A computation-universal two-dimensional 8-state triangular reversible cellular automaton
计算机专业英语测试题及答案

计算机专业英语测试题一、词汇测试题(每小题1分,共20分)(一).Translate the following words and expressions into Chinese (共10分,每题1分)1.Cyber cafe2.microcomputer3.ROM4.Object-oriented programming5.utility program6.system specification7.database administrator8.modulator-demodulator9.client/server model10.spreadsheet program(二).Translate the following terms or phrases from Chinese into English (共10分,每题1分)1.中央处理器2.广域网3.超级计算机4.电子商务5.计算机安全6.计算机文化7.网站8.域名9.数据库管理系统10.软件工程二、完型填空题(每空1分,共20分)Fill in each of the blanks with one of the words given in the list following each paragraph, making changes if necessary:1. Computer hardware is the involved in the of a computer and consists of the that can be physically handled. The function of these components is typically divided into three main categories: , , and . Components in these categories connect to , specifically, the computer’s central unit (CPU), the electronic that provides the computational ability and control of the computer, via wires or circuitry called bus.microprocessors component processing functionoutput equipment input circuitry storage2.In the relational model, data is organized in two-dimensionalcalled . There is no or structure imposed on the data. The tables or relations are, however, related to each other. The database management system (RDBMS) the data so that its external is a of relation or tables. This does not mean that data is stored as tables: the physical of the data is independent of the way in which the is logically organized.hierarchical set organize relational relationdata storage view network table三、英译汉题(每题10分,共20分)Translate the following passage from English into Chinese:1.The field of computer science has grown rapidly since the1950s due to the increase in their use. Computer programs have undergone many changes during this time in response to user need and advances in technology. Newer ideas in computing such as parallel computing, distributed computing, and artificial intelligence, have radically altered the traditional concepts that once determined program form and function. In parallelcomputing parts of a problem are worked on simultaneously by different processors, and this speed up the solution of the problem. Another type of parallel computing called distributed computing use CPUs from many interconnected computers to solve problems. Research into artificial intelligence (AI) has led to several other new styles of programming.2.High-level languages are commonly classified asprocedure-oriented, functional, objected-oriented, logic languages. The most common high-level languages today are procedure-oriented languages. In these languages, one or more related blocks of statements that perform some complete function are grouped together into a program module, or procedure, and given a name such as “procedure A”. If the same sequence of operations is needed elsewhere in the program, a simple statement can be used to refer back to the procedure. In essence, a procedure is just a mini-program. A large program can be constructed by grouping together procedures that perform different tasks.四、汉译英题(20分)最著名的互联网例子是因特网。
学数学的作文英语

Learning mathematics is an essential part of education that not only sharpens analytical skills but also fosters logical thinking and problemsolving abilities.Heres a detailed essay on the importance of studying mathematics and how it can be approached effectively.Title:The Significance of Mathematics in EducationIntroduction:Mathematics is often referred to as the language of the universe.It is a subject that transcends cultural and linguistic barriers,providing a universal framework for understanding the world around us.From counting to complex calculations,math is an integral part of our daily lives.Importance of Mathematics:1.Foundation for Other Subjects:Mathematics is a fundamental building block for other sciences such as physics,chemistry,and engineering.It provides the necessary tools to analyze and interpret data,making it indispensable for scientific inquiry.2.Critical Thinking:Studying math helps develop critical thinking skills.It encourages students to approach problems systematically and logically,breaking them down into manageable parts.3.ProblemSolving Abilities:Mathematical problems often have multiple solutions, which teaches students to think creatively and find the most efficient method to solve a problem.4.RealWorld Applications:Mathematics is not confined to the classroom.It is used in everyday life,from managing finances to understanding statistical data in various fields such as economics,medicine,and social sciences.Approaches to Learning Mathematics:1.Understanding Concepts:Rather than memorizing formulas,it is crucial to understand the underlying concepts.This approach helps in applying mathematical principles to new and unfamiliar problems.2.Practice:Mathematics is a subject that requires consistent practice.Regular exercises and problemsolving can significantly improve ones ability to tackle complex problems.e of Technology:Utilizing technology such as calculators,computer software,and online resources can enhance the learning experience.They can provide visual representations of mathematical concepts and offer interactive ways to solve problems.4.Group Work:Collaborative learning can be beneficial in mathematics.Discussing problems with peers can lead to new insights and a deeper understanding of the subject.Challenges in Learning Mathematics:1.Perception of Difficulty:Many students find mathematics challenging and may develop a fear of the subject.Overcoming this fear is essential for embracing the subject fully.ck of RealWorld Connection:Sometimes,the abstract nature of mathematics can make it difficult for students to see its relevance.Connecting mathematical concepts to realworld applications can help bridge this gap.Conclusion:In conclusion,mathematics is a subject of immense importance that offers a wealth of benefits to students.It not only equips them with analytical and problemsolving skills but also prepares them for various professional fields.By adopting effective learning strategies and recognizing the relevance of mathematics in everyday life,students can overcome challenges and excel in this fascinating subject.。
