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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
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Entrepreneurship Entrepreneurship FIFTH EDITION Andrew Zacharakis Babson College Andrew C. Corbett Babson College William D. Bygrave Babson College VP AND EDITORIAL DIRECTOR Mike McDonald EXECUTIVE EDITOR Lise Johnson EDITORIAL MANAGER Judy Howarth CONTENT MANAGEMENT DIRECTOR Lisa Wojcik CONTENT MANAGER Nichole Urban SENIOR CONTENT SPECIALIST Nicole Repasky PRODUCTION EDITOR Indirakumari, S. COVER PHOTO CREDIT © PitukTV/Shutterstock This book was set in 10/12pt Times by SPi Global, Pondicherry, India and printed and bound by Quad Graphics. Founded in 1807, John Wiley & Sons, Inc. has been a valued source of knowledge and understanding for more than 200 years, helping people around the world meet their needs and fulfill their aspirations. Our company is built on a foundation of principles that include responsibility to the communities we serve and where we live and work. In 2008, we launched a Corporate Citizenship Initiative, a global effort to address the environmental, social, economic, and ethical challenges we face in our business. Among the issues we are addressing are carbon impact, paper specifications and procurement, ethical conduct within our business and among our vendors, and community and charitable support. For more information, please visit our website: www.wiley.com/go/citizenship. Copyright © 2020, 2017, 2014, 2011, 2008 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923 (Web site: www.copyright.com). -
Reducing Redundancy in Data Organization and Arithmetic Calculation for Stencil Computations
Reducing Redundancy in Data Organization and Arithmetic Calculation for Stencil Computations Kun Li Liang Yuan Yunquan Zhang Institute of Computing Technology, Institute of Computing Technology, Institute of Computing Technology, Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of Beijing, China Beijing, China Sciences [email protected] [email protected] Beijing, China [email protected] Yue Yue Hang Cao Pengqi Lu Institute of Computing Technology, Institute of Computing Technology, Institute of Computing Technology, Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of University of Chinese Academy of University of Chinese Academy of Sciences Sciences Sciences Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] Abstract ACM Reference Format: Stencil computation is one of the most important kernels Kun Li, Liang Yuan, Yunquan Zhang, Yue Yue, Hang Cao, and Pengqi in various scientific and engineering applications. A variety Lu. 2021. Reducing Redundancy in Data Organization and Arith- metic Calculation for Stencil Computations. In Proceedings of ACM of work has focused on vectorization techniques, aiming at Conference (Conference’21). ACM, Seoul, South Korea, 12 pages. exploiting the in-core data parallelism. Briefly, they either https://doi.org/10.1145/nnnnnnn.nnnnnnn incur data alignment conflicts or hurt the data locality when integrated with tiling. In this paper, a novel transpose lay- out is devised to preserve the data locality for tiling in the 1 Introduction data space and reduce the data reorganization overhead for Stencil is one of the most important kernels widely used vectorization simultaneously. -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
Topology in Distributed Computing
Die approbierte Originalversion dieser Diplom-/Masterarbeit ist an der Hauptbibliothek der Technischen Universität Wien aufgestellt (http://www.ub.tuwien.ac.at). The approved original version of this diploma or master thesis is available at the main library of the Vienna University of Technology (http://www.ub.tuwien.ac.at/englweb/). Topology in Distributed Computing DIPLOMARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur im Rahmen des Studiums Technische Informatik ausgeführt von Thomas Nowak Matrikelnummer 0425201 an der Fakultät für Informatik der Technischen Universität Wien Betreuer: Univ.Prof. Dr. Ulrich Schmid Wien, 18.03.2010 _______________________ ______________________ (Unterschrift Verfasser) (Unterschrift Betreuer) Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43/(0)1/58801-0 http://www.tuwien.ac.at Erklärung Thomas Nowak Rechte Wienzeile 73/23 1050 Wien Hiermit erkläre ich, dass ich diese Arbeit selbstständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe. Wien, 18.03.2010 ______________________ (Unterschrift) Abstract Topology is the general mathematical theory of convergence. Distributed com- puting is the formal investigation of communicating concurrent processes. We explore applications of topology to distributed computing in two directions: (1) Point-set topology and (2) algebraic topology. We use the former to study the topological structure of infinite execution trees. This enables us to unify a number of impossibility proofs, in particular, the impossibility of distributed consensus — the task of all processes in a system agreeing on a single value — in various (close to) asynchronous systems with crash failures. -
Lzw Compression and Decompression
