Lecture 18: Compression

Size: px
Start display at page:

Download "Lecture 18: Compression"

Transcription

1 Lectre 8: Coression CS2: Great Insights in Coter Science Michael L Littan Sring 2006 Overview When we decide how to reresent soething in its there are soe coeting interests: easil anilated/rocessed short Coon to se two reresentations: one direct to allow for eas rocessing one terse (coressed) to save storage and conication costs

2 Plan I going to tr to descrie one neat idea ilicit in Chater 6: Hffan coding For ore inforation see wiiedia: htt://enwiiediaorg/wii/ Hffan_coding Gettsrg Address For score and seven ears ago or fathers roght forth on this continent a new nation conceived in Liert and dedicated to the roosition that all en are created eal Now we are engaged in a great civil war testing whether that nation or an nation so conceived and so dedicated can long endre We are et on a great attlefield of that war We have coe to dedicate a ortion of that field as a final resting lace for those who here gave their lives that that nation ight live It is altogether fitting and roer that we shold do this Bt in a larger sense we can not dedicate we can not consecrate we can not hallow this grond The rave en living and dead who strggled here have consecrated it far aove or oor ower to add or detract The world will little note nor long reeer what we sa here t it can never forget what the did here It is for s the living rather to e dedicated here to the nfinished wor which the who foght here have ths far so nol advanced It is rather for s to e here dedicated to the great tas reaining efore s that fro these honored dead we tae increased devotion to that case for which the gave the last fll easre of devotion that we here highl resolve that these dead shall not have died in vain that this nation nder God shall have a new irth of freedo and that governent of the eole the eole for the eole shall not erish fro the earth

3 Character Conts For silicit let s trn the ercase letters into lowercase letters That leaves s with: 2 <s> <> 0? 2 a c 58 d 65 e 27 f g 80 h 68 i 0 j 2 l 77 n 9 o 79 r s 26 t 2 v w 0 x 0 z Attet #: ASCII The standard forat for reresenting characters ses 8 its er character The address is 82 characters long so a total of 856 its is needed sing this reresentation 8 its er character 856 total its 0% the size of ASCII reresentation

4 Attet #2: Coact Note that at least in its lowercase for there are onl 2 different characters needed Therefore each can e assigned a 5 it code (2 different 5its atterns) 5 its er character 7 total its 625% the size of ASCII reresentation 5it Patterns <s> 0000 <> ? 00 a c 00 d 0 e 0 f 00 g 0 h 0 i 0 j l 0 0 n 0 o r 00 s 0 t v 0 w x z

5 Attet #: Variale Len Soe characters are ch ore coon than others Give the ost coon characters a it code and the reaining a 6it code How an its do we need now? Variale Length Patterns 000 <s> 00 e 0 t 0 a 0000 o 000 h 00 r 00 n 00 i 0 d 0 s 0 l 00 c 0 w g f 0 v <> 00? 0 j x z

6 Decodailit Note that the code was chosen so that the first it of each character tells o whether the code is short (0) or long () This choice ensres that a essage can actall e decoded: h i <s> t h e r e 2 its not 5 Bt harder to wor with What Gives? We had assigned all 2 characters 5it codes Now we ve got that have it codes and that are 6it codes So ore than half of the characters have actall gotten longer How can that change hel? Need to factor in how an of each characters there are

7 Adding U the Bits How an its to write down jst the letter? Well there are s and each taes 6 its So 60 its (It was 50 efore) How aot t? There are 26 and each taes its That s 78 (was 60) So how do we total the all? Let c e a character fre(c) the ner of ties it aears and len(c) its encoding length Total its =! c fre(c) x len(c) Sing It U 2x + 65x + 26x +2x + 9x6+ 80x6 + 79x x6 + 0x6 = <s> 65 e 26 t 2 a 9 o 80 h 79 r 77 n 68 i 58 d s 2 l c w g 27 f 2 v <> 0? 0 j 0 x 0 z

8 Attet #: Sar Total for this exale: 6 its er character 6867 total its 579% the size of ASCII reresentation Attet #: Sorted 0 <s> e t a o Total for this exale: 7 its er character 67 total its 88% the size of ASCII reresentation

9 Attet #5: Yor Trn Mae sre it is decodale! 2 <s> 65 e 26 t 2 a 9 o 80 h 79 r 77 n 68 i 58 d s 2 l c w g 27 f 2 v <> 0? 0 j 0 x 0 z Can We Do Better? Shannon invented inforation theor which tals aot its and randoness and encodings Fano and Shannon wored together on finding inial size codes The fond a good heristic Fano assigned the role to his class Hffan solved it not nowing his rof had nsccessfll strggled with it

10 Tree (Prefix) Code First notice that a code can e drawn as a tree Left = 0 right = So e = 00 w = 0 Tree strctre ensres code is decodale: Bits tell o naigosl which character <s> e t a o h r n i d s l c w g f v <>? j x z Hffan Coding Mae each character a stree ( loc ) with cont eal to its freenc Tae two locs with sallest conts and erge the into left and right ranches The cont for the new loc is the s of the conts of the locs it is ade ot of Reeat ntil all locs have een erged into one ig loc (single tree) Read the code off the ranches in the tree

