A Mathematical Theory of Communication
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Instructional Communication 8 the Emergence of a Field
Instructional Communication 8 The Emergence of a Field Scott A. Myers ince its inception, the field of instructional communication has Senjoyed a healthy existence. Unlike its related subareas of communi- cation education and developmental communication (Friedrich, 1989), instructional communication is considered to be a unique area of study rooted in the tripartite field of research conducted among educational psychology, pedagogy, and communication studies scholars (Mottet & Beebe, 2006). This tripartite field focuses on the learner (i.e., how students learn affectively, behaviorally, and cognitively), the instructor (i.e., the skills and strategies necessary for effective instruction), and the meaning exchanged in the verbal, nonverbal, and mediated messages between and among instructors and students. As such, the study of instructional com- munication centers on the study of the communicative factors in the teaching-learning process that occur across grade levels (e.g., K–12, post- secondary), instructional settings (e.g., the classroom, the organization), and subject matter (Friedrich, 1989; Staton, 1989). Although some debate exists as to the events that precipitated the emer- gence of instructional communication as a field of study (Rubin & Feezel, 1986; Sprague, 1992), McCroskey and McCroskey (2006) posited that the establishment of instructional communication as a legitimate area of scholarship originated in 1972, when the governing board of the Inter- national Communication Association created the Instructional Communi- cation Division. The purpose of the Division was to “focus attention on the role of communication in all teaching and training contexts, not just the teaching of communication” (p. 35), and provided instructional communication researchers with the opportunity to showcase their scholarship at the Association’s annual convention and to publish their research in Communication Yearbook, a yearly periodical sponsored by the Association. -
Package 'Infotheo'
Package ‘infotheo’ February 20, 2015 Title Information-Theoretic Measures Version 1.2.0 Date 2014-07 Publication 2009-08-14 Author Patrick E. Meyer Description This package implements various measures of information theory based on several en- tropy estimators. Maintainer Patrick E. Meyer <[email protected]> License GPL (>= 3) URL http://homepage.meyerp.com/software Repository CRAN NeedsCompilation yes Date/Publication 2014-07-26 08:08:09 R topics documented: condentropy . .2 condinformation . .3 discretize . .4 entropy . .5 infotheo . .6 interinformation . .7 multiinformation . .8 mutinformation . .9 natstobits . 10 Index 12 1 2 condentropy condentropy conditional entropy computation Description condentropy takes two random vectors, X and Y, as input and returns the conditional entropy, H(X|Y), in nats (base e), according to the entropy estimator method. If Y is not supplied the function returns the entropy of X - see entropy. Usage condentropy(X, Y=NULL, method="emp") Arguments X data.frame denoting a random variable or random vector where columns contain variables/features and rows contain outcomes/samples. Y data.frame denoting a conditioning random variable or random vector where columns contain variables/features and rows contain outcomes/samples. method The name of the entropy estimator. The package implements four estimators : "emp", "mm", "shrink", "sg" (default:"emp") - see details. These estimators require discrete data values - see discretize. Details • "emp" : This estimator computes the entropy of the empirical probability distribution. • "mm" : This is the Miller-Madow asymptotic bias corrected empirical estimator. • "shrink" : This is a shrinkage estimate of the entropy of a Dirichlet probability distribution. • "sg" : This is the Schurmann-Grassberger estimate of the entropy of a Dirichlet probability distribution. -
Information Theory Techniques for Multimedia Data Classification and Retrieval
