Jing Gao

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data.In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neuralnetworks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

More specifically, emphasizing service-based networking, the book introduces structured processes for network planning, provides systematic approaches for network analysis and architecture, develops network planning specifications, and discusses high-level network architectural models from various perspectives.

This book covers network analysis and architecture for large-scale computer network planning. Networks and the Internet are essential for modern industries and societies. Building a new network, upgrading an existing network, or planning to use a public network requires integrating various network mechanisms and technologies in a cohesive fashion. This demands a deep understanding of the concepts, principles, processes, approaches, and good practices of advanced network planning. More specifically, emphasizing service-based networking, the book introduces structured processes for network planning, provides systematic approaches for network analysis and architecture, develops network planning specifications, and discusses high-level network architectural models from various perspectives. It also offers detailed discussions on component-based architecture about addressing, routing, performance, management, and security and privacy. Recent developments in data centers, virtualization,and cloud are also embedded into the network architecture. Moreover, the book includes a comprehensive introduction to building practical TCP/IP network communications via sockets with practical examples. The book is suitable for use as a textbook for senior undergraduate and postgraduate students or as a reference book for network practitioners looking to develop or enhance their skills in network planning.

Explore the "not traditional but personal" Sichuan flavors of Fly By Jing's cult favorite spicy and savory chili crisp through 85 hot recipes for everything from dumplings to cocktails to desserts. Born in Chengdu and raised everywhere, chef and entrepreneur Jing Gao has introduced America to the hot, tingly sensation of chili crisp and the Sichuan flavors that inspire it, first through her wildly successful Kickstarter campaign and currently through thousands of grocery stores across the United States. Now, in The Book of Sichuan Chili Crisp, Jing shows how nearly every dish can be elevated with Sichuan's complex flavors, taking you on a unique journey from her hometown to your own kitchen stove, all while sharing her personal story and reflections on this storied cuisine and the challenges she's encountered along the way. Rooted in tradition but adapted for the modern kitchen, these 85 recipes invite you to explore the nuances of Sichuan flavors and experiment with new ingredients. With gorgeous photography and punchy writing, Jing shows you how to incorporate these flavors in just about everything, including: snacks like Zhong Dumplings and Deviled Tea Eggsmains like Hongshao Carnitas Tacos, Fish Fragrant Crispy Eggplant, and Spicy Scallion Oil Noodlesdesserts and drinks like Chili Crisp Sundae with Fish Sauce Caramel Brittle, Poached Pear in Sichuan Pepper Syrup, and Baijiu Negroni The Book of Sichuan Chili Crisp is an ode to chili crisp and a story of resilience, breaking free from tradition, and writing new narratives. Grab yourself a jar of Sichuan Chili Crisp and dive in