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9 Jul 2023: THE 3RD ASIA PACIFIC REGION ALTERNATIVE FINANCE INDUSTRY REPORTNovember 2018. In collaboration with:. The Cambridge Centre for Alternative Finance (CCAF) is an international and interdisciplinary research centre based at the University of Cambridge Judge Business School. It is dedicated to the study of innovative instruments, channels, and systems emerging outside of traditional finance. This includes, among others, crowdfunding, marketplace lending, alternative credit and investment analytics, alternative payment systems, cryptoasset, distributed ledger technology (e.g. blockchain) as well as related regulations and regulatory innovations (e.g. sandboxes and RegTech). TABLE OF CONTENTS. Research Team. 5. Acknowledgements. 8. Forewords. 14CCAF. 14AIF, Zhejiang University. 15ADBI. 16KPMG. 17Invesco. 18. Executive Summary. 19. Methodology. 22. Chapter 1: Market Fundamentals. 25Size and Growth of the Asia Pacific Alternative Finance Market. 25Geography of Asia Pacific Alternative Finance. 28Diversity of Models in Asia Pacific: A working Taxonomy. 30Alternative Finance Market Volumes by Model in China. 31Alternative Finance Market Volumes by Model in Asia Pacific. 33The Use of Online Alternative Finance by Businesses. 36. China.

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