Solutions to Exercises, Set 3

Size: px
Start display at page:

Download "Solutions to Exercises, Set 3"

Transcription

1 Shool of Computer Siene, University of Nottinghm G5MAL Mhines nd their Lnguges, Spring 1 Thorsten Altenkirh Solutions to Exerises, Set 3 Fridy 3rd Mrh 1 1. () () L(+ +ǫ) = {L(E +F) = L(E) L(F)} L() L( ) L(ǫ) = {L(EF) = L(E)L(F)} L()L() L( )L( ) L(ǫ)L() = {L( ) = } L()L() L( ) L(ǫ)L() = {S = } L()L() L(ǫ)L() = {L(ǫ) = {ǫ}} L()L() {ǫ}l() = {{ǫ}s = S} L()L() L() = {L(x) = {x}} {}{} {} = {Contention of Lnguges} {} {} = {Set Union} {, } L((+)+( +)ǫ) = {L(E +F) = L(E) L(F)} L((+)) L(( +)ǫ) = {L(EF) = L(E)L(F)} L()L(+)L() L( +)L(ǫ) = {L(ǫ) = {ǫ}} L()L(+)L() L( +){ǫ} = {S{ǫ} = S} L()L(+)L() L( +) = {L(E +F) = L(E) L(F)} L()(L() L())L() L( ) L() = {L( ) = } L()(L() L())L() L() = {L(x) = {x}} {}({} {}){} {} = {Set Union} {}{,}{} {} = {Contention of Lnguges} {,} {} = {Set Union} {,, }. () ǫ+(+)(ǫ+) () 1

2 3. () (++) () (+) () (++) (++) (d) (+) (+) (++) (e) (+) (+) (f) (++(+) ) (g) (ǫ+)((+)(ǫ+)) (h) (ǫ++)((+)(ǫ++)). () Let us strt y onstruting NFAs for nd. I hve nmed the sttes to mke it esier for you to see how they orrespond to the finl NFA, ut normlly you wouldn t nme them until lter. 1 3 An NFA for is then onstruted y omposing the two NFAs sequentilly: 1 3 Stte 1 is now ded-end stte nd n e removed: 3 An NFA for () is then onstruted y dupliting ll rrows tht point to n epting stte to point to ll initil sttes, nd then dding n initil epting stte: 3

3 Finlly, n NFA for +() is onstruted y dding n NFA for in prllel: 3 () Let us strt with n NFA for +: 1 3 An NFA for (+) is then s follows: 1 3 3

4 To onstrut n NFA for (+), we first ple the previous NFA side y side with n NFA for : 1 3 The two re then omposed sequentilly: 1 3 Note tht the initil rrow ws removed from Stte 5, then dded gin s the initil rrow for Stte ws duplited to point to Stte 5.

5 At this point, it s good ide to remove ded-end sttes. Next let s onsider. First we ple the NFAs for nd side y side: We now onstrut n NFA for y omposing them in sequene. As N( ) hs no epting sttes, this just requires setting ll sttes from N() to not e initil The NFA for +(+) +ǫ onsists of N( ), N((+) ) nd N(ǫ) in prllel. 5

6 Finlly, Stte 7 n e eliminted s it is ded-end stte, nd sttes 8 nd 9 n e eliminted s they re unrehle () The empty word is just the empty list: ε :: Word σ ε = [] () Contention for finite lnguges n e defined using list omprehension: lngcont :: Lnguge σ Lnguge σ Lnguge σ lngcont l 1 l = [w 1 + w w 1 l 1,w l ] 6