LZW COMPRESSION AND DECOMPRESSION December 4, 2015 1 Contents 1 INTRODUCTION 3 2 CONCEPT 3 3 COMPRESSION 3 4 DECOMPRESSION: 4 5 ADVANTAGES OF LZW: 6 6 DISADVANTAGES OF LZW: 6 2 1 INTRODUCTION LZW stands for Lempel-Ziv-Welch. This algorithm was created in 1984 by these people namely Abraham Lempel, Jacob Ziv, and Terry Welch. This algorithm is very simple to implement. In 1977, Lempel and Ziv published a paper on the \sliding-window" compression followed by the \dictionary" based compression which were named LZ77 and LZ78, respectively. later, Welch made a contri- bution to LZ78 algorithm, which was then renamed to be LZW Compression algorithm. 2 CONCEPT Many files in real time, especially text files, have certain set of strings that repeat very often, for example " The ","of","on"etc., . With the spaces, any string takes 5 bytes, or 40 bits to encode. But what if we need to add the whole string to the list of characters after the last one, at 256. Then every time we came across the string like" the ", we could send the code 256 instead of 32,116,104 etc.,. This would take 9 bits instead of 40bits. This is the algorithm of LZW compression. It starts with a "dictionary" of all the single character with indexes from 0 to 255. It then starts to expand the dictionary as information gets sent through. Pretty soon, all the strings will be encoded as a single bit, and compression would have occurred. LZW compression replaces strings of characters with single codes. It does not analyze the input text. -
Why Extension-Based Proofs Fail
Why Extension-Based Proofs Fail Dan Alistarh James Aspnes Faith Ellen IST Austria Yale University of Toronto [email protected] [email protected] [email protected] Rati Gelashvili Leqi Zhu University of Toronto University of Michigan [email protected] [email protected] August 2, 2020 Abstract We introduce extension-based proofs, a class of impossibility proofs that includes valency arguments. They are modelled as an interaction between a prover and a protocol. Using proofs based on combinatorial topology, it has been shown that it is impossible to deterministically solve k-set agreement among n > k 2 processes in a wait-free manner in certain asynchronous models. However, it was unknown whether≥ proofs based on simpler techniques were possible. We show that this impossibility result cannot be obtained for one of these models by an extension- based proof and, hence, extension-based proofs are limited in power. 1 Introduction One of the most well-known results in the theory of distributed computing, due to Fischer, Lynch, and Paterson [FLP85], is that there is no deterministic, wait-free protocol solving consensus among n 2 processes in an asynchronous message passing system, even if at most one process may ≥ crash. Their result has been extended to asynchronous shared memory systems where processes communicate by reading from and writing to shared registers [Abr88, CIL87, Her91, LAA87]. Moses and Rajsbaum [MR02] gave a unified framework for proving the impossibility of consensus in a number of different systems. Chaudhuri [Cha93] conjectured that the impossibility of consensus could be generalized to the k-set agreement problem. -
NASA Contrattbr 4Report 178229 S E M I a N N U a L R E P O
NASA Contrattbr 4Report 178229 ICASE SEMIANNUAL REPORT April 1, 1986 through September 30, 1986 Contract No. NAS1-18107 January 1987 INSTITUTE FOR COMPUTER APPLICATIONS IN SCIENCE AND ENGINEERING NASA Langley Research Center, Hampton, Virginia 23665 Operatsd by the Universities Space Research Association National Aeronautics and Space Ad minis t rat ion bnglay Research Center HamDton,Virginia23665 CONTENTS Page Introduction .............................................................. iii Research in erogress ...................................................... 1 Reports and Abstracts ..................................................... 32 ICASE Colloquia........................................................... 49 ICASE Summer Activities ................................................... 52 Other Activities .......................................................... 58 ICASE Staff ............................................................... 60 i INTRODUCTION The Institute for Computer Applications in Science and Engineering (ICASE) is operated at the Langley Research Center (LaRC) of NASA by the Universities Space Research Associat€on (USRA) under a contract with the Center. USRA is a nonpro€it consortium of major U. S. colleges and universities. The Institute conducts unclassified basic research in applied mathematics, numerical analysis, and computer science in order to extend and improve problem-solving capabilities in science and engineering, particularly in aeronautics and space. ICASE has a small permanent staff. Research -
Oral History Interview with Bernard A. Galler
An Interview with BERNARD A. GALLER OH 236 Conducted by Enid H. Galler on 8, 10-11, and 16 August 1991 Sutton's Bay, MI Ann Arbor, MI Charles Babbage Institute Center for the History of Information Processing University of Minnesota, Minneapolis Copyright, Charles Babbage Institute 1 Bernard A. Galler Interview 8, 10-11, and 16 August 1991 Abstract In this wide-ranging interview, Galler describes the development of computer science at the University of Michigan from the 1950s through the 1980s and discusses his own work in computer science. Prominent subjects in Galler's description of his work at Michigan include: his arrival and classes with John Carr, research use of International Business Machines (IBM) and later Amdahl mainframe computers, the establishment of the Statistical Laboratory in the Mathematics Dept., the origin of the computer science curriculum and the Computer Science Dept. in the 1950s, interactions with Massachusetts Institute of Technology and IBM about timesharing in the 1960s, the development of the Michigan Algorithm Decoder, and the founding of the MERIT network. Galler also discusses Michigan's relationship with ARPANET, CSNET, and BITNET. He describes the atmosphere on campus in the 1960s and early 1970s and his various administrative roles at the university. Galler discusses his involvement with the Association for Computing Machinery, the American Federation of Information Processing Societies, the founding of the Charles Babbage Institute, and his work with the Annals of the History of Computing. He describes his consultative work with Israel and his consulting practice in general, his work as an expert witness, and his interaction with the Patent Office on issues surrounding the patenting of software and his role in the establishment of the Software Patent Institute. -
