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COM 113 INTRO to COMPUTER PROGRAMMING Theory Book
1 [Type the document title] UNESCO -NIGERIA TECHNICAL & VOCATIONAL EDUCATION REVITALISATION PROJECT -PHASE II NATIONAL DIPLOMA IN COMPUTER TECHNOLOGY Computer Programming COURSE CODE: COM113 YEAR I - SE MESTER I THEORY Version 1: December 2008 2 [Type the document title] Table of Contents WEEK 1 Concept of programming ................................................................................................................ 6 Features of a good computer program ............................................................................................ 7 System Development Cycle ............................................................................................................ 9 WEEK 2 Concept of Algorithm ................................................................................................................... 11 Features of an Algorithm .............................................................................................................. 11 Methods of Representing Algorithm ............................................................................................ 11 Pseudo code .................................................................................................................................. 12 WEEK 3 English-like form .......................................................................................................................... 15 Flowchart ..................................................................................................................................... -
Jet Development in Leading Log QCD.Pdf
17 CALT-68-740 DoE RESEARCH AND DEVELOPMENT REPORT Jet Development in Leading Log QCD * by STEPHEN WOLFRAM California Institute of Technology, Pasadena. California 91125 ABSTRACT A simple picture of jet development in QCD is described. Various appli- cations are treated, including transverse spreading of jets, hadroproduced y* pT distributions, lepton energy spectra from heavy quark decays, soft parton multiplicities and hadron cluster formation. *Work supported in part by the U.S. Department of Energy under Contract No. DE-AC-03-79ER0068 and by a Feynman Fellowship. 18 According to QCD, high-energy e+- e annihilation into hadrons is initiated by the production from the decaying virtual photon of a quark and an antiquark, ,- +- each with invariant masses up to the c.m. energy vs in the original e e collision. The q and q then travel outwards radiating gluons which serve to spread their energy and color into a jet of finite angle. After a time ~ 1/IS, the rate of gluon emissions presumably decreases roughly inversely with time, except for the logarithmic rise associated with the effective coupling con 2 stant, (a (t) ~ l/log(t/A ), where It is the invariant mass of the radiating s . quark). Finally, when emissions have degraded the energies of the partons produced until their invariant masses fall below some critical ~ (probably c a few times h), the system of quarks and gluons begins to condense into the observed hadrons. The probability for a gluon to be emitted at times of 0(--)1 is small IS and may be est~mated from the leading terms of a perturbation series in a (s). -
PARTON and HADRON PRODUCTION in E+E- ANNIHILATION Stephen Wolfram California Institute of Technology, Pasadena, California 91125
549 * PARTON AND HADRON PRODUCTION IN e+e- ANNIHILATION Stephen Wolfram California Institute of Technology, Pasadena, California 91125 Ab stract: The production of showers of partons in e+e- annihilation final states is described according to QCD , and the formation of hadrons is dis cussed. Resume : On y decrit la production d'averses d� partons dans la theorie QCD qui forment l'etat final de !'annihilation e+ e , ainsi que leur transform ation en hadrons. *Work supported in part by the U.S. Department of Energy under contract no. DE-AC-03-79ER0068. SSt1 Introduction In these notes , I discuss some attemp ts e ri the cl!evellope1!1t of - to d s c be hadron final states in e e annihilation events ll>Sing. QCD. few featm11res A (barely visible at available+ energies} of this deve l"'P"1'ent amenab lle· are lt<J a precise and formal analysis in QCD by means of pe·rturbatirnc• the<J ry. lfor the mos t part, however, existing are quite inadle<!i""' lte! theoretical mett:l�ods one must therefore simply try to identify dl-iimam;t physical p.l:ten<0melilla tt:he to be expected from QCD, and make estll!att:es of dne:i.r effects , vi.th the hop·e that results so obtained will provide a good appro�::illliatimn to eventllLal'c ex act calculations . In so far as such estllma�es r necessa�y� pre�ise �r..uan- a e titative tests of QCD are precluded . On the ott:her handl , if QC!Ji is ass11l!med correct, then existing experimental data to inve•stigate its be may \JJ:SE•«f havior in regions not yet explored by theoreticalbe llJleaums . -
Defining Computer Program Parts Under Learned Hand's Abstractions Test in Software Copyright Infringement Cases
