On any given day, nearly half a million children are served by foster care services in the U.S. at an annual cost of over $25 billion. Growing demand and shrinking funds have so greatly stressed the child welfare system that calls for orphanages have re-entered the public debate for the first time in nearly half a century. New ideas are desperately needed to transform a system in crisis, guarantee better outcomes for children in foster care, and reduce the need for out-of-home care in the first place. Yet little is known about what works in foster care. Very few studies have examined how alumni have fared as adults or tracked long-term health effects, and even fewer have directly compared different foster care services. In one of the most comprehensive studies of adults formerly in foster care ever conducted, the Northwest Foster Care Alumni Study found that quality foster care services for children pay big dividends when they grow into adults. Key investments in highly trained staff, low caseloads, and robust supplementary services can dramatically reduce the rates of mental disorders and substance abuse later in life and increase the likelihood of completing education beyond high school and remaining employed. The results of this unparalleled study document not only the more favorable outcomes for youth who receive better services but the overall return when an investment is made in high quality foster care: every dollar invested in a child generates $1.50 in benefits to society. These findings form the core of this book's blueprint for reform. By keeping more children with their families and investing additional funds in enhanced foster care services, child welfare agencies have the opportunity to greatly improve the health, well being, and economic prospects for foster care alumni. What Works in Foster Care? presents a model foster care program that promises to revolutionize the way policymakers, administrators, case workers, and researchers think about protecting our most vulnerable youth.

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Abstract: "This report describes the design, construction and field evaluation of State-of-the-art eddy correlation instruments. Energy balance results over Eucalypt forest and over grassland are evaluated and compared. Two sources of error in the instrumentation are identified. In the infrared hygrometer 2 mechnical shortcomings may give rise to errors in the measurement of the latent heat flux. However, it is not possible to quantify these problems. A further problem in the instrumentation arises from numerical truncation of calculations performed in the OHIO microcomputer. Errors of 5-10% are predicted using a simulation model of the calculation procedure (A)." -- verso p.

Abstract: "Viruses, trojan horses, and other malware are a growing problem for computer users, but current tools and research do not adequately aid users in fighting these threats. One approach to increasing security is to partition all applications and data based on general task types, or 'roles, ' such as 'Personal, ' 'Work, ' and 'Communications.' This can limit the effects of malware to a single role rather than allowing it to affect the entire computer. We are developing a prototype to investigate the usability of this security model. Our initial investigation uses cognitive walkthrough and think-aloud user studies of paper prototypes to look at this model in the context of realistic tasks, and to compare different user interface mechanisms for managing data and applications in a role-based system. For most participants, our interface was simple to understand and use. In addition to a security model that is intrinsically useful, we believe development of this system will inform issues in the design and implementation of usable security interfaces, such as refinement of design guidelines."