人工智能的数学基础 英文

人工智能的数学基础英文The Mathematical Foundations of Artificial Intelligence Artificial Intelligence (AI) is the field of computer science that aims to create intelligent machines that can learn and solve problems like humans. The mathematical foundations of AI are key to understanding and developing the algorithms and models that power this technology. Here wewill take a closer look at the math behind AI.1. Linear AlgebraLinear Algebra is an essential part of building algorithms that can learn from data. Most of the data in AI is represented as vectors and matrices. Linear algebra helps us to manipulate and transform these vectors and matrices in efficient ways. The concepts of vectors, matrices, and linear transformations are fundamental to understanding thestructure of the data and the models used in AI.2. CalculusCalculus is the study of how things change. It is used in AI to optimize the parameters of models to minimize the error in predictions. The gradient of a function gives us the direction of greatest increase, which is used to update the parameters in the optimization process. Calculus is particularly important in deep learning algorithms, which use neural networks with many layers of interconnected nodes to process data.3. Probability and StatisticsProbability and Statistics are essential for building probabilistic models in AI. These models are used to makepredictions based on uncertain or incomplete data.Probability theory gives us the tools to calculate the likelihood of different outcomes, while statistical inference lets us draw conclusions about a population based on a sample of data. Many machine learning algorithms such as naïve Bayes, decision trees, and random forests are based on probabilistic models.4. Graph TheoryGraph Theory is used to represent and analyze the relationships between objects in AI. Graphs are used to model the structure of data, such as social networks or the connections between web pages. Graph algorithms are used in search algorithms, clustering, and recommendation systems.ConclusionIn conclusion, these are some of the main mathematical foundations of artificial intelligence. Linear algebra, calculus, probability, statistics, and graph theory providethe tools to build algorithms that can learn from data, optimize models, make predictions, and represent and analyze the relationships between objects. As AI continues to develop and impact our daily lives, a solid understanding of these mathematical concepts is essential for anyone interested inthis field.。
reducedimension method

reducedimension method1. IntroductionThe dimensionality reduction technique known as "Reducedimension" refers to a method used to reduce the number of features in a dataset while preserving the most important information. It is commonly used in machine learning and data analysis tasks to overcome the curse of dimensionality and improve computational efficiency. In this article, we will discuss the principles and procedures of the Reducedimension method.2. Principles of ReducedimensionReducedimension is based on the assumption that the data lies on a low-dimensional manifold embedded in a high-dimensional space. The method aims to find a transformation that maps the original high-dimensional space to a lower-dimensional space while preserving the intrinsic structure and relationships of the data. By reducing the dimensionality, it becomes easier to visualize, analyze, and model the data.3. Procedure of ReducedimensionThe Reducedimension method can be performed in several steps:a. Data preprocessing: Before applying Reducedimension, it is necessary to preprocess the data. This includes handling missing values, normalizing or standardizing the features, and dealing with categorical variables. Data preprocessing ensures that the algorithm performs optimally.b. Covariance matrix computation: The covariance matrix is a symmetric positive semi-definite matrix that represents therelationship between the features in the dataset. It is computed to capture the linear dependencies between the variables.c. Eigenvalue decomposition: By decomposing the covariance matrix, we obtain its eigenvalues and eigenvectors. The eigenvalues represent the amount of variance explained by each eigenvector. The eigenvectors are the directions along which the data varies the most.d. Selection of principal components: The principal components are selected based on the eigenvalues. The eigenvectors corresponding to the largest eigenvalues capture most of the variance in the data. These eigenvectors are chosen as the principal components.e. Projection: The original high-dimensional data is projected onto the newly defined lower-dimensional space spanned by the principal components. The projection reduces the dimensionality of the data while preserving its essential properties and minimizing information loss.f. Reconstruction: If needed, the reduced-dimensional data can be reconstructed back into the original high-dimensional space using the inverse projection matrix. This allows for analysis or visualization in the original feature space.4. Advantages of ReducedimensionThe Reducedimension method offers several advantages:a. Dimensionality reduction: The method reduces thedimensionality of the dataset, which helps to overcome the curse of dimensionality. It simplifies the data representation and improves computational efficiency.b. Feature selection: Reducedimension automatically selects the most informative features by calculating the eigenvalues and eigenvectors. It eliminates redundant or irrelevant features, resulting in a more concise and interpretable dataset.c. Intrinsic structure preservation: Reducedimension aims to preserve the intrinsic structure and relationships of the data during the dimensionality reduction process. It ensures that the important information is retained while discarding noise and irrelevant variations.5. Applications