Detection of radioactive material entering national ports: A Bayesian approach to radiation portal data

Given the potential for illicit nuclear material being used for terrorism, most ports now inspect a large number of goods entering national borders for radioactive cargo. The U.S. Department of Homeland Security is moving toward one hundred percent inspection of all containers entering the U.S. at various ports of entry for nuclear material. We propose a Bayesian classification approach for the real-time data collected by the inline Polyvinyl Toluene radiation portal monitors. We study the computational and asymptotic properties of the proposed method and demonstrate its efficacy in simulations. Given data available to the authorities, it should be feasible to implement this approach in practice.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS334 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Disposition Effect and Momentum

Prior experimental and empirical research documents that many investors have a lower propensity to sell those stocks on which they have a capital loss. This behavioral phenomenon, known as 'the disposition effect,' has implications for equilibrium prices. We investigate the temporal pattern of stock prices in an equilibrium that aggregates the demand functions of both rational and disposition investors. The disposition effect creates a spread between a stock's fundamental value -- the stock price that would exist in the absence of a disposition effect -- and its market price. Even when a stock's fundamental value follows a random walk, and thus is unpredictable, its equilibrium price will tend to underreact to information. Spread convergence, arising from the random evolution of fundamental values, generates predictable equilibrium prices. This convergence implies that stocks with large past price runups and stocks on which most investors experienced capital gains have higher expected returns that those that have experienced large declines and capital losses. The profitability of a momentum strategy, which makes use of this spread, depends on the path of past stock prices. Crosssectional empirical tests of the model find that stocks with large aggregate unrealized capital gains tend to have higher expected returns than stocks with large aggregate unrealized capital losses and that this capital gains 'overhang' appears to be the key variable that generates the profitability of a momentum strategy. When this capital gains variable is used as a regressor along with past returns and volume to predict future returns, the momentum effect disappears.

Promotion Tournaments and Capital Rationing

We analyze capital allocation in a conglomerate where divisional managers with uncertain abilities compete for promotion to CEO. A manager can sometimes gain by unobservably adding variance to divisional performance. Capital rationing can limit this distortion, increase productive efficiency, and allow the owner to make more accurate promotion decisions. Firms for which CEO talent is more important for firm performance are more likely to ration capital. A rationed manager is more likely to be promoted even though all managers are identical ex ante. When the tournament payoff is relatively small, offering an incentive wage can be more efficient than rationing capital; however, when tournament incentives are paramount, rationing is more efficient.

Novel Strategies in the Treatment of COPD:Focus on oxidative stress and a-kinase anchoring proteins

Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation and airway inflammation. Since the current medications are not always effective and fail to reduce the progression of COPD, studies for novel strategies are necessary. The objective of this thesis was to investigate possible targets for the treatments of COPD, with a focus on the involvement of A-kinase anchoring proteins (AKAPs) and oxidative stress. AKAPs enable compartmentalized cAMP signaling, which plays an important role in regulation of processes that are involved in the pathophysiology of COPD, such as airway smooth muscle (ASM) contraction and ASM proliferation.By interrupting AKAP-PKA interactions, the peptide st-Ht31 increased ASM contraction. This is likely caused by the fact the st-Ht31 increased the expression of contractile proteins, such as α-SMA and calponin on a post-transcriptional level, in a complex that presumably also involves proteasomes. Moreover, st-Ht31 increased proliferative markers, presumably by lowering the expression of AKAP8 which is known to regulate the cell cycle. Overproduction of reactive oxygen species (ROS) can induce oxidative stress, which is believed to play a central role in the pathophysiology of COPD. By directly neutralizing ROS, we found that the novel compound Sul-121 reduced activation of NF-κB, thereby preventing IL-8 release and subsequent airway neutrophilia in lipopolysaccharide (LPS)-treated guinea pigs as an animal model for COPD. Moreover, Sul-121 prevented LPS-induced airway hyperresponsiveness in this guinea pig model of COPD, presumably by inhibiting the LPS-induced lung inflammation. The studies described in this thesis by Bing Han highlight new insights in the pathophysiology of COPD that may lead to novel treatments for COPD

DETECTING CANCER-RELATED GENES AND GENE-GENE INTERACTIONS BY MACHINE LEARNING METHODS

To understand the underlying molecular mechanisms of cancer and therefore to improve pathogenesis, prevention, diagnosis and treatment of cancer, it is necessary to explore the activities of cancer-related genes and the interactions among these genes. In this dissertation, I use machine learning and computational methods to identify differential gene relations and detect gene-gene interactions. To identify gene pairs that have different relationships in normal versus cancer tissues, I develop an integrative method based on the bootstrapping K-S test to evaluate a large number of microarray datasets. The experimental results demonstrate that my method can find meaningful alterations in gene relations. For gene-gene interaction detection, I propose to use two Bayesian Network based methods: DASSO-MB (Detection of ASSOciations using Markov Blanket) and EpiBN (Epistatic interaction detection using Bayesian Network model) to address the two critical challenges: searching and scoring. DASSO-MB is based on the concept of Markov Blanket in Bayesian Networks. In EpiBN, I develop a new scoring function, which can reflect higher-order gene-gene interactions and detect the true number of disease markers, and apply a fast Branch-and-Bound (B&B) algorithm to learn the structure of Bayesian Network. Both DASSO-MB and EpiBN outperform some other commonly-used methods and are scalable to genome-wide data

Hardware implementation of multiple-input multiple-output transceiver for wireless communication

This dissertation proposes an efficient hardware implementation scheme for iterative multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) transceiver. The transmitter incorporates linear precoder designed with instantaneous channel state information (CSI). The receiver implements MMSE-IC (minimum mean square error interference cancelation) detector, channel estimator, low-density parity-check (LDPC) decoder and other supporting modules. The proposed implementation uses QR decomposition (QRD) of complex-valued matrices with four co-ordinate rotation digital computer (CORDIC) cores and back substitution to achieve the best tradeoff between resource and throughput. The MIMO system is used in field test and the results indicate that the instantaneous CSI varies very fast in practices and the performance of linear precoder designed with instantaneous CSI is limited. Instead, statistic CSI had to be used. This dissertation also proposes a higher-rank principle Kronecker model (PKM). That exploits the statistic CSI to simulate the fading channels. The PKM is constructed by decomposing the channel correlation matrices with the higher-order singular value decomposition (HOSVD) method. The proposed PKM-HOSVD model is validated by extensive field experiments conducted for 4-by-4 MIMO systems in both indoor and outdoor environments. The results confirm that the statistic CSI varies slowly and the PKM-HOSVD will be helpful in the design of linear precoders. --Abstract, page iv