Content-based Information Retrieval via Nearest Neighbor Search

Content-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function Learning (HFL) is considered to accelerate the retrieval process. On one hand, a new local metric learning framework is proposed - Reduced-Rank Local Metric Learning (R2LML). By considering a conical combination of Mahalanobis metrics, the proposed method is able to better capture information like data\u27s similarity and location. A regularization to suppress the noise and avoid over-fitting is also incorporated into the formulation. Based on the different methods to infer the weights for the local metric, we considered two frameworks: Transductive Reduced-Rank Local Metric Learning (T-R2LML), which utilizes transductive learning, while Efficient Reduced-Rank Local Metric Learning (E-R2LML)employs a simpler and faster approximated method. Besides, we study the convergence property of the proposed block coordinate descent algorithms for both our frameworks. The extensive experiments show the superiority of our approaches. On the other hand, *Supervised Hash Learning (*SHL), which could be used in supervised, semi-supervised and unsupervised learning scenarios, was proposed in the dissertation. By considering several codewords which could be learned from the data, the proposed method naturally derives to several Support Vector Machine (SVM) problems. After providing an efficient training algorithm, we also study the theoretical generalization bound of the new hashing framework. In the final experiments, *SHL outperforms many other popular hash function learning methods. Additionally, in order to cope with large data sets, we also conducted experiments running on big data using a parallel computing software package, namely LIBSKYLARK

Untangling Socioeconomic Health Inequalities:Reinforcing the Evidence Base for Public Health

Health inequalities persist as a major challenge to public health. We hypothesized that public health policy may have mis-prioritized the determinants and risks for poor health and, consequently may have been barking up the wrong tree because of unmeasured, biased, and neglected risk factors at population level. This thesis investigated those hypotheses using data from the Dutch Lifelines Cohort and Biobank. The first part focused on unraveling the complexity of socioeconomic determinants of health and highlights socioeconomic disparities in health status and health outcomes. The second part explored the use of objective measurements of nutritional factors to uncover blind spots to make good quality data on diet available for promotion of public health. The results indicate that 1) public health policy should give particular attention to individuals with low socioeconomic status because they are at higher risks of practicing unhealthy lifestyle and of chronic and infectious diseases; 2) it is crucial to consider not only individual factors, but also neighborhood conditions in prevention policies, especially when targeting behavioral changes, as we neighborhood conditions independently influence both lifestyle and health; 3) regulatory authorities need to create healthier food environments to make healthy choices easier and discourage unhealthy ones; 4) objective measurements of nutritional factors are powerful for better prioritizing of nutrition targets in public health policy.Addressing socioeconomic determinants and broader determinants of health in disease prevention and health promotion is needed for the health and prosperity of both individual citizens and society as a whole

Strain Engineering for Advanced Silicon Transistors

Ph.DDOCTOR OF PHILOSOPH

High-quality reduced graphene oxide-nanocrystalline platinum hybrid materials prepared by simultaneous co-reduction of graphene oxide and chloroplatinic acid

Reduced graphene oxide-nanocrystalline platinum (RGO-Pt) hybrid materials were synthesized by simultaneous co-reduction of graphene oxide (GO) and chloroplatinic acid with sodium citrate in water at 80°C, of pH 7 and 10. The resultant RGO-Pt hybrid materials were characterized using transmission electron microscopy (TEM), powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy, and thermogravimetric analysis. Platinum (Pt) nanoparticles were anchored randomly onto the reduced GO (RGO) sheets with average mean diameters of 1.76 (pH 7) and 1.93 nm (pH 10). The significant Pt diffraction peaks and the decreased intensity of (002) peak in the XRD patterns of RGO-Pt hybrid materials confirmed that the Pt nanoparticles were anchored onto the RGO sheets and intercalated into the stacked RGO layers at these two pH values. The Pt loadings for the hybrid materials were determined as 36.83 (pH 7) and 49.18% (pH 10) by mass using XPS analysis. With the assistance of oleylamine, the resultant RGO-Pt hybrid materials were soluble in the nonpolar organic solvents, and the dispersion could remain stable for several months

Flux Balance Analysis of Dynamic Metabolism in Shewanella oneidensis MR-1 Using a Static Optimization Approach

Shewanella bacteria are facultative anaerobes isolated from aquatic and sedimentary environments (Hau and Gralnick 2007) with a broad capacity for reduction of multiple electron receptors (Pinchuk et al. 2009; Serres and Riley 2006), including Fe(III), Mn(IV), sulfur, nitrate, and fumarate. With the accomplishment of complete genome sequencing of several Shewanella bacteria, the general pictures of the carbon metabolism have been revealed (Serres and Riley 2006). metabolism. One of the most physiological methods to decipher the time-variant metabolic regulation is to determine the dynamic distribution of intracellular metabolic fluxes since it reveals the final response of cellular metabolism to genomic, transcriptional and post-transcriptional regulations (Sauer 2006; Tang et al. 2009). In order to track the dynamic intracellular metabolic regulation, dynamic flux balance analysis (DFBA) was developed (Mahadevan et al. 2002), in which cell growth phase was divided into numerous stages, assuming that at each stage a new metabolic steady state was maintained. All the metabolic fluxes were then searched to satisfy the objective functions set for each stage. By solving this nonlinear optimization model using a cutting-edge nonlinear optimization solver (IPOPT), we confirmed the changing of carbon sources for the growth of Shewanella oneidensis MR-1 and deciphered the dynamic regulation of intracellular metabolism