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27 Mar 2024: Wan Yin Lim PhD student   Current situation Member of Chinese Annual Conference (CAC), Methodist Church of Malaysia   Subject Peacebuilding in Malaysia: An Interdisciplinary Examination of Chinese Methodist Peace Theology and Peace Studies &#xa0

30 Nov 2023: LIONEL D. SMITH __________________________________________________________________. Faculty of Law, University of Cambridge 10 West Road, Cambridge UK CB3 9DZ. Fellow of Gonville and Caius College, Cambridge CB2 1TA Telephone: ( 44) 01223 330079. lionel.smith@law.cam.ac.uk. EMPLOYMENT. 2022 – 2017-2023 2000-2022 2013-2016. Downing Professor of the Laws of England Faculty of Law, University of Cambridge Fellow, Gonville and Caius College, Cambridge Director, Cambridge Private Law Centre Courses taught: Equity (undergraduate level); Restitution, Advanced Private Law, English, Comparative and Transnational Secured Transactions Law (postgraduate level). Visiting Professor, Faculty of Law, University of Oxford. Courses taught: Trusts, Land Law (undergraduate level); Advanced Property and Trusts, Restitution (postgraduate level). Professor, Faculty of Law, McGill University. Full Professor (2007-2022); Sir William C. Macdonald Professor of Law (2014-2022); James McGill Professor of Law (2004-2014); Associate Professor (2000-2007); William Dawson Scholar in Law (2003-2004). 2007-2014: Director, Paul-André Crépeau Centre for Private and Comparative Law (until 2012, the Quebec Research Centre of Private and Comparative Law). 2001-2004:

13 Apr 2024: The Betty Behrens Seminar on Classics of Historiography. Wednesday 22 May 2024, 2pm-4pm. Michael Nylan. Distinguished Professor, University of California, Berkeley. Documents [Shū] (ca. 221 BC) by Fu Sheng. Two chapters (ch. 1, pp. 1-3; ch. 14, pp. 1-4) from the text in English follow. (out of a total of 30 that were in circulation in the Han). Ch. 1 is also in Chinese to see how much emendation Professor Nylan brings to the text, based on the extant sources. 1. Canon of Yao <a>1A.1 encomium in verse<a> Yao, Examiner of Old Heroes, named "Propagator of Merit," was impressive and clear-sighted. He knew how to put all things in order and at ease. Truly attentive to his duties, he was capable of relinquishing his powers and privileges to worthy men. His bright influence shone wide to earth's four corners, reaching above and below. He was able to make his instructive example shine forth, to draw the Nine Clans close to him. Once the Nine Clans were settled, then Yao distinguished by insignia the Hundred Families. And once the Hundred Families themselves became shining examples, that induced harmony in the myriad polities, at which point the masses flourished, and the era came to be at peace. <a>1A.2 account of Xi and He and

3 Apr 2024: 1. C U R R I C U L U M V I T A E. Wah Sung Vincent Mak（麥華嵩）. Address: Cambridge Judge Business School University of Cambridge Trumpington Street Cambridge CB2 1AG United Kingdom Tel: 44 (0)1223 764295 Email: v.mak@jbs.cam.ac.uk Faculty webpage: www.jbs.cam.ac.uk/people/vincent-mak/. PROFESSIONAL EXPERIENCE.  Professor of Marketing & Decision Sciences, Cambridge Judge Business School, University of Cambridge (since 2019; with tenure).  Reader (Associate Professor) in Marketing & Decision Sciences, Cambridge Judge Business School, University of Cambridge (2016-2019; with tenure).  University Lecturer (Assistant Professor) in Marketing & Decision Sciences, Cambridge Judge Business School, University of Cambridge (2014-2016; with tenure).  University Lecturer (Assistant Professor) in Marketing, Cambridge Judge Business School, University of Cambridge (2009-2014).  Visiting Assistant Professor, Department of Marketing, The Hong Kong University of Science and Technology (2008-2009). EDUCATION.  The Hong Kong University of Science and Technology.  2008 PhD in Marketing.  Visiting Scholar, The Fuqua School of Business, Duke University (January-May 2008).  University of Cambridge, Cambridge, UK

