Performance Analysis of Spectral Clustering on Compressed, Incomplete and Inaccurate Measurements

Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged as prevailing methods for efficiently recovering sparse and partially observed signals respectively. We combine the distance preserving measurements of compressed sensing and matrix completion with the power of robust spectral clustering. Our analysis provides rigorous bounds on how small errors in the affinity matrix can affect the spectral coordinates and clusterability. This work generalizes the current perturbation results of two-class spectral clustering to incorporate multi-class clustering with k eigenvectors. We thoroughly track how small perturbation from using compressed sensing and matrix completion affect the affinity matrix and in succession the spectral coordinates. These perturbation results for multi-class clustering require an eigengap between the kth and (k+1)th eigenvalues of the affinity matrix, which naturally occurs in data with k well-defined clusters. Our theoretical guarantees are complemented with numerical results along with a number of examples of the unsupervised organization and clustering of image data

Increasing Quantum Limited Sensitivity of Interferometers Using Electromagnetically Induced Transparency

We explore the properties of electromagnetically induced transparency (EIT) and its applications as a frequency filter in the field of gravitational wave interferometry. Through modeling and simulation, we determine parameters for atom-light configurations of multi- state atoms which will theoretically allow for transmission frequencies and intensities of squeezed light in a range suitable for increasing sensitiviy levels in gravitational wave interferometers. This corresponds to contrasts greater than 50% and linewidths of 100 Hz or less. We produce EIT experimentally and characterize the distributions by fitting them to a generalized Lorentzian. The largest contrast observed is 3.9% with a linewidth of 657 Hz. The smallest linewidth observed is 202 Hz with a contrast of 0.84%

Geosocial Graph-Based Community Detection

We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.Comment: 5 pages, 4 figures Workshop paper for the IEEE International Conference on Data Mining 2012: Workshop on Social Media Analysis and Minin

Multislice Modularity Optimization in Community Detection and Image Segmentation

Because networks can be used to represent many complex systems, they have attracted considerable attention in physics, computer science, sociology, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In this paper, we algorithmically detect communities in social networks and image data by optimizing multislice modularity. A key advantage of modularity optimization is that it does not require prior knowledge of the number or sizes of communities, and it is capable of finding network partitions that are composed of communities of different sizes. By optimizing multislice modularity and subsequently calculating diagnostics on the resulting network partitions, it is thereby possible to obtain information about network structure across multiple system scales. We illustrate this method on data from both social networks and images, and we find that optimization of multislice modularity performs well on these two tasks without the need for extensive problem-specific adaptation. However, improving the computational speed of this method remains a challenging open problem.Comment: 3 pages, 2 figures, to appear in IEEE International Conference on Data Mining PhD forum conference proceeding

SOLENOPSIS INVICTA VIRUS (SINV-1) INFECTION AND INSECTICIDE INTERACTIONS IN THE RED IMPORTED FIRE ANT (HYMENOPTERA: FORMICIDAE)

Controlling invasive species is a growing concern; however, pesticides can be detrimental for non-target organisms. The red imported fire ant (Solenopsis invicta Buren; Hymenoptera: Formicidae) has aggressively invaded ~138 million ha in the USA and causes over $6 billion in damage and control efforts annually (Valles 2011). Myriad research studies have been conducted to discover safe biological control agents to manage these invasive pests (Valles et al. 2004; Milks et al. 2008; Oi et al. 2009; Yang et al. 2009; Wang et al. 2010; Callcott et al. 2011; Porter et al. 2011; Tufts et al. 2011). Viruses may be lethal due to modifications of cellular processes and induction of defense responses or may produce distinct survival outcomes depending on species (i.e. ascoviruses) (Stasiak et al. 2005). The Solenopsis invicta virus (SINV-1) is a positive sense, single-stranded RNA virus, which can only infect the genus Solenopsis at all stages of development, and is verticallytransmitted within a colony (Valles et al. 2004; Valles 2012)

Pigment and Ink Analysis of University of Portland Library’s Illuminated Manuscripts using Spectroscopic Techniques

Raman and XRF spectroscopy were used to analyze pigments and inks of five illuminated manuscripts from the University of Portland’s Clark Library Special Collections. The five manuscripts were acquired at different times. Some were collected by members at the university and have been in the Special Collections for years. Others were recently acquired from Marylhurst University after the school’s closure in 2018. To address questions regarding their authenticity and possible origin, this study, which is the first of its kind on these manuscripts, was begun. Pigment analysis found the presence of phthalocyanine green dark, first made in the 1930s, in the first manuscript. Burnt sienna, not known as a pigment until the 18th-century, was also found in this same manuscript. In two sheets, analysis revealed the presence of vermilion, which is a common pigment for the time period that these manuscripts were thought to be from. Due to interrupted access to the manuscripts as a result of the pandemic, more information was unable to be collected, meaning few conclusions could be made about all five manuscripts. The work presented here aims to inform future analysis of these manuscripts, so that the authenticity and origin of these manuscripts can be better understood

Unmanned Aircraft Systems for Archaeology Using Photogrammetry and LiDAR in Southwestern United States

Researchers can use small unmanned aircraft systems (sUAS), also known as drones, to make observations of historical sites, help interpret locations, and make new discoveries that may not be visible to the naked eye. A student team from Embry-Riddle Aeronautical University gathered data for historical site documentation in New Mexico using the DJI Phantom 4 Pro V2, DJI Mavic Pro 2, DJI M210 and DJI M600, and senseFly eBee. Utilizing these drones, student analysts were able to take the data gathered and create georectified orthomosaic images and 3D virtual objects. At Tularosa Canyon, at a site known as the Creekside Village, work aimed at imaging an amphitheater like structure (i.e., kiva) that dates back to 600 AD. The team used photogrammetry and LiDAR to determine the location of other manmade structures at the same location. Images were processed with Pix4Dmapper Pro. Team members generated LiDAR point clouds and post processed data in search of undiscovered features and structures