This book systemically describes the mechanisms underlying the neural regulation of metabolism. Metabolic diseases, including obesity and its associated conditions, currently affect more than 500 million people worldwide. Recent research has shown that the neural regulation of metabolism is a central mechanism that controls metabolic status physiologically and pathophysiologically. The book first introduces the latest studies on the neural and cellular mechanisms of hypothalamic neurons, hypothalamic glial cells, neural circuitries, cellular signaling pathways, and synaptic plasticity in the control of appetite, body weight, feeding-related behaviors and metabolic disorders. It then summarizes the humoral mechanisms by which critical adipocyte-derived hormones and lipoprotein lipase regulate lipid and glucose metabolism, and examines the role of the hypothalamus-sympathetic nerve, a critical nerve pathway from CNS to peripheral nervous system (PNS), in the regulation of metabolism in multiple tissues/organs. Furthermore, the book discusses the functions of adipose tissue in energy metabolism. Lastly, it explores dietary interventions to treat neural diseases and some of the emerging technologies used to study the neural regulation of metabolism. Presenting cutting-edge developments in the neural regulation of metabolism, the book is a valuable reference resource for graduate students and researchers in the field of neuroscience and metabolism.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

Despite China's rise to the status of global power, many Chinese youths are anxious about their personal future, in large measure because the rapid changes have left them feeling adrift. This book, available in open access, provides a manifesto of intellectual activism that counsels China's young people to think by themselves and for themselves. Consisting of three conversations between Xiang Biao, a social anthropologist, and Wu Qi, a rising journalist, the book probes how China has reached its current stage and how young people can make changes. The conversations touch on issues of mobility, education, family, relations between the self and the authority, centers and margins, China, and the world. The Chinese version was named the "most impactful book of 2021" by Douban, China's premier website for rating books, films, and music. The English version is translated by David Ownby, who also penned an introduction.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

Despite China's rise to the status of global power, many Chinese youths are anxious about their personal future, in large measure because the rapid changes have left them feeling adrift. This book, available in open access, provides a manifesto of intellectual activism that counsels China's young people to think by themselves and for themselves. Consisting of three conversations between Xiang Biao, a social anthropologist, and Wu Qi, a rising journalist, the book probes how China has reached its current stage and how young people can make changes. The conversations touch on issues of mobility, education, family, relations between the self and the authority, centers and margins, China, and the world. The Chinese version was named the "most impactful book of 2021" by Douban, China's premier website for rating books, films, and music. The English version is translated by David Ownby, who also penned an introduction.

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world 'mean' to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity in which semantics are extracted, used, or both. Within these broad categories, we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics such as mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where semantics is or is likely to play a key role. In creating this survey, we hope to provide researchers across academia and industry with a comprehensive reference that helps facilitate future research in this exciting field.