11 Partial Exale 2 l 85 s 2 v a 9 o 95 7 g 27 f 55 w d c <> t 77 n 68 i l s 2 v g 27 f w 58 d c <> 2 l s 2 v g 27 f w 58 d c 8 <> Partial Exale <> <> 8 <> 8 8 <> <> <> <>

12 Coleted Code Tree n i 26 t 8 8 <> c 58 d w g f 2 a 95 9 o 2 v s l 65 e h 79 r 2 <s> Created Code <s> 0 e 000 t 00 a 0 o h r n 0000 i 00 d 0 s 0 l 000 c 00 w 00 g 00 f 000 v <>

13 Hffan: Sar Total for this exale: its er character 65 total its 57% the size of ASCII reresentation Minial for this te of code Other Codes error detecting: Know if soething has een odified (it fli) error correcting: Know which it has een odified lticharacter: Encode seences (lie the ) with their own codes Can get ch closer to ini ossile code length: Shannon s entro

14 What To Know constrct a Hffan code fro freencies decode a essage sing a Hffan code encode a essage sing a Hffan code (Let s tr soe exales as tie erits) Next Tie Hillis Chater 8

Chapter 6: Memory: Information and Secret Codes. CS105: Great Insights in Computer Science. Overview

Chapter 6: Memory: Information and Secret Codes. CS105: Great Insights in Computer Science. Overview Chater 6: Meor: Inforation and Secret Codes CS5: Great Insights in Coter Science Overview When we decide how to reresent soething in its there are soe coeting interests: easil anilated/rocessed short Coon

More information

4 A Survey of Congruent Results 12

4 A Survey of Congruent Results 12 4 A urvey of Congruent Results 1 ECTION 4.5 Perfect Nubers and the iga Function By the end of this section you will be able to test whether a given Mersenne nuber is rie understand what is eant be a erfect

More information

Approximation in Stochastic Scheduling: The Power of LP-Based Priority Policies

Approximation in Stochastic Scheduling: The Power of LP-Based Priority Policies Approxiation in Stochastic Scheduling: The Power of -Based Priority Policies Rolf Möhring, Andreas Schulz, Marc Uetz Setting (A P p stoch, r E( w and (B P p stoch E( w We will assue that the processing

More information

Handout 6 Solutions to Problems from Homework 2

Handout 6 Solutions to Problems from Homework 2 CS 85/185 Fall 2003 Lower Bounds Handout 6 Solutions to Probles fro Hoewor 2 Ait Charabarti Couter Science Dartouth College Solution to Proble 1 1.2: Let f n stand for A 111 n. To decide the roerty f 3

More information

Place value and fractions. Explanation and worked examples We read this number as two hundred and fifty-six point nine one.

Place value and fractions. Explanation and worked examples We read this number as two hundred and fifty-six point nine one. 3 3 Place vale and ractions Exlanation and worked examles Level Yo shold know and nderstand which digit o a nmer shows the nmer o: ten thosands 0 000 thosands 000 hndreds 00 tens 0 nits As well as the

More information

This model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t.

This model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t. CS 493: Algoriths for Massive Data Sets Feb 2, 2002 Local Models, Bloo Filter Scribe: Qin Lv Local Models In global odels, every inverted file entry is copressed with the sae odel. This work wells when

More information

Quadratic Reciprocity. As in the previous notes, we consider the Legendre Symbol, defined by

Quadratic Reciprocity. As in the previous notes, we consider the Legendre Symbol, defined by Math 0 Sring 01 Quadratic Recirocity As in the revious notes we consider the Legendre Sybol defined by $ ˆa & 0 if a 1 if a is a quadratic residue odulo. % 1 if a is a quadratic non residue We also had

More information

Ballistic Pendulum. Introduction

Ballistic Pendulum. Introduction Ballistic Pendulu Introduction The revious two activities in this odule have shown us the iortance of conservation laws. These laws rovide extra tools that allow us to analyze certain asects of hysical

More information

5. Dimensional Analysis. 5.1 Dimensions and units

5. Dimensional Analysis. 5.1 Dimensions and units 5. Diensional Analysis In engineering the alication of fluid echanics in designs ake uch of the use of eirical results fro a lot of exerients. This data is often difficult to resent in a readable for.

More information

EXACT BOUNDS FOR JUDICIOUS PARTITIONS OF GRAPHS

EXACT BOUNDS FOR JUDICIOUS PARTITIONS OF GRAPHS EXACT BOUNDS FOR JUDICIOUS PARTITIONS OF GRAPHS B. BOLLOBÁS1,3 AND A.D. SCOTT,3 Abstract. Edwards showed that every grah of size 1 has a biartite subgrah of size at least / + /8 + 1/64 1/8. We show that

More information

Reversibility of Turing Machine Computations

Reversibility of Turing Machine Computations Reversiility of Turing Machine Coputations Zvi M. Kede NYU CS Technical Report TR-2013-956 May 13, 2013 Astract Since Bennett s 1973 seinal paper, there has een a growing interest in general-purpose, reversile

