Jun Chen – författare

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:

It will be of great interest to researchers in computational finance, machine learning and data science.

About the Authors

Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:

It will be of great interest to researchers in computational finance, machine learning and data science.

About the Authors

Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

This book aims to summarize and report the major research achievements and validation results under the global land cover (GLC) initiative led by the Group Earth Observation (GEO). The first part of the book introduces the major tasks and challenges facing the validation of finer-resolution GLC maps and presents the concepts and overall framework of the GEO-led initiative. Chapters 2-5 provide systematic introductions to the major methodology of finer-resolution GLC map validation, including sampling design, reference data collection, sample labeling, and accuracy assessment. Chapter 6 introduces the online validation tools that have been developed, including their design, considerations, and functionalities. Chapter 7 presents the international validation practices and the results of validating GlobeLand30 at country, regional, and global scales. Future directions are also discussed in the Conclusion chapter.

Features

Global Land Cover Validation: Methodology, Tools, and Practices serves as a reference for those engaged in land cover validation, especially at a global scale, including students, professionals, researchers, and general practitioners. It is also an excellent resource for professionals involved in sustainable development monitoring, environmental change studies, natural resource management, disaster assessment and mitigation, and many other applications.

This book aims to summarize and report the major research achievements and validation results under the global land cover (GLC) initiative led by the Group Earth Observation (GEO). The first part of the book introduces the major tasks and challenges facing the validation of finer-resolution GLC maps and presents the concepts and overall framework of the GEO-led initiative. Chapters 2-5 provide systematic introductions to the major methodology of finer-resolution GLC map validation, including sampling design, reference data collection, sample labeling, and accuracy assessment. Chapter 6 introduces the online validation tools that have been developed, including their design, considerations, and functionalities. Chapter 7 presents the international validation practices and the results of validating GlobeLand30 at country, regional, and global scales. Future directions are also discussed in the Conclusion chapter.

Features

Global Land Cover Validation: Methodology, Tools, and Practices serves as a reference for those engaged in land cover validation, especially at a global scale, including students, professionals, researchers, and general practitioners. It is also an excellent resource for professionals involved in sustainable development monitoring, environmental change studies, natural resource management, disaster assessment and mitigation, and many other applications.

Focusing on “human-in-the-loop” and “human-on-the-loop” mechanisms, cognitive intelligent interaction, and the practical modeling, implementation, and application of limited human intervention in modern aerial systems, this book explores cooperative decision-making technologies between manned and unmanned aerial vehicles.

It systematically discusses two primary approaches to cooperative decision-making mechanisms: limited human intervention and cognitive intelligent interaction. The authors introduce modeling methods for applying limited intervention in typical mission scenarios, such as obstacle avoidance, threat mitigation, and attack decision-making. They also explain how to assess human workload and cognitive load in manned-unmanned operations. Additionally, it outlines interactive cognitive models that support situational awareness, threat evaluation, task allocation, route planning, and decision simulation. These insights address the growing need for effective human-machine collaboration in complex operational environments driven by rapid advancements in information science, control science, cognitive science, and artificial intelligence.

This title will appeal to researchers, engineers, and professionals specializing in command-and-control systems and intelligent decision-making systems. It will also serve as an essential reference for students and educators in information-related disciplines.

Focusing on “human-in-the-loop” and “human-on-the-loop” mechanisms, cognitive intelligent interaction, and the practical modeling, implementation, and application of limited human intervention in modern aerial systems, this book explores cooperative decision-making technologies between manned and unmanned aerial vehicles.

It systematically discusses two primary approaches to cooperative decision-making mechanisms: limited human intervention and cognitive intelligent interaction. The authors introduce modeling methods for applying limited intervention in typical mission scenarios, such as obstacle avoidance, threat mitigation, and attack decision-making. They also explain how to assess human workload and cognitive load in manned-unmanned operations. Additionally, it outlines interactive cognitive models that support situational awareness, threat evaluation, task allocation, route planning, and decision simulation. These insights address the growing need for effective human-machine collaboration in complex operational environments driven by rapid advancements in information science, control science, cognitive science, and artificial intelligence.

This title will appeal to researchers, engineers, and professionals specializing in command-and-control systems and intelligent decision-making systems. It will also serve as an essential reference for students and educators in information-related disciplines.

Despite numerous recent studies and exciting discoveries in the field, only limited treatment is available today for the victims of acute neurological injuries. Animal Models of Acute Neurological Injuries provides a standardized methodology manual designed to eliminate the inconsistent preparations and variability that currently jeopardizes advances in the field. Contributed by top experts and many original developers of the models, each chapter contains a step-by-step, proven procedure and visual aids covering the most commonly used animal models of neurological injury in order to highlight the practical applications of animal models rather than the theoretical issues. This intensive volume presents its readily reproducible protocols with great clarity and consistency to best aid neuroscientists and neurobiologists in laboratory testing and experimentation.

Comprehensive and cutting-edge, Animal Models of Acute Neurological Injuries is an ideal guide for scientists and researchers who wish to pursue this vital course of study with the proficiency and precision that the field requires.

The successful previous volume on this topic provided a detailed benchwork manual for the most commonly used animal models of acute neurological injuries including cerebral ischemia, hemorrhage, vasospasm, and traumatic brain and spinal cord injuries.  Animal Models of Acute Neurological Injuries II: Injury and Mechanistic Assessments aims to collect chapters on assessing these disorders from cells and molecules to behavior and imaging. These comprehensive assessments are the key for understanding disease mechanisms as well as developing novel therapeutic strategies to ameliorate or even prevent damages to the nervous system.  Volume 1 examines general assessments in morphology, physiology, biochemistry and molecular biology, neurobehavior, and neuroimaging, as well as extensive sections on subarachnoid hemorrhage, cerebral vasospasm, and intracerebral hemorrhage.  Designed to provide both expert guidance and step-by-step procedures, chapters serve to increase understanding in what, why, when, where, and how a particular assessment is used.

Accessible and essential, Animal Models of Acute Neurological Injuries II: Injury and Mechanistic Assessments will be useful for trainees or beginners in their assessments of acute neurological injuries, for experienced scientists from other research fields who are interested in either switching fields or exploring new opportunities, and for established scientists within the field who wish to employ new assessments.

The successful previous volume on this topic provided a detailed benchwork manual for the most commonly used animal models of acute neurological injuries including cerebral ischemia, hemorrhage, vasospasm, and traumatic brain and spinal cord injuries.  Animal Models of Acute Neurological Injuries II: Injury and Mechanistic Assessments aims to collect chapters on assessing these disorders from cells and molecules to behavior and imaging. These comprehensive assessments are the key for understanding disease mechanisms as well as developing novel therapeutic strategies to ameliorate or even prevent damages to the nervous system.  Volume 2 examines global cerebral ischemia, focal cerebral ischemia, and neonatal hypoxia-ischemia, as well as intensive sections covering traumatic brain injury and spinal cord injury.  Designed to provide both expert guidance and step-by-step procedures, chapters serve to increase understanding in what, why, when, where, and how a particular assessment is used.