This book focuses on ways to better manage and prevent aircraft-based homicide events while in flight using alternate technology to replace the Cockpit Voice Recorder (CVR) and/or Digital Flight Data Recorder (DFDR) functions. While these events are infrequent, the implementation of real-time predictive maintenance allows aircraft operators to better manage both scheduled and unscheduled maintenance events. Aviation Safety and Security: Utilizing Technology to Prevent Aircraft Fatality explores historical events of in-flight homicide and includes relevant accident case study excerpts from the National Transportation Safety Board (NTSB) and Air Accidents Investigation Branch (AAIB). FEATURES Explores historical events of in-flight homicide and offers solutions for ways to mitigate risk Explains how alternate technologies can be implemented to address in-flight safety issues Demonstrates that metrics for change are not solely for safety but also for financial savings for aircraft operation Includes relevant accident case study excerpts from the NTSB and AAIB Expresses the need for real-time predictive maintenance Stephen J Wright is an academic Professor at the faculty of Engineering and Natural Sciences at Tampere University, Finland, specializing in aviation, aeronautical engineering, and aircraft systems.

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.

Creation or evolution? Did God speak everything into existence or was it all made through natural processes? Does it really matter, anyway? Dr. Jeff Duncan, the newly hired biology professor at Grogan University, is about to discover the truth. The Genesis Creation, Noah's Ark, the Tower of Babel...Jeff was taught all the classic Bible stories as a child, and although he accepted them at face value, he was never taught how to defend his faith, nor were his Sunday school teachers able to provide answers to the many questions he asked. Faced with a series of life-altering events, Jeff is forced to choose between his childhood faith and the evolutionary biology he learned in college and has now been hired to teach. If the historical accounts in Genesis are not true, then what about the rest of the Bible? Jeff begins to question everything he had been taught as a child and almost denies his faith altogether, but God has something else in store for him. Why couldn't Jeff ever establish a firm foundation as a child and understand his faith back then? Shouldn't the people who were teaching him in church every Sunday be able to answer his questions? Were his Sunday school teachers deceiving him all those years by just telling about Bible "stories," or, now as a professor of evolution, is Jeff part of...the deception?

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.