Achievements in medicine 1985-1995

Staff list with Chinese names.published_or_final_versionForeword Wang Gungwu Wang, Gungwu vRheumatology C.S. Lau and Raymond W.S. Wong Lau, C. S. Wong, Raymond, W. S. 162Respiratory Medicine W.K. Lam, Mary S.M. Ip and Jane C.K. Chan Lam, W. K. Ip, Mary, S. M. Chan, Jane, C. K. 153Neurology Y.L. Yu, Jason K.Y. Fong and S.L. Ho Yu, Y. L. Fong, Jason, K. Y. Ho, S. L. 143Nephrology Ignatius K.P. Cheng and Daniel T.M. Chan Cheng, Ignatius, K. P. Chan, Daniel, T. M. 132Molecular Medicine Vivian N.Y. Chan Chan, Vivian, N. Y. 125Haematology and Oncology Raymond H.S. Liang Liang, Raymond, H. S. 103Geriatric Medicine L.W. Chu Chu, L. W. 101General Practice Unit Anthony S. Dixon and Cindy L.K. Lam Dixon, Anthony S. Lam, Cindy, L. K. 91Gastroenterology and Hepatology S.K. Lam, C.L. Lai, C.K. Ching and Benjamin C.Y. Wong Lam, S. K. Lai, C. L. Ching, C. K. Wong, Benjamin, C. Y. 74Endocrinology Karen S.L. Lam and Annie W.C. Kung Lam, Karen, S. L. Kung, Annie, W. C. 62Clinical Pharmacology Cyrus R. Kumana and Bernard M.Y. Cheung Kumana, Cyrus R. Cheung, Bernard, M. Y. 55Cardiology C.P. Lau, K.L. Cheung, W.H. Chow and David S.W. Ho Lau, C. P. Cheung, K. L. Chow, W. H. Ho, David, S. W. 37Curriculum Vitae of Professor Chan Tai-kwong 25Professor Chan Tai-kwong - a Personal Tribute David Todd Todd, David 21The Department of Medicine: Today and Tomorrow S.K. Lam Lam, S. K. 14Vision and Mission - a History of the Department of Medicine Rosie T.T. Young Young, Rosie, T. T. 1Appendix Staff List 171Subspecialty Divisions and the General Practice UnitPreface Y.L. Yu Yu, Y. L. xiMission and Objectives of the Department of Medicine ixUniversity's Mission Statement vii

The impact of the automated teller machine on Hong Kong's banking industry: review and outlook.

by Chan Lai-ming, Raymond, Mok Ngai-shun, Esmond.Thesis (M.B.A.)--Chinese University of Hong Kong, 1988.Bibliography: leaves 65-68

A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing

The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem [M. Mignotte. An energy-based model for the image edge-histogram specification problem. IEEE Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently

Integrated Solutions Development Capital Solutions Interim Report Volume I: Report

This report analyzes the water quality, flood, erosion, and integrated scores of multiple Austin watersheds, including Waller Creek.Waller Creek Working Grou

Prospect and Markowitz Stochastic Dominance

Levy and Levy (2002, 2004) develop the Prospect and Markowitz stochastic dominance theory with S-shaped and reverse S-shaped utility functions for investors. In this paper, we extend Levy and Levy's Prospect Stochastic Dominance theory (PSD) and Markowitz Stochastic Dominance theory (MSD) to the first three orders and link the corresponding S-shaped and reverse S-shaped utility functions to the first three orders. We also provide experiments to illustrate each case of the MSD and PSD to the first three orders and demonstrate that the higher order MSD and PSD cannot be replaced by the lower order MSD and PSD. Prospect theory has been regarded as a challenge to the expected utility paradigm. Levy and Levy (2002) prove that the second order PSD and MSD satisfy the expected utility paradigm. In our paper we take Levy and Levy's results one step further by showing that both PSD and MSD of any order are consistent with the expected utility paradigm. Furthermore, we formulate some other properties for the PSD and MSD including the hierarchy that exists in both PSD and MSD relationships; arbitrage opportunities that exist in the first orders of both PSD and MSD; and that for any two prospects under certain conditions, their third order MSD preference will be ???the opposite??? of or ???the same??? as their counterpart third order PSD preference. By extending Levy and Levy's work, we provide investors with more tools for empirical analysis, with which they can identify the first order PSD and MSD prospects and discern arbitrage opportunities that could increase his/her utility as well as wealth and set up a zero dollar portfolio to make huge profit. Our tools also enable investors to identify the third order PSD and MSD prospects and make better choices.Prospect stochastic dominance, Markowitz stochastic dominance, risk seeking, risk averse, S-shaped utility function, reverse S-shaped utility function