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A captivating blend of reportage and personal narrative that explores the untold history of women’s exercise culture--from jogging and Jazzercise to Jane Fonda--and how women have parlayed physical strength into other forms of power. For much of the twentieth century, sweating was considered “unladylike” and girls grew up believing physical exertion would cause their uterus to “fall out.” It was only in the Sixties that, thanks to a few forward-thinking fitness pioneers, women began to move en masse. In Let's Get Physical, journalist Danielle Friedman reveals the fascinating untold history of contemporary fitness culture, chronicling in vivid, cinematic prose how exercise evolved from a beauty tool pitched almost exclusively as a way to “reduce” into one millions have harnessed as a path to mental, emotional, and physical well-being. Let’s Get Physical takes us into the workout studios and onto the mats to reclaim these forgotten origin stories—and shine a spotlight on the trailblazers who made it possible for women to move. Each chapter uncovers the birth of an fitness movement that laid the foundation for working out today: the invention of the barre method in the Swinging Sixties, jogging’s path to liberation in the Seventies, the explosion of aerobics and weight-training in the Eighties, the rise of yoga in the Nineties, and the ongoing push for a more socially inclusive fitness culture—one that celebrates every body. Ultimately, it tells the story of how women discovered the joy of physical competence and strength—and how, by moving together to transform fitness from a privilege into a right, we can create a more powerful sisterhood.
Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization
This book offers a timely and engaging account of how technologies of communication media impact nationalist challenges to global order, shedding new light on how they matter, how they have changed, and how their evolution transforms the conditions of possibility for nationalist order challengers. In the 21st century, we have become accustomed to close entanglements between resurgent nationalism and digital media. In Mediatizing the Nation, Ordering the World, Andrew Dougall shows that the relationship between media and nationalist order contestation is far older. Comparing Trump's breakthrough in the 21st century United States with a similar - but unsuccessful - movement in 19th century Britain, the book argues that communication media shaped these episodes by differently patterning the constitution and distribution of meaning on which they relied. Underpinning this argument is a novel theorization of media in world politics that draws on insights from media and communications scholarship, in addition to international relations. Among the book's key contributions are to explain how media affect vertical challenges to the structure of international orders; to reframe IR's theoretical engagement with the relationship between media and order; and to situate the internet within a longer history of this relationship, contributing to a more balanced view of its impact.
This book constitutes the refereed proceedings of the 19th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2006, held in Annecy, France, June 2006. The book presents 134 revised full papers together with 3 invited contributions, organized in topical sections on multi-agent systems, decision-support, genetic algorithms, data-mining and knowledge discovery, fuzzy logic, knowledge engineering, machine learning, speech recognition, systems for real life applications, and more.
This book is an interdisciplinary theoretical effort to explain the mind-body problem. Conscious mind is the hard problem to be explained and is the utmost existential question for any scientific mind. Neither a reductionist identity theory nor a commonsense-religious dualism can answer the problem. Human cognitive system can have a natural explanation rather than a religious description. To reduce the mind as what the brain does is too premature and to separate the mind and brain as two independent realities is too trivial. The hypothesis of the book identifies the conscious mind with the emergent functionality of the human brain. And, this is definitely an approximate guess. This informed guess is a challenge to many previously established theories and is an invitation for further research. It demystifies the age old homunculus mind and does not explains it away. To elaborate the theme, the author has incorporated themes such as complex system dynamics, evolution, cosmology, thermodynamics, information and emergence. The philosophical discussion on the first three chapters govern as an intuitive background for the theoretical development in further chapters. It affirms that the mind and brain are neither two dichotomized substances nor are they one and same substance. Chapters from four to eight deal with various themes from natural science with respect to the theme of mind-brain. they involve system dynamics, cosmology, thermodynamics, evolutionary theory and information model. Last chapter assimilates the discussions of previous chapters to propose the key hypothesis of the book viz. mind-brain is the emergent functionality of the human brain which is the matter-energy-information complex system. The universe, which itself is a matter-energy-information system, at least in one occasion, becomes conscious of itself through humans.
This book constitutes the refereed papers of the 2nd International Conference on Contemporary Computing, which was held in Noida (New Delhi), India, in August 2009. The 61 revised full papers presented were carefully reviewed and selected from 213 submissions and focus on topics that are of contemporary interest to computer and computational scientists and engineers. The papers are organized in topical sections on Algorithms, Applications, Bioinformatics, and Systems.
Strength Training Past 50 is the authoritative guide for active adults. The all-new third edition features 83 exercises for free weights, machines, kettlebells, stability balls, and elastic bands and 30 programs for endurance, speed, and strength.
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.