More information

2E1252 Control Theory and Practice

2E1252 Control Theory and Practice 2E1252 Control Theory and Practice Lectre 11: Actator satration and anti wind-p Learning aims After this lectre, yo shold nderstand how satration can case controller states to wind p know how to modify

More information

Bayesian Learning. Chapter 6: Bayesian Learning. Bayes Theorem. Roles for Bayesian Methods. CS 536: Machine Learning Littman (Wu, TA)

Bayesian Learning. Chapter 6: Bayesian Learning. Bayes Theorem. Roles for Bayesian Methods. CS 536: Machine Learning Littman (Wu, TA) Bayesian Learning Chapter 6: Bayesian Learning CS 536: Machine Learning Littan (Wu, TA) [Read Ch. 6, except 6.3] [Suggested exercises: 6.1, 6.2, 6.6] Bayes Theore MAP, ML hypotheses MAP learners Miniu

More information

Chapter 8 Markov Chains and Some Applications ( 馬哥夫鏈 )

Chapter 8 Markov Chains and Some Applications ( 馬哥夫鏈 ) Chater 8 arkov Chains and oe Alications ( 馬哥夫鏈 Consider a sequence of rando variables,,, and suose that the set of ossible values of these rando variables is {,,,, }, which is called the state sace. It

More information

USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS. By: Ian Blokland, Augustana Campus, University of Alberta

USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS. By: Ian Blokland, Augustana Campus, University of Alberta 1 USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS By: Ian Bloland, Augustana Capus, University of Alberta For: Physics Olypiad Weeend, April 6, 008, UofA Introduction: Physicists often attept to solve

More information

Your Suggestions. Board/slides. Too fast/too slow. Book does not have enough examples.

Your Suggestions. Board/slides. Too fast/too slow. Book does not have enough examples. Your Suggestions Sale robles and eales in lecture. Donload recitation robles before recitation. Colete eercises in recitations. Reorganize eb site. Have oer oint slides available earlier. Overvie class

More information

CHAPTER 2 THERMODYNAMICS

CHAPTER 2 THERMODYNAMICS CHAPER 2 HERMODYNAMICS 2.1 INRODUCION herodynaics is the study of the behavior of systes of atter under the action of external fields such as teerature and ressure. It is used in articular to describe

More information

Computability and Complexity Random Sources. Computability and Complexity Andrei Bulatov

Computability and Complexity Random Sources. Computability and Complexity Andrei Bulatov Coputabilit and Copleit 29- Rando Sources Coputabilit and Copleit Andrei Bulatov Coputabilit and Copleit 29-2 Rando Choices We have seen several probabilistic algoriths, that is algoriths that ake soe

More information

Note-A-Rific: Mechanical

Note-A-Rific: Mechanical Note-A-Rific: Mechanical Kinetic You ve probably heard of inetic energy in previous courses using the following definition and forula Any object that is oving has inetic energy. E ½ v 2 E inetic energy

More information

the vibrant colors and further behind each design. Other early pioneers include Théo Ballmer Max THEINTERNA-

the vibrant colors and further behind each design. Other early pioneers include Théo Ballmer Max THEINTERNA- a l s o k n o w n a s t h e S w i s s S, i s a g r a p h i c d e s i g n He did s t y l e d e v e l o p e d i n S w i t z e r n o t l a n d i n t h e 1 9 5 0 s t h a t e m teach a p h a s i z e s c l e

More information

1. (2.5.1) So, the number of moles, n, contained in a sample of any substance is equal N n, (2.5.2)

1. (2.5.1) So, the number of moles, n, contained in a sample of any substance is equal N n, (2.5.2) Lecture.5. Ideal gas law We have already discussed general rinciles of classical therodynaics. Classical therodynaics is a acroscoic science which describes hysical systes by eans of acroscoic variables,

More information

The Frequent Paucity of Trivial Strings

The Frequent Paucity of Trivial Strings The Frequent Paucity of Trivial Strings Jack H. Lutz Departent of Coputer Science Iowa State University Aes, IA 50011, USA lutz@cs.iastate.edu Abstract A 1976 theore of Chaitin can be used to show that

More information

Multiplication and division. Explanation and worked examples. First, we ll look at work you should know at this level.

Multiplication and division. Explanation and worked examples. First, we ll look at work you should know at this level. x Mltilication and division Exlanation and worked examles Level First, we ll look at work yo shold know at this level. Work ot these mltilication and division sms: a) ) 96 c) 8 d) 6 Soltions: a) 9 6 Yo

More information

languages are not CFL and hence are not This lemma enables us to prove that some recognizable by any PDA.

languages are not CFL and hence are not This lemma enables us to prove that some recognizable by any PDA. Introdction to Comtabilit Theor Lectre7: The Pming Lemma for Contet Free Langages Prof. Amos Israeli 1 The Pming Lemma Let Abe a contet free langage. There eists a nmber sch that for eer w A, if w then

More information

Lecture 8.2 Fluids For a long time now we have been talking about classical mechanics, part of physics which studies macroscopic motion of

Lecture 8.2 Fluids For a long time now we have been talking about classical mechanics, part of physics which studies macroscopic motion of Lecture 8 luids or a long tie now we have een talking aout classical echanics part of physics which studies acroscopic otion of particle-like ojects or rigid odies Using different ethods we have considered