of ReducedimensionThe Reducedimension method has various applications in various fields, including:a. Image and signal processing: It is used to reduce the dimensionality of image and signal data, enabling efficient compression, denoising, and feature extraction.b. Pattern recognition: Reducedimension is applied to extract discriminative features from high-dimensional datasets, improving the performance of pattern recognition algorithms.c. Bioinformatics: It is used to analyze and visualize genomic and proteomic data, enabling the identification of key genes or proteins associated with specific diseases or biological processes.d. Financial analysis: Reducedimension is used to analyze and model financial data, identifying the key factors that drive stock prices or predicting market trends.6. ConclusionThe Reducedimension method provides an effective approach for reducing the dimensionality of high-dimensional datasets while preserving the most relevant information. Its principles and procedures, including data preprocessing, covariance matrix computation, eigenvalue decomposition, principal component selection, projection, and reconstruction, enable efficient analysis, modeling, and visualization of complex datasets in various fields.。
Quantum Computing for Computer Scientists

More informationQuantum Computing for Computer ScientistsThe multidisciplinaryfield of quantum computing strives to exploit someof the uncanny aspects of quantum mechanics to expand our computa-tional horizons.Quantum Computing for Computer Scientists takes read-ers on a tour of this fascinating area of cutting-edge research.Writtenin an accessible yet rigorous fashion,this book employs ideas and tech-niques familiar to every student of computer science.The reader is notexpected to have any advanced mathematics or physics background.Af-ter presenting the necessary prerequisites,the material is organized tolook at different aspects of quantum computing from the specific stand-point of computer science.There are chapters on computer architecture,algorithms,programming languages,theoretical computer science,cryp-tography,information theory,and hardware.The text has step-by-stepexamples,more than two hundred exercises with solutions,and program-ming drills that bring the ideas of quantum computing alive for today’scomputer science students and researchers.Noson S.Yanofsky,PhD,is an Associate Professor in the Departmentof Computer and Information Science at Brooklyn College,City Univer-sity of New York and at the PhD Program in Computer Science at TheGraduate Center of CUNY.Mirco A.Mannucci,PhD,is the founder and CEO of HoloMathics,LLC,a research and development company with a focus on innovative mathe-matical modeling.He also serves as Adjunct Professor of Computer Sci-ence at George Mason University and the University of Maryland.QUANTUM COMPUTING FORCOMPUTER SCIENTISTSNoson S.YanofskyBrooklyn College,City University of New YorkandMirco A.MannucciHoloMathics,LLCMore informationMore informationcambridge university pressCambridge,New York,Melbourne,Madrid,Cape Town,Singapore,S˜ao Paulo,DelhiCambridge University Press32Avenue of the Americas,New York,NY10013-2473,USAInformation on this title:/9780521879965C Noson S.Yanofsky and Mirco A.Mannucci2008This publication is in copyright.Subject to statutory exceptionand to the provisions of relevant collective licensing agreements,no reproduction of any part may take place withoutthe written permission of Cambridge University Press.First published2008Printed in the United States of AmericaA catalog record for this publication is available from the British Library.Library of Congress Cataloging in Publication dataYanofsky,Noson S.,1967–Quantum computing for computer scientists/Noson S.Yanofsky andMirco A.Mannucci.p.cm.Includes bibliographical references and index.ISBN978-0-521-87996-5(hardback)1.Quantum computers.I.Mannucci,Mirco A.,1960–II.Title.QA76.889.Y352008004.1–dc222008020507ISBN978-0-521-879965hardbackCambridge University Press has no responsibility forthe persistence or accuracy of URLs for external orthird-party Internet Web sites referred to in this publicationand does not guarantee that any content on suchWeb sites is,or will remain,accurate or appropriate.More informationDedicated toMoishe and Sharon Yanofskyandto the memory ofLuigi and Antonietta MannucciWisdom is one thing:to know the tho u ght by which all things are directed thro u gh allthings.˜Heraclitu s of Ephe s u s(535–475B C E)a s quoted in Dio g ene s Laertiu s’sLives and Opinions of Eminent PhilosophersBook IX,1. More informationMore informationContentsPreface xi1Complex Numbers71.1Basic Definitions81.2The Algebra of Complex Numbers101.3The Geometry of Complex Numbers152Complex Vector Spaces292.1C n as the Primary Example302.2Definitions,Properties,and Examples342.3Basis and Dimension452.4Inner Products and Hilbert Spaces532.5Eigenvalues and Eigenvectors602.6Hermitian and Unitary Matrices622.7Tensor Product of Vector Spaces663The Leap from Classical to Quantum743.1Classical Deterministic Systems743.2Probabilistic Systems793.3Quantum Systems883.4Assembling Systems974Basic Quantum Theory1034.1Quantum States1034.2Observables1154.3Measuring1264.4Dynamics1294.5Assembling Quantum Systems1325Architecture1385.1Bits and Qubits138viiMore informationviii Contents5.2Classical Gates1445.3Reversible Gates1515.4Quantum Gates1586Algorithms1706.1Deutsch’s Algorithm1716.2The Deutsch–Jozsa Algorithm1796.3Simon’s Periodicity Algorithm1876.4Grover’s Search Algorithm1956.5Shor’s Factoring Algorithm2047Programming Languages2207.1Programming in a Quantum World2207.2Quantum Assembly Programming2217.3Toward Higher-Level Quantum Programming2307.4Quantum Computation Before Quantum Computers2378Theoretical Computer Science2398.1Deterministic and Nondeterministic Computations2398.2Probabilistic Computations2468.3Quantum Computations2519Cryptography2629.1Classical Cryptography2629.2Quantum Key Exchange I:The BB84Protocol2689.3Quantum Key Exchange II:The B92Protocol2739.4Quantum Key Exchange III:The EPR Protocol2759.5Quantum Teleportation27710Information Theory28410.1Classical