I. Personal Information (1/11/2021) I.A. UID, Last Name, First Name, Middle Name, Contact Information UID: 101001302 Last
I. Personal Information (1/11/2021) I.A. UID, Last Name, First Name, Middle Name, Contact Information UID: 101001302 Last Name: Shneiderman First Name: Ben Mailing Address: Brendan Iribe Building, Dept. of Computer Science, University of Maryland, College Park, MD 20742 Email: [email protected] Personal URL: http://www.cs.umd.edu/~ben HCIL URL: http://www.cs.umd.edu/hcil Wikipedia: https://en.wikipedia.org/wiki/Ben_Shneiderman Google Scholar: https://scholar.google.com/citations?user=h4i4fh8AAAAJ&hl=en ORCID: orcid.org/0000-0002-8298-1097 I.B. Academic Appointments at UMD 2017- Emeritus Distinguished University Professor 2013- Distinguished University Professor 2013- Affiliate Professor, Glenn L. Martin Professor of Engineering 2005- Affiliate Professor, College of Information Studies 2005- Affiliate Professor, College of Engineering 1991- 2005 Member, Institute for Systems Research 1989- Professor, Department of Computer Science 1987- Member, Institute for Advanced Computer Studies 1980- 1989 Associate Professor, Department of Computer Science 1976- 1980 Assistant Professor, Department of Information Systems Management I.C. Administrative Appointments at UMD 1983- 2000 Founding Director, Human-Computer Interaction Lab, Institute for Advanced Computer Studies I.D. Other Employment 1973- 1976 Indiana University Assistant Professor, Department of Computer Science 1972- 1973 State University of NY Instructor, Department of Computer Science at Stony Brook 1968- 1972 State University of NY Instructor, Department of Data Processing at Farmingdale -
Excel Excel Excel Excel Excel Excel
CIVL 1112 Origins of Spreadsheets 1/2 Excel Excel Spreadsheets on computers Spreadsheets on computers The word "spreadsheet" came from "spread" in its While other have made contributions to computer-based sense of a newspaper or magazine item that covers spreadsheets, most agree the modern electronic two facing pages, extending across the center fold and spreadsheet was first developed by: treating the two pages as one large one. The compound word "spread-sheet" came to mean the format used to present book-keeping ledgers—with columns for categories of expenditures across the top, invoices listed down the left margin, and the amount of each payment in the cell where its row and column intersect. Dan Bricklin Bob Frankston Excel Excel Spreadsheets on computers Spreadsheets on computers Because of Dan Bricklin and Bob Frankston's Bricklin has spoken of watching his university professor implementation of VisiCalc on the Apple II in 1979 and the create a table of calculation results on a blackboard. IBM PC in 1981, the spreadsheet concept became widely known in the late 1970s and early 1980s. When the professor found an error, he had to tediously erase and rewrite a number of sequential entries in the PC World magazine called VisiCalc the first electronic table, triggering Bricklin to think that he could replicate the spreadsheet. process on a computer, using the blackboard as the model to view results of underlying formulas. His idea became VisiCalc, the first application that turned the personal computer from a hobby for computer enthusiasts into a business tool. Excel Excel Spreadsheets on computers Spreadsheets on computers VisiCalc was the first spreadsheet that combined all VisiCalc went on to become the first killer app, an essential features of modern spreadsheet applications application that was so compelling, people would buy a particular computer just to use it. -
The People Who Invented the Internet Source: Wikipedia's History of the Internet
The People Who Invented the Internet Source: Wikipedia's History of the Internet PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 22 Sep 2012 02:49:54 UTC Contents Articles History of the Internet 1 Barry Appelman 26 Paul Baran 28 Vint Cerf 33 Danny Cohen (engineer) 41 David D. Clark 44 Steve Crocker 45 Donald Davies 47 Douglas Engelbart 49 Charles M. Herzfeld 56 Internet Engineering Task Force 58 Bob Kahn 61 Peter T. Kirstein 65 Leonard Kleinrock 66 John Klensin 70 J. C. R. Licklider 71 Jon Postel 77 Louis Pouzin 80 Lawrence Roberts (scientist) 81 John Romkey 84 Ivan Sutherland 85 Robert Taylor (computer scientist) 89 Ray Tomlinson 92 Oleg Vishnepolsky 94 Phil Zimmermann 96 References Article Sources and Contributors 99 Image Sources, Licenses and Contributors 102 Article Licenses License 103 History of the Internet 1 History of the Internet The history of the Internet began with the development of electronic computers in the 1950s. This began with point-to-point communication between mainframe computers and terminals, expanded to point-to-point connections between computers and then early research into packet switching. Packet switched networks such as ARPANET, Mark I at NPL in the UK, CYCLADES, Merit Network, Tymnet, and Telenet, were developed in the late 1960s and early 1970s using a variety of protocols. The ARPANET in particular led to the development of protocols for internetworking, where multiple separate networks could be joined together into a network of networks. In 1982 the Internet Protocol Suite (TCP/IP) was standardized and the concept of a world-wide network of fully interconnected TCP/IP networks called the Internet was introduced.