More information

26 Impulse and Momentum

26 Impulse and Momentum 6 Ipulse and Moentu First, a Few More Words on Work and Energy, for Coparison Purposes Iagine a gigantic air hockey table with a whole bunch of pucks of various asses, none of which experiences any friction

More information

ESE 523 Information Theory

ESE 523 Information Theory ESE 53 Inforation Theory Joseph A. O Sullivan Sauel C. Sachs Professor Electrical and Systes Engineering Washington University 11 Urbauer Hall 10E Green Hall 314-935-4173 (Lynda Marha Answers) jao@wustl.edu

More information

Finite fields. and we ve used it in various examples and homework problems. In these notes I will introduce more finite fields

Finite fields. and we ve used it in various examples and homework problems. In these notes I will introduce more finite fields Finite fields I talked in class about the field with two eleents F 2 = {, } and we ve used it in various eaples and hoework probles. In these notes I will introduce ore finite fields F p = {,,...,p } for

More information

Math 116 First Midterm October 14, 2009

Math 116 First Midterm October 14, 2009 Math 116 First Midterm October 14, 9 Name: EXAM SOLUTIONS Instrctor: Section: 1. Do not open this exam ntil yo are told to do so.. This exam has 1 pages inclding this cover. There are 9 problems. Note

More information

DCSP-3: Minimal Length Coding. Jianfeng Feng

DCSP-3: Minimal Length Coding. Jianfeng Feng DCSP-3: Minimal Length Coding Jianfeng Feng Department of Computer Science Warwick Univ., UK Jianfeng.feng@warwick.ac.uk http://www.dcs.warwick.ac.uk/~feng/dcsp.html Automatic Image Caption (better than

More information

Derivative at a point

Derivative at a point Roberto s Notes on Differential Calculus Capter : Definition of derivative Section Derivative at a point Wat you need to know already: Te concept of liit and basic etods for coputing liits. Wat you can

More information

#A62 INTEGERS 16 (2016) REPRESENTATION OF INTEGERS BY TERNARY QUADRATIC FORMS: A GEOMETRIC APPROACH

#A62 INTEGERS 16 (2016) REPRESENTATION OF INTEGERS BY TERNARY QUADRATIC FORMS: A GEOMETRIC APPROACH #A6 INTEGERS 16 (016) REPRESENTATION OF INTEGERS BY TERNARY QUADRATIC FORMS: A GEOMETRIC APPROACH Gabriel Durha Deartent of Matheatics, University of Georgia, Athens, Georgia gjdurha@ugaedu Received: 9/11/15,

More information

Unit 14 Harmonic Motion. Your Comments

Unit 14 Harmonic Motion. Your Comments Today s Concepts: Periodic Motion Siple - Mass on spring Daped Forced Resonance Siple - Pendulu Unit 1, Slide 1 Your Coents Please go through the three equations for siple haronic otion and phase angle

More information

Lesson 24: Newton's Second Law (Motion)

Lesson 24: Newton's Second Law (Motion) Lesson 24: Newton's Second Law (Motion) To really appreciate Newton s Laws, it soeties helps to see how they build on each other. The First Law describes what will happen if there is no net force. The

More information

Physically Based Modeling CS Notes Spring 1997 Particle Collision and Contact

Physically Based Modeling CS Notes Spring 1997 Particle Collision and Contact Physically Based Modeling CS 15-863 Notes Spring 1997 Particle Collision and Contact 1 Collisions with Springs Suppose we wanted to ipleent a particle siulator with a floor : a solid horizontal plane which

More information

Birth-Death Processes. Outline. EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Relationship Among Stochastic Processes.

Birth-Death Processes. Outline. EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Relationship Among Stochastic Processes. EEC 686/785 Modelig & Perforace Evaluatio of Couter Systes Lecture Webig Zhao Deartet of Electrical ad Couter Egieerig Clevelad State Uiversity webig@ieee.org based o Dr. Raj jai s lecture otes Relatioshi

More information

Honors Lab 4.5 Freefall, Apparent Weight, and Friction

Honors Lab 4.5 Freefall, Apparent Weight, and Friction Nae School Date Honors Lab 4.5 Freefall, Apparent Weight, and Friction Purpose To investigate the vector nature of forces To practice the use free-body diagras (FBDs) To learn to apply Newton s Second

More information

A Self-adaptive Predictive Congestion Control Model for Extreme Networks

A Self-adaptive Predictive Congestion Control Model for Extreme Networks A Self-adative Predictive Congestion Control Model for Extree etworks Yaqin Li, Min Song, and W. Steven Gray ECE Deartent, Old Doinion University 3 Kafan Hall, orfolk, VA 359 {yli, song, sgray}@od.ed ABSTRACT

More information

Quadratic forms and a some matrix computations

Quadratic forms and a some matrix computations Linear Algebra or Wireless Conications Lectre: 8 Qadratic ors and a soe atri coptations Ove Edors Departent o Electrical and Inoration echnology Lnd University it Stationary points One diension ( d d =