INFORMATION THEORY TECHNIQUES FOR MULTIMEDIA DATA CLASSIFICATION AND RETRIEVAL Marius Vila Duran Dipòsit legal: Gi. 1379-2015 http://hdl.handle.net/10803/302664 http://creativecommons.org/licenses/by-nc-sa/4.0/deed.ca Aquesta obra està subjecta a una llicència Creative Commons Reconeixement- NoComercial-CompartirIgual Esta obra está bajo una licencia Creative Commons Reconocimiento-NoComercial- CompartirIgual This work is licensed under a Creative Commons Attribution-NonCommercial- ShareAlike licence DOCTORAL THESIS Information theory techniques for multimedia data classification and retrieval Marius VILA DURAN 2015 DOCTORAL THESIS Information theory techniques for multimedia data classification and retrieval Author: Marius VILA DURAN 2015 Doctoral Programme in Technology Advisors: Dr. Miquel FEIXAS FEIXAS Dr. Mateu SBERT CASASAYAS This manuscript has been presented to opt for the doctoral degree from the University of Girona List of publications Publications that support the contents of this thesis: "Tsallis Mutual Information for Document Classification", Marius Vila, Anton • Bardera, Miquel Feixas, Mateu Sbert. Entropy, vol. 13, no. 9, pages 1694-1707, 2011. "Tsallis entropy-based information measure for shot boundary detection and • keyframe selection", Marius Vila, Anton Bardera, Qing Xu, Miquel Feixas, Mateu Sbert. Signal, Image and Video Processing, vol. 7, no. 3, pages 507-520, 2013. "Analysis of image informativeness measures", Marius Vila, Anton Bardera, • Miquel Feixas, Philippe Bekaert, Mateu Sbert. IEEE International Conference on Image Processing pages 1086-1090, October 2014. "Image-based Similarity Measures for Invoice Classification", Marius Vila, Anton • Bardera, Miquel Feixas, Mateu Sbert. Submitted. List of figures 2.1 Plot of binary entropy..............................7 2.2 Venn diagram of Shannon’s information measures............ 10 3.1 Computation of the normalized compression distance using an image compressor.................................... -
Combinatorial Reasoning in Information Theory
Combinatorial Reasoning in Information Theory Noga Alon, Tel Aviv U. ISIT 2009 1 Combinatorial Reasoning is crucial in Information Theory Google lists 245,000 sites with the words “Information Theory ” and “ Combinatorics ” 2 The Shannon Capacity of Graphs The (and) product G x H of two graphs G=(V,E) and H=(V’,E’) is the graph on V x V’, where (v,v’) = (u,u’) are adjacent iff (u=v or uv є E) and (u’=v’ or u’v’ є E’) The n-th power Gn of G is the product of n copies of G. 3 Shannon Capacity Let α ( G n ) denote the independence number of G n. The Shannon capacity of G is n 1/n n 1/n c(G) = lim [α(G )] ( = supn[α(G )] ) n →∞ 4 Motivation output input A channel has an input set X, an output set Y, and a fan-out set S Y for each x X. x ⊂ ∈ The graph of the channel is G=(X,E), where xx’ є E iff x,x’ can be confused , that is, iff S S = x ∩ x′ ∅ 5 α(G) = the maximum number of distinct messages the channel can communicate in a single use (with no errors) α(Gn)= the maximum number of distinct messages the channel can communicate in n uses. c(G) = the maximum number of messages per use the channel can communicate (with long messages) 6 There are several upper bounds for the Shannon Capacity: Combinatorial [ Shannon(56)] Geometric [ Lovász(79), Schrijver (80)] Algebraic [Haemers(79), A (98)] 7 Theorem (A-98): For every k there are graphs G and H so that c(G), c(H) ≤ k and yet c(G + H) kΩ(log k/ log log k) ≥ where G+H is the disjoint union of G and H. -
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (Chair), Bobby Kleinberg September 14Th, 2020
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (chair), Bobby Kleinberg September 14th, 2020 SIGACT Mission Statement: The primary mission of ACM SIGACT (Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory) is to foster and promote the discovery and dissemination of high quality research in the domain of theoretical computer science. The field of theoretical computer science is the rigorous study of all computational phenomena - natural, artificial or man-made. This includes the diverse areas of algorithms, data structures, complexity theory, distributed computation, parallel computation, VLSI, machine learning, computational biology, computational geometry, information theory, cryptography, quantum computation, computational number theory and algebra, program semantics and verification, automata theory, and the study of randomness. Work in this field is often distinguished by its emphasis on mathematical technique and rigor. 