Michigan Law Review Volume 91 Issue 3 1992 Defining Computer Program Parts Under Learned Hand's Abstractions Test in Software Copyright Infringement Cases John W.L. Ogilive University of Michigan Law School Follow this and additional works at: https://repository.law.umich.edu/mlr Part of the Computer Law Commons, Intellectual Property Law Commons, and the Judges Commons Recommended Citation John W. Ogilive, Defining Computer Program Parts Under Learned Hand's Abstractions Test in Software Copyright Infringement Cases, 91 MICH. L. REV. 526 (1992). Available at: https://repository.law.umich.edu/mlr/vol91/iss3/5 This Note is brought to you for free and open access by the Michigan Law Review at University of Michigan Law School Scholarship Repository. It has been accepted for inclusion in Michigan Law Review by an authorized editor of University of Michigan Law School Scholarship Repository. For more information, please contact [email protected]. NOTE Defining Computer Program Parts Under Learned Hand's Abstractions Test in Software Copyright Infringement Cases John W.L. Ogilvie INTRODUCTION Although computer programs enjoy copyright protection as pro tectable "literary works" under the federal copyright statute, 1 the case law governing software infringement is confused, inconsistent, and even unintelligible to those who must interpret it.2 A computer pro gram is often viewed as a collection of different parts, just as a book or play is seen as an amalgamation of plot, characters, and other familiar parts. However, different courts recognize vastly different computer program parts for copyright infringement purposes. 3 Much of the dis array in software copyright law stems from mutually incompatible and conclusory program part definitions that bear no relation to how a computer program is actually designed and created. -
The Problem of Distributed Consensus: a Survey Stephen Wolfram*
The Problem of Distributed Consensus: A Survey Stephen Wolfram* A survey is given of approaches to the problem of distributed consensus, focusing particularly on methods based on cellular automata and related systems. A variety of new results are given, as well as a history of the field and an extensive bibliography. Distributed consensus is of current relevance in a new generation of blockchain-related systems. In preparation for a conference entitled “Distributed Consensus with Cellular Automata and Related Systems” that we’re organizing with NKN (= “New Kind of Network”) I decided to explore the problem of distributed consensus using methods from A New Kind of Science (yes, NKN “rhymes” with NKS) as well as from the Wolfram Physics Project. A Simple Example Consider a collection of “nodes”, each one of two possible colors. We want to determine the majority or “consensus” color of the nodes, i.e. which color is the more common among the nodes. A version of this document with immediately executable code is available at writings.stephenwolfram.com/2021/05/the-problem-of-distributed-consensus Originally published May 17, 2021 *Email: [email protected] 2 | Stephen Wolfram One obvious method to find this “majority” color is just sequentially to visit each node, and tally up all the colors. But it’s potentially much more efficient if we can use a distributed algorithm, where we’re running computations in parallel across the various nodes. One possible algorithm works as follows. First connect each node to some number of neighbors. For now, we’ll just pick the neighbors according to the spatial layout of the nodes: The algorithm works in a sequence of steps, at each step updating the color of each node to be whatever the “majority color” of its neighbors is. -
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Mel says, “This is swell! But it’s not ideal—it’s a free, grainy PDF.” Attain your ideals! Purchase a nicer, printable PDF of this issue. Or nicest of all, subscribe to the paper version of the Annals of Improbable Research (six issues per year, delivered to your doorstep!). To purchase pretty PDFs, or to subscribe to splendid paper, go to http://www.improbable.com/magazine/ ANNALS OF Special Issue THE 2009 IG® NOBEL PRIZES Panda poo spinoff, Tequila-based diamonds, 11> Chernobyl-inspired bra/mask… NOVEMBER|DECEMBER 2009 (volume 15, number 6) $6.50 US|$9.50 CAN 027447088921 The journal of record for inflated research and personalities Annals of © 2009 Annals of Improbable Research Improbable Research ISSN 1079-5146 print / 1935-6862 online AIR, P.O. Box 380853, Cambridge, MA 02238, USA “Improbable Research” and “Ig” and the tumbled thinker logo are all reg. U.S. Pat. & Tm. Off. 617-491-4437 FAX: 617-661-0927 www.improbable.com [email protected] EDITORIAL: [email protected] The journal of record for inflated research and personalities Co-founders Commutative Editor VP, Human Resources Circulation (Counter-clockwise) Marc Abrahams Stanley Eigen Robin Abrahams James Mahoney Alexander Kohn Northeastern U. Research Researchers Webmaster Editor Associative Editor Kristine Danowski, Julia Lunetta Marc Abrahams Mark Dionne Martin Gardiner, Tom Gill, [email protected] Mary Kroner, Wendy Mattson, General Factotum (web) [email protected] Dissociative Editor Katherine Meusey, Srinivasan Jesse Eppers Rose Fox Rajagopalan, Tom Roberts, Admin Tom Ulrich Technical Eminence Grise Lisa Birk Psychology Editor Dave Feldman Robin Abrahams Design and Art European Bureau Geri Sullivan Art Director emerita Kees Moeliker, Bureau Chief Contributing Editors PROmote Communications Peaco Todd Rotterdam Otto Didact, Stephen Drew, Ernest Lois Malone Webmaster emerita [email protected] Ersatz, Emil Filterbag, Karen Rich & Famous Graphics Steve Farrar, Edinburgh Desk Chief Hopkin, Alice Kaswell, Nick Kim, Amy Gorin Erwin J.O. -