More information

CSE 421 Greedy: Huffman Codes

CSE 421 Greedy: Huffman Codes CSE 421 Greedy: Huffman Codes Yin Tat Lee 1 Compression Example 100k file, 6 letter alphabet: File Size: ASCII, 8 bits/char: 800kbits 2 3 > 6; 3 bits/char: 300kbits better: 2.52 bits/char 74%*2 +26%*4:

More information

Algebraic Multigrid. Multigrid

Algebraic Multigrid. Multigrid Algebraic Mltigrid We re going to discss algebraic ltigrid bt irst begin b discssing ordinar ltigrid. Both o these deal with scale space eaining the iage at ltiple scales. This is iportant or segentation

More information

Huffman Coding. C.M. Liu Perceptual Lab, College of Computer Science National Chiao-Tung University

Huffman Coding. C.M. Liu Perceptual Lab, College of Computer Science National Chiao-Tung University Huffman Coding C.M. Liu Perceptual Lab, College of Computer Science National Chiao-Tung University http://www.csie.nctu.edu.tw/~cmliu/courses/compression/ Office: EC538 (03)573877 cmliu@cs.nctu.edu.tw

More information

Phase field modelling of microstructural evolution using the Cahn-Hilliard equation: A report to accompany CH-muSE

Phase field modelling of microstructural evolution using the Cahn-Hilliard equation: A report to accompany CH-muSE Phase field odelling of icrostructural evolution using the Cahn-Hilliard equation: A reort to accoany CH-uSE 1 The Cahn-Hilliard equation Let us consider a binary alloy of average coosition c 0 occuying

More information

Lecture 21 Principle of Inclusion and Exclusion

Lecture 21 Principle of Inclusion and Exclusion Lecture 21 Principle of Inclusion and Exclusion Holden Lee and Yoni Miller 5/6/11 1 Introduction and first exaples We start off with an exaple Exaple 11: At Sunnydale High School there are 28 students

More information

[95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER. Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow

[95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER. Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow [95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow ABSTRACT The aer discusses a well-known condition [95%/95%],

More information

General Physical Chemistry I

General Physical Chemistry I General Physical Cheistry I Lecture 12 Aleksey Kocherzhenko Aril 2, 2015" Last tie " Gibbs free energy" In order to analyze the sontaneity of cheical reactions, we need to calculate the entroy changes

More information

The Transactional Nature of Quantum Information

The Transactional Nature of Quantum Information The Transactional Nature of Quantu Inforation Subhash Kak Departent of Coputer Science Oklahoa State University Stillwater, OK 7478 ABSTRACT Inforation, in its counications sense, is a transactional property.

More information

Support Vector Machines MIT Course Notes Cynthia Rudin

Support Vector Machines MIT Course Notes Cynthia Rudin Support Vector Machines MIT 5.097 Course Notes Cynthia Rudin Credit: Ng, Hastie, Tibshirani, Friedan Thanks: Şeyda Ertekin Let s start with soe intuition about argins. The argin of an exaple x i = distance

More information

Now multiply the left-hand-side by ω and the right-hand side by dδ/dt (recall ω= dδ/dt) to get:

Now multiply the left-hand-side by ω and the right-hand side by dδ/dt (recall ω= dδ/dt) to get: Equal Area Criterion.0 Developent of equal area criterion As in previous notes, all powers are in per-unit. I want to show you the equal area criterion a little differently than the book does it. Let s

More information

Estimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples

Estimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples Open Journal of Statistics, 4, 4, 64-649 Published Online Septeber 4 in SciRes http//wwwscirporg/ournal/os http//ddoiorg/436/os4486 Estiation of the Mean of the Eponential Distribution Using Maiu Ranked

More information

The Semantics of Data Flow Diagrams. P.D. Bruza. Th.P. van der Weide. Dept. of Information Systems, University of Nijmegen

The Semantics of Data Flow Diagrams. P.D. Bruza. Th.P. van der Weide. Dept. of Information Systems, University of Nijmegen The Seantics of Data Flow Diagras P.D. Bruza Th.P. van der Weide Det. of Inforation Systes, University of Nijegen Toernooiveld, NL-6525 ED Nijegen, The Netherlands July 26, 1993 Abstract In this article

More information

Math Review. Week 1, Wed Jan 10

Math Review. Week 1, Wed Jan 10 Uniersity of British Colbia CPSC 4 Coter Grahics Jan-Ar 007 Taara Mnzner Math Reiew Week, Wed Jan 0 htt://www.grad.cs.bc.ca/~cs4/vjan007 News sign sheet with nae, eail, rogra Reiew: Coter Grahics Defined

More information

Module 9: Further Numbers and Equations. Numbers and Indices. The aim of this lesson is to enable you to: work with rational and irrational numbers

Module 9: Further Numbers and Equations. Numbers and Indices. The aim of this lesson is to enable you to: work with rational and irrational numbers Module 9: Further Numers and Equations Lesson Aims The aim of this lesson is to enale you to: wor with rational and irrational numers wor with surds to rationalise the denominator when calculating interest,

More information

Binomial and Poisson Probability Distributions

Binomial and Poisson Probability Distributions Binoial and Poisson Probability Distributions There are a few discrete robability distributions that cro u any ties in hysics alications, e.g. QM, SM. Here we consider TWO iortant and related cases, the

