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| data science domain emphasis berkeley: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science domain emphasis berkeley: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| data science domain emphasis berkeley: Digital Humanities Looking at the World Sílvia Araújo, |
| data science domain emphasis berkeley: Targeted Learning Mark J. van der Laan, Sherri Rose, 2011-06-17 The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies. |
| data science domain emphasis berkeley: Discrete Choice Methods with Simulation Kenneth Train, 2009-07-06 This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing. |
| data science domain emphasis berkeley: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| data science domain emphasis berkeley: Introduction to Embedded Systems, Second Edition Edward Ashford Lee, Sanjit Arunkumar Seshia, 2017-01-06 An introduction to the engineering principles of embedded systems, with a focus on modeling, design, and analysis of cyber-physical systems. The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes. The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems. |
| data science domain emphasis berkeley: System Design, Modeling, and Simulation Claudius Ptolemaeus, 2013-09-27 This book is a definitive introduction to models of computation for the design of complex, heterogeneous systems. It has a particular focus on cyber-physical systems, which integrate computing, networking, and physical dynamics. The book captures more than twenty years of experience in the Ptolemy Project at UC Berkeley, which pioneered many design, modeling, and simulation techniques that are now in widespread use. All of the methods covered in the book are realized in the open source Ptolemy II modeling framework and are available for experimentation through links provided in the book. The book is suitable for engineers, scientists, researchers, and managers who wish to understand the rich possibilities offered by modern modeling techniques. The goal of the book is to equip the reader with a breadth of experience that will help in understanding the role that such techniques can play in design. |
| data science domain emphasis berkeley: Attention Deficit Hyperactivity Disorder Lily Trokenberg Hechtman, 2017 Comprehensive description of adult outcome in educational, occupational, emotional, social, substance use, legal, antisocial functioning is described via the best well-controlled prospective follow-up studies of children with ADHD into adulthood. Predictors of outcome, e.g., medication and psychosocial treatment, IQ, severity of ADHD, comorbidity, SES, parental pathology and family functioning are all explored. Prognosis and issues that need to be addressed to promote more positive outcome are thus addressed. |
| data science domain emphasis berkeley: The Discipline of Organizing: Professional Edition Robert J. Glushko, 2014-08-25 Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the Internet of Things, and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead. |
| data science domain emphasis berkeley: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data. |
| data science domain emphasis berkeley: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science domain emphasis berkeley: Sophie's World Jostein Gaarder, 2007-03-20 A page-turning novel that is also an exploration of the great philosophical concepts of Western thought, Jostein Gaarder's Sophie's World has fired the imagination of readers all over the world, with more than twenty million copies in print. One day fourteen-year-old Sophie Amundsen comes home from school to find in her mailbox two notes, with one question on each: Who are you? and Where does the world come from? From that irresistible beginning, Sophie becomes obsessed with questions that take her far beyond what she knows of her Norwegian village. Through those letters, she enrolls in a kind of correspondence course, covering Socrates to Sartre, with a mysterious philosopher, while receiving letters addressed to another girl. Who is Hilde? And why does her mail keep turning up? To unravel this riddle, Sophie must use the philosophy she is learning—but the truth turns out to be far more complicated than she could have imagined. |
| data science domain emphasis berkeley: Fundamentals of Wireless Communication David Tse, Pramod Viswanath, 2005-05-26 This textbook takes a unified view of the fundamentals of wireless communication and explains cutting-edge concepts in a simple and intuitive way. An abundant supply of exercises make it ideal for graduate courses in electrical and computer engineering and it will also be of great interest to practising engineers. |
| data science domain emphasis berkeley: How Learning Works Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman, 2010-04-16 Praise for How Learning Works How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning. —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching. —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues. —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book. —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning |
| data science domain emphasis berkeley: The Adult Learner Malcolm S. Knowles, Elwood F. Holton III, Richard A. Swanson, RICHARD SWANSON, Petra A. Robinson, 2020-12-20 How do you tailor education to the learning needs of adults? Do they learn differently from children? How does their life experience inform their learning processes? These were the questions at the heart of Malcolm Knowles’ pioneering theory of andragogy which transformed education theory in the 1970s. The resulting principles of a self-directed, experiential, problem-centred approach to learning have been hugely influential and are still the basis of the learning practices we use today. Understanding these principles is the cornerstone of increasing motivation and enabling adult learners to achieve. The 9th edition of The Adult Learner has been revised to include: Updates to the book to reflect the very latest advancements in the field. The addition of two new chapters on diversity and inclusion in adult learning, and andragogy and the online adult learner. An updated supporting website. This website for the 9th edition of The Adult Learner will provide basic instructor aids including a PowerPoint presentation for each chapter. Revisions throughout to make it more readable and relevant to your practices. If you are a researcher, practitioner, or student in education, an adult learning practitioner, training manager, or involved in human resource development, this is the definitive book in adult learning you should not be without. |
| data science domain emphasis berkeley: Cornerstones of Attachment Research Robbie Duschinsky, 2020 This is an open access title available under the terms of a [CC BY-NC-ND 4.0 International] licence. It is free to read at Oxford Clinical Psychology Online and offered as a free PDF download from OUP and selected open access locations. Attachment theory is among the most popular theories of human socioemotional development, with a global research community and widespread interest from clinicians, child welfare professionals, educationalists and parents. It has been considered one of the most generative contemporary ideas about family life in modern society. It is one of the last of the grand theories of human development that still retains an active research tradition. Attachment theory and research speak to fundamental questions about human emotions, relationships and development. They do so in terms that feel experience-near, with a remarkable combination of intuitive ideas and counter-intuitive assessments and conclusions. Over time, attachment theory seems to have become more, rather than less, appealing and popular, in part perhaps due to alignment with current concern with the lifetime implications of early brain development Cornerstones of Attachment Research re-examines the work of key laboratories that have contributed to the study of attachment. In doing so, the book traces the development in a single scientific paradigm through parallel but separate lines of inquiry. Chapters address the work of Bowlby, Ainsworth, Main and Hesse, Sroufe and Egeland, and Shaver and Mikulincer. Cornerstones of Attachment Research utilises attention to these five research groups as a lens on wider themes and challenges faced by attachment research over the decades. The chapters draw on a complete analysis of published scholarly and popular works by each research group, as well as much unpublished material. |
| data science domain emphasis berkeley: Promoting Social and Emotional Learning Maurice J. Elias, 1997 The authors draw upon scientific studies, theories, site visits, nd their own extensive experiences to describe approaches to social and emotional learning for all levels. |
| data science domain emphasis berkeley: Computer Architecture John L. Hennessy, David A. Patterson, 2017-11-23 Computer Architecture: A Quantitative Approach, Sixth Edition has been considered essential reading by instructors, students and practitioners of computer design for over 20 years. The sixth edition of this classic textbook from Hennessy and Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, is fully revised with the latest developments in processor and system architecture. The text now features examples from the RISC-V (RISC Five) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard. It also includes a new chapter on domain-specific architectures and an updated chapter on warehouse-scale computing that features the first public information on Google's newest WSC. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design. - Winner of a 2019 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association - Includes a new chapter on domain-specific architectures, explaining how they are the only path forward for improved performance and energy efficiency given the end of Moore's Law and Dennard scaling - Features the first publication of several DSAs from industry - Features extensive updates to the chapter on warehouse-scale computing, with the first public information on the newest Google WSC - Offers updates to other chapters including new material dealing with the use of stacked DRAM; data on the performance of new NVIDIA Pascal GPU vs. new AVX-512 Intel Skylake CPU; and extensive additions to content covering multicore architecture and organization - Includes Putting It All Together sections near the end of every chapter, providing real-world technology examples that demonstrate the principles covered in each chapter - Includes review appendices in the printed text and additional reference appendices available online - Includes updated and improved case studies and exercises - ACM named John L. Hennessy and David A. Patterson, recipients of the 2017 ACM A.M. Turing Award for pioneering a systematic, quantitative approach to the design and evaluation of computer architectures with enduring impact on the microprocessor industry |
| data science domain emphasis berkeley: Autonomous Horizons Greg Zacharias, 2019-04-05 Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology. |
| data science domain emphasis berkeley: Rights and Retrenchment Stephen B. Burbank, Sean Farhang, 2017-04-18 This groundbreaking book contributes to an emerging literature that examines responses to the rights revolution that unfolded in the United States during the 1960s and 1970s. Using original archival evidence and data, Stephen B. Burbank and Sean Farhang identify the origins of the counterrevolution against private enforcement of federal law in the first Reagan Administration. They then measure the counterrevolution's trajectory in the elected branches, court rulemaking, and the Supreme Court, evaluate its success in those different lawmaking sites, and test key elements of their argument. Finally, the authors leverage an institutional perspective to explain a striking variation in their results: although the counterrevolution largely failed in more democratic lawmaking sites, in a long series of cases little noticed by the public, an increasingly conservative and ideologically polarized Supreme Court has transformed federal law, making it less friendly, if not hostile, to the enforcement of rights through lawsuits. |
| data science domain emphasis berkeley: The Non-designer's Design Book Robin Williams, 2015 This guide provides a simple, step-by-step process to better design. Techniques promise immediate results that forever change a reader's design eye. It contains dozens of examples. |
| data science domain emphasis berkeley: Mathematical Thinking and Problem Solving Alan H. Schoenfeld, Alan H. Sloane, 2016-05-06 In the early 1980s there was virtually no serious communication among the various groups that contribute to mathematics education -- mathematicians, mathematics educators, classroom teachers, and cognitive scientists. Members of these groups came from different traditions, had different perspectives, and rarely gathered in the same place to discuss issues of common interest. Part of the problem was that there was no common ground for the discussions -- given the disparate traditions and perspectives. As one way of addressing this problem, the Sloan Foundation funded two conferences in the mid-1980s, bringing together members of the different communities in a ground clearing effort, designed to establish a base for communication. In those conferences, interdisciplinary teams reviewed major topic areas and put together distillations of what was known about them.* A more recent conference -- upon which this volume is based -- offered a forum in which various people involved in education reform would present their work, and members of the broad communities gathered would comment on it. The focus was primarily on college mathematics, informed by developments in K-12 mathematics. The main issues of the conference were mathematical thinking and problem solving. |
| data science domain emphasis berkeley: The Coding Manual for Qualitative Researchers Johnny Saldana, 2009-02-19 The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example. |
| data science domain emphasis berkeley: Life Skills Education for Youth Joan DeJaeghere, Erin Murphy-Graham, 2021-11-23 This open access volume critically reviews a diverse body of scholarship and practice that informs the conceptualization, curriculum, teaching and measurement of life skills in education settings around the world. It discusses life skills as they are implemented in schools and non-formal education, providing both qualitative and quantitative evidence of when, with whom, and how life skills do or do not impact young women’s and men’s lives in various contexts. Specifically, it examines the nature and importance of life skills, and how they are taught. It looks at the synergies and differences between life skills educational programmes and the way in which they promote social and emotional learning, vocational/employment education, and health and sexuality education. Finally, it explores how life skills may be better incorporated into education and how such education can address structures and relations of power to help youth achieve desired future outcomes, and goals set out in the Sustainable Development Goals (SDGs). Life skills education has gained considerable attention by education policymakers, researchers and educators as being the sine qua non for later achievements in life. It is nearly ubiquitous in global and national education policies, including the SDGs, because life skills are regarded as essential for a diverse set of purposes: reducing poverty, achieving gender equality, promoting economic growth, addressing climate change, fostering peace and global citizenship, and creating sustainable and healthy communities. Yet, to achieve these broad goals, questions persist as to which life skills are important, who needs to learn them, how they can be taught, and how they are best measured. This book addresses these questions. |
| data science domain emphasis berkeley: Architectural Research Methods Linda N. Groat, David Wang, 2013-04-03 A practical guide to research for architects and designers—now updated and expanded! From searching for the best glass to prevent glare to determining how clients might react to the color choice for restaurant walls, research is a crucial tool that architects must master in order to effectively address the technical, aesthetic, and behavioral issues that arise in their work. This book's unique coverage of research methods is specifically targeted to help professional designers and researchers better conduct and understand research. Part I explores basic research issues and concepts, and includes chapters on relating theory to method and design to research. Part II gives a comprehensive treatment of specific strategies for investigating built forms. In all, the book covers seven types of research, including historical, qualitative, correlational, experimental, simulation, logical argumentation, and case studies and mixed methods. Features new to this edition include: Strategies for investigation, practical examples, and resources for additional information A look at current trends and innovations in research Coverage of design studio–based research that shows how strategies described in the book can be employed in real life A discussion of digital media and online research New and updated examples of research studies A new chapter on the relationship between design and research Architectural Research Methods is an essential reference for architecture students and researchers as well as architects, interior designers, landscape architects, and building product manufacturers. |
| data science domain emphasis berkeley: Industry Standard FDSOI Compact Model BSIM-IMG for IC Design Chenming Hu, Sourabh Khandelwal, Yogesh Singh Chauhan, Thomas Mckay, Josef Watts, Juan Pablo Duarte, Pragya Kushwaha, Harshit Agarwal, 2019-05-22 Industry Standard FDSOI Compact Model BSIM-IMG for IC Design helps readers develop an understanding of a FDSOI device and its simulation model. It covers the physics and operation of the FDSOI device, explaining not only how FDSOI enables further scaling, but also how it offers unique possibilities in circuits. Following chapters cover the industry standard compact model BSIM-IMG for FDSOI devices. The book addresses core surface-potential calculations and the plethora of real devices and potential effects. Written by the original developers of the industrial standard model, this book is an excellent reference for the new BSIM-IMG compact model for emerging FDSOI technology. The authors include chapters on step-by-step parameters extraction procedure for BSIM-IMG model and rigorous industry grade tests that the BSIM-IMG model has undergone. There is also a chapter on analog and RF circuit design in FDSOI technology using the BSIM-IMG model. |
| data science domain emphasis berkeley: Annual Review of Information Science & Technology Blaise Cronin, 2006-10 ARIST, published annually since 1966, is a landmark publication within the information science community. It surveys the landscape of information science and technology, providing an analytical, authoritative, and accessible overview of recent trends and significant developments. The range of topics varies considerably, reflecting the dynamism of the discipline and the diversity of theoretical and applied perspectives. While ARIST continues to cover key topics associated with classical information science (e.g., bibliometrics, information retrieval), editor Blaise Cronin is selectively expanding its footprint in an effort to connect information science more tightly with cognate academic and professional communities. |
| data science domain emphasis berkeley: Coordination Dynamics: Issues and Trends Viktor K. Jirsa, Scott Kelso, 2013-11-01 This book brings together scientists from all over the world who have defined and developed the field of Coordination Dynamics. Grounded in the concepts of self-organization and the tools of nonlinear dynamics, appropriately extended to handle informational aspects of living things, Coordination Dynamics aims to understand the coordinated functioning of a variety of different systems at multiple levels of description. The book addresses the themes of Coordination Dynamics and Dynamic Patterns in the context of the following topics: Coordination of Brain and Behavior, Perception-Action Coupling, Control, Posture, Learning, Intention, Attention, and Cognition. |
| data science domain emphasis berkeley: China’s Grand Strategy Andrew Scobell, Edmund J. Burke, Cortez A. Cooper III, Sale Lilly, Chad J. R. Ohlandt, Eric Warner, J.D. Williams, 2020-07-27 To explore what extended competition between the United States and China might entail out to 2050, the authors of this report identified and characterized China’s grand strategy, analyzed its component national strategies (diplomacy, economics, science and technology, and military affairs), and assessed how successful China might be at implementing these over the next three decades. |
| data science domain emphasis berkeley: Phonological Typology Larry M. Hyman, Frans Plank, 2018-04-09 Despite earlier work by Trubetzkoy, Jakobson and Greenberg, phonological typology is often underrepresented in typology textbooks. At the same time, most phonologists do not see a difference between phonological typology and cross-linguistic (formal) phonology. The purpose of this book is to bring together leading scholars to address the issue of phonological typology, both in terms of the unity and the diversity of phonological systems. |
| data science domain emphasis berkeley: The Oxford Handbook of Berkeley , 2022-01-18 The Oxford Handbook of Berkeley is a compendious examination of a vast array of topics in the philosophy of George Berkeley (1685-1753), Anglican Bishop of Cloyne, the famous idealist and most illustrious Irish philosopher. Berkeley is best known for his denial of the existence of material substance and his insistence that the only things that exist in the universe are minds (including God) and their ideas; however, Berkeley was a polymath who contributed to a variety of different disciplines, not well distinguished from philosophy in the eighteenth century, including the theory and psychology of vision, the nature and functioning of language, the debate over infinitesimals in mathematics, political philosophy, economics, chemistry (including his favoured panacea, tar-water), and theology. This volume includes contributions from thirty-four expert commentators on Berkeley's philosophy, some of whom provide a state-of-the-art account of his philosophical achievements, and some of whom place his philosophy in historical context by comparing and contrasting it with the views of his contemporaries (including Mandeville, Collier, and Edwards), as well as with philosophers who preceded him (such as Descartes, Locke, Malebranche, and Leibniz) and others who succeeded him (such as Hume, Reid, Kant, and Shepherd). |
| data science domain emphasis berkeley: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| data science domain emphasis berkeley: Handbook of Research on Student Engagement Sandra L. Christenson, Amy L. Reschly, CATHY WYLIE, 2012-02-23 For more than two decades, the concept of student engagement has grown from simple attention in class to a construct comprised of cognitive, emotional, and behavioral components that embody and further develop motivation for learning. Similarly, the goals of student engagement have evolved from dropout prevention to improved outcomes for lifelong learning. This robust expansion has led to numerous lines of research across disciplines and are brought together clearly and comprehensively in the Handbook of Research on Student Engagement. The Handbook guides readers through the field’s rich history, sorts out its component constructs, and identifies knowledge gaps to be filled by future research. Grounding data in real-world learning situations, contributors analyze indicators and facilitators of student engagement, link engagement to motivation, and gauge the impact of family, peers, and teachers on engagement in elementary and secondary grades. Findings on the effectiveness of classroom interventions are discussed in detail. And because assessing engagement is still a relatively new endeavor, chapters on measurement methods and issues round out this important resource. Topical areas addressed in the Handbook include: Engagement across developmental stages. Self-efficacy in the engaged learner. Parental and social influences on engagement and achievement motivation. The engaging nature of teaching for competency development. The relationship between engagement and high-risk behavior in adolescents. Comparing methods for measuring student engagement. An essential guide to the expanding knowledge base, the Handbook of Research on Student Engagement serves as a valuable resource for researchers, scientist-practitioners, and graduate students in such varied fields as clinical child and school psychology, educational psychology, public health, teaching and teacher education, social work, and educational policy. |
| data science domain emphasis berkeley: Analytics, Data Science, and Artificial Intelligence Ramesh Sharda, Dursun Delen, Efraim Turban, 2020-03-06 For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT. |
| data science domain emphasis berkeley: The Most Human Human Brian Christian, 2012-03-06 A playful, profound book that is not only a testament to one man's efforts to be deemed more human than a computer, but also a rollicking exploration of what it means to be human in the first place. “Terrific. ... Art and science meet an engaged mind and the friction produces real fire.” —The New Yorker Each year, the AI community convenes to administer the famous (and famously controversial) Turing test, pitting sophisticated software programs against humans to determine if a computer can “think.” The machine that most often fools the judges wins the Most Human Computer Award. But there is also a prize, strange and intriguing, for the “Most Human Human.” Brian Christian—a young poet with degrees in computer science and philosophy—was chosen to participate in a recent competition. This |
| data science domain emphasis berkeley: BioBuilder Natalie Kuldell PhD., Rachel Bernstein, Karen Ingram, Kathryn M Hart, 2015-06-22 Today’s synthetic biologists are in the early stages of engineering living cells to help treat diseases, sense toxic compounds in the environment, and produce valuable drugs. With this manual, you can be part of it. Based on the BioBuilder curriculum, this valuable book provides open-access, modular, hands-on lessons in synthetic biology for secondary and post-secondary classrooms and laboratories. It also serves as an introduction to the field for science and engineering enthusiasts. Developed at MIT in collaboration with award-winning high school teachers, BioBuilder teaches the foundational ideas of the emerging synthetic biology field, as well as key aspects of biological engineering that researchers are exploring in labs throughout the world. These lessons will empower teachers and students to explore and be part of solving persistent real-world challenges. Learn the fundamentals of biodesign and DNA engineering Explore important ethical issues raised by examples of synthetic biology Investigate the BioBuilder labs that probe the design-build-test cycle Test synthetic living systems designed and built by engineers Measure several variants of an enzyme-generating genetic circuit Model bacterial photography that changes a strain’s light sensitivity Build living systems to produce purple or green pigment Optimize baker’s yeast to produce ?-carotene |
| data science domain emphasis berkeley: Alciphron, Or, The Minute Philosopher George Berkeley, 1732 |
| data science domain emphasis berkeley: The Psychology of Gratitude Robert A. Emmons, Michael E. McCullough, 2004-02-26 Gratitude, like other positive emotions, has inspired many theological and philosophical writings, but it has inspired very little vigorous, empirical research. In an effort to remedy this oversight, this volume brings together prominent scientists from various disciplines to examine what has become known as the most-neglected emotion. The volume begins with the historical, philosophical, and theoretical foundations of gratitude, then presents the current research perspectives from social, personality, and developmental psychology, as well as from primatology, anthropology, and biology. The volume also includes a comprehensive, annotated bibliography of research on gratitude. This work contributes a great deal to the growing positive psychology initiative and to the scientific investigation of positive human emotions. It will be an invaluable resource for researchers and students in social, personality, and developmental, clinical, and health psychology, as well as to sociologists and cultural anthropologists. |
| data science domain emphasis berkeley: Pattern Recognition and Machine Learning Christopher M. Bishop, 2016-08-23 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
