Can Christian identity and national identity be reconciled? For Christians in China, this question is particularly fraught. While Sinicization offers the indigenous church one path forward, it fails to provide a tenable solution for believers unwilling to submit their love of God under love of country. Dr. Jue Wang explores an alternative roadmap for Chinese Christian identity in the writings of Zhang Yijing. The editor of True Light, a Chinese Baptist publication, Zhang was also a Chinese patriot, Confucian, and life-long proponent of science and reason. Utilizing the lens of identity studies, Dr. Wang examines Zhang’s process of reconciling faith and culture in his quest to be both authentically Christian and authentically Chinese. This study offers a fascinating glimpse into the modern history of the Chinese church, while uncovering the significance of an often-overlooked Chinese Christian apologist. Zhang’s example offers encouragement and hope for believers around the world seeking to integrate social, cultural, and national identities under the lordship of Christ.
Irreducible to conventional labels usually applied to him, the Tang poet Du Fu (712–770) both defined and was defined by the literary, intellectual, and socio-political cultures of the Song dynasty (960–1279). Jue Chen not only argues in his work that Du Fu was constructed according to particular literary and intellectual agendas of Song literati but also that conventional labels applied to Du Fu do not accurately represent this construction campaign. He also discusses how Du Fu’s image as the greatest poet sheds unique light on issues that can deepen our understanding of the subtleties in the poetic culture of Song China.
This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
Image and Video Matting: A Survey provides a comprehensive review of existing image and video matting algorithms and systems, with an emphasis on the advanced techniques that have been recently proposed. Various methods are described and contrasted and finally tested against a uniform set of examples.
With the rapid development of economic globalization and information technology, the field of economic forecasting continues its expeditious advancement, providing business and government with applicable technologies. This book discusses various business intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, TEI@I, fuzzy systems, text mining, and many more. It serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.
This book introduces current perspectives on Rasch measurement theory with an emphasis on developing Rasch-based scales. Authors George Engelhard Jr and Jue Wang introduce Rasch measurement theory step by step, with chapters on scale construction, evaluation, maintenance, and use. Points are illustrated and techniques are demonstrated through an extended example: The Food Insecurity Experience (FIE) Scale.
This book introduces current perspectives on Rasch measurement theory with an emphasis on developing Rasch-based scales. Rasch measurement theory represents a paradigm shift in measurement theory away from classical test theory and creates a framework for scaling that can yield invariant measurement. Rasch Models for Solving Measurement Problems: Invariant Measurement in the Social Sciences is a broadly accessible text. Authors George Engelhard Jr and Jue Wang introduce Rasch measurement theory step by step, with chapters on scale construction, evaluation, maintenance, and use. Points are illustrated and techniques are demonstrated through an extended example: The Food Insecurity Experience (FIE) Scale. The Rasch analyses in the book are run using the Facets computer program. Facets syntax, and R code for the ERMA program created by the authors to obtain parameter estimates and to examine model-data fit, together with sample data sets are all available on a website for the book.
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