More information

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2016

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2016 OPTI-502 Optical Design and Instrmentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2016 Name Closed book; closed notes. Time limit: 120 mintes. An eqation sheet is attached and can be

More information

ANOVA INTERPRETING. It might be tempting to just look at the data and wing it

ANOVA INTERPRETING. It might be tempting to just look at the data and wing it Introdction to Statistics in Psychology PSY 2 Professor Greg Francis Lectre 33 ANalysis Of VAriance Something erss which thing? ANOVA Test statistic: F = MS B MS W Estimated ariability from noise and mean

More information

Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay

Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture - 13 Competitive Optimality of the Shannon Code So, far we have studied

More information

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2015

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2015 OPTI-502 Optical Design and Instrmentation I John E. Greivenkamp Final Exam In Class Page 1/16 Fall, 2015 Name Closed book; closed notes. Time limit: 120 mintes. An eqation sheet is attached and can be

More information

The Realm of Hydrogeology

The Realm of Hydrogeology The Real of Hydrogeology In class exercise Stagnant Flow Plot hydraulic head and ressure vs. deth for (also indicate the hydrostatic line) Stagnant flow (no flow) Steady downward flow Steady uward flow

More information

CS 331: Artificial Intelligence Naïve Bayes. Naïve Bayes

CS 331: Artificial Intelligence Naïve Bayes. Naïve Bayes CS 33: Artificial Intelligence Naïe Bayes Thanks to Andrew Moore for soe corse aterial Naïe Bayes A special type of Bayesian network Makes a conditional independence assption Typically sed for classification

More information

List Scheduling and LPT Oliver Braun (09/05/2017)

List Scheduling and LPT Oliver Braun (09/05/2017) List Scheduling and LPT Oliver Braun (09/05/207) We investigate the classical scheduling proble P ax where a set of n independent jobs has to be processed on 2 parallel and identical processors (achines)

More information

For those who want to skip this chapter and carry on, that s fine, all you really need to know is that for the scalar expression: 2 H

For those who want to skip this chapter and carry on, that s fine, all you really need to know is that for the scalar expression: 2 H 1 Matrices are rectangular arrays of numbers. hey are usually written in terms of a capital bold letter, for example A. In previous chapters we ve looed at matrix algebra and matrix arithmetic. Where things

More information

abhi shelat

abhi shelat L15 4102.17.2016 abhi shelat Huffman image: wikimedia Alice m Bob m Alice m Bob MOSCOW President Vladimir V. Putin s typically theatrical order to withdraw the bulk of Russian forces from Syria, a process

More information

6.02 Fall 2012 Lecture #1

6.02 Fall 2012 Lecture #1 6.02 Fall 2012 Lecture #1 Digital vs. analog communication The birth of modern digital communication Information and entropy Codes, Huffman coding 6.02 Fall 2012 Lecture 1, Slide #1 6.02 Fall 2012 Lecture

More information

3 Thermodynamics and Statistical mechanics

3 Thermodynamics and Statistical mechanics Therodynaics and Statistical echanics. Syste and environent The syste is soe ortion of atter that we searate using real walls or only in our ine, fro the other art of the universe. Everything outside the

More information

10.2 Solving Quadratic Equations by Completing the Square

10.2 Solving Quadratic Equations by Completing the Square . Solving Qadratic Eqations b Completing the Sqare Consider the eqation ( ) We can see clearl that the soltions are However, What if the eqation was given to s in standard form, that is 6 How wold we go

More information

EGN 3353C Fluid Mechanics

EGN 3353C Fluid Mechanics Lecture 4 When nondiensionalizing an equation, nondiensional araeters often aear. Exale Consider an object falling due to gravity in a vacuu d z ays: (1) the conventional diensional aroach, and () diensionless

More information

!! Let x n = x 1,x 2,,x n with x j! X!! We say that x n is "-typical with respect to p(x) if

!! Let x n = x 1,x 2,,x n with x j! X!! We say that x n is -typical with respect to p(x) if Quantu Inforation Theory and Measure Concentration Patrick Hayden (McGill) Overview!! What is inforation theory?!! Entropy, copression, noisy coding and beyond!! What does it have to do with quantu echanics?!!

More information

Section 9. Paraxial Raytracing

Section 9. Paraxial Raytracing OPTI-/ Geometrical and Instrmental Optics Copright 8 John E. Greivenkamp 9- Section 9 Paraxial atracing YNU atrace efraction (or reflection) occrs at an interface between two optical spaces. The transfer

More information

Gaussians. Andrew W. Moore Professor School of Computer Science Carnegie Mellon University.

Gaussians. Andrew W. Moore Professor School of Computer Science Carnegie Mellon University. Note to other teachers and users of these slides. Andrew would be delighted if you found this source aterial useful in giing your own lectures. Feel free to use these slides erbati, or to odify the to

More information

New upper bound for the B-spline basis condition number II. K. Scherer. Institut fur Angewandte Mathematik, Universitat Bonn, Bonn, Germany.