Information and Shannon Entropy28410.2Quantum Information and von Neumann Entropy28810.3Classical and Quantum Data Compression29510.4Error-Correcting Codes30211Hardware30511.1Quantum Hardware:Goals and Challenges30611.2Implementing a Quantum Computer I:Ion Traps31111.3Implementing a Quantum Computer II:Linear Optics31311.4Implementing a Quantum Computer III:NMRand Superconductors31511.5Future of Quantum Ware316Appendix A Historical Bibliography of Quantum Computing319 by Jill CirasellaA.1Reading Scientific Articles319A.2Models of Computation320More informationContents ixA.3Quantum Gates321A.4Quantum Algorithms and Implementations321A.5Quantum Cryptography323A.6Quantum Information323A.7More Milestones?324Appendix B Answers to Selected Exercises325Appendix C Quantum Computing Experiments with MATLAB351C.1Playing with Matlab351C.2Complex Numbers and Matrices351C.3Quantum Computations354Appendix D Keeping Abreast of Quantum News:QuantumComputing on the Web and in the Literature357by Jill CirasellaD.1Keeping Abreast of Popular News357D.2Keeping Abreast of Scientific Literature358D.3The Best Way to Stay Abreast?359Appendix E Selected Topics for Student Presentations360E.1Complex Numbers361E.2Complex Vector Spaces362E.3The Leap from Classical to Quantum363E.4Basic Quantum Theory364E.5Architecture365E.6Algorithms366E.7Programming Languages368E.8Theoretical Computer Science369E.9Cryptography370E.10Information Theory370E.11Hardware371Bibliography373Index381More informationPrefaceQuantum computing is a fascinating newfield at the intersection of computer sci-ence,mathematics,and physics,which strives to harness some of the uncanny as-pects of quantum mechanics to broaden our computational horizons.This bookpresents some of the most exciting and interesting topics in quantum computing.Along the way,there will be some amazing facts about the universe in which we liveand about the very notions of information and computation.The text you hold in your hands has a distinctflavor from most of the other cur-rently available books on quantum computing.First and foremost,we do not assumethat our reader has much of a mathematics or physics background.This book shouldbe readable by anyone who is in or beyond their second year in a computer scienceprogram.We have written this book specifically with computer scientists in mind,and tailored it accordingly:we assume a bare minimum of mathematical sophistica-tion,afirst course in discrete structures,and a healthy level of curiosity.Because thistext was written specifically for computer people,in addition to the many exercisesthroughout the text,we added many programming drills.These are a hands-on,funway of learning the material presented and getting a real feel for the subject.The calculus-phobic reader will be happy to learn that derivatives and integrals are virtually absent from our text.Quite simply,we avoid differentiation,integra-tion,and all higher mathematics by carefully selecting only those topics that arecritical to a basic introduction to quantum computing.Because we are focusing onthe fundamentals of quantum computing,we can restrict ourselves to thefinite-dimensional mathematics that is required.This turns out to be not much more thanmanipulating vectors and matrices with complex entries.Surprisingly enough,thelion’s share of quantum computing can be done without the intricacies of advancedmathematics.Nevertheless,we hasten to stress that this is a technical textbook.We are not writing a popular science book,nor do we substitute hand waving for rigor or math-ematical precision.Most other texts in thefield present a primer on quantum mechanics in all its glory.Many assume some knowledge of classical mechanics.We do not make theseassumptions.We only discuss what is needed for a basic understanding of quantumxiMore informationxii Prefacecomputing as afield of research in its own right,although we cite sources for learningmore about advanced topics.There are some who consider quantum computing to be solely within the do-main of physics.Others think of the subject as purely mathematical.We stress thecomputer science aspect of quantum computing.It is not our intention for this book to be the definitive treatment of quantum computing.There are a few topics that we do not even touch,and there are severalothers that we approach briefly,not exhaustively.As of this writing,the bible ofquantum computing is Nielsen and Chuang’s magnificent Quantum Computing andQuantum Information(2000).Their book contains almost everything known aboutquantum computing at the time of its publication.We would like to think of ourbook as a usefulfirst step that can prepare the reader for that text.FEATURESThis book is almost entirely self-contained.We do not demand that the reader comearmed with a large toolbox of skills.Even the subject of complex numbers,which istaught in high school,is given a fairly comprehensive review.The book contains many solved problems and easy-to-understand descriptions.We do not merely present the theory;rather,we explain it and go through severalexamples.The book also contains many exercises,which we strongly recommendthe serious reader should attempt to solve.There is no substitute for rolling up one’ssleeves and doing some work!We have also incorporated plenty of programming drills throughout our text.These are hands-on exercises that can be carried out on your laptop to gain a betterunderstanding of the concepts presented here(they are also a great way of hav-ing fun).We hasten to point out that we are entirely language-agnostic.The stu-dent should write the programs in the language that feels most comfortable.Weare also paradigm-agnostic.If declarative programming is your favorite method,gofor it.If object-oriented programming is your game,use that.The programmingdrills build on one another.Functions created in one programming drill will be usedand modified in later drills.Furthermore,in Appendix C,we show how to makelittle quantum computing emulators with MATLAB or how to use a ready-madeone.