Two-Sided Matching and Spread Determinants in the Loan Market

Empirical work on bank loans typically regresses loan spreads (markups of loan interest rates over a benchmark rate) on observed characteristics of banks, firms, and loans. The estimation is problematic when some of these characteristics are only partially observed and the matching of banks and firms is endogenously determined because they prefer partners that have higher quality. We study the U.S. bank loan market with a two-sided matching model to control for the endogenous matching, and obtain Bayesian inference using a Gibbs sampling algorithm with data augmentation. We find evidence of positive assortative matching of sizes, explained by similar relationships between quality and size on both sides of the market. Banks' risk and firms' risk are important factors in their quality. Controlling for the endogenous matching has a strong impact on estimated coefficients in the loan spread equation.Two-sided matching; Loan spread; Bayesian inference; Gibbs sampling with data augmentation

VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion

Mass Spectrometry for Determination of Conformation and Dynamics of Proteins and Structure and Biosynthesis of Bacterial Peptidoglycan

Mass spectrometry: MS) has emerged as an important tool for analyzing and characterizing large biomolecules. In this dissertation, two aspects of the development and application of MS-based approaches are presented; they include: 1) protein conformation and folding dynamics: in Chapters 2 to 5) and bacterial peptidoglycan: PG) structure and biosynthesis: Chapters 6 to 8). Chapter 1 serves as the introduction for both aspects. Part I of the dissertation focuses on the development of analytical methods combining fast photochemical oxidation of proteins: FPOP) and mass spectrometry analysis. In chapter 2 to 4, we discuss protein folding with sub-millisecond time resolution by a new pump/probe procedure. Perturbations in protein structure are by temperature jump of the protein solution, followed by fast photochemical oxidation of proteins: FPOP) as the probe. The hydroxyl radical lifetime was predicted by a dosimeter experiment: Chapter 2). The T jump-induced folding constant was measured at the global protein level: Chapter 3), and the residue level detail was revealed by proteolysis and liquid chromatography-mass spectrometry: LC-MS): Chapter 4). Chapter 5 discusses the development of a new FPOP reagent, iodobenzoic acid, and its application on studying conformational differences between apo- and holo- proteins. Part II of the thesis discusses the development and application of MS-based methods to investigate bacterial peptidoglycan. Chapter 6 focuses on the methodology of the bottom-up MS to characterize the fine structure of enterococcus faecium: E. faecium) peptidoglycan. Furthermore, we developed a time-dependent isotopic labeling strategy and applied it to E. faecium during the cell wall growth to determine quantitatively the percentage of heavy isotope incorporation into different muropeptides through peptidoglycan growth cycles, discussed in chapter 7. The results are important for understanding tertiary structure and designing novel drugs for antibiotic-resistant pathogens. In chapter 8, we applied the above approaches to investigate methicillin-resistant staphylococcus aureus: S. aureus) and its fem-mutants. We emphasize the peptidoglycan composition, fine structures, and biosynthesis

Linear-Combined-Code-Based Unambiguous Code Discriminator Design for Multipath Mitigation in GNSS Receivers

Unambiguous tracking and multipath mitigation for Binary Offset Carrier (BOC) signals are two important requirements of modern Global Navigation Satellite Systems (GNSS) receivers. A GNSS discriminator design method based on optimization technique is proposed in this paper to meet these requirements. Firstly, the discriminator structure based on a linear-combined code is given. Then the requirements of ideal discriminator function are converted into the mathematical constraints and the objective function to form a non-linear optimization problem. Finally, the problem is solved and the local code is generated according to the results. The theoretical analysis and simulation results indicate that the proposed method can completely remove the false lock points for BOC signals and provide superior multipath mitigation performance compared with traditional discriminator and high revolution correlator (HRC) technique. Moreover, the proposed discriminator is easy to implement for not increasing the number of correlators