Frame Theory In Data Science

This book establishes brand-new frame theory and technical implementation in data science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis. Given that data science is unanimously recognized as a core driver for achieving Sustainable Development Goals of the United Nations, these frame techniques bring fundamental changes to multi-channel data mining systems and support the development of digital Earth platforms. This book integrates the authors' frame research in the past twenty years and provides cutting-edge techniques and depth for scientists, professionals, and graduate students in data science, applied mathematics, environmental science, and geoscience.

As acceptance and commitment therapy (ACT) increases in popularity among clinicians, it becomes more and more vital to understand its theoretical basis, relational frame theory (RFT). RFT is a psychological theory of human language and cognition, developed by Steven C. Hayes. It focuses on how humans learn language and how language connects them to their environment. In essence, our thoughts, feelings, and behaviors are dependent on our experiences and the context that these experiences provide. Edited by leading relational frame theory (RFT) scholars, Simon Dymond, PhD, and Bryan Roche, PhD, Advances in Relational Frame Theory presents advances in all aspects of RFT research over the last decade, and provides a greater understanding of the core principles of acceptance and commitment therapy (ACT). The book also contains chapters written by Steven C. Hayes and Kelly Wilson, both research-active experts from the RFT community around the world. Because ACT is focused largely on accepting one’s thoughts, it is important to understand where these thoughts come from. And while many books on RFT are abstract and require extensive knowledge of behavior analysis, this is the first book to comprehensively but accessibly introduce RFT to ACT mental health professionals. Gaining a deeper knowledge of the relational concepts of RFT can help you understand why a person's behavior does not always match up with their self-professed values. Whether you are a mental health professional, or simply someone who is interested in the connection between language and experience, this book is an invaluable resource.

Hilbert space frames have long served as a valuable tool for signal and image processing due to their resilience to additive noise, quantization, and erasures, as well as their ability to capture valuable signal characteristics. More recently, finite frame theory has grown into an important research topic in its own right, with a myriad of applications to pure and applied mathematics, engineering, computer science, and other areas. The number of research publications, conferences, and workshops on this topic has increased dramatically over the past few years, but no survey paper or monograph has yet appeared on the subject. Edited by two of the leading experts in the field, Finite Frames aims to fill this void in the literature by providing a comprehensive, systematic study of finite frame theory and applications. With carefully selected contributions written by highly experienced researchers, it covers topics including: * Finite Frame Constructions; * Optimal Erasure Resilient Frames; * Quantization of Finite Frames; * Finite Frames and Compressed Sensing; * Group and Gabor Frames; * Fusion Frames. Despite the variety of its chapters' source and content, the book's notation and terminology are unified throughout and provide a definitive picture of the current state of frame theory. With a broad range of applications and a clear, full presentation, this book is a highly valuable resource for graduate students and researchers across disciplines such as applied harmonic analysis, electrical engineering, quantum computing, medicine, and more. It is designed to be used as a supplemental textbook, self-study guide, or reference book.

Relational frame theory, or RFT, is the little-understood behavioral theory behind a recent development in modern psychology: the shift from the cognitive paradigm underpinning cognitive behavioral therapy to a new understanding of language and cognition. Learning RFT presents a basic yet comprehensive introduction to this fascinating theory, which forms the basis of acceptance and commitment therapy. The book also offers practical guidance for directly applying it in clinical work. In the book, author Niklas Törneke presents the building blocks of RFT: language as a particular kind of relating, derived stimulus relations, and transformation of stimulus functions. He then shows how these concepts are essential to understanding acceptance and commitment therapy and other therapeutic models. Learning RFT shows how to use experiential exercises and metaphors in psychological treatment and explains how they can help your clients. This book belongs on the bookshelves of psychologists, psychotherapists, students, and others seeking to deepen their understanding of psychological treatment from a behavioral perspective.

This volume contains the proceedings of the AMS Special Sessions on Frames, Wavelets and Gabor Systems and Frames, Harmonic Analysis, and Operator Theory, held from April 16-17, 2016, at North Dakota State University in Fargo, North Dakota. The papers appearing in this volume cover frame theory and applications in three specific contexts: frame constructions and applications, Fourier and harmonic analysis, and wavelet theory.

This volume goes beyond theory and gives the empirical and conceptual tools to conduct an experimental analysis of virtually every substantive topic in human language and cognition, both basic and applied. It challenges behavioral psychology to abandon many of the specific theoretical formulations of its most prominent historical leader in the domain of complex human behavior, especially in human language and cognition, and approach the field from a new direction. It will be of interest to behavior theorists, cognitive psychologists, therapists, and educators.

DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.