(Our choice of MATLAB was dictated by the fact that it makes very easy-to-build,quick-and-dirty prototypes,thanks to its vast amount of built-in mathematicaltools.)This text appears to be thefirst to handle quantum programming languages in a significant way.Until now,there have been only research papers and a few surveyson the topic.Chapter7describes the basics of this expandingfield:perhaps some ofour readers will be inspired to contribute to quantum programming!This book also contains several appendices that are important for further study:Appendix A takes readers on a tour of major papers in quantum computing.This bibliographical essay was written by Jill Cirasella,Computational SciencesSpecialist at the Brooklyn College Library.In addition to having a master’s de-gree in library and information science,Jill has a master’s degree in logic,forwhich she wrote a thesis on classical and quantum graph algorithms.This dualbackground uniquely qualifies her to suggest and describe further readings.More informationPreface xiii Appendix B contains the answers to some of the exercises in the text.Othersolutions will also be found on the book’s Web page.We strongly urge studentsto do the exercises on their own and then check their answers against ours.Appendix C uses MATLAB,the popular mathematical environment and an es-tablished industry standard,to show how to carry out most of the mathematicaloperations described in this book.MATLAB has scores of routines for manip-ulating complex matrices:we briefly review the most useful ones and show howthe reader can quickly perform a few quantum computing experiments with al-most no effort,using the freely available MATLAB quantum emulator Quack.Appendix D,also by Jill Cirasella,describes how to use online resources to keepup with developments in quantum computing.Quantum computing is a fast-movingfield,and this appendix offers guidelines and tips forfinding relevantarticles and announcements.Appendix E is a list of possible topics for student presentations.We give briefdescriptions of different topics that a student might present before a class of hispeers.We also provide some hints about where to start looking for materials topresent.ORGANIZATIONThe book begins with two chapters of mathematical preliminaries.Chapter1con-tains the basics of complex numbers,and Chapter2deals with complex vectorspaces.Although much of Chapter1is currently taught in high school,we feel thata review is in order.Much of Chapter2will be known by students who have had acourse in linear algebra.We deliberately did not relegate these chapters to an ap-pendix at the end of the book because the mathematics is necessary to understandwhat is really going on.A reader who knows the material can safely skip thefirsttwo chapters.She might want to skim over these chapters and then return to themas a reference,using the index and the table of contents tofind specific topics.Chapter3is a gentle introduction to some of the ideas that will be encountered throughout the rest of the ing simple models and simple matrix multipli-cation,we demonstrate some of the fundamental concepts of quantum mechanics,which are then formally developed in Chapter4.From there,Chapter5presentssome of the basic architecture of quantum computing.Here one willfind the notionsof a qubit(a quantum generalization of a bit)and the quantum analog of logic gates.Once Chapter5is understood,readers can safely proceed to their choice of Chapters6through11.Each chapter takes its title from a typical course offered in acomputer science department.The chapters look at that subfield of quantum com-puting from the perspective of the given course.These chapters are almost totallyindependent of one another.We urge the readers to study the particular chapterthat corresponds to their favorite course.Learn topics that you likefirst.From thereproceed to other chapters.Figure0.1summarizes the dependencies of the chapters.One of the hardest topics tackled in this text is that of considering two quan-tum systems and combining them,or“entangled”quantum systems.This is donemathematically in Section2.7.It is further motivated in Section3.4and formallypresented in Section4.5.The reader might want to look at these sections together.xivPrefaceFigure 0.1.Chapter dependencies.There are many ways this book can be used as a text for a course.We urge instructors to find their own way.May we humbly suggest the following three plans of action:(1)A class that provides some depth might involve the following:Go through Chapters 1,2,3,4,and 5.Armed with that background,study the entirety of Chapter 6(“Algorithms”)in depth.One can spend at least a third of a semester on that chapter.After wrestling a bit with quantum algorithms,the student will get a good feel for the entire enterprise.(2)If breadth is preferred,pick and choose one or two sections from each of the advanced chapters.Such a course might look like this:(1),2,3,4.1,4.4,5,6.1,7.1,9.1,10.1,10.2,and 11.This will permit the student to see the broad outline of quantum computing and then pursue his or her own path.(3)For a more advanced class (a class in which linear algebra and some mathe-matical sophistication is assumed),we recommend that students be told to read Chapters 1,2,and 3on their own.A nice course can then commence with Chapter 4and plow through most of the remainder of the book.If this is being used as a text in a classroom setting,we strongly recommend that the students make presentations.There are selected topics mentioned in Appendix E.There is no substitute for student participation!Although we have tried to include many topics in this text,inevitably some oth-ers had to be left out.Here are a few that we omitted because of space considera-tions:many of the more complicated proofs in Chapter 8,results about oracle computation,the details of the (quantum)Fourier transforms,and the latest hardware implementations.We give references for further study on these,as well as other subjects,throughout the text.More informationMore informationPreface xvANCILLARIESWe are going to maintain a Web page for the text at/∼noson/qctext.html/The Web page will containperiodic updates to the book,links to interesting books and