New upper bound for the B-spline basis condition number II. K. Scherer. Institut fur Angewandte Mathematik, Universitat Bonn, Bonn, Germany. New upper bound for the B-spline basis condition nuber II. A proof of de Boor's 2 -conjecture K. Scherer Institut fur Angewandte Matheati, Universitat Bonn, 535 Bonn, Gerany and A. Yu. Shadrin Coputing

More information

SUPPORTING INFORMATION FOR. Mass Spectrometrically-Detected Statistical Aspects of Ligand Populations in Mixed Monolayer Au 25 L 18 Nanoparticles

SUPPORTING INFORMATION FOR. Mass Spectrometrically-Detected Statistical Aspects of Ligand Populations in Mixed Monolayer Au 25 L 18 Nanoparticles SUPPORTIG IFORMATIO FOR Mass Sectroetrically-Detected Statistical Asects of Lig Poulations in Mixed Monolayer Au 25 L 8 anoarticles Aala Dass,,a Kennedy Holt, Joseh F. Parer, Stehen W. Feldberg, Royce

More information

0.1. Linear transformations

0.1. Linear transformations Suggestions for midterm review #3 The repetitoria are usually not complete; I am merely bringing up the points that many people didn t now on the recitations Linear transformations The following mostly

More information

A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words)

A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words) 1 A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine (1900 words) Contact: Jerry Farlow Dept of Matheatics Univeristy of Maine Orono, ME 04469 Tel (07) 866-3540 Eail: farlow@ath.uaine.edu

More information

Experiment 2: Hooke s Law

Experiment 2: Hooke s Law COMSATS Institute of Inforation Technology, Islaabad Capus PHYS-108 Experient 2: Hooke s Law Hooke s Law is a physical principle that states that a spring stretched (extended) or copressed by soe distance

More information

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/14 Fall, 2017

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/14 Fall, 2017 OPTI-50 Optical Design and Instrmentation I John E. Greivenkamp Final Exam In Class Page 1/14 Fall, 017 Name Closed book; closed notes. Time limit: 10 mintes. An eqation sheet is attached and can be removed.

More information

t s time we revisit our friend, the equation of a line: y = mx + b

t s time we revisit our friend, the equation of a line: y = mx + b CH PARALLEL AND PERPENDICULAR LINES Introduction I t s time we revisit our friend, the equation of a line: mx + b SLOPE -INTERCEPT To be precise, b is not the -intercept; b is the -coordinate of the -intercept.

More information

The Schrödinger Equation and the Scale Principle

The Schrödinger Equation and the Scale Principle Te Scrödinger Equation and te Scale Princile RODOLFO A. FRINO Jul 014 Electronics Engineer Degree fro te National Universit of Mar del Plata - Argentina rodolfo_frino@aoo.co.ar Earlier tis ear (Ma) I wrote

More information

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/12 Fall, 2011

OPTI-502 Optical Design and Instrumentation I John E. Greivenkamp Final Exam In Class Page 1/12 Fall, 2011 OPTI-502 Optical Design and Instrmentation I John E. Greivenkamp Final Exam In Class Page 1/12 Fall, 2011 Name Closed book; closed notes. Time limit: 2 hors. An eqation sheet is attached and can be removed.

More information

Fundamentals of Image Compression

Fundamentals of Image Compression Fundaentals of Iage Copression Iage Copression reduce the size of iage data file while retaining necessary inforation Original uncopressed Iage Copression (encoding) 01101 Decopression (decoding) Copressed

More information

Birthday Paradox Calculations and Approximation

Birthday Paradox Calculations and Approximation Birthday Paradox Calculations and Approxiation Joshua E. Hill InfoGard Laboratories -March- v. Birthday Proble In the birthday proble, we have a group of n randoly selected people. If we assue that birthdays

More information

SIGNAL COMPRESSION Lecture Shannon-Fano-Elias Codes and Arithmetic Coding

SIGNAL COMPRESSION Lecture Shannon-Fano-Elias Codes and Arithmetic Coding SIGNAL COMPRESSION Lecture 3 4.9.2007 Shannon-Fano-Elias Codes and Arithmetic Coding 1 Shannon-Fano-Elias Coding We discuss how to encode the symbols {a 1, a 2,..., a m }, knowing their probabilities,

More information

Source-Channel-Sink Some questions

Source-Channel-Sink Some questions Source-Channel-Snk Soe questons Source Channel Snk Aount of Inforaton avalable Source Entro Generall nos and a be te varng Introduces error and lts the rate at whch data can be transferred ow uch nforaton

More information

Math 144 Activity #10 Applications of Vectors

Math 144 Activity #10 Applications of Vectors 144 p 1 Math 144 Actiity #10 Applications of Vectors In the last actiity, yo were introdced to ectors. In this actiity yo will look at some of the applications of ectors. Let the position ector = a, b

More information

ma x = -bv x + F rod.

ma x = -bv x + F rod. Notes on Dynaical Systes Dynaics is the study of change. The priary ingredients of a dynaical syste are its state and its rule of change (also soeties called the dynaic). Dynaical systes can be continuous

More information

Random Process Examples 1/23

Random Process Examples 1/23 ando Process Eaples /3 E. #: D-T White Noise Let ] be a sequence of V s where each V ] in the sequence is uncorrelated with all the others: E{ ] ] } 0 for This DEFINES a DT White Noise Also called Uncorrelated

More information

Exploiting Matrix Symmetries and Physical Symmetries in Matrix Product States and Tensor Trains

Exploiting Matrix Symmetries and Physical Symmetries in Matrix Product States and Tensor Trains Exloiting Matrix Syetries and Physical Syetries in Matrix Product States and Tensor Trains Thoas K Huckle a and Konrad Waldherr a and Thoas Schulte-Herbrüggen b a Technische Universität München, Boltzannstr

More information

Handout 7. and Pr [M(x) = χ L (x) M(x) =? ] = 1.

