127 research outputs found
Shi jiu shi ji zhong ye ru zhe dui Jidu zong jiao zhi li jie: yi Yao Ying ji Wei Yuan wei zhong xin = Mid-19th century Confucians' understanding of Christianity : Yao Ying and Wei Yuan.
No full text本文旨在闡釋十九世紀中葉的儒者如何理解基督宗教。筆者以兩位中層官員（姚瑩與魏源）作為主要例子，說明儒者思想和信仰構成的世界觀對世俗世界和超越世界都有一套完整的理解，亦引伸出儒者「經世」的信念。本文認為儒者對基督宗教的理解與他們的世界觀有密不可分的關係，但由於理解的目的在於「經世」，他們亦會在一些情況下基於現實考慮或新資訊的出現而暫時放棄自身的觀感，並進而修正他們的世界觀。藉著相類案例的比較，本文嘗試說明十九世紀中葉的儒者並非單純以自身的信仰批判基督宗教，亦非純粹以帝國官員的立場以純粹的管治和外交原則應對異國的宗教。對他們而言，理解基督宗教的過程就是世界觀、新資訊與現實考慮三者互動的結果。This thesis attempts to explain Confucian understanding of Christianity in the mid-nineteeth century through the views of two government officials, Yau Yin and Wai Yuin, who based their beliefs on both the immanent and transcendent aspects of "Jinshi" or the philosophy of statecraft. This work asserts that the Confucian understanding of Christianity was very much tied to the officials’ perception of secular society. Further, with the increase in knowledge and information, their view of Christianity began to change. Chinese officials’ criticism of Christianity was based on a complex set of factors which included the influx of new ideas, information, government edicts, diplomatic considerations as well as their status in the imperial government. In other words, understanding of Chrisitanity in China developed and evolved with new understanding of historical realities.Detailed summary in vernacular field only.孔德維.Parallel title from English abstract.Thesis (M.Phil.) Chinese University of Hong Kong, 2015.Includes bibliographical references (leaves 289-341).Abstracts in English and Chinese.Kong Dewei
Estimation of train dwell time at short stops based on track occupation event data
No full textTrain dwell time is one of the most unpredictable components of railway operations mainly due to the varying volumes of alighting and boarding passengers. For reliable estimations of train running times and route conflicts on main lines it is however necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a big challenge for a more reliable, efficient and robust train operation. Previous research has shown that dwell time is highly dependent on the number of boarding and alighting passengers. However, the latter numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information, by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hour separately. The peak hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the previous trains. The off-peak hour dwell times are estimated using a non-parametric regression model. There are two major advantages of the proposed estimation model. The model does not need passenger flow data which is usually impossible to know in real time in practice. Also, detailed parameters of rolling stock configuration and platform layout are not required, which eases implementation.Transport & PlanningCivil Engineering and Geoscience
Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT
No full textFor the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewe
High‐Performance Ta2O5/Al‐Doped Ag Electrode for Resonant Light Harvesting in Efficient Organic Solar Cells
No full textPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113744/1/aenm201500768.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113744/2/aenm201500768-sup-0001-S1.pd
Asymptotic techniques for selective inference
No full textAsymptotic approaches are widely used in statistics. Generally, I recognize two applications of asymptotic. First, asymptotics can solve some problems which cannot be solved exactly in mathematics. For example, density and mass functions and distribution functions of some statistics often cannot be found exactly. Asymptotic approaches will be used for finding the asymptotic density and mass functions and distribution functions under such circumstances. The error between asymptotic methods and truth is controlled within tolerance, like O(1/n) or something else. Chapter 1 presents this kind of problem. The two-stage Mann-Whitney statistic has known mass and distribution functions. But these exact representations are given only recursively, and the recursion is complicated. It means that we cannot express them mathematically. With the help of an asymptotic method, the Edgeworth expansion, we can express the distribution functions. Moments and cumulants are necessary for the Edgeworth expansion and I focus on the calculation of them in Chapter 1.
The second use of asymptotics is to compare two different methods or functions and find how they are close. When various methods are proposed to approximate something, one may just determine whether they are asymptotically correct. If asymptotically, the methods are correct, the error between them should be determined. Furthermore, how close they are to the truth must be determined. Chapter 2 is a typical example of this kind of problem. The traditional approach is called the studentized bootstrap and the new one is the tilted bootstrap. We compare the two approaches in multi-dimension and conclude the difference between their p-values is o(1) based on some assumptions.
Chapters 3 and 4 discuss a significance test to perform a variable selection for regression. The test is called the covariance test. The test is based on the exponential distribution, but the statistic does not follow it exactly but asymptotically. We investigate the properties of the test statistic and proposeanother covariance test based on the gamma distribution. This topic is a combination of the two problems mentioned above. We compare all available methods and provide an alternative better approach.
Chapter 5 presents a method for calculating the order of error numerically. It is derived from Chapters 3 and 4. We have to find the order of error numerically when it is too hard to find it analytically. Many examples are illustrated to demonstrate effectiveness.Ph.D.Includes bibliographical reference
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