articles on quantum computing,some answers to certain exercises not solved in Appendix B,anderrata.The reader is encouraged to send any and all corrections tonoson@Help us make this textbook better!ACKNOLWEDGMENTSBoth of us had the great privilege of writing our doctoral theses under the gentleguidance of the recently deceased Alex Heller.Professor Heller wrote the follow-ing1about his teacher Samuel“Sammy”Eilenberg and Sammy’s mathematics:As I perceived it,then,Sammy considered that the highest value in mathematicswas to be found,not in specious depth nor in the overcoming of overwhelmingdifficulty,but rather in providing the definitive clarity that would illuminate itsunderlying order.This never-ending struggle to bring out the underlying order of mathematical structures was always Professor Heller’s everlasting goal,and he did his best to passit on to his students.We have gained greatly from his clarity of vision and his viewof mathematics,but we also saw,embodied in a man,the classical and sober ideal ofcontemplative life at its very best.We both remain eternally grateful to him.While at the City University of New York,we also had the privilege of inter-acting with one of the world’s foremost logicians,Professor Rohit Parikh,a manwhose seminal contributions to thefield are only matched by his enduring com-mitment to promote younger researchers’work.Besides opening fascinating vis-tas to us,Professor Parikh encouraged us more than once to follow new directionsof thought.His continued professional and personal guidance are greatly appre-ciated.We both received our Ph.D.’s from the Department of Mathematics in The Graduate Center of the City University of New York.We thank them for providingus with a warm and friendly environment in which to study and learn real mathemat-ics.Thefirst author also thanks the entire Brooklyn College family and,in partic-ular,the Computer and Information Science Department for being supportive andvery helpful in this endeavor.1See page1349of Bass et al.(1998).More informationxvi PrefaceSeveral faculty members of Brooklyn College and The Graduate Center were kind enough to read and comment on parts of this book:Michael Anshel,DavidArnow,Jill Cirasella,Dayton Clark,Eva Cogan,Jim Cox,Scott Dexter,EdgarFeldman,Fred Gardiner,Murray Gross,Chaya Gurwitz,Keith Harrow,JunHu,Yedidyah Langsam,Peter Lesser,Philipp Rothmaler,Chris Steinsvold,AlexSverdlov,Aaron Tenenbaum,Micha Tomkiewicz,Al Vasquez,Gerald Weiss,andPaula Whitlock.Their comments have made this a better text.Thank you all!We were fortunate to have had many students of Brooklyn College and The Graduate Center read and comment on earlier drafts:Shira Abraham,RachelAdler,Ali Assarpour,Aleksander Barkan,Sayeef Bazli,Cheuk Man Chan,WeiChen,Evgenia Dandurova,Phillip Dreizen,C.S.Fahie,Miriam Gutherc,RaveHarpaz,David Herzog,Alex Hoffnung,Matthew P.Johnson,Joel Kammet,SerdarKara,Karen Kletter,Janusz Kusyk,Tiziana Ligorio,Matt Meyer,James Ng,SeverinNgnosse,Eric Pacuit,Jason Schanker,Roman Shenderovsky,Aleksandr Shnayder-man,Rose B.Sigler,Shai Silver,Justin Stallard,Justin Tojeira,John Ma Sang Tsang,Sadia Zahoor,Mark Zelcer,and Xiaowen Zhang.We are indebted to them.Many other people looked over parts or all of the text:Scott Aaronson,Ste-fano Bettelli,Adam Brandenburger,Juan B.Climent,Anita Colvard,Leon Ehren-preis,Michael Greenebaum,Miriam Klein,Eli Kravits,Raphael Magarik,JohnMaiorana,Domenico Napoletani,Vaughan Pratt,Suri Raber,Peter Selinger,EvanSiegel,Thomas Tradler,and Jennifer Whitehead.Their criticism and helpful ideasare deeply appreciated.Thanks to Peter Rohde for creating and making available to everyone his MAT-LAB q-emulator Quack and also for letting us use it in our appendix.We had a gooddeal of fun playing with it,and we hope our readers will too.Besides writing two wonderful appendices,our friendly neighborhood librar-ian,Jill Cirasella,was always just an e-mail away with helpful advice and support.Thanks,Jill!A very special thanks goes to our editor at Cambridge University Press,HeatherBergman,for believing in our project right from the start,for guiding us through thisbook,and for providing endless support in all matters.This book would not existwithout her.Thanks,Heather!We had the good fortune to have a truly stellar editor check much of the text many times.Karen Kletter is a great friend and did a magnificent job.We also ap-preciate that she refrained from killing us every time we handed her altered draftsthat she had previously edited.But,of course,all errors are our own!This book could not have been written without the help of my daughter,Hadas-sah.She added meaning,purpose,and joy.N.S.Y.My dear wife,Rose,and our two wondrous and tireless cats,Ursula and Buster, contributed in no small measure to melting my stress away during the long andpainful hours of writing and editing:to them my gratitude and love.(Ursula is ascientist cat and will read this book.Buster will just shred it with his powerful claws.)M.A.M.。
Computational Geometry Theory and Applications

Max-Planck-lnstitut fiir Informatik, Im Stadtwald, 66123 Saarbriicken, Germany b RIB Bausoftware GmbH, Vaihinger Str. 151, 70507 Stuttgart, Germany c Fachbereich Mathematik und lnformatik, Martin-Luther Universitiit Halle-Wittenberg, 06120 Halle, Germany
Communicated by C.M. Hoffmann;submitted 15 August 1997; accepted 6 January 1998
Abstract
In this paper we describe and discuss a kernel for higher-dimensional computational geometry and we present its application in the calculation of convex hulls and Delaunay triangulations. The kernel is available in form of a software library module programmed in C++ extending LEDA. We introduce the basic data types like points, vectors, directions, hyperplanes, segments, rays, lines, spheres, affine transformations, and operations connecting these types. The description consists of a motivation for the basic class layout as well as topics like layered software design, runtime correctness via checking routines and documentation issues. Finally we shortly describe the usage of the kernel in the application domain. © 1998 Elsevier Science B.V.