1. Awards ▪ 2020 Gödel Prize: This was awarded to Robin A. Moser and Gábor Tardos for their paper “A constructive proof of the general Lovász Local Lemma”, Journal of the ACM, Vol 57 (2), 2010. The Lovász Local Lemma (LLL) is a fundamental tool of the probabilistic method. It enables one to show the existence of certain objects even though they occur with exponentially small probability. The original proof was not algorithmic, and subsequent algorithmic versions had significant losses in parameters. This paper provides a simple, powerful algorithmic paradigm that converts almost all known applications of the LLL into randomized algorithms matching the bounds of the existence proof. The paper further gives a derandomized algorithm, a parallel algorithm, and an extension to the “lopsided” LLL. -
Information Theory for Intelligent People
Information Theory for Intelligent People Simon DeDeo∗ September 9, 2018 Contents 1 Twenty Questions 1 2 Sidebar: Information on Ice 4 3 Encoding and Memory 4 4 Coarse-graining 5 5 Alternatives to Entropy? 7 6 Coding Failure, Cognitive Surprise, and Kullback-Leibler Divergence 7 7 Einstein and Cromwell's Rule 10 8 Mutual Information 10 9 Jensen-Shannon Distance 11 10 A Note on Measuring Information 12 11 Minds and Information 13 1 Twenty Questions The story of information theory begins with the children's game usually known as \twenty ques- tions". The first player (the \adult") in this two-player game thinks of something, and by a series of yes-no questions, the other player (the \child") attempts to guess what it is. \Is it bigger than a breadbox?" No. \Does it have fur?" Yes. \Is it a mammal?" No. And so forth. If you play this game for a while, you learn that some questions work better than others. Children usually learn that it's a good idea to eliminate general categories first before becoming ∗Article modified from text for the Santa Fe Institute Complex Systems Summer School 2012, and updated for a meeting of the Indiana University Center for 18th Century Studies in 2015, a Colloquium at Tufts University Department of Cognitive Science on Play and String Theory in 2016, and a meeting of the SFI ACtioN Network in 2017. Please send corrections, comments and feedback to [email protected]; http://santafe.edu/~simon. 1 more specific, for example. If you ask on the first round \is it a carburetor?" you are likely wasting time|unless you're playing the game on Car Talk. -
Chapter 4 Information Theory
Chapter Information Theory Intro duction This lecture covers entropy joint entropy mutual information and minimum descrip tion length See the texts by Cover and Mackay for a more comprehensive treatment Measures of Information Information on a computer is represented by binary bit strings Decimal numb ers can b e represented using the following enco ding The p osition of the binary digit 3 2 1 0 Bit Bit Bit Bit Decimal Table Binary encoding indicates its decimal equivalent such that if there are N bits the ith bit represents N i the decimal numb er Bit is referred to as the most signicant bit and bit N as the least signicant bit To enco de M dierent messages requires log M bits 2 Signal Pro cessing Course WD Penny April Entropy The table b elow shows the probability of o ccurrence px to two decimal places of i selected letters x in the English alphab et These statistics were taken from Mackays i b o ok on Information Theory The table also shows the information content of a x px hx i i i a e j q t z Table Probability and Information content of letters letter hx log i px i which is a measure of surprise if we had to guess what a randomly chosen letter of the English alphab et was going to b e wed say it was an A E T or other frequently o ccuring letter If it turned out to b e a Z wed b e surprised The letter E is so common that it is unusual to nd a sentence without one An exception is the page novel Gadsby by Ernest Vincent Wright in which -
Guide for the Use of the International System of Units (SI)
