Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of features greatly exceeds the number of training examples, but where only a few features suffice for accurate classification. We show that sum-product GAMP can be used to (approximately) minimize the classification error rate and max-sum GAMP can be used to minimize a wide variety of regularized loss functions. Furthermore, we describe an expectation-maximization (EM)-based scheme to learn the associated model parameters online, as an alternative to cross-validation, and we show that GAMP's state-evolution framework can be used to accurately predict the misclassification rate. Finally, we present a detailed numerical study to confirm the accuracy, speed, and flexibility afforded by our GAMP-based approaches to binary linear classification and feature selection

Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing

In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the literature, the ability to perform inference on high-dimensional problems in a computationally efficient manner remains elusive. In response, we propose a probabilistic dynamic CS signal model that captures both amplitude and support correlation structure, and describe an approximate message passing algorithm that performs soft signal estimation and support detection with a computational complexity that is linear in all problem dimensions. The algorithm, DCS-AMP, can perform either causal filtering or non-causal smoothing, and is capable of learning model parameters adaptively from the data through an expectation-maximization learning procedure. We provide numerical evidence that DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety of operating conditions. We further describe the result of applying DCS-AMP to two real dynamic CS datasets, as well as a frequency estimation task, to bolster our claim that DCS-AMP is capable of offering state-of-the-art performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure

Modulation of Hypoglossal Motoneurons by Nitric Oxide

Obstructive sleep apnea (OSA)- the occurrence of repetitive episodes of airway obstruction during sleep- is considered a major health problem affecting up to 9% of adults in the United States (Parish & Somers, 2004). The hypoglossal motor nucleus (HMN) controls genioglossus muscle tone and is critically important for maintaining airway patency; loss of excitatory input to the HMN during sleep results in disfacilitation of hypoglossal motoneurons, increased airway resistance and contributes to the development of OSA (Horner R. L., 2007). However, a fundamental question of sleep medicine that remains unresolved is what mechanisms help maintain airway patency during sleep? A potential source of sleep-activated compensatory drive is nitric oxide released from cholinergic terminals in the HMN (Pose et al. 2005; Vincent & Kimura, 1992). Here we show that NO functions as an excitatory transmitter in the HMN by a cGMP-dependent inhibition of a background TASK-like conductance and an S-nitrosylation-dependent activation of the instantaneous but not the time-dependent component of the hyperpolarization-activated current (Ih) generated by hyperpolarization-activated cyclic nucleotide gated (HCN) channels. These results suggest that sleep-induced nitrergic innervation of the HMN helps compensate for respiratory motoneuron disfacilitation and disruption of NO/cGMP signaling may contribute to the etiology of OSA. Although a causal link between disruption of NO/cGMP signaling and occurrence of OSA has yet to be established, it is well known that patients with metabolic syndrome have high levels of uric acid- a potent NO scavenger- and, perhaps consequently, are at much higher risk of developing OSA (Mota, 2010)

Variables Impacting Southern Illinois Airport Activity Between The Years 2000 And 2010

Southern Illinois Airport is a publicly used and operated airport that forecasts its airport activity for airport planning purposes. This research uses linear regression analysis to identify independent variables impacting based aircraft, local civilian operations and itinerant general aviation aircraft operations at Southern Illinois Airport between 2000 and 2010. Regression analysis is a Federal Aviation Administration approved method in determining relationships between airport activity factors and other variables, but is typically used in large scale airport system planning and not at general aviation airports such as Southern Illinois Airport. The results appear promising for future use in airport planning as the test did identify significant relationships between Southern Illinois Airport activity and independent variables

Groundwater Recharge Response to Reduced Irrigation Pumping in Western Nebraska

Given current and continued investment in irrigation scheduling technologies, a need exists to better estimate the longevity and magnitude of water savings at watershed level to avoid the paradox of irrigation efficiency. This paradox occurs within a watershed as not all irrigation inefficiencies lead to the system losing water. For example, irrigation pumping rates in excess of crop water demand may lead to enhanced groundwater recharge or surface runoff that migrates to a stream. Thus, increases in efficiency may not lead to similar magnitudes of water savings. I hypothesize that water savings longevities are short given previous work demonstrating rapid responses of groundwater recharge rates to changing surface conditions. To test this hypothesis, I used numerical modeling and hydrogeological field techniques. This work provides localized ranges of: weather, management, soil variability, depth to groundwater, and water fluxes. In chapter two, utilizing a crop modeling and numerical modeling of soil moisture redistribution, I found that irrigation practices within the study area could be reduced by 120 mm yr-1 with impact on yield less than 3% when compared to a long-term dataset of irrigation pumping rates for ~50 fields within the study area. From work in chapter three, I found that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. This work then informed an intermediate core sampling framework in chapter four to constrain how soil hydraulic fluxes vary on subfield scale. In chapter four, I compared deep drainage outputs of a numerical model parameterized with localized measurements to a chemical tracer analysis and find agreement within 80% despite a wide range of fluxes observed (135-515mm yr-1). Scenario testing informed using the parameterized numerical model and the irrigation reduction potential from chapter two indicated that a 120mm yr-1 reduction of pumping leads to modest water savings (1-3 years; 50-200mm over 10 years). However, when applied over a number of fields, similar irrigation efficiency programs may be competitive with other water resource management programs. Adviser: Trenton E. Fran

