This book helps your child further develop the visual skills necessar y to learn to read and write. In Excel English Early Ski lls: Learning About Sequencing, your child will learn to: f ollow instructions systematically follow a sequence of events recognise and write some basic symbols (eg. tick, cross, circle) identify the place and position of objects (eg. under, above, top, bottom) The activities in each book are divided into double pages. Each double page allows your child to practise one particu lar skill many times so that the skill is reinforced. A clear, easy-to-f ollow page design has been developed, with clear explanations to help bo th parents and children.

Task Sequencing and Instructed Second Language Learning provides theoretical rationales for, and empirical studies of, the effects of sequencing language learning tasks to maximize second language learning. Examples of task sequences, and both laboratory and classroom-based research into them, are presented. This is the first collection of so far under-researched studies on the effects of task sequencing, framed within the Cognition Hypothesis of Task-based Language Teaching (TBLT) and the SSARC model for task sequencing. Perspectives include -- laboratory-based and classroom-based research designs -- implications for teacher training -- laboratory and classroom research methods -- conversational interaction -- task sequencing and Task Based Language Teaching syllabus design

Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

Next-Generation Sequencing (NGS) is increasingly common and has applications in various fields such as clinical diagnosis, animal and plant breeding, and conservation of species. This incredible tool has become cost-effective. However, it generates a deluge of sequence data that requires efficient analysis. The highly sought-after skills in computational and statistical analyses include machine learning and, are essential for successful research within a wide range of specializations, such as identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine, and increased crop yields in agriculture.This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform specific analyses, from raw sequenced data to answer important biological questions using machine learning methods. It is an excellent hands-on material for lecturers who conduct courses in bioinformatics and as reference material for professionals. The chapters are standalone recipes making them suitable for readers who wish to self-learn selected topics. Readers gain the essential skills necessary to work on sequenced data from NGS platforms; hence, making themselves more attractive to employers who need skilled bioinformaticians.

This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.

Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this b

While public affairs faculty study administration and management techniques, few administrators of public affairs programs receive formal training in the nuts and bolts of academic administration. Even those faculty who come to academia after distinguished careers in managerial positions may not be ready for the very different (and difficult) environment of university administration. The Public Affairs Faculty Manual argues that public affairs as a field needs to ensure that knowledge about administration and management is applied to the running of its academic programs, and brings together major leaders in the discipline to explore key features of academic administration. Many of these leaders have served as Master of Public Administration (MPA) directors, chairs, and deans at the nation’s top public affairs programs. Crucial issues of academic administration discussed include the basics of public affairs programs, models of governance, roles of different administrative leaders, planning and budgeting for programs, navigating the accreditation process, assessing and improving student learning, ensuring social equity and cultural competency, mentoring faculty, developing curriculum, and helping provide service and applied research to community partners. Contributors have served as MPA directors, chairs, and deans at the nation’s topic public affairs programs. Themes running throughout the book’s chapters are examined, and additional resources to help manage public affairs programs are offered. This collection of essays and the strategies within it are designed to encourage faculty to assume positions of leadership in their programs and manage those programs in an effective, efficient, and fair manner. The Public Affairs Faculty Manual is required reading for new, seasoned, and aspiring academic administrators in public administration, public policy, and nonprofit management programs, as well as schools of government.

In the past decade, the field of comparative cognition has grown and thrived. No less rigorous than purely behavioristic investigations, examinations of animal intelligence are useful for scientists and psychologists alike in their quest to understand the nature and mechanisms of intelligence. Extensive field research of various species has yielded exciting new areas of research, integrating findings from psychology, behavioral ecology, and ethology in a unique and wide-ranging synthesis of theory and research on animal cognition. The Oxford Handbook of Comparative Cognition contains sections on perception and illusion, attention and search, memory processes, spatial cognition, conceptualization and categorization, problem solving and behavioral flexibility, and social cognition processes including findings in primate tool usage, pattern learning, and counting. The authors have incorporated findings and theoretical approaches that reflect the current state of the field. This comprehensive volume will be a must-read for students and scientists who want to know about the state of the art of the modern science of comparative cognition.