4 Dec 2023: A1-1 A1-2 A2-1 A2-2 B1 B2 C1 C2. 聞く. 身近な場面でよく使われ. る基本的な語句がわかる。. 限られた範囲であれば、自. 分の専門分野でよく使わ. れる語句がわかる。. 身近な場面でよく使われ. る簡単な表現がわかる。. 自分の専門分野で必要な. 基本的な語句や表現が. わかる。. 日常的な話題について短. くはっきり話されれば、. 大切なポイントを聞き取. ることができる。. 日常のコミュニケーショ. ンでよく使われる表現が. わかる。自分の専門分野. でよく使われる語句を. 含む簡単な指示や説明が. わかる。. よく知っている話題なら、メ. ディア（音声メディア・テレ. ビ・動画など）から、主な内容. やデータを推測しながら聞き. 取ることができる。自分の専. 門分野でよく耳にする指示や. 説明がだいたいわかる。. 自分の専門分野の話題につい. て、構成がまとまっていれば. 理解できる。特定のポイント. を聞き分け、示すことができ. る。メディア（動画・講義・ウ. ェビナーなど）から、主要な事.

5 Apr 2024: Small Data, Big Time—A retrospect of the first weeks of COVID-19. Qingyuan Zhao. [To be read before The Royal Statistical Society at the Society’s 2021 annual conference held inManchester on Wednesday, September 8th, 2021, the President, Professor Sylvia Richardson, in theChair]. Abstract. This article reviews some early investigations and research studies in the first weeks of the coron-avirus disease 2019 (COVID-19) pandemic from a statistician’s perspective. These investigations werebased on very small datasets but were momentous in the initial global reactions to the pandemic. Thearticle discusses the initial evidence of high infectiousness of COVID-19 and why that conclusion wasnot reached faster than in reality. Further reanalyses of some published COVID-19 studies show thatthe epidemic growth was dramatically underestimated by compartmental models, and the lack of fitcould have been clearly identified by simple data visualization. Finally, some lessons for statisticiansare discussed.Keywords: Infectious disease modeling; Selection bias; COVID-19; Model diagnostics. 1 Introduction. Starting from a regional disease outbreak in Wuhan, China, the coronavirus disease 2019 (COVID-19)rapidly grew into a once-in-a-lifetime

21 Nov 2023: Multilingual Models in Neural MachineTranslation. Guangyu Yang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Clare Hall August 2023. Dedicated to my loving parents. Declaration. I, Guangyu Yang of Clare Hall, being a candidate for the MPhil in Machine Learning andMachine Intelligence, hereby declare that this report and the work described in it are my ownwork, unaided except as may be specified below, and that the report does not contain materialthat has already been used to any substantial extent for a comparable purpose. All software used in this thesis was written in Python and PyTorch. The open-sourceHugging Face transformers library1 [47] was used for downloading pre-trained models andinference. The sacreBLEU library2 [26] was used to download datasets and evaluate theperformance of the models using the BLEU metric. The BLEURT library3 [34] was used toevaluate the performance of the models using the BLEURT metric. The OpenICL frameworkwas used to experiment with in-context learning. And the Direct Preference Optimizationcodebase4 [28] was used for unsupervised preference optimization with MBR. The

24 Nov 2023: Multilingual Models in Neural MachineTranslation. Guangyu Yang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Clare Hall August 2023. Dedicated to my loving parents. Declaration. I, Guangyu Yang of Clare Hall, being a candidate for the MPhil in Machine Learning andMachine Intelligence, hereby declare that this report and the work described in it are my ownwork, unaided except as may be specified below, and that the report does not contain materialthat has already been used to any substantial extent for a comparable purpose. All software used in this thesis was written in Python and PyTorch. The open-sourceHugging Face transformers library1 [47] was used for downloading pre-trained models andinference. The sacreBLEU library2 [26] was used to download datasets and evaluate theperformance of the models using the BLEU metric. The BLEURT library3 [34] was used toevaluate the performance of the models using the BLEURT metric. The OpenICL frameworkwas used to experiment with in-context learning. And the Direct Preference Optimizationcodebase4 [28] was used for unsupervised preference optimization with MBR. The