Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models

This paper proposes a nonparametric simultaneous test for parametric specification of the conditional mean and variance functions in a time series regression model. The test is based on an empirical likelihood (EL) statistic that measures the goodness of fit between the parametric estimates and the nonparametric kernel estimates of the mean and variance functions. A unique feature of the test is its ability to distribute natural weights automatically between the mean and the variance components of the goodness of fit. To reduce the dependence of the test on a single pair of smoothing bandwidths, we construct an adaptive test by maximizing a standardized version of the empirical likelihood test statistic over a set of smoothing bandwidths. The test procedure is based on a bootstrap calibration to the distribution of the empirical likelihood test statistic. We demonstrate that the empirical likelihood test is able to distinguish local alternatives which are different from the null hypothesis at an optimal rate.Bootstrap, empirical likelihood, goodness{of{t test, kernel estimation, least squares empirical likelihood, rate-optimal test

Growth Engine: Effects of China’s Trade and Investment on the Economies of East and Southeast Asia

The emergence of China as an economic superpower through globalization and fragmentation of production has impacted global trade relations, particularly in East and Southeast Asia (ESA). China has become a major trading partner for ESA economies not only through exporting goods to ESA countries, but also importing goods to satisfy China’s energy and consumer needs. This thesis studies the impact trade and investment relationships with China have on ESA economies. This study will include ten developed and developing ESA countries: Japan, Republic of Korea, Singapore, Thailand, Malaysia, Indonesia, Vietnam, Cambodia, Lao DPR, and Philippines. The results obtained include: 1) the effects that exports to China and inbound FDI from China have on ESA countries’ real GDP per capita, 2) the impact of trade and investment with China depends on the countries’ degree of advancement, 3) emphasis on the importance of regional cooperation in Asia

A test for model specification of diffusion processes

We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The empirical likelihood is used to formulate a statistic, for each kernel smoothing bandwidth, which is effectively a Studentized $L_2$-distance between the kernel transitional density estimator and the parametric transitional density implied by the parametric process. To reduce the sensitivity of the test on smoothing bandwidth choice, the final test statistic is constructed by combining the empirical likelihood statistics over a set of smoothing bandwidths. To better capture the finite sample distribution of the test statistic and data dependence, the critical value of the test is obtained by a parametric bootstrap procedure. Properties of the test are evaluated asymptotically and numerically by simulation and by a real data example.Comment: Published in at http://dx.doi.org/10.1214/009053607000000659 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Advanced CFD model of multiphase photobioreactors for microalgal derived biomass production

Development of more efficient algal photobioreactors (PBRs) is driven by increasing interest in algaculture for the production of fuels, chemicals, food, animal feed, and medicine, as well as carbon capture. While at present, the cost and microalgae production capacity are one of its restrictions when competition with other biodiesel feedstock. The objective of the present work is to develop and validate better computational models to investigate the interplay between fluid hydrodynamics, radiation transport and algae growth, which is crucial to determine the performance and scalability of algae photobioreactors. First, a detailed review of the pertinent information required for developing a comprehensive computation model for photobioreactors was conducted. The current status of the submodels, including hydrodynamics and mass transfer multiphase CFD models, radiation transport models, microalgae growth rate models, and coupling method for developing a comprehensive model for PBRs was outlined. Second, an Eulerian two-fluid model for gas-liquid Taylor-Couette flow was proposed and validated. The CFD was based on the RANS approach with constitutive closures for interphase forces and liquid turbulence. The model was validated by comparison with previously published experimental data. The mechanism of bubble radial non-uniformity distribution was discussed and the relative importance of various interphase forces was demonstrated. Third, the validated two fluid CFD model was employed to simulate the local values of the mass transfer coefficient based on the Higbie theory. A novel approach was proposed to estimate the mass transfer exposure time. This approach automatically selects the appropriate expression (either the penetration model or eddy cell model) based on local flow conditions. The simulation predictions agree well with experimental foundlings, which demonstrates that the adaptive mass transfer model has the ability to correctly description of both local and global mass transfer of oxygen in a semi-batch gas–liquid Taylor–Couette reactor. Forth, microalgae culture experiment was conducted to identify the limiting factor in the Taylor-Couette photobioreactor. The characteristic time scales for mixing, mass transfer and biomass growth was compared. It is found that algal growth rate in Taylor vortex reactors is not limited by fluid mixing or interphase mass transfer, and therefore the observed biomass productivity improvements are likely attributable to improved light utilization efficiency (high-frequency light/dark cycles). Fifth, a commonly used Lagrangian strategy for coupling the various factors influencing algal growth was employed whereby results from computational fluid dynamics and radiation transport simulations were used to compute numerous microorganism light exposure histories, and this information, in turn, was used to estimate the global biomass specific growth rate. The simulation predictions were compared with experimental measurements and the origin of weaknesses of the commonly used Lagrangian approach model was traced. Sixth, an alternative Eulerian computational approach for predicting photobioreactor performance was proposed, wherein a transport equation for algal growth kinetics is solved, thereby obviating the need to carry out thousands of particle tracking simulations. The simulation predictions were compared with experimental measurements and commonly used Lagrangian approach model

FPGA Implementation of Post-Quantum Cryptography Recommended by NIST

In the next 10 to 50 years, the quantum computer is expected to be available and quantum computing has the potential to defeat RSA (Rivest-Shamir-Adleman Cryptosystem) and ECC (Elliptic Curve Cryptosystem). Therefore there is an urgentneed to do research on post-quantum cryptography and its implementation. In this thesis, four new Truncated Polynomial Multipliers (TPM), namely, TPM-I, TPM-II, TPM-III, and TPM-IV for NTRU Prime system are proposed. To the best of our knowledge, this is the first time to focus on time-efficient hardware architectures and implementation of NTRU Prime with FPGA. TPM-I uses a modified linear feedback shift register (LFSR) based architecture for NTRU prime system. TPM-II makes use of x^2-net structure for NTRU Prime system, which scans two consecutive coefficients in the control input polynomial r(x) in one clock cycle. In TPM-III and TPM-IV, three consecutive zeros and consecutive zeros in the control input polynomial r(x) are scanned during one clock cycle, respectively. FPGA implementation results are obtained for the four proposed polynomial multiplication architectures and a comparison between the proposed multiplier FPGA results for NTRU Prime system and the existing work on NTRUEncrypt is shown. Regarding space complexity, TPM-I can reduce the area consumption with the least logical elements, although it takes more latency time among the four proposed multipliers and NTRUEncrypt work [12]. TPM-II has the best performance of latency with parameter sets ees401ep1, ees449ep1, ees677ep1 in security levels: 112-bit, 128-bit, and 192-bit, respectively. TPM-IV uses the smallest latency time with the parameter set ees1087ep2 in security level 256, compared to the other three latency time of proposed multipliers. Both TPM-II and TPM-IV have a lower latency time compared to NTRUEncrypt work [12] in different security levels. Note that NTRU Prime has enhanced security in comparison with NTRUEncrypt due to the fact, the former uses a new truncated polynomial ring, which has a more secure structure

Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks

Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel\u27s law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today\u27s scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier\u27s objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes