Products related to Computation:
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Quantum Computation and Quantum Information : 10th Anniversary Edition
One of the most cited books in physics of all time, Quantum Computation and Quantum Information remains the best textbook in this exciting field of science.This 10th anniversary edition includes an introduction from the authors setting the work in context.This comprehensive textbook describes such remarkable effects as fast quantum algorithms, quantum teleportation, quantum cryptography and quantum error-correction.Quantum mechanics and computer science are introduced before moving on to describe what a quantum computer is, how it can be used to solve problems faster than 'classical' computers and its real-world implementation.It concludes with an in-depth treatment of quantum information.Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering.
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Jumping Computation : Updating Automata and Grammars for Discontinuous Information Processing
Jumping Computation: Updating Automata and Grammars for Discontinuous Information Processing is primarily a theoretically oriented treatment of jumping automata and grammars, covering all essential theoretical topics concerning them, including their power, properties, and transformations.From a practical viewpoint, it describes various concepts, methods, algorithms, techniques, case studies and applications based upon these automata and grammars. In today’s computerized world, the scientific development and study of computation, referred to as the theory of computation, plays a crucial role.One important branch, language theory, investigates how to define and study languages and their models, which formalize algorithms according to which their computation is executed.These language-defining models are classified into two basic categories: automata, which define languages by recognizing their words, and grammars, which generate them.Introduced many decades ago, these rules reflect classical sequential computation.However, today’s computational methods frequently process information in a fundamentally different way, frequently “jumping” over large portions of the information as a whole.This book adapts classical models to formalize and study this kind of computation properly.Simply put, during their language-defining process, these adapted versions, called jumping automata and grammars, jump across the words they work on. The book selects important models and summarizes key results about them in a compact and uniform way.It relates each model to a particular form of modern computation, such as sequential, semi-parallel and totally parallel computation, and explains how the model in question properly reflects and formalizes the corresponding form of computation, thus allowing us to obtain a systematized body of mathematically precise knowledge concerning the jumping computation.The book pays a special attention to power, closure properties, and transformations, and also describes many algorithms that modify jumping grammars and automata so they satisfy some prescribed properties without changing the defined language.The book will be of great interest to anyone researching the theory of computation across the fields of computer science, mathematics, engineering, logic and linguistics.
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Turbulent Flow Computation
In various branches of fluid mechanics, our understanding is inhibited by the presence of turbulence.Although many experimental and theoretical studies have significantly helped to increase our physical understanding, a comp- hensive and predictive theory of turbulent flows has not yet been established.Therefore, the prediction of turbulent flow relies heavily on simulation stra- gies.The development of reliable methods for turbulent flow computation will have a significant impact on a variety of technological advancements.These range from aircraft and car design, to turbomachinery, combustors, and process engineering.Moreover, simulation approaches are important in materials - sign, prediction of biologically relevant flows, and also significantly contribute to the understanding of environmental processes including weather and climate forecasting.The material that is compiled in this book presents a coherent account of contemporary computational approaches for turbulent flows.It aims to p- vide the reader with information about the current state of the art as well as to stimulate directions for future research and development.The book puts part- ular emphasis on computational methods for incompressible and compressible turbulent flows as well as on methods for analysing and quantifying nume- cal errors in turbulent flow computations.In addition, it presents turbulence modelling approaches in the context of large eddy simulation, and unfolds the challenges in the field of simulations for multiphase flows and computational fluid dynamics (CFD) of engineering flows in complex geometries.Apart from reviewing main research developments, new material is also included in many of the chapters.
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Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.
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What is neural computation?
Neural computation refers to the process by which the brain and nervous system process and transmit information. It involves the complex interactions between neurons, which are the basic building blocks of the nervous system. Neural computation encompasses a wide range of functions, including sensory perception, motor control, learning, and memory. This field of study seeks to understand how neural networks process information and how these processes can be replicated or simulated in artificial systems.
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How does neural computation work?
Neural computation refers to the process by which the brain processes and analyzes information. It involves the transmission of electrical signals between neurons, which are specialized cells that make up the nervous system. These signals are passed through synapses, or connections between neurons, and can be either excitatory or inhibitory. The brain integrates and processes these signals to generate responses and behaviors. Overall, neural computation is a complex and dynamic process that underlies all cognitive functions and behaviors.
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How does symbolic computation work?
Symbolic computation involves manipulating mathematical expressions and equations using symbols and rules, rather than numerical values. This allows for the manipulation of complex mathematical expressions, including algebraic, trigonometric, and calculus operations, without needing to compute specific numerical values. Symbolic computation systems use algorithms and rules to perform operations such as simplification, differentiation, integration, and solving equations symbolically, providing exact solutions in terms of symbols and variables. This approach is particularly useful in mathematics, engineering, and scientific research, where exact symbolic solutions are often needed for further analysis and interpretation.
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What is neural computation 2?
Neural computation 2 refers to the process of information processing and decision-making within the brain using neural networks. It involves the complex interactions between neurons, synapses, and neurotransmitters to perform tasks such as learning, memory, and problem-solving. Neural computation 2 also encompasses the study of artificial neural networks, which are computational models inspired by the structure and function of the brain, and are used in various applications such as pattern recognition, machine learning, and artificial intelligence.
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Linear Programming Computation
This monograph represents a historic breakthrough in the field of linear programming (LP)since George Dantzig first discovered the simplex method in 1947. Being both thoughtful and informative, it focuses on reflecting and promoting the state of the art by highlighting new achievements in LP.This new edition is organized in two volumes. The first volume addresses foundations of LP, including the geometry of feasible region, the simplex method and its implementation, duality and the dual simplex method, the primal-dual simplex method, sensitivity analysis and parametric LP, the generalized simplex method, the decomposition method, the interior-point method and integer LP method.The second volume mainly introduces contributions of the author himself, such as efficient primal/dual pivot rules, primal/dual Phase-I methods, reduced/D-reduced simplex methods, the generalized reduced simplex method, primal/dual deficient-basis methods, primal/dual face methods, a new decomposition principle, etc. Many important improvements were made in this edition.The first volume includes new results, such as the mixed two-phase simplex algorithm, dual elimination, fresh pricing scheme for reduced cost, bilevel LP models and intercepting of optimal solution set.In particular, the chapter Integer LP Method was rewritten with great gains of the objective cutting for new ILP solvers {it controlled-cutting/branch} methods, as well as with an attractive implementation of the controlled-branch method. In the second volume, the `simplex feasible-point algorithm' was rewritten, and removed from the chapter Pivotal Interior-Point Method to form an independent chapter with the new title `Simplex Interior-Point Method', as it represents a class of efficient interior-point algorithms transformed from traditional simplex algorithms.The title of the original chapter was then changed to `Facial Interior-Point Method', as the remaining algorithms represent another class of efficient interior-point algorithms transformed from normal interior-point algorithms.Without exploiting sparsity, the original primal/dual face methods were implemented using Cholesky factorization.In order to deal with sparse computation, two new chapters discussing LU factorization were added to the second volume.The most exciting improvement came from the rediscovery of the reduced simplex method.In the first edition, the derivation of its prototype was presented in a chapter with the same title, and then converted into the so-called `improved' version in another chapter.Fortunately, the author recently found a quite concise new derivation, so he can now introduce the distinctive fresh simplex method in a single chapter.It is exciting that the reduced simplex method can be expected to be the best LP solver ever. With a focus on computation, the current edition contains many novel ideas, theories and methods, supported by solid numerical results.Being clear and succinct, its content reveals in a fresh manner, from simple to profound.In particular, a larger number of examples were worked out to demonstrate algorithms.This book is a rare work in LP and an indispensable tool for undergraduate and graduate students, teachers, practitioners, and researchers in LP and related fields.