Guide for the Use of the International System of Units (SI) m kg s cd SI mol K A NIST Special Publication 811 2008 Edition Ambler Thompson and Barry N. Taylor NIST Special Publication 811 2008 Edition Guide for the Use of the International System of Units (SI) Ambler Thompson Technology Services and Barry N. Taylor Physics Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899 (Supersedes NIST Special Publication 811, 1995 Edition, April 1995) March 2008 U.S. Department of Commerce Carlos M. Gutierrez, Secretary National Institute of Standards and Technology James M. Turner, Acting Director National Institute of Standards and Technology Special Publication 811, 2008 Edition (Supersedes NIST Special Publication 811, April 1995 Edition) Natl. Inst. Stand. Technol. Spec. Publ. 811, 2008 Ed., 85 pages (March 2008; 2nd printing November 2008) CODEN: NSPUE3 Note on 2nd printing: This 2nd printing dated November 2008 of NIST SP811 corrects a number of minor typographical errors present in the 1st printing dated March 2008. Guide for the Use of the International System of Units (SI) Preface The International System of Units, universally abbreviated SI (from the French Le Système International d’Unités), is the modern metric system of measurement. Long the dominant measurement system used in science, the SI is becoming the dominant measurement system used in international commerce. The Omnibus Trade and Competitiveness Act of August 1988 [Public Law (PL) 100-418] changed the name of the National Bureau of Standards (NBS) to the National Institute of Standards and Technology (NIST) and gave to NIST the added task of helping U.S. -
Social Scientific Theories for Media and Communication JMC:6210:0001/ 019:231:001 Spring 2014
Social Scientific Theories for Media and Communication JMC:6210:0001/ 019:231:001 Spring 2014 Professor: Sujatha Sosale Room: E254 AJB Office: W331 AJB Time: M 11:30-2:15 Phone: 319-335-0663 Office hours: Wed. 11:30 – 2:30 E-mail: [email protected] and by appointment Course description and objectives This course explores the ways in which media impact society and how individuals relate to the media by examining social scientific-based theories that relate to media effects, learning, and public opinion. Discussion includes the elements necessary for theory development from a social science perspective, plus historical and current contexts for understanding the major theories of the field. The objectives of this course are: • To learn and critique a variety of social science-based theories • To review mass communication literature in terms of its theoretical relevance • To study the process of theory building Textbook: Bryant, J. & Oliver M.B. (2009). Media Effects: Advances in Theory and Research. 3rd Ed. New York: Routledge. [Available at the University Bookstore] Additional readings will be posted on the course ICON. Teaching Policies & Resources — CLAS Syllabus Insert (instructor additions in this font) Administrative Home The College of Liberal Arts and Sciences is the administrative home of this course and governs matters such as the add/drop deadlines, the second-grade-only option, and other related issues. Different colleges may have different policies. Questions may be addressed to 120 Schaeffer Hall, or see the CLAS Academic Policies Handbook at http://clas.uiowa.edu/students/handbook. Electronic Communication University policy specifies that students are responsible for all official correspondences sent to their University of Iowa e-mail address (@uiowa.edu). -
Management of Data Elements in Information Processing
PB-249-530 MANAGEMENT OF DATA ELEMENTS IN INFORMATION PROCESSING U.S. DEPARTMENT OF COMMERCE / National Bureau of Standards SECOND NATIONAL SYMPOSIUM NATIONAL BUREAU OF STANDARDS GAITHERSBURG, MARYLAND 1975 OCTOBER 23-24 Available by purchase from the National Technical Information Service, 5285 Port Royal Road, Springfield, Va. 221 Price: $9.25 hardcopy; $2.25 microfiche. National Technical Information Service U. S. DEPARTMENT OF COMMERCE PB-249-530 Management of Data Elements in Information Processing Proceedings of a Second Symposium Sponsored by the American National Standards Institute and by The National Bureau of Standards 1975 October 23-24 NBS, Gaithersburg, Maryland Hazel E. McEwen, Editor Institute for Computer Sciences and Technology National Bureau of Standards Washington, D.C. 20234 U.S. DEPARTMENT OF COMMERCE, Elliot L. Richardson, Secrefary NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acfing Direc/or Table of Contents