From the award-winning author of The First Rule of Punk and Strange Birds, a dazzling novel about a young girl who collects the missing pieces of her origin story from the family of legendary luchadores she’s never met. A 2023 Pura Belpré Author Honor Book Twelve-year-old Adela “Addie” Ramírez has a big decision to make when her stepfather proposes adoption. Addie loves Alex, the only father figure she’s ever known, but with a new half brother due in a few months and a big school theater performance on her mind, everything suddenly feels like it’s moving too fast. She has a million questions, and the first is about the young man in the photo she found hidden away in her mother’s things. Addie’s sleuthing takes her to a New Mexico ranch, and her world expands to include the legendary Bravos: Rosie and Pancho, her paternal grandparents and former professional wrestlers; Eva and Maggie, her older identical twin cousins who love to spar in and out of the ring; Uncle Mateo, whose lucha couture and advice are unmatched; and Manny, her biological father, who’s in the midst of a career comeback. As luchadores, the Bravos’s legacy is strong. But being part of a family is so much harder—it’s about showing up, taking off your mask, and working through challenges together. COVER MAY VARY.
A 2018 Pura Belpré Author Honor Book The First Rule of Punk is a wry and heartfelt exploration of friendship, finding your place, and learning to rock out like no one’s watching. There are no shortcuts to surviving your first day at a new school—you can’t fix it with duct tape like you would your Chuck Taylors. On Day One, twelve-year-old Malú (María Luisa, if you want to annoy her) inadvertently upsets Posada Middle School’s queen bee, violates the school’s dress code with her punk rock look, and disappoints her college-professor mom in the process. Her dad, who now lives a thousand miles away, says things will get better as long as she remembers the first rule of punk: be yourself. The real Malú loves rock music, skateboarding, zines, and Soyrizo (hold the cilantro, please). And when she assembles a group of like-minded misfits at school and starts a band, Malú finally begins to feel at home. She'll do anything to preserve this, which includes standing up to an anti-punk school administration to fight for her right to express herself! Black and white illustrations and collage art by award-winning author Celia C. Pérez are featured throughout. "Malú rocks!" —Victoria Jamieson, author and illustrator of the New York Times bestselling and Newbery Honor-winning Roller Girl
From the award-winning author of The First Rule of Punk comes the story of four kids who form an alternative Scout troop that shakes up their sleepy Florida town. *"Writing with wry restraint that's reminiscent of Kate DiCamillo... a beautiful tale." —Kirkus Reviews, starred review When three very different girls find a mysterious invitation to a lavish mansion, the promise of adventure and mischief is too intriguing to pass up. Ofelia Castillo (a budding journalist), Aster Douglas (a bookish foodie), and Cat Garcia (a rule-abiding birdwatcher) meet the kid behind the invite, Lane DiSanti, and it isn't love at first sight. But they soon bond over a shared mission to get the Floras, their local Scouts, to ditch an outdated tradition. In their quest for justice, independence, and an unforgettable summer, the girls form their own troop and find something they didn't know they needed: sisterhood.
A veteran real estate agent shares simple but powerful techniques to connect with more customers, close more sales, and maximize success. There are more than 1.7 million real estate agents in the United States and Canada. Thousands of new agents enter the profession each year hoping to make a comfortable living. But more than 80 percent of them will not be successful. To do well in this business, you need to take your career seriously and equip yourself with training, information, and proven strategies. This guidebook provides you with the tools you need, including hundreds of marketing tips to help you ?nd business; advice on responding to objections from clients; the thirty answers to the most common questions youll be asked; strategies to ensure that open houses are successful; tips on how to interact with people on the phone and in person. While this guidebook o?ers hundreds of ideas, youll prefer certain marketing and selling techniques over others. The goal is to ensure that you have every strategy out there so you can sell and succeed. You should know what to say, when to say it, and how to say it. You will get the advice you need to close more sales with Rules for Real Estate Success.
This new edition has been extensively revised to reflect the progress in error control coding over the past few years. Over 60% of the material has been completely reworked, and 30% of the material is original. Convolutional, turbo, and low density parity-check (LDPC) coding and polar codes in a unified framework Advanced research-related developments such as spatial coupling A focus on algorithmic and implementation aspects of error control coding
Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: Neural Networks with Parallel and GPU Computing Deep Learning Optimize Neural Network Training Speed and Memory Improve Neural Network Generalization and Avoid Overfitting Create and Train Custom Neural Network Architectures Deploy Training of Neural Networks Perceptron Neural Networks Linear Neural Networks Hopfield Neural Network Neural Network Object Reference Neural Network Simulink Block Library Deploy Neural Network Simulink Diagrams
Contains research articles by nearly 40 leading mathematicians from North and South America, Europe, Africa, and Asia, presented at the Fourth International Conference on p-adic Functional Analysis held recently in Nijmegen, The Netherlands. Includes numerous new open problems documented with extensive comments and references.