Language Translators
Student Notes Theory LANGUAGE TRANSLATORS A. HIGH AND LOW LEVEL LANGUAGES Programming languages Low – Level Languages High-Level Languages Example: Assembly Language Example: Pascal, Basic, Java Characteristics of LOW Level Languages: They are machine oriented : an assembly language program written for one machine will not work on any other type of machine unless they happen to use the same processor chip. Each assembly language statement generally translates into one machine code instruction, therefore the program becomes long and time-consuming to create. Example: 10100101 01110001 LDA &71 01101001 00000001 ADD #&01 10000101 01110001 STA &71 Characteristics of HIGH Level Languages: They are not machine oriented: in theory they are portable , meaning that a program written for one machine will run on any other machine for which the appropriate compiler or interpreter is available. They are problem oriented: most high level languages have structures and facilities appropriate to a particular use or type of problem. For example, FORTRAN was developed for use in solving mathematical problems. Some languages, such as PASCAL were developed as general-purpose languages. Statements in high-level languages usually resemble English sentences or mathematical expressions and these languages tend to be easier to learn and understand than assembly language. Each statement in a high level language will be translated into several machine code instructions. Example: number:= number + 1; 10100101 01110001 01101001 00000001 10000101 01110001 B. GENERATIONS OF PROGRAMMING LANGUAGES 4th generation 4GLs 3rd generation High Level Languages 2nd generation Low-level Languages 1st generation Machine Code Page 1 of 5 K Aquilina Student Notes Theory 1. MACHINE LANGUAGE – 1ST GENERATION In the early days of computer programming all programs had to be written in machine code. -
13. Stored-Program Computers
13. Stored-Program Computers 13.1 Introduction This chapter concentrates on the low-level usage and structure of stored program computers. We focus on a particular hypothetical machine known as the ISC, describing its programming in assembly language. We show how recursion and switch statements are compiled into machine language, and how memory-mapped overlapped I/O is achieved. We also show the logic implement of the ISC, in terms of registers, buses, and finite-state machine controllers. 13.2 Programmer's Abstraction for a Stored-Program Computer By stored-program computer, we mean a machine in which the program, as well as the data, are stored in memory, each word of which can be accessed in uniform time. Most of the high-level language programming the reader has done will likely have used this kind of computer implicitly. However, the program that is stored is not high-level language text. If it were, then it would be necessary to constantly parse this text, which would slow down execution immensely. Instead one of two other forms of storage is used: An abstract syntax representation of the program could be stored. The identifiers in this representation are pre-translated, and the structure is traversed dynamically as needed during execution. This is the approach used by an interpreter for the language. A second approach is to use a compiler for the language. The compiler translates the program into the very low-level language native to the machine, appropriately called machine language. The native machine language acts as a least-common-denominator language for the computer. -
Programming Fundamentals - I Basic Concepts Fall-Semester 2016
Programming Fundamentals - I Basic Concepts Fall-Semester 2016 Prepared By: Rao Muhammad Umer Lecturer, Web: raoumer.github.io Department of Computer Science & IT, The University of Lahore. What is computer? The term "computer" was originally given to humans who performed numerical calculations using mechanical calculators, such as the abacus and slide rule. The term was later given to a mechanical device as they began replacing the human computers. Today's computers are electronic devices that accept data such as numbers, text, sound, image, animations, video, etc., (input), process that data (converts data to information) , produce output, and then store (storage) the results. A basic computer consists of 4 components: 1. Input devices 2. Central Processing Unit or CPU 3. Output devices 4. Memory Input Devices are used to provide input to the computer basic input devices include keyboard, mouse, touch screens etc. Central Processing Unit acts like a brain, it processes all instructions and data in the computer, the instructions are computer commands, these commands are given to CPU by input devices, some of the instructions are generated by the computer itself Output devices are used to receive computer output, the output, some basic output devices are hard drive disk (HDD, commonly known as hard disk), printers, computer screens (Monitors and LCDs) The computer memory is a temporary storage area. It holds the data and instructions that the Central Processing Unit (CPU) needs. Before a program can be run, the program is loaded from some storage device such as into the memory, the CPU loads the program or part of the program from the memory and executes it. -
