Yun Fu

Building with earthquakes is a familiar yet persistent design problem for resilient construction on all continents. This book elaborates on various factors for earthquake-resilient architecture in six thematic chapters that explore the design strategies of lightness, quickness, exactitude, visibility, multiplicity and consistency. These factors allow designers to develop contextual solutions that marry technical know-how with social and cultural understanding, ranging in scale from buildings to furniture and urban master plans. 120 case studies from roughly 30 countries, including some highly prestigious buildings, provide a comprehensive overview of the different design strategies.

Southeast Asian modernity – an overview Peter Rowe and Yun Fu’s second volume on the modernization of architecture in the Far East deals with Southeast Asia and Austronesia, including the 12 nation states of Vietnam, Cambodia, Laos, Myanmar, Thailand, Malaysia, Singapore, Indonesia, Brunei, East Timor, Philippines and Taiwan, as well as the ocean peoples of Polynesia, Melanesia and Micronesia. The modern architecture of these culturally and nationally heterogenous regions echoes local vernacular traditions and colonial as well as postcolonial hegemonies from both the East and the West. The book tells the stories of these separate roots and their culmination into contemporary architectural production, analyzing the distinctiveness and quality of approx. 65 building projects that have emerged in the past half century. Second volume of the history of Modern architecture in the Far East Excellent photographic presentations of approx. 65 buildings Important contribution on the globalization of architecture

Korean modernity – a defining overview The development of modern architecture in Korea and, more recently, South Korea, is closely tied to the country’s dramatic transformations since the late 19th century. The authors interrogate major periods from the Late Joseon Dynasty to the vibrant democratic present, showing how architecture, by making technological and stylistic leaps, has played a important role in the construction of the nation’s identity. The architectural analyses, ranging from Hwaseong Fortress to 21st-century constructions like Paju Book City, Ssamziegil Shopping Center, the Boutique Monaco skyscraper, and the Bauzium Sculpture Museum, focus on buildings in which the formation of a specifically Korean modernism is particularly observable. The appendix includes biographical descriptions of major architectural figures. Emergence and development of modern architecture in Korea Written by a team with unique expertise in East-Asian architecture and urbanism A timely contribution to the topic of identity in architecture

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.

This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.