Peter G. Rowe

Yun Fu

Francesca R. Forlini

Chenghe Guan

Yingying Lu

The Mumbai Metropolitan Region and Palava City: a Brief Account and Evaluation

Peter Rowe

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.

From Roots to Contemporary Turns

Southeast Asian Modern

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.

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.

On Architecture in Seismic Regions

Thinking and Building on Shaky Ground

This book provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. Topics and features: includes perspectives from an international and interdisciplinary selection of pre-eminent authorities; presents balanced coverage of both detailed theoretical analysis and real-world applications; examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications; reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities; discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation.

Human-Centered Social Media Analytics

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Low-Rank and Sparse Modeling for Visual Analysis

Thomas S. Huang

Zhangyang Wang

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. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Deep Learning through Sparse and Low-Rank Modeling

Zhengming Ding

Handong Zhao

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.

Models and Applications

Learning Representation for Multi-View Data Analysis

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.

Yun Fu

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