7 It gets it trikier for infinite lnguges, s we wnt to ensure tht ll words get enumerted. For exmple, if l is infinite, then, using the ove definition, only the first word from l 1 will ever pper in the resultnt lnguge. As eh word in the resultnt lnguge is pir of words (one from eh rgument lnguge), the prolem is to ssign ountle ordering to ll suh pirs. This n e hieved y using the Cntor Piring Funtion to ssign n order of enumertion (see, for exmple, However, for this prolem, iterting through the pirs is ll tht is required, so defining n iterting funtion is suffiient (rther thn diretly defining the inverse piring funtion). nextpir :: (Int,Int) (Int,Int) nextpir (,n) = (n +1,) nextpir (m,n) = (m 1,n +1) Contention n now e defined reursively s follows: lngcont :: Lnguge σ Lnguge σ Lnguge σ lngcont l 1 l = laux (,) where laux (m,n) 1 = ((l 1!! m) + (l!! n)) : laux (nextpir (m,n)) 1 = laux (nextpir (m,n)) otherwise = [] where 1 = inbound m l 1 = inbound n l inbound :: Int [] Bool inbound n = not null drop n Note tht s the lnguges my e finite, we need to use inbound to hek tht we do not index list (lnguge in this se) out of ounds. If the ounds of oth lnguges re exeeded, then ll words hve een enumerted nd the reursion termintes. Further note tht this funtion ould e mde muh more effiient. For exmple, speilised uxiliry funtions ould e introdued for the ses when one of the two lnguges is disovered to e finite. () Exponentition of lnguges n e defined y primitive reursion: lngexp :: Lnguge σ Int Lnguge σ lngexp l = [ε] lngexp l n = lngcont l (lngexp l (n 1)) (d) The definition L = n= L n n e enoded firly diretly: kleenestr :: Eq σ Lnguge σ Lnguge σ kleenestr l = unions [lngexp l n n [..]] 7

8 (e) Finlly, enumerting the lnguge of regulr expression is strightforwrd enoding of its semntis using the funtions lredy defined: relng :: Eq σ RE σ Lnguge σ relng Empty = [] relng Epsilon = [ε] relng (Symol x) = [[x]] relng (Plus r1 r) = relng r1 union relng r relng (Sequene r1 r) = lngcont (relng r1) (relng r) relng (Str r) = kleenestr (relng r) relng (Pren r) = relng r 8

9.3. Regular Languages

9.3. Regular Languages 9.3. REGULAR LANGUAGES 139 9.3. Regulr Lnguges 9.3.1. Properties of Regulr Lnguges. Recll tht regulr lnguge is the lnguge ssocited to regulr grmmr, i.e., grmmr G = (N, T, P, σ) in which every production

More information

Cache CPI and DFAs and NFAs. CS230 Tutorial 10

Cache CPI and DFAs and NFAs. CS230 Tutorial 10 Cche CPI nd DFAs nd NFAs CS230 Tutoril 10 Multi-Level Cche: Clculting CPI When memory ccess is ttempted, wht re the possible results? ccess miss miss CPU L1 Cche L2 Cche Memory L1 cche hit L2 cche hit

More information

Trigonometry - Activity 21 General Triangle Solution: Given three sides.

Trigonometry - Activity 21 General Triangle Solution: Given three sides. Nme: lss: p 43 Mths Helper Plus Resoure Set. opyright 003 rue. Vughn, Tehers hoie Softwre Trigonometry - tivity 1 Generl Tringle Solution: Given three sides. When the three side lengths '', '' nd '' of

More information

Drawing Finite State Machines in L A TEX and TikZ A Tutorial

Drawing Finite State Machines in L A TEX and TikZ A Tutorial Drwing Finite Stte Mchines in L A TEX nd TikZ A Tutoril Styki Sikdr nd Dvid Ching ssikdr@nd.edu Version 3 Jnury 7, 208 Introduction L A TEX (pronounced ly-tek) is n open-source, multipltform document preprtion

More information

Pushdown Automata. Courtesy: Costas Busch RPI

Pushdown Automata. Courtesy: Costas Busch RPI Pushdown Automt Courtesy: Costs Busch RPI Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Non-Determinism:NPDA PDAs re non-deterministic: non-deterministic

More information

INF 4130 Exercise set 4

INF 4130 Exercise set 4 INF 4130 Exercise set 4 Exercise 1 List the order in which we extrct the nodes from the Live Set queue when we do redth first serch of the following grph (tree) with the Live Set implemented s LIFO queue.