Handout 7. and Pr [M(x) = χ L (x) M(x) =? ] = 1. Notes on Coplexity Theory Last updated: October, 2005 Jonathan Katz Handout 7 1 More on Randoized Coplexity Classes Reinder: so far we have seen RP,coRP, and BPP. We introduce two ore tie-bounded randoized

More information

UNIT I INFORMATION THEORY. I k log 2

UNIT I INFORMATION THEORY. I k log 2 UNIT I INFORMATION THEORY Claude Shannon 1916-2001 Creator of Information Theory, lays the foundation for implementing logic in digital circuits as part of his Masters Thesis! (1939) and published a paper

More information

Equivalence between transition systems. Modal logic and first order logic. In pictures: forth condition. Bisimilation. In pictures: back condition

Equivalence between transition systems. Modal logic and first order logic. In pictures: forth condition. Bisimilation. In pictures: back condition odal logic and first order logic odal logic: local ie of the strctre ( here can I get by folloing links from here ). First order logic: global ie of the strctre (can see eerything, qantifiers do not follo

More information

Multiple Testing Issues & K-Means Clustering. Definitions related to the significance level (or type I error) of multiple tests

Multiple Testing Issues & K-Means Clustering. Definitions related to the significance level (or type I error) of multiple tests StatsM254 Statistical Methods in Coputational Biology Lecture 3-04/08/204 Multiple Testing Issues & K-Means Clustering Lecturer: Jingyi Jessica Li Scribe: Arturo Rairez Multiple Testing Issues When trying

More information

Graphs and Networks Lecture 5. PageRank. Lecturer: Daniel A. Spielman September 20, 2007

Graphs and Networks Lecture 5. PageRank. Lecturer: Daniel A. Spielman September 20, 2007 Graphs and Networks Lectre 5 PageRank Lectrer: Daniel A. Spielman September 20, 2007 5.1 Intro to PageRank PageRank, the algorithm reportedly sed by Google, assigns a nmerical rank to eery web page. More

More information

Reduced Length Checking Sequences

Reduced Length Checking Sequences Reduced Length Checing Sequences Robert M. Hierons 1 and Hasan Ural 2 1 Departent of Inforation Systes and Coputing, Brunel University, Middlesex, UB8 3PH, United Kingdo 2 School of Inforation echnology

More information

Lecture 1: Shannon s Theorem

Lecture 1: Shannon s Theorem Lecture 1: Shannon s Theorem Lecturer: Travis Gagie January 13th, 2015 Welcome to Data Compression! I m Travis and I ll be your instructor this week. If you haven t registered yet, don t worry, we ll work

More information

We have also learned that, thanks to the Central Limit Theorem and the Law of Large Numbers,

We have also learned that, thanks to the Central Limit Theorem and the Law of Large Numbers, Cofidece Itervals III What we kow so far: We have see how to set cofidece itervals for the ea, or expected value, of a oral probability distributio, both whe the variace is kow (usig the stadard oral,

More information

Limited Failure Censored Life Test Sampling Plan in Burr Type X Distribution

Limited Failure Censored Life Test Sampling Plan in Burr Type X Distribution Journal of Modern Applied Statistical Methods Volue 15 Issue 2 Article 27 11-1-2016 Liited Failure Censored Life Test Sapling Plan in Burr Type X Distribution R. R. L. Kanta Acharya Nagarjuna University,

More information

Entropy as a measure of surprise

Entropy as a measure of surprise Entropy as a measure of surprise Lecture 5: Sam Roweis September 26, 25 What does information do? It removes uncertainty. Information Conveyed = Uncertainty Removed = Surprise Yielded. How should we quantify

More information

Discrete Memoryless Channels

Discrete Memoryless Channels Dscrete Meorless Channels Source Channel Snk Aount of Inforaton avalable Source Entro Generall nos, dstorted and a be te varng ow uch nforaton s receved? ow uch s lost? Introduces error and lts the rate

More information

Algorithms for parallel processor scheduling with distinct due windows and unit-time jobs

Algorithms for parallel processor scheduling with distinct due windows and unit-time jobs BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES Vol. 57, No. 3, 2009 Algoriths for parallel processor scheduling with distinct due windows and unit-tie obs A. JANIAK 1, W.A. JANIAK 2, and

More information

Fibonacci Coding for Lossless Data Compression A Review

Fibonacci Coding for Lossless Data Compression A Review RESEARCH ARTICLE OPEN ACCESS Fibonacci Coding for Lossless Data Compression A Review Ezhilarasu P Associate Professor Department of Computer Science and Engineering Hindusthan College of Engineering and

More information