Impacts And Implications Of Polytypism On The Evolution Of Aposematic Coloration

Aposematic signaling is a comdefensive strategy whereby prey species use conspicuous signals (i.e., bright coloration) to warn predators of the risks of predation due to a secondary defense. Theoretical, lab, and field experiments have demonstrated that individuals who project novel conspicuous signals should experience disproportionately high predation pressure as predators will not associate novel signals with a secondary defense. Thus, aposematic signaling should be subject to strong positive frequency-dependent selection (FDS). Numerous species show considerable intrapopulation variation (polymorphism) in direct conflict with the expectations of FDS. Interpopulation variation (polytypism) can adhere to expectations of FDS, but as its origins likely involve polymorphism, FDS likely plays a role in the establishment of such populations. Both the Dyeing Poison Frog (Dendrobates tinctorius) and the Australian Brood Frogs (Pseudophryne) exhibit considerable inter- and intrapopulation variation making them excellent candidates to investigate how aposematic signaling can evolve and under what circumstances of FDS can be relaxed. Consequently, I approach this question by systematically investigating how predators perceive conspicuous colors, how secondary defense influences predator response, how predators respond to known and novel colors, and the role that gene flow plays in promoting or limiting phenotypic divergence. By using model, naïve predators for learning experiments, I found that the hue color component influences predators’ abilities to learn to avoid an aposematic signal. I provide the first among-population characterization of alkaloid toxins in D. tinctorius and, when using a model avian predator to examine unpalatability of alkaloid toxins, found that a subset of these alkaloids is driving the predator response. In examining how predators respond to known and novel phenotypes, I elucidate the function of conspicuous signals in Pseudophryne and how predators may generalize to novel signals. Finally, I find that despite field and lab experiments that indicate a selective disadvantage of a weak aposematic signal, it can persist when isolated with limited gene flow. Together, these studies provide evidence for the evolution of aposematic signals and propose mechanisms that can allow the relaxation of FDS and thus allow for phenotypic polymorphism in aposematic species

Vortex Glass is a Metal: Unified Theory of the Magnetic Field and Disorder-Tuned Bose Metals

We consider the disordered quantum rotor model in the presence of a magnetic field. We analyze the transport properties in the vicinity of the multicritical point between the superconductor, phase glass and paramagnetic phases. We find that the magnetic field leaves metallic transport of bosons in the glassy phase in tact. In the vicinity of the vicinity of the superconductivity-to-Bose metal transition, the resistitivy turns on as $(H-H_c)^{2}$ with $H_c$. This functional form is in excellent agreement with the experimentally observed turn-on of the resistivity in the metallic state in MoGe, namely $R\approx R_c(H-H_c)^\mu$, $1<\mu<3$. The metallic state is also shown to presist in three spatial dimensions. In addition, we also show that the metallic state remains intact in the presence of Ohmic dissipation in spite of recent claims to the contrary. As the phase glass in $d=3$ is identical to the vortex glass, we conclude that the vortex glass is, in actuality, a metal rather than a superconductor at T=0. Our analysis unifies the recent experiments on vortex glass systems in which the linear resistivity remained non-zero below the putative vortex glass transition and the experiments on thin films in which a metallic phase has been observed to disrupt the direct transition from a superconductor to an insulator.Comment: Published version with an appendix showing that the claim in cond-mat/0510380 (and cond-mat/0606522) that Ohmic dissipation in the phase glass leads to a superconducting state is false. A metal persists in this case as wel

Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, is capable of exploiting temporal correlations in the amplitudes of non-zero coefficients, and provides soft estimates of the signal vectors as well as the underlying support. Central to the proposed approach is an extension of recently developed approximate message passing techniques to the amplitude-correlated MMV setting. Aided by these techniques, AMP-MMV offers a computational complexity that is linear in all problem dimensions. In order to allow for automatic parameter tuning, an expectation-maximization algorithm that complements AMP-MMV is described. Finally, a detailed numerical study demonstrates the power of the proposed approach and its particular suitability for application to high-dimensional problems.Comment: 28 pages, 9 figure

When do young birds disperse? : Tests from studies of golden eagles in Scotland

Peer reviewedPublisher PD

High-Power, High-Speed Electro-Optic Pockels Cell Modulator

Electro-optic modulators rely on a change in the index of refraction for the optical wave as a function of an applied voltage. The corresponding change in index acts to delay the wavefront in the waveguide. The goal of this work was to develop a high-speed, high-power waveguide- based modulator (phase and amplitude) and investigate its use as a pulse slicer. The key innovation in this effort is the use of potassium titanyl phosphate (KTP) waveguides, making the highpower, polarization-based waveguide amplitude modulator possible. Furthermore, because it is fabricated in KTP, the waveguide component will withstand high optical power and have a significantly higher RF modulation figure of merit (FOM) relative to lithium niobate. KTP waveguides support high-power TE and TM modes - a necessary requirement for polarization-based modulation as with a Pockels cell. High-power fiber laser development has greatly outpaced fiber-based modulators in terms of its maturity and specifications. The demand for high-performance nonlinear optical (NLO) devices in terms of power handling, efficiency, bandwidth, and useful wavelength range has driven the development of bulk NLO options, which are limited in their bandwidth, as well as waveguide based LN modulators, which are limited by their low optical damage threshold. Today, commercially available lithium niobate (LN) modulators are used for laser formatting; however, because of photorefractive damage that can reduce transmission and increase requirements on bias control, LN modulators cannot be used with powers over several mW, dependent on wavelength. The high-power, high-speed modulators proposed for development under this effort will enable advancements in several exciting fields including lidarbased remote sensing, atomic interferometry, free-space laser communications, and others