Dead Ends Of Transition

After war, many countries, such as Bosnia, Kosovo, Afghanistan, or Iraq, the transition to a democratic market economy extremely difficult. This failure to thrive, Dead Ends of Transition demonstrates, is often the result of national reliance on foreign aid. Rentier states, the contributors to this study argue, have few incentives to respond to the needs of their societies. Taking a closer look at the policies of rentier economies, this book further identifies new ways in which these countries and their international partners could work together to ease the critical transition to democracy.

A riddle rarely makes sense the first time you hear it. The connection between Dane, a bully, and Billy D, a guy with Down Syndrome, doesn't even make sense the second time you hear it. But it's a collection of riddles that solidify their unlikely friendship. Dane doesn't know who his dad is. Billy doesn't know where his dad is. So when Billy asks for Dane's help solving the riddles his dad left in an atlas, Dane can't help but agree. The unmarked towns lead them closer to secrets of the past. But there's one secret Billy isn't sharing. It's a secret Dane might have liked to know before he stole his mom's car and her lottery winnings and set off on a road trip that will put him face to face with Billy's dad.

Success comes in many forms and in synthesis it can be a failure that results in their ultimate successful solutions. This long-awaited sequel to "Dead Ends and Detours" retains the proven concept while featuring over 20 new case studies of failed strategies and their (successful) solutions in natural product total synthesis. Additionally, computational models are used to discuss the problem in much more detail and to provide readers with additional information not found in the primary literature. The topics range from classic synthetic reactions (e.g. Diels Alder reaction), metal-mediated coupling reactions, metathesis, and asymmetric catalysis to the importance of protecting and activating groups. This book will benefit not only graduate students in organic chemistry but also advanced researchers as they gain knowledge derived from the step-by-step analysis of mistakes made in the past and, thus be able to improve their own chemical reaction planning. With its coverage of the most commonly applied reaction types, the book perfectly complements its predecessor, which focuses on general aspects, such as reactivity and selectivity.

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. - Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Life doesn't always go according to our best-laid plans.Let Staci Boden show you how to navigate the unknown wisely and well.Whether we like it or not, control is an illusion. You may want to be the architect of your life, but often living fully includes facing loss and things you'd rather avoid. Life is change. And when change comes knocking, it can feel overwhelming, especially with challenges like divorce, illness and death.But here's some good news: embracing the uncertainty inside change can become fertile ground for new beginnings. The key is to let go of control and follow the energy. To help you do this, Staci Boden draws upon 25 years of energy healing and somatic practice to introduce eight teachers: fear, awareness, choice, body, intuition, energy, intention, and surrender.You'll learn to call on the wisdom of your body, your intuition, of deep surrender, even the awareness that comes from fear, to navigate life as it is. In the end, you'll find a kind of power and resilience that you can carry into any life circumstance because it lives inside. Start today and discover the boundless power of moving from control to trust. This book will change your life if you let it. "At a time when all of us are seeking a way out of the chaos, this practical guide can help us embrace our fear of change by letting go and allowing Life's Mystery to reveal the way."-Jyoti Ma, Grandmother Vision Keeper, co-founder of Center for Sacred Studies "Staci Boden is the wise friend, the generous teacher, the compassionate soul-sister you've always wished for."-Sage Cohen, author of Fierce on the Page and The Productive Writer

Algorithmic Composition offers new ways of thinking about the organization of sound that we call music

Argues that South Africa’s apartheid system of racial segregation relied on an unexamined but interrelated system of sexed oppression that was at once both rigid and flexible.

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes