Products related to Computational:
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Designing the Computational Image, Imagining Computational Design
During the three decades following the Second World War, and before the advent of personal computers, government investment in university research in North America and the UK funded multidisciplinary projects to investigate the use of computers for manufacturing and design.Designing the Computational Image, Imagining Computational Design explores this period of remarkable inventiveness, and traces its repercussions on architecture and other creative fields through a selection of computational designers working today.Situating contemporary expressions of design in relation to broader historical, disciplinary, and technical frames, the book showcases the confluence, during the second half of the 20th century, of publicly funded technical innovations in software, geometry, and hardware with a cultural imaginary of design endowing computer-generated images with both geometric plasticity and a new type of agency as operative design artifacts.
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Computational Humanities
The first book to intervene in debates on computation in the digital humanities Bringing together leading experts from across North America and Europe, Computational Humanities redirects debates around computation and humanities digital scholarship from dualistic arguments to nuanced discourse centered around theories of knowledge and power.This volume is organized around four questions: Why or why not pursue computational humanities?How do we engage in computational humanities? What can we study using these methods? Who are the stakeholders? Recent advances in technologies for image and sound processing have expanded computational approaches to cultural forms beyond text, and new forms of data, from listservs and code repositories to tweets and other social media content, have enlivened debates about what counts as digital humanities scholarship.Providing case studies of collaborations between humanities-centered and computation-centered researchers, this volume highlights both opportunities and frictions, showing that data and computation are as much about power, prestige, and precarity as they are about p-values. Contributors: Mark Algee-Hewitt, Stanford U; David Bamman, U of California, Berkeley; Kaspar Beelen, U of London; Peter Bell, Philipps U of Marburg; Tobias Blanke, U of Amsterdam; Julia Damerow, Arizona State U; Quinn Dombrowski, Stanford U; Crystal Nicole Eddins, U of Pittsburgh; Abraham Gibson, U of Texas at San Antonio; Tassie Gniady; Crystal Hall, Bowdoin College; Vanessa M.Holden, U of Kentucky; David Kloster, Indiana U; Manfred D.Laubichler, Arizona State U; Katherine McDonough, Lancaster U; Barbara McGillivray, King’s College London; Megan Meredith-Lobay, Simon Fraser U; Federico Nanni, Alan Turing Institute; Fabian Offert, U of California, Santa Barbara; Hannah Ringler, Illinois Institute of Technology; Roopika Risam, Dartmouth College; Joshua D.Rothman, U of Alabama; Benjamin M. Schmidt; Lisa Tagliaferri, Rutgers U; Jeffrey Tharsen, U of Chicago; Marieke van Erp, Royal Netherlands Academy of Arts and Sciences; Lee Zickel, Case Western Reserve U.
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Computational Aerodynamics
Computational aerodynamics is a relatively new field in engineering that investigates aircraft flow fields via the simulation of fluid motion and sophisticated numerical algorithms.This book provides an excellent reference to the subject for a wide audience, from graduate students to experienced researchers and professionals in the aerospace engineering field.Opening with the essential elements of computational aerodynamics, the relevant mathematical methods of fluid flow and numerical methods for partial differential equations are presented.Stability theory and shock capturing schemes, and vicious flow and time integration methods are then comprehensively outlined.The final chapters treat more advanced material, including energy stability for nonlinear problems, and higher order methods for unstructured and structured meshes.Presenting over 150 illustrations, including representative calculations on unstructured meshes in color.This book is a rich source of information that will be of interest and importance in this pioneering field.
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Computational Phytochemistry
Computational Phytochemistry, Second Edition, explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operations, refocusing and expanding the possibilities of phytochemical studies.By applying computational aids and mathematical models to extraction, isolation, structure determination, and bioactivity testing, researchers can obtain highly detailed information about phytochemicals and optimize working approaches.This book aims to support and encourage researchers currently working with or looking to incorporate computational methods into their phytochemical work.Topics in this book include computational methods for predicting medicinal properties, optimizing extraction, isolating plant secondary metabolites, and building dereplicated phytochemical libraries.The roles of high-throughput screening, spectral data for structural prediction, plant metabolomics, and biosynthesis are all reviewed before the application of computational aids for assessing bioactivities and virtual screening is discussed.Illustrated with detailed figures and supported by practical examples, this book is an indispensable guide for all those involved with the identification, extraction, and application of active agents from natural products.This new edition captures remarkable advancements in mathematical modeling and computational methods that have been incorporated in phytochemical research, addressing, e.g., extraction, isolation, structure determination, and bioactivity testing of phytochemicals.
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What is a computational rule?
A computational rule is a set of instructions or guidelines that dictate how a computer or computational system should process or manipulate data. These rules can be in the form of algorithms, formulas, or logical operations that define the steps necessary to perform a specific task or solve a problem. Computational rules are essential for programming and designing software, as they provide the framework for how data should be input, processed, and output by a computer system.
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How can one improve computational performance?
One can improve computational performance by optimizing algorithms to reduce time complexity, utilizing parallel processing techniques to distribute workloads across multiple processors, and implementing efficient data structures to minimize memory usage. Additionally, optimizing code by reducing unnecessary computations and memory allocations can also help improve computational performance. Regularly profiling and benchmarking the code to identify bottlenecks and areas for improvement is essential in achieving optimal computational performance.
