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Event(s) on August 2018


  • Friday, 3rd August, 2018

    Title: Blockchain Design for Supply Chain Management
    Speaker: Prof SHI Junmin, Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, USA
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Blockchain research is still in its infancy stage, with most exiting work focused on security and scalability and few applications for controlling physical devices. Very scant research looks at its impact and design issues on management perspectives, especially from the perspective of Supply Chain Management. To investigate the impact of blockchain technology (BCT) on supply chain performance and the inherent design issue, we study a generic stochastic model, where a manufacturer seeks to maximize the total expected discounted profit, by jointly managing (i) blockchain design, (ii) production or ordering decision, and (iii) dynamic pricing and selling. The leverage of blockchain can help firms reduce order quantity, lower selling price and reduce the target inventory level to carry over. It is also shown that the volatility pertaining to either supply or demand market will lower the expected profit. While facing higher volatility, the firm prefers to leverage high degree of blockchain. Finally, our numerical study illustrates rich managerial insight. For example, considering tech-savvy customer behavior, some types of goods (e.g., credence goods and experience goods) appreciate BCT, but it might not be beneficial to leverage BCT for some others (e.g., Search goods); considering the lifecycle of the product, it is recommended to adopt BCT as early as possible and leverage a higher adoption degree at an earlier stage; high volatility of supply chain (e.g., yield and demand) raises the intension to adopt BCT.


  • Friday, 3rd August, 2018

    Title: Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets
    Speaker: Prof Yuedong WANG, Department of Statistics and Applied Probability, University of California, Santa Barbara, USA
    Time/Place: 13:00  -  14:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Spline smoothing is a widely used nonparametric method that allows data to speak for themselves. Due to its complexity and flexibility, fitting smoothing spline models is usually computationally intensive which may become prohibitive with large datasets. To overcome memory and CPU limitations, we propose four divide and recombine (D&R) approaches for fitting cubic splines with large datasets. We consider two approaches to divide the data: random and sequential. For each approach of division, we consider two approaches to recombine. These D&R approaches are implemented in parallel without communication. Extensive simulations show that these D&R approaches are scalable and have comparable performance as the method that uses the whole data. The sequential D&R approaches are spatially adaptive which lead to better performance than the method that uses the whole data when the underlying function is spatially inhomogeneous.


  • Friday, 10th August, 2018

    Title: Kernel-based least-squares approximations: theories and applications
    Speaker: Ms LI Siqing
    Time/Place: 14:30  -  16:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Kernel-based meshless methods for approximating functions and solutions of partial differential equations have many applications in engineering fields. Since only scattered data are used, meshless methods by radial basis functions can be extended to complicated geometry and high dimensional problems. In this thesis, kernel based least-squares methods will be used to solve several direct and inverse problems.


  • Monday, 13th August, 2018

    Title: Intrinsic meshless methods for PDEs on manifolds and applications
    Speaker: Ms CHEN Meng
    Time/Place: 10:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Radial Basis function (RBF) methods for partial differential equations (PDEs) either in bulk domains, on surfaces, or coupled of the formers arise in a wide range of practical applications. This thesis proposes numerical approaches of RBF-based meshless techniques for solving these three kinds of PDEs on stationary and nonstationary surfaces and domains.


  • Monday, 13th August, 2018

    Title: The integrative analysis of genomic data in complex diseases
    Speaker: Dr Xiang WAN, Shenzhen Research Institute of Big Data, Shenzhen, China
    Time/Place: 16:00  -  17:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Many common human diseases, such as type-1 and type-2 diabetes, depression, schizophrenia, and prostate cancer, are influenced by several genetic and environmental factors. Scientists and public health officials have struggled to find genetic patterns associated with complex diseases, not only to advance our understanding of multi-gene disorders, but also to provide more insights into complex diseases. However, most of the genetic factors that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. As high-throughput data acquisition becomes popular in biomedical research, it is timely to propose some novel approaches to mining the large-scale genomic data to find new genetic patterns. In this talk, I will present our on-going works on the integrative analysis of multiple large-scale genomic data sets. Some preliminary results have shown that our approaches have greater power, less false positives, and more accurate estimations of genetic effects in the study of complex diseases.


  • Tuesday, 14th August, 2018

    Title: Numerical Algorithms for Data Analysis with Imaging and Financial Applications
    Speaker: Mr SIU Ka Wai
    Time/Place: 09:30  -  11:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: We study modellings and numerical algorithms to data analysis with applications to image processing and financial forecast. The presentation is composed of two parts, namely the tensor regression and data assimilation methods for image restoration. The tensor regression is a generalization of a classical regression in order to adopt and analyze much more information by using multi-dimensional arrays. Since the regression problem is subject to multiple solutions, we propose a regularized tensor regression model to the problem. By imposing a low-rank property of the solution and considering the structure of the tensor product,we develop an algorithm which is suitable for scalable implementations. The regularization method is used to select useful solutions which depend on applications. The proposed model is solved by the alternating minimization method and we prove the convergence of the objective function values and iterates by the maximization-minimization (MM) technique. We study different factors which affects the performance of the algorithm, including sample sizes, solution ranks and the noise levels. Applications include image compressing and financial forecast. Another part of presentation refers to adopting filtering methods in data assimilation to image restoration problems. Traditionally, data assimilation methods optimally combine a predictive state from a dynamical system with real partially observations. The motivation is to improve the model forecast by real observation. We construct an artificial dynamics to the non-blind deblurring problems. By making use of spatial information of a single image, a span of ensemble members is constructed. A two-stage use of ensemble transform Kalman filter (ETKF) is adopted to deblur corrupted images. The theoretical background of ETKF and the use of artificial dynamics by stage augmentation method are provided. Numerical experiments include image and video processing.


  • Tuesday, 14th August, 2018

    Title: Numerical Methods of Data Assimilation in Weather Forecasting
    Speaker: Ms YAN Hanjun
    Time/Place: 13:30  -  15:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation.


  • Tuesday, 14th August, 2018

    Title: A Study of Forecasting Performance of Alternative Option Pricing Models on Option Return and Market Volatility
    Speaker: Mr OU Jitao
    Time/Place: 15:30  -  17:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: We investigate the forecasting problem for option returns and future volatilities in financial market. We study the option return skewness effect and the negative correlation between asset returns and volatility, and propose an ex-ante measure of option return skewness which accommodates the negative return-volatility relationship in asset returns. We investigate how time-to-expiration and moneyness affect the skewness and return of an option. Furthermore, we show that our proposed measure has extra benefits in forecasting option returns. We also test the information contents of implied volatility derived from stochastic volatility option pricing model and also examine the potential benefit of including the models implied volatility risk and ex-ante option return skewness in forecasting future volatility and volatility risk premium. Our study finds that the inclusion of volatility risk factor has significantly reduced the downward bias of the slope coefficients. Most importantly, both option implied ex-ante volatility risk and return skewness have significant predictive power on the ex-post volatility premium.


  • Thursday, 23rd August, 2018

    Title: Statistical Methods for Integrative Analysis of Genomic Data
    Speaker: Ms Jingsi MING
    Time/Place: 14:30  -  16:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in the non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve a deeper understanding of the genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.