Page Introduction to the Program of the Second National Symposium on The Management of Data Elements in Information Processing ix David V. Savidge, Program Chairman On-Line Tactical Data Inputting: Research in Operator Training and Performance 1 Irving Alderman, Ph.D. "Turning the Corner" on MIS, A Proposed Program of Data Standards in Post-Secondary Education 9 Donald R. Arnold, Ph.D. ASCII - The Data Alphabet That Will Endure 17 Robert W. Bemer Techniques in Developing Standard Procedures for Data Editing 23 George W. Covill An Adaptive File Management Systems 45 Dennis L. Dance and Udo W. Pooch (Given by Dance) A Focus on the Role of the Data Manager 57 Ruth M, Davis, Ph.D. A Proposed Standard Routine for Generating Proposed Standard Check Characters 61 Paul -Andre Desjardins Methodology for Development of Standard Data Elements within Multiple Public Agencies 69 L. -
Understanding Shannon's Entropy Metric for Information
Understanding Shannon's Entropy metric for Information Sriram Vajapeyam [email protected] 24 March 2014 1. Overview Shannon's metric of "Entropy" of information is a foundational concept of information theory [1, 2]. Here is an intuitive way of understanding, remembering, and/or reconstructing Shannon's Entropy metric for information. Conceptually, information can be thought of as being stored in or transmitted as variables that can take on different values. A variable can be thought of as a unit of storage that can take on, at different times, one of several different specified values, following some process for taking on those values. Informally, we get information from a variable by looking at its value, just as we get information from an email by reading its contents. In the case of the variable, the information is about the process behind the variable. The entropy of a variable is the "amount of information" contained in the variable. This amount is determined not just by the number of different values the variable can take on, just as the information in an email is quantified not just by the number of words in the email or the different possible words in the language of the email. Informally, the amount of information in an email is proportional to the amount of “surprise” its reading causes. For example, if an email is simply a repeat of an earlier email, then it is not informative at all. On the other hand, if say the email reveals the outcome of a cliff-hanger election, then it is highly informative. -
On the Irresistible Efficiency of Signal Processing Methods in Quantum
On the Irresistible Efficiency of Signal Processing Methods in Quantum Computing 1,2 2 Andreas Klappenecker∗ , Martin R¨otteler† 1Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA 2 Institut f¨ur Algorithmen und Kognitive Systeme, Universit¨at Karlsruhe,‡ Am Fasanengarten 5, D-76 128 Karlsruhe, Germany February 1, 2008 Abstract We show that many well-known signal transforms allow highly ef- ficient realizations on a quantum computer. We explain some elemen- tary quantum circuits and review the construction of the Quantum Fourier Transform. We derive quantum circuits for the Discrete Co- sine and Sine Transforms, and for the Discrete Hartley transform. We show that at most O(log2 N) elementary quantum gates are necessary to implement any of those transforms for input sequences of length N. arXiv:quant-ph/0111039v1 7 Nov 2001 §1 Introduction Quantum computers have the potential to solve certain problems at much higher speed than any classical computer. Some evidence for this statement is given by Shor’s algorithm to factor integers in polynomial time on a quan- tum computer. A crucial part of Shor’s algorithm depends on the discrete Fourier transform. The time complexity of the quantum Fourier transform is polylogarithmic in the length of the input signal. It is natural to ask whether other signal transforms allow for similar speed-ups. We briefly recall some properties of quantum circuits and construct the quantum Fourier transform. The main part of this paper is concerned with ∗e-mail: [email protected] †e-mail: [email protected] ‡research group Quantum Computing, Professor Thomas Beth the construction of quantum circuits for the discrete Cosine transforms, for the discrete Sine transforms, and for the discrete Hartley transform.