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Conducting research with children raises significant ethical and practical difficulties; when the context is rural China, where there has been no tradition of qualitative research with children, these become especially heightened. This article, written by a student and her supervisor, introduces a pilot study conducted in 2018 as part of a Master's degree programme at a Scottish university. The study was designed to trial two child participatory methods with the aim of scaling these up in a full PhD project; the research focused on the experiences and needs of 'left-behind children' in a town of South-West China. The study threw up a number of challenges for the student which are explored in the article. Whilst not wishing to over-claim on the basis of a student project, we suggest that these highlight the reality that methodologies and 'good practice' guidelines developed in a 'Western'/'minority world' context may not always be wholly compatible with a very different research environment such as this one. This conclusion presents a significant challenge for all those who are conducting research with children in the 'Global South'/'majority world', as well as for those who are supporting research students who may experience similar dilemmas in the 'real world' of research.

For the 50th Anniversary of the Academy of Marketing Science, the 2021 AMS Annual Conference and Marketing World Congress celebrates the history of marketing while also exploring its future. This includes research on possible new theory discoveries and findings that could lead to more efficient and impactful responses by marketers to the current multi-faceted global challenge array. The volume proposes that marketers strive to continue to offer value in a socially responsible way to the consumers within. Articles in this volume explore the influence of marketing innovations leveraged by the rising influence of artificial intelligence, virtual reality, mechanamorphics, a proliferation of data, changing economic power concentration, and a myriad of other factors. Founded in 1971, the Academy of Marketing Science is an international organization dedicated to promoting timely explorations of phenomena related to the science of marketing in theory, research, and practice. Among its services to members and the community at large, the Academy offers conferences, congresses, and symposia that attract delegates from around the world. Presentations from these events are published in this Proceedings series, which offers a comprehensive archive of volumes reflecting the evolution of the field. Volumes deliver cutting-edge research and insights, complementing the Academys flagship journals, the Journal of the Academy of Marketing Science (JAMS) and AMS Review. Volumes are edited by leading scholars and practitioners across a wide range of subject areas in marketing science.

Intro -- Preface to the Second Edition -- Teaching and Learning from This Book -- Introduction to Autonomous Driving -- 1.1 Autonomous Driving Technologies Overview -- 1.2 Autonomous Driving Algorithms -- 1.2.1 Sensing -- 1.2.2 Perception -- 1.2.3 Object Recognition and Tracking -- 1.2.4 Action -- 1.3 Autonomous Driving Client System -- 1.3.1 Robot Operating System (ROS) -- 1.3.2 Hardware Platform -- 1.4 Autonomous Driving Cloud Platform -- 1.4.1 Simulation -- 1.4.2 HD Map Production -- 1.4.3 Deep Learning Model Training -- 1.5 It Is Just the Beginning -- Autonomous Vehicle Localization -- 2.1 Localization with GNSS -- 2.1.1 GNSS Overview -- 2.1.2 GNSS Error Analysis -- 2.1.3 Satellite-Based Augmentation Systems -- 2.1.4 Real-Time Kinematic and Differential GPS -- 2.1.5 Precise Point Positioning -- 2.1.6 GNSS INS Integration -- 2.2 Localization with LiDAR and High-Definition Maps -- 2.2.1 LiDAR Overview -- 2.2.2 High-Definition Maps Overview -- 2.2.3 Localization with LiDAR and HD Map -- 2.3 Visual Odometry -- 2.3.1 Stereo Visual Odometry -- 2.3.2 Monocular Visual Odometry -- 2.3.3 Visual Inertial Odometry -- 2.4 Dead Reckoning and Wheel Odometry -- 2.4.1 Wheel Encoders -- 2.4.2 Wheel Odometry Errors -- 2.4.3 Reduction of Wheel Odometry Errors -- 2.5 Sensor Fusion -- 2.5.1 CMU Boss for Urban Challenge -- 2.5.2 Stanford Junior for Urban Challenge -- 2.5.3 Bertha from Mercedes Benz -- 2.6 References -- Perception in Autonomous Driving -- 3.1 Introduction -- 3.2 Datasets -- 3.3 Detection -- 3.4 Segmentation -- 3.5 Stereo, Optical Flow, and Scene Flow -- 3.5.1 Stereo and Depth -- 3.5.2 Optical Flow -- 3.5.3 Scene Flow -- 3.6 Tracking -- 3.7 Conclusion -- 3.8 References -- Deep Learning in Autonomous Driving Perception -- 4.1 Convolutional Neural Networks -- 4.2 Detection -- 4.3 Semantic Segmentation -- 4.4 Stereo and Optical Flow -- 4.4.1 Stereo.

AbstractPersonalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy and efficiency of the recommendation algorithms or are more concerned on ways to reduce perceived risks and thus increase consumer satisfaction. Unlike these studies, our study begins from the decision‐making process of consumers, using consumers' two‐stage decision‐making system and preference inconsistency theory as a basis, to reveal the mechanisms involved in consumers' acceptance of recommendations. This paper analyzes the effect of personalized recommendations from two angles, recommendation timing and product portfolio, tries to point out differences in consumer preferences between similar products and related products, and verifies that consumers demand diversity in the recommended content. The study analyzes differences in the acceptance of personalized recommendations between practical products and hedonic products and discovers that recommendations of hedonic products are more effective than that of practical products. Based on the research earlier, the study provides suggestions on how to better plan and operate a personalized recommendation system. Copyright © 2016 John Wiley & Sons, Ltd.