Products related to Computation:
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Software for Artists Book: School(s) for Poetic Computation
Artists and organizers explore experimental learning initiatives fostering solidarity and dialogueWith a focus on labor organizing within the arts and tech industries, Software for Artists Book 3 explores the potential of creating alternative networks of education and challenging the material conditions that prevent us from enacting new realities with dignity and security. When we defy existing models for institution-building, what possibilities for gathering can materialize and what kinds of learning and unlearning can we practice together?Released alongside Pioneer Works’ eighth Software for Artists Day and the School for Poetic Computation’s 10th anniversary in November 2023, the book convenes educators, labor organizers and artists exploring experimental learning initiatives to be in solidarity and conversation with each other. Contributors include: Kandis Williams, manuel arturo abreu, Rahel Aima, Kameela Janan Rasheed, American Artist, Shani Peters, Neema Githere, Ayana Cotton, Lynn Yunn, Yatu Espinosa, Norm O’Hagan, Sam Lavigne, Or Zublasky and Dana Kopel.
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Mathematics and Computation : A Theory Revolutionizing Technology and Science
From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophyMathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation.With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges.He explains the ideas and motivations leading to key models, notions, and results.In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences.Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics.He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society.For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields.Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyondHigh-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific disciplineHistorical accounts of the evolution and motivations of central concepts and modelsA broad view of the theory of computation's influence on science, technology, and societyExtensive bibliography
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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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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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Biomechanics : Concepts and Computation
This is the first textbook to integrates both general and specific topics, theoretical background and biomedical engineering applications, as well as analytical and numerical approaches.This quantitative approach integrates the classical concepts of mechanics and computational modelling techniques, in a logical progression through a wide range of fundamental biomechanics principles.Online MATLAB-based software along with examples and problems using biomedical applications will motivate undergraduate biomedical engineering students to practise and test their skills.The book covers topics such as kinematics, equilibrium, stresses and strains, and also focuses on large deformations and rotations and non-linear constitutive equations, including visco-elastic behaviour and the behaviour of long slender fibre-like structures.This is the definitive textbook for students.
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A Quantum Computation Workbook
Teaching quantum computation and information is notoriously difficult, because it requires covering subjects from various fields of science, organizing these subjects consistently in a unified way despite their tendency to favor their specific languages, and overcoming the subjects’ abstract and theoretical natures, which offer few examples of actual realizations.In this book, we have organized all the subjects required to understand the principles of quantum computation and information processing in a manner suited to physics, mathematics, and engineering courses as early as undergraduate studies.In addition, we provide a supporting package of quantum simulation software from Wolfram Mathematica, specialists in symbolic calculation software.Throughout the book’s main text, demonstrations are provided that use the software package, allowing the students to deepen their understanding of each subject through self-practice.Readers can change the code so as to experiment with their own ideas and contemplate possible applications.The information in this book reflects many years of experience teaching quantum computation and information.The quantum simulation-based demonstrations and the unified organization of the subjects are both time-tested and have received very positive responses from the students who have experienced them.
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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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Why is integrated business management standard software like the SAP system so successful?
Integrated business management standard software like the SAP system is successful because it offers a comprehensive solution for managing various business processes such as finance, human resources, supply chain, and customer relationship management. This integrated approach allows for seamless data flow and communication across different departments, leading to improved efficiency and decision-making. Additionally, the SAP system is highly customizable and scalable, allowing businesses to adapt and grow with their changing needs. Its widespread adoption and large user community also contribute to its success, as it provides a network of support and resources for users.
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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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