This book constitutes the thoroughly refereed post-proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2000, held in Yorktown Heights, NY, USA, in August 2000. The 22 revised full papers presented together with 5 posters were carefully selected during two rounds of reviewing and improvement. All current aspects of parallel processing are addressed with emphasis on issues in optimizing compilers, languages, and software environments in high-performance computing.

Part of a new series on reproductive medicine, this book is a complete guide to assisted reproductive technology (ART). Divided into 39 chapters, the book covers all aspects of ART, providing clinicians with a step by step guide through the processes. The text covers ovarian stimulation, embryo transfer, imaging, hormone analysis, endometrial receptivity, surgical procedures and much more. Many different causes of infertility and their management through ART are described in detail. The book presents the latest advances in the field and each chapter includes key points and references for further reading. Clinical photographs, diagrams and tables further enhance the comprehensive text. Other titles in the series include: Practical Guide in Infertility, Practical Guide in Reproductive Surgery and Practical Guide in Andrology and Embryology. Key points Comprehensive guide to assisted reproductive technology Part of new series on reproductive medicine Covers numerous ART procedures for management of infertility Chapters include key points and detailed references for further reading

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book * Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more * Analyze and extract actionable insights from your social data using various Python tools * A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn * Understand the basics of social media mining * Use PyMongo to clean, store, and access data in MongoDB * Understand user reactions and emotion detection on Facebook * Perform Twitter sentiment analysis and entity recognition using Python * Analyze video and campaign performance on YouTube * Mine popular trends on GitHub and predict the next big technology * Extract conversational topics on public internet forums * Analyze user interests on Pinterest * Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.