This volume examines the diversified and challenging experiences of Chinese international STEM doctoral students at Australian institutes of higher education, exploring how intersections between research, personal life, and social experiences can be negotiated to achieve academic success and personal transformation. By drawing on a range of qualitative and longitudinal research methods, the book foregrounds student narratives and utilizes a novel three-dimensional multi-world framework as an effective approach for understanding student experiences in a holistic way. It integrates Chinese philosophical perspectives and theories in the fields of educational psychology, international education, and doctoral education to interpret the nuances, complexity, and particularities of the cross-cultural STEM PhD experience, highlighting the importance of the supervisor-mentee relationship and the role of students' cultural, social, and philosophical values in supporting their successful completion of the PhD degree. The analysis thus provides new insights into the ways in which these experiences vary across students, and might apply in other national contexts, and to non-STEM student cohorts. This book will be a valuable resource for researchers and academics engaged in cross-cultural education, the sociology of education, and international and comparative education. It will be of particular interest to those with a focus on international doctoral education and cultural Asian studies.

The goal of data compression is to reduce the number of bits needed to represent useful information. Neural, or learned compression, is the application of neural networks and related machine learning techniques to this task. This monograph aims to serve as an entry point for machine learning researchers interested in compression by reviewing the prerequisite background and representative methods in neural compression. Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial networks. This monograph introduces this field of research to a broader machine learning audience by reviewing the necessary background in information theory (e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image quality assessment, perceptual metrics), and providing a curated guide through the essential ideas and methods in the literature thus far. Instead of surveying the vast literature, essential concepts and methods in neural compression are covered, with a reader in mind who is versed in machine learning but not necessarily data compression.