大学计算机导论练习题及答案

考题练习
• If a computer can execute 2× 109 instructions per second, what is the computer’s performance in MFLOPS? [1] 20 [2] 2000 [3] 2000000 [4] 200000000 • The time unit, 400 x 10-10 seconds, is equivalent to . [1] 40 nanoseconds [2] 4000 miniseconds [3] 400 microseconds [4] 4 bitseconds • The disk size is 100 x 240 bytes that is equivalent to 100 . [1] KiloBytes (KB) [2] MegaBytes (MB) [3] GigaBytes (GB) [4] TeraBytes (TB) • A _________ is the method or technique that improves the efficiency of a search process, possibly by sacrificing optimality. [1] Turing test [2] Ad-hoc [3] Trade off [4] Heuristics
8
Lec. 2 Number Systems
考题练习
• • • Which of the following representation is erroneous? [1] (1001001)2 [2] (765)8 [3] (ING)16 [4] 1001001 The decimal fraction, 0.15625, is equal to: [1] 2 -1 + 2 -2 [2] 2 -2 + 2 -4 [3] 2 3 + 2 5 [4] 2 -3 + 2 -5 The decimal fraction, 0.515625, is equal to: [1] 2 -1 + 2 -2 [2] 2 -2 + 2 -4 [3] 2 -1 + 2 -6 [4] 2 -1 + 2 -5 Which of the following representation is erroneous? [1] (1001001) 2 [2] (789) 8 [3] (FCF) 16 [4] 1001001 The base of the hexadecimal number systems is ____? [1] 2 [2] 8 [3] 10 [4] 16 The octal system is a positional number system that uses __8_symbols to represent a number.
各国对数学的看法英语作文

Mathematics is a universal language that transcends cultural and geographical boundaries.However,the perception and approach to mathematics vary significantly among different countries around the world.Here is an exploration of how various nations view and engage with the subject of mathematics.China:Mastery and ExcellenceIn China,mathematics is highly valued as a subject that requires mastery and precision. The Chinese education system places a strong emphasis on math,with students expected to achieve high proficiency levels.The country is known for its rigorous curriculum and competitive exams,such as the Gaokao,where math scores play a crucial role in determining university admissions.India:A Tradition of Mathematical GeniusIndia has a rich history of mathematical innovation,dating back to ancient times.The country is renowned for its mathematical prodigies and has made significant contributions to the field.In contemporary times,Indian students are encouraged to excel in mathematics,with the subject being a core part of the education system.The competitive nature of Indian education fosters a deep understanding and appreciation for the subject.United States:A Mixed BagThe United States has a diverse approach to mathematics education.While some students excel and pursue advanced studies in the field,others struggle with basic concepts.The cation system has been criticized for not providing equal opportunities for all students to develop strong math skills.However,there is a growing emphasis on improving math education and fostering a love for the subject.Russia:Deep Understanding and ApplicationRussian education is known for its focus on developing a deep understanding of mathematical concepts.The curriculum is designed to challenge students to think critically and apply mathematical knowledge to realworld problems.Russian students often excel in international math competitions,reflecting the countrys commitment to highquality math education.France:A Strong Emphasis on TheoryIn France,mathematics is approached with a strong emphasis on theory and abstract thinking.French students are encouraged to explore the beauty and logic of mathematical concepts.The French education system is known for its rigorous training in theoretical mathematics,which has produced many renowned mathematicians.Japan:A Balance of Theory and Practical ApplicationJapanese education seeks to balance the theoretical understanding of mathematics with practical application.The curriculum is designed to be comprehensive,covering a wide range of topics and ensuring that students have a solid foundation in the subject.Japanese students are known for their strong problemsolving skills and ability to apply mathematical concepts to various situations.Germany:A Focus on Logical Thinking and PrecisionGermanys approach to mathematics education emphasizes logical thinking and precision. The curriculum is structured to develop students analytical skills and their ability to solve complex problems.German students are encouraged to explore the subject in depth,with a focus on understanding the underlying principles and concepts.ConclusionWhile the approach to mathematics education varies from country to country,the subject remains a critical component of the global educational landscape.Each nations unique perspective on mathematics contributes to the rich tapestry of global mathematical knowledge and innovation.Regardless of cultural differences,the pursuit of mathematical excellence remains a shared goal across the world.。
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A reversible cellular automaton (RCA) is a cellular automaton (CA) whose global function is injective and every con guration has at most one predecessor. Margolus showed that there is a computation-universal two-dimensional 2-state RCA. But his RCA has nonuniform neighbor, so Morita and Ueno proposed 16-state computation-universal RCA using partitioned cellular automata (PCA). Because PCA can be regarded as a subclass of standard CA, their models has standard neighbor. In this paper, we show that the number of states of Morita and Ueno's models can be reducible. To decrease the number of states from their models with preserving isotropic and bit-preserving properties, we used triangular 3neighbor, and thus 8-state RCA can be possible. This is the smallest state two-dimensional RCA under the condition of isotropic property on the framework of PCA. We show that our model can simulate basic circuit elements such as unit wires, delay elements, crossing wires, switch gates and inverse switch gates. And it is possible to construct a Fredkin gate by combining these elements. Since Fredkin gate is known to be a universal logic gate, our model has computation-universality.