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Big Data tools (Parallel Computing Toolbox). Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering-Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance. his book develops cluster analysis and pattern recognition
Publisher's Note: Products purchased from 3rd Party sellers are not guaranteed by the Publisher for quality, authenticity, or access to any online entitlements included with the product. For more than 30 years, Perez and Brady's Principles and Practice of Radiation Oncology has been the must-have standard reference for radiation oncologists and radiation oncology residents who need a comprehensive text covering both the biological and physical science aspects of this complex field as well as disease site-specific information on the integrated, multidisciplinary management of patients with cancer. The book has established itself as the discipline’s "text-of-record," belonging on the shelf of all of those working in the field. The Seventh Edition continues this tradition of excellence with extensive updates throughout, many new chapters, and more than 1,400 full-color illustrations that highlight key concepts in tumor pathogenesis, diagnosis, and targeted radiation therapy.
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques
Inside the Sixth Edition of this now-reference, you will discover encyclopedic coverage of topics ranging from basic science to sophisticated computer-based radiation therapy treatment planning and supportive care. The book's comprehensive scope and abundantly illustrated format provide you with better understanding of the natural history of cancer, the physical methods of radiation application, the effects of radiation on normal tissues, and the most judicious ways in which you can employ radiation therapy in patient care. Including epidemiology, pathology, diagnostic work-up, prognostic factors, treatment techniques, applications of surgery and chemotherapy, end results, and more. Increased emphasis on new approaches and technologies improve your understanding of three-dimensional treatment planning, intensity-modulated radiotherapy, combined modality therapy, and particle therapy. Digital version includes the complete text, index-based search, note sharing, regular content updates integrated into the text, and much more.
This volume contains research articles based on lectures given at the Seventh International Conference on $p$-adic Functional Analysis. The articles, written by leading international experts, provide a complete overview of the latest contributions in basic functional analysis (Hilbert and Banach spaces, locally convex spaces, orthogonality, inductive limits, spaces of continuous functions, strict topologies, operator theory, automatic continuity, measure and integrations, Banach and topological algebras, summability methods, and ultrametric spaces), analytic functions (meromorphic functions, roots of rational functions, characterization of injective holomorphic functions, and Gelfand transforms in algebras of analytic functions), differential equations, Banach-Hopf algebras, Cauchy theory of Levi-Civita fields, finite differences, weighted means, $p$-adic dynamical systems, and non-Archimedean probability theory and stochastic processes. The book is written for graduate students and research mathematicians. It also would make a good reference source for those in related areas, such as classical functional analysis, complex analytic functions, probability theory, dynamical systems, orthomodular spaces, number theory, and representations of $p$-adic groups.
Part One, the inaugural volume in the Fishes of the Western North Atlantic series, describes lancelets, hagfishes, lampreys, and sharks. Specialist authorships of its sections include detailed species descriptions with keys, life history and general habits, abundance, range, and relation to human activity, such as economic and sporting importance. The text is written for an audience of amateur and professional ichthyologists, sportsmen, and fishermen, based on new revisions, original research, and critical reviews of existing information. Species are illustrated by exceptional black and white line drawings, accompanied by distribution maps and tables of meristic data.
In rangelands and grasslands, land degradation has an immediate and local impact by disrupting ecosystems from functioning, threatening livelihoods and negatively affecting social cohesion. It also threatens productivity while dovetailing with the threats of climate change in these ecologically fragile areas. The understanding of land degradation in rangelands and grasslands is weak, which is attributed to a lack of robust data and a misunderstanding of management objectives. The day-to-day management of land by pastoral communities is intricately linked to local and traditional knowledge that needs to be taken into account when monitoring the health of ecosystems and designing management interventions. Sustainable Development Goal (SDG) 15 Life on land includes Land Degradation Neutrality (LDN) as a target, which requires that the process of degradation is halted and reversed. This publication presents a rationale for participatory approaches to achieve LDN in pastoral areas while showing how this can be achieved using the Participatory Rangelands and Grasslands Assessment (PRAGA) that has been piloted in Kenya, the Niger, Burkina Faso, Uruguay and Kyrgyzstan.
Non-Archimedean functional analysis, where alternative but equally valid number systems such as p-adic numbers are fundamental, is a fast-growing discipline widely used not just within pure mathematics, but also applied in other sciences, including physics, biology and chemistry. This book is the first to provide a comprehensive treatment of non-Archimedean locally convex spaces. The authors provide a clear exposition of the basic theory, together with complete proofs and new results from the latest research. A guide to the many illustrative examples provided, end-of-chapter notes and glossary of terms all make this book easily accessible to beginners at the graduate level, as well as specialists from a variety of disciplines.
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