Introduction to Computer Programming
Introduction to Computer Programming CISC1600/1610 Computer Science I/Lab Spring 2016 CISC1600 Yanjun Li 1 Outline This Course Computer Programming Spring 2016 CISC1600 Yanjun Li 2 1 This is a course In Programming For beginners who want to become professionals or who would like to know something about programming who are assumed to be bright Though not (necessarily) geniuses who are willing to work hard Though do need sleep occasionally, and take a normal course load Using the C++ programming language Spring 2016 CISC1600 Yanjun Li 3 Learning Goals Learn Fundamental programming concepts Key useful techniques Basic Standard C++ facilities After the course, you’ll be able to Write small sound C++ programs Read much larger programs Learn the basics of many other languages by yourself Proceed with an “advanced” C++ programming course After the course, you will not (yet) be An expert programmer A C++ language expert An expert user of advanced libraries Spring 2016 CISC1600 Yanjun Li 4 2 The Means Lectures Attend every one Outside of lectures Read a chapter ahead, and read the chapter again after each lecture Read actively: with questions in mind, try to reorganize/rephrase the key points in your mind Review questions/Terms in chapters Drills Always do the drills, before the exercises Exercises Spring 2016 CISC1600 Yanjun Li 5 The Means (Cont.) Lab projects That’s where the most fun and the best learning takes place Don’t wait until lab section to start the project Start to think about the project early Finish up & get help during labs Exams Midterms Final Spring 2016 CISC1600 Yanjun Li 6 3 How to be Successful? Don’t study alone when you don’t have to Form study groups Do help each other (without plagiarizing) If in doubt if a collaboration is legitimate: ask! Don’t claim to have written code that you copied from others Don’t give anyone else your code (to hand in for a grade) When you rely on the work of others, explicitly list all of your sources – i.e. -
Cellular Automation
John von Neumann Cellular automation A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays.[2] A cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighborhood is defined relative to the specified cell. An initial state (time t = 0) is selected by assigning a state for each cell. A new generation is created (advancing t by 1), according to some fixed rule (generally, a mathematical function) that determines the new state of each cell in terms of the current state of the cell and the states of the cells in its neighborhood. Typically, the rule for updating the state of cells is the same for each cell and does not change over time, and is applied to the whole grid simultaneously, though exceptions are known, such as the stochastic cellular automaton and asynchronous cellular automaton. The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. -
A New Kind of Science
Wolfram|Alpha, A New Kind of Science A New Kind of Science Wolfram|Alpha, A New Kind of Science by Bruce Walters April 18, 2011 Research Paper for Spring 2012 INFSY 556 Data Warehousing Professor Rhoda Joseph, Ph.D. Penn State University at Harrisburg Wolfram|Alpha, A New Kind of Science Page 2 of 8 Abstract The core mission of Wolfram|Alpha is “to take expert-level knowledge, and create a system that can apply it automatically whenever and wherever it’s needed” says Stephen Wolfram, the technologies inventor (Wolfram, 2009-02). This paper examines Wolfram|Alpha in its present form. Introduction As the internet became available to the world mass population, British computer scientist Tim Berners-Lee provided “hypertext” as a means for its general consumption, and coined the phrase World Wide Web. The World Wide Web is often referred to simply as the Web, and Web 1.0 transformed how we communicate. Now, with Web 2.0 firmly entrenched in our being and going with us wherever we go, can 3.0 be far behind? Web 3.0, the semantic web, is a web that endeavors to understand meaning rather than syntactically precise commands (Andersen, 2010). Enter Wolfram|Alpha. Wolfram Alpha, officially launched in May 2009, is a rapidly evolving "computational search engine,” but rather than searching pre‐existing documents, it actually computes the answer, every time (Andersen, 2010). Wolfram|Alpha relies on a knowledgebase of data in order to perform these computations, which despite efforts to date, is still only a fraction of world’s knowledge. Scientist, author, and inventor Stephen Wolfram refers to the world’s knowledge this way: “It’s a sad but true fact that most data that’s generated or collected, even with considerable effort, never gets any kind of serious analysis” (Wolfram, 2009-02).