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The Physics of Quantum Information : Quantum Cryptography, Quantum Teleportation, Quantum Computation
Information is stored, transmitted and processed by physical means.Thus, the concept of information and computation can be formulated in the con text of a physical theory and the study of information requires ultimately experimentation.This sentence, innocuous at first glance, leads to non-trivial consequences.Following Moore's law, about every 18 months microprocessors double their speed and, it seems, the only way to make them significantly faster is to make them smaller.In the not too distant future they will reach the point where the logic gates are so small that they consist of only a few atoms each.Then quantum-mechanical effects will become important.Thus, if computers are to continue to become faster (and therefore smaller), new, quantum technology must replace or supplement what we have now.But it turns out that such technology can offer much more than smaller and faster microprocessors.Several recent theoretical results have shown that quantum effects may be harnessed to provide qualitatively new modes of communication and computation, in some cases much more powerful than their classical counterparts.This new quantum technology is being born in many laboratories.The last two decades have witnessed experiments in which single quantum particles of different kinds were controlled and manipulated with an unprecedented preci sion.Many "gedanken" experiments, so famous in the early days of quantum mechanics, have been carried out.
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Effective Computation in Physics
More physicists today are taking on the role of software developer as part of their research, but software development isn't always easy or obvious, even for physicists.This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field.Written by two PhDs in nuclear engineering, this book includes practical examples drawn from a working knowledge of physics concepts.You'll learn how to use the Python programming language to perform everything from collecting and analyzing data to building software and publishing your results.In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your findings, and collaborate efficiently; dive into software licenses, ownership, and copyright procedures
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The Nature of Computation
Computational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology.But this beauty is often buried underneath layers of unnecessary formalism, and exciting recent results like interactive proofs, phase transitions, and quantum computing are usually considered too advanced for the typical student.This book bridges these gaps by explaining the deep ideas of theoretical computer science in a clear and enjoyable fashion, making them accessible to non-computer scientists and to computer scientists who finally want to appreciate their field from a new point of view.The authors start with a lucid and playful explanation of the P vs.NP problem, explaining why it is so fundamental, and so hard to resolve.They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing.At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible.The book is intended for graduate and undergraduate students, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.
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How does neuronal computation work through summation?
Neuronal computation works through summation by integrating the input signals received from other neurons. When a neuron receives multiple inputs, these signals are combined through a process called summation. There are two types of summation: spatial summation, which involves the integration of signals from different neurons at the same time, and temporal summation, which involves the integration of signals from the same neuron over a short period of time. The combined input signals are then processed and, if the resulting signal exceeds a certain threshold, the neuron will generate an action potential, transmitting the signal to other neurons. This process allows for complex information processing and decision-making within the brain.
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What is the assignment sheet for neural computation?
The assignment sheet for neural computation typically includes a set of problems or tasks related to the study of neural networks and their computational properties. It may include exercises on topics such as neural network architecture, learning algorithms, and applications in pattern recognition or data analysis. The sheet may also provide guidelines for implementing and testing neural network models using programming languages or simulation tools. Additionally, it may outline the evaluation criteria and deadlines for the completion of the assignment.
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Are TV and radio stations obligated to broadcast news?
TV and radio stations are not legally obligated to broadcast news, but many choose to do so as part of their commitment to serving the public interest. In some countries, there may be regulations or licensing requirements that mandate a certain amount of news programming, but this varies by jurisdiction. Ultimately, the decision to include news in their programming is up to the individual stations and their management.
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Can someone explain this task to me? It's about neural computation. Photo attached.
Certainly! The task in the photo appears to be related to neural computation, which is a field that studies how the brain processes information. In this task, it seems like you are required to analyze the neural network architecture depicted in the diagram and possibly identify the flow of information or the activation patterns within the network. By understanding how neural networks process information, researchers can develop more efficient algorithms for tasks such as pattern recognition, machine learning, and artificial intelligence.
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