c c cx x cx z c y y+z c
Figure 2: An S-gate and an inverse S-gate
Figure 3: A realization of a Fredkin gate by S-gates and inverse S-gates
Though BBMCA uses a non-uniform neighborhood, Morita and Ueno constructed 16-State twodimensional computation-universal RCA using the framework of partitioned cellular automata (PCA) 5]. PCA is regarded as the subclass of standard CA. Each cell is partitioned into the equal number of parts to the neighborhood size and the information stored in each part is sent to only one of the neighboring cells. In PCA, injectivity of global function is equivalent to injectivity of local function, thus a PCA is reversible if its local function is injective 6]. Their models used 4-neighbor PCA and g. 4 shows its domain and range of the local function.
the control signal c. They showed that S-gates can be constructed on their Billiard Ball Model (BBM) and using two S-gates and two inverse S-gates, it is possible to construct an F-gate (Fig. 3).
A computation-universal two-dimensional 8-state triangular reversible cellular automa来自onKatsunobu
Imai, Kenichi Morita
Faculty of Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan fimai, moritag@ke.sys.hiroshima-u.ac.jp
2.1 Fredkin Gate
2 Computation-universal RCA and BBM
Figure 1: A Fredkin gate They also introduced a switch gate (S-gate) and its inverse gate (Fig. 2). An S-gate is a 2-input, 3-output reversible and bit-preserving logic gate. An S-gate switches the input x by 2
On BBMCA and 16-state RPCA models, BBM is used for showing their computation-universality. First we make a brief description about BBM and a Fredkin gate. A Fredkin gate (F-gate) is a basic element in the theory of Conservative Logic proposed by Fredkin and To oli 3]. It is reversible and bit-preserving logic gate (Fig. 1). They showed that AND, OR, NOT, and fan-out gate can be constructed by an F-gate and any circuits can be constructed by F-gates and unit delays.
Keywords:
Abstract
cellular automata, reversibility, computation-universality, conservative logic.
1
1 Introduction
A reversible cellular automaton (RCA) is a cellular automaton (CA) whose global function is injective and every con guration has at most one predecessor. Reversibility is a very strong constraint, but computation-universality of RCA has been showed 2]. On two-dimensional CA, their computation-universality can be proved by embedding universal logic elements. For example, computation-universality of the game of life was proved by constructing AND, OR, NOT, and fan-out gates on its cellular space. Sequences of glider patterns was used as signal carriers 1]. Using this approach, one can construct small state computationuniversal CA. But on RCA, erasing informations are inhibited and such irreversible logic gates can not be embedded directly. Margolus proposed a two-dimensional computation-universal RCA (BBMCA) 4]. He realized its universality by embedding Fredkin and To oli's Billiard Ball Model (BBM). BBM is a computing model in which logical operations are performed by elastic collisions of balls. They showed that 3-input, 3-output reversible and bit-preserving Fredkin gate (F-gate) can be embedded in their BBM, and combining F-gates and unit delays, any logic circuits can be constructed using BBM. Although BBMCA is simple RCA, it has non-uniform neighbor. So Morita and Ueno proposed a di erent type of 4-neighbor 16-state computation-universal RCA 5]. They used a partitioned cellular automaton (PCA). It is regarded as a subclass of standard CA. In PCA, the injectivity of a global function is equivalent to the injectivity of local function and it makes ease of constructing RCA 6]. In this paper, we show that the number of states of Morita and Ueno's models can be reduced. To decrease the number of states from their models with preserving isotropic and bit-preserving properties, we used triangular 3-neighbor, and thus 8-state RCA can be possible. This is the smallest state two-dimensional RCA under the condition of isotropic property on the framework of PCA.