving 3D medical image reconstruction tasks such as sparse-view tomography, limited angle tomography, compressed sensing MRI from pre-trained 2D diffusion models. In essence, we propose to augment the 2D diffusion prior with a model-based prior in the remaining direction at test time, such that one can achieve coherent reconstructions across all dimensions. Our method can be run in a single commodity GPU, and establishes the new state-of-the-art, showing that the proposed method can perform reconstructions of high fidelity and accuracy even in the most extreme cases (e.g. 2-view 3D tomography). We further reveal that the generalization capacity of the proposed method is surprisingly high, and can be used to reconstruct volumes that are entirely different from the training dataset.Comment: 14 pages, 10 figure

NASA ExoPAG Study Analysis Group 11: Preparing for the WFIRST Microlensing Survey

NASA's proposed WFIRST-AFTA mission will discover thousands of exoplanets with separations from the habitable zone out to unbound planets, using the technique of gravitational microlensing. The Study Analysis Group 11 of the NASA Exoplanet Program Analysis Group was convened to explore scientific programs that can be undertaken now, and in the years leading up to WFIRST's launch, in order to maximize the mission's scientific return and to reduce technical and scientific risk. This report presents those findings, which include suggested precursor Hubble Space Telescope observations, a ground-based, NIR microlensing survey, and other programs to develop and deepen community scientific expertise prior to the mission.Comment: 35 pages, 5 Figures. A brief overview of the findings is presented in the Executive Summary (2 pages