More information

Suffix trees and their uses

Suffix trees and their uses Prt I Suffi trees nd their uses Introdution to suffi trees A suffi tree is dt struture tht eposes the internl struture of string in deeper wy thn does the fundmentl preproessing disussed in Setion?? Suffi

More information

Outline. CSE 326: Data Structures. Priority Queues Leftist Heaps & Skew Heaps. Announcements. New Heap Operation: Merge

Outline. CSE 326: Data Structures. Priority Queues Leftist Heaps & Skew Heaps. Announcements. New Heap Operation: Merge CSE 26: Dt Structures Priority Queues Leftist Heps & Skew Heps Outline Announcements Leftist Heps & Skew Heps Reding: Weiss, Ch. 6 Hl Perkins Spring 2 Lectures 6 & 4//2 4//2 2 Announcements Written HW

More information

1 Manipulation for binary voters

1 Manipulation for binary voters STAT 206A: Soil Choie nd Networks Fll 2010 Mnipultion nd GS Theorem Otoer 21 Leturer: Elhnn Mossel Srie: Kristen Woyh In this leture we over mnipultion y single voter: whether single voter n lie out his

More information

Patterns and functions recursive number patterns Write the next 3 numbers in each sequence by following the rule:

Patterns and functions recursive number patterns Write the next 3 numbers in each sequence by following the rule: Ptterns n funtions reursive numer ptterns Look roun you, n you see pttern? A pttern is n rrngement of shpes, numers or ojets forme oring to rule. Ptterns re everywhere, you n fin them in nture, rt, musi

More information

Interest. Interest. Curriculum Ready ACMNA: 211, 229,

Interest. Interest. Curriculum Ready ACMNA: 211, 229, Inteest Cuiulum Redy ACMNA: 211, 229, 234 www.mthletis.om INTEREST The whole point of Finnil Mths is to pedit wht will hppen to money ove time. This is so you n e peped y knowing how muh money you will

More information

Section 2.2 Trigonometry: The Triangle Identities

Section 2.2 Trigonometry: The Triangle Identities Se. 2.2 Trigonometry: The Tringle Identities 7 Setion 2.2 Trigonometry: The Tringle Identities DJENT ND OPPOSITE SIDES The study of tringle trigonometry is entered round the ute ngles in right tringle.

More information

The phases of a simple compiler: Compiler Construction SMD163. From Intermediate To Target: An Optimizing Compiler: Lecture 13: Instruction selection

The phases of a simple compiler: Compiler Construction SMD163. From Intermediate To Target: An Optimizing Compiler: Lecture 13: Instruction selection Compiler Constrution SMD163 The phses of simple ompiler: Lexer Prser Stti Anlysis IA32 Code Genertor Leture 13: Instrution seletion Viktor Leijon & Peter Jonsson with slides y John Nordlnder Contins mteril

More information

EFFECTS OF THE SINGLE EUROPEAN MARKET ON WELFARE OF THE PARTICIPATING COUNTRIES (theoretical approach)

EFFECTS OF THE SINGLE EUROPEAN MARKET ON WELFARE OF THE PARTICIPATING COUNTRIES (theoretical approach) EFFECTS OF THE SINGLE EUROEAN MARKET ON ELFARE OF THE ARTICIATING COUNTRIES theoretil pproh O ELHD&DU\:DUVDZ6FKRRORIFRRLFV:DUVDZRODG Agnieszk Rusinowsk, rsw Shool of Eonomis, rsw, olnd In this note we

More information

3/1/2016. Intermediate Microeconomics W3211. Lecture 7: The Endowment Economy. Today s Aims. The Story So Far. An Endowment Economy.

3/1/2016. Intermediate Microeconomics W3211. Lecture 7: The Endowment Economy. Today s Aims. The Story So Far. An Endowment Economy. 1 Intermedite Microeconomics W3211 Lecture 7: The Endowment Economy Introduction Columbi University, Spring 2016 Mrk Den: mrk.den@columbi.edu 2 The Story So Fr. 3 Tody s Aims 4 Remember: the course hd

More information

ECE 410 Homework 1 -Solutions Spring 2008

ECE 410 Homework 1 -Solutions Spring 2008 ECE 410 Homework 1 -Solution Spring 2008 Prolem 1 For prolem 2-4 elow, ind the voltge required to keep the trnitor on ppling the rule dicued in cl. Aume VDD = 2.2V FET tpe Vt (V) Vg (V) Vi (V) n-tpe 0.5

More information

Suffix Trees. Outline. Introduction Suffix Trees (ST) Building STs in linear time: Ukkonen s algorithm Applications of ST.