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What is an astrophysics computational problem?
An astrophysics computational problem refers to a problem in the field of astrophysics that requires the use of computational methods and techniques to analyze and solve. These problems often involve complex mathematical models, large datasets, and simulations of astronomical phenomena such as the behavior of stars, galaxies, and the evolution of the universe. Astrophysics computational problems can include tasks such as modeling the formation of galaxies, simulating the behavior of black holes, or analyzing the distribution of dark matter in the universe. These problems require the use of advanced computational tools and techniques to understand and explore the complexities of the universe.
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Which university is the best for computational linguistics?
The best university for computational linguistics can vary depending on individual preferences and goals. Some top universities known for their strong programs in computational linguistics include Stanford University, University of Edinburgh, and University of Washington. These universities have renowned faculty, cutting-edge research opportunities, and a history of producing successful graduates in the field of computational linguistics. It is important for prospective students to research each university's specific program offerings, faculty expertise, and industry connections to determine which university aligns best with their academic and career goals.
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Computational Physics
First published in 2007, this second edition describes the computational methods used in theoretical physics.New sections were added to cover finite element methods and lattice Boltzmann simulation, density functional theory, quantum molecular dynamics, Monte Carlo simulation, and diagonalisation of one-dimensional quantum systems.It covers many different areas of physics research and different computational methodologies, including computational methods such as Monte Carlo and molecular dynamics, various electronic structure methodologies, methods for solving partial differential equations, and lattice gauge theory.Throughout the book the relations between the methods used in different fields of physics are emphasised.Several new programs are described and can be downloaded from www.cambridge.org/9781107677135.The book requires a background in elementary programming, numerical analysis, and field theory, as well as undergraduate knowledge of condensed matter theory and statistical physics.It will be of interest to graduate students and researchers in theoretical, computational and experimental physics.
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Computational Chemistry
The renowned Oxford Chemistry Primers series, which provides focused introductions to a range of important topics in chemistry, has been refreshed and updated to suit the needs of today's students, lecturers, and postgraduate researchers.The rigorous, yet accessible, treatment of each subject area is ideal for those wanting a primer in a given topic to prepare them for more advanced study or research.The learning features provided, including exercises at the end of every chapter and online multiple-choice questions, encourage active learning and promote understanding.Moreover, cutting-edge examples and applications throughout the texts show the relevance to current research and industry of the chemistry being described. Computational Chemistry provides a user-friendly introduction to this powerful way of characterizing and modelling chemical systems.This primer provides the perfect introduction to the subject, leading the reader through the basic principles before showing the variety of ways in which computational chemistry is applied in practice to study real molecules, all illustrated by frequent examples. Online Resource CentreThe Online Resource Centre to accompany Computational Chemistry features: For registered adopters of the text: · Figures from the book available to download For students: · Multiple-choice questions for self-directed learning
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Computational Thinking
An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology.More recently, “computational thinking” has become part of the K–12 curriculum.But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it.The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes.Mathematically trained experts (known as “computers”) who performed complex calculations as teams engaged in CT long before electronic computers.The authors identify six dimensions of today's highly developed CT—methods, machines, computing education, software engineering, computational science, and design—and cover each in a chapter.Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.
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Computational Imaging
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How can one earn money with computational power?
One can earn money with computational power by participating in activities such as cryptocurrency mining, cloud computing, and distributed computing. Cryptocurrency mining involves using computational power to solve complex mathematical problems and validate transactions on a blockchain network, in return for earning cryptocurrency rewards. Cloud computing allows individuals to rent out their computational power to organizations and individuals in need of computing resources, earning money in exchange for providing these services. Distributed computing involves contributing computational power to a network of computers to solve complex problems or process large amounts of data, and individuals can earn money by participating in these distributed computing projects.
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Should one study computational linguistics or rather mathematics?
The choice between studying computational linguistics or mathematics depends on individual interests and career goals. If someone is passionate about language and communication, and wants to work on developing language technologies, then computational linguistics would be a better fit. On the other hand, if someone is more interested in abstract problem-solving and theoretical concepts, then mathematics might be a better choice. Both fields offer diverse career opportunities, so it's important to consider personal interests and strengths when making this decision.
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What do abstraction and automation mean in computational thinking?
Abstraction in computational thinking refers to the process of simplifying complex systems by focusing on the most important details while ignoring unnecessary details. This allows for easier problem-solving and understanding of the system. Automation, on the other hand, involves the use of technology to perform tasks without human intervention. In computational thinking, automation often involves writing code to execute repetitive tasks or processes, making them more efficient and less prone to human error. Both abstraction and automation are key concepts in computational thinking, as they enable the development of efficient and scalable solutions to complex problems.
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Can the computational power be shared or distributed between computers?
Yes, computational power can be shared or distributed between computers through techniques like parallel computing, grid computing, and cloud computing. These methods allow multiple computers to work together to solve complex problems or process large amounts of data more efficiently. By distributing computational tasks across multiple machines, the overall processing power can be increased, leading to faster results and improved performance.
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