Li Xiao

Audio and Visual Analytics in Marketing addresses questions around the application and use of audio/visual data analytics for business decision making in the post-digital economy. It does so by: (1) developing a framework for understanding the internal states of individuals based on audio/visual signals and incorporating works in the domain of audio/visual (A/V) data analytics into marketing research in order to identify future research opportunities; (2) providing an overview of methodologies that are commonly used in conducting research with A/V data; (3) providing a review of A/V analytics-based research in various business contexts; and (4) reviewing the business practices using A/V analytics and identifying the future trends in both research and business applications. The rest of the monograph is organized as follows: The authors first propose a framework for A/V data-based research in the business domain and discuss how A/V data analytics can be used to support business decision making in various contexts. They then provide an overview of the key techniques and tools used in A/V data analytics and discuss the procedures and key methodological questions. Finally, they discuss how the A/V analytics has been used in business practices and its trend and future development.

This groundbreaking book, Machine Learning Methods for Scientific Data Compression, delivers an essential exploration into the rapidly evolving field of data reduction for scientific applications. As scientific simulations generate petabytes of data, traditional compression methods falter in maintaining critical fidelity. This work introduces novel machine learning approaches, from advanced autoencoders to generative foundation models, all designed to achieve unprecedented compression ratios while rigorously guaranteeing the accuracy of both primary data and quantities of interest. Dive into comprehensive chapters covering autoencoders, constrained and guaranteed autoencoders, adaptive data reduction, and attention-based hierarchical methods. Discover the power of guaranteed conditional diffusion and the revolutionary potential of foundation models for scientific data. The book culminates in a unified framework for scalable, high-fidelity data reduction, showcasing practical GPU-accelerated pipelines and experimental results across diverse domains like climate modeling, turbulent flow, and plasma physics. This resource provides the tools and insights needed to accelerate scientific discovery by getting smarter faster with data. The book is a must-read for researchers, data scientists, and engineers grappling with the challenges of managing and analyzing colossal scientific datasets in the age of exascale computing.