Suffix Trees. Outline. Introduction Suffix Trees (ST) Building STs in linear time: Ukkonen s algorithm Applications of ST. Suffi Trees Outline Introduction Suffi Trees (ST) Building STs in liner time: Ukkonen s lgorithm Applictions of ST 2 Introduction 3 Sustrings String is ny sequence of chrcters. Sustring of string S is

More information

Addition and Subtraction

Addition and Subtraction Addition nd Subtrction Nme: Dte: Definition: rtionl expression A rtionl expression is n lgebric expression in frction form, with polynomils in the numertor nd denomintor such tht t lest one vrible ppers

More information

Roadmap of This Lecture

Roadmap of This Lecture Reltionl Model Rodmp of This Lecture Structure of Reltionl Dtbses Fundmentl Reltionl-Algebr-Opertions Additionl Reltionl-Algebr-Opertions Extended Reltionl-Algebr-Opertions Null Vlues Modifiction of the

More information

Lecture 6 Phylogenetic Analysis. Algorithm. Types of Data. UPGMA Neighbor Joining. Minimum Evolution

Lecture 6 Phylogenetic Analysis. Algorithm. Types of Data. UPGMA Neighbor Joining. Minimum Evolution Leture 6 Phylogeneti Anlysis Algorithms Types of Dt Distnes Nuleotie sites Tree-uiling Metho Clustering Algorithm Optimlity Criterion UPGMA Neighor Joining Minimum Evolution Mximum Prsimony Mximum Likelihoo

More information

Lecture 9: The E/R Model II. 2. E/R Design Considerations 2/7/2018. Multiplicity of E/R Relationships. What you will learn about in this section

Lecture 9: The E/R Model II. 2. E/R Design Considerations 2/7/2018. Multiplicity of E/R Relationships. What you will learn about in this section Leture 9: The E/R Moel II Leture n tivity ontents re se on wht Prof Chris Ré use in his CS 45 in the fll 06 term with permission.. E/R Design Consiertions Wht you will lern out in this setion Multipliity

More information

Arithmetic and Geometric Sequences

Arithmetic and Geometric Sequences Arithmetic nd Geometric Sequences A sequence is list of numbers or objects, clled terms, in certin order. In n rithmetic sequence, the difference between one term nd the next is lwys the sme. This difference

More information

Measuring Search Trees

Measuring Search Trees Mesuring Serch Trees Christin Bessiere 1, Bruno Znuttini 2, nd Cèsr Fernández 3 1 LIRMM-CNRS, Montpellier, Frnce 2 GREYC, Cen, Frnce 3 Univ. de Lleid, Lleid, Spin Astrct. The SAT nd CSP communities mke

More information

Revision Topic 14: Algebra

Revision Topic 14: Algebra Revision Topi 1: Algebr Indies: At Grde B nd C levels, you should be fmilir with the following rules of indies: b b y y y i.e. dd powers when multiplying; y b b y y i.e. subtrt powers when dividing; b

More information

The New Circus. Main ideas are the most important ideas in a passage. They are the messages the writer

The New Circus. Main ideas are the most important ideas in a passage. They are the messages the writer The New Cirus SUBJECT READING SKILL TEXT TYPE Culture n People Fining min ies n etils Content-se pssge Fining min ies n etils Min ies re the most importnt ies in pssge. They re the messges the writer wnts

More information

CH 71 COMPLETING THE SQUARE INTRODUCTION FACTORING PERFECT SQUARE TRINOMIALS

CH 71 COMPLETING THE SQUARE INTRODUCTION FACTORING PERFECT SQUARE TRINOMIALS CH 7 COMPLETING THE SQUARE INTRODUCTION I t s now time to py our dues regrding the Qudrtic Formul. Wht, you my sk, does this men? It mens tht the formul ws merely given to you once or twice in this course,

More information

Reinforcement Learning. CS 188: Artificial Intelligence Fall Grid World. Markov Decision Processes. What is Markov about MDPs?

Reinforcement Learning. CS 188: Artificial Intelligence Fall Grid World. Markov Decision Processes. What is Markov about MDPs? CS 188: Artificil Intelligence Fll 2010 Lecture 9: MDP 9/2/2010 Reinforcement Lerning [DEMOS] Bic ide: Receive feedbck in the form of rewrd Agent utility i defined by the rewrd function Mut (lern to) ct

More information

CSCI 104 Splay Trees. Mark Redekopp

CSCI 104 Splay Trees. Mark Redekopp CSCI 0 Sply Trees Mrk edekopp Soures / eding Mteril for these slides ws derived from the following soures https://www.s.mu.edu/~sletor/ppers/selfdjusting.pdf http://digitl.s.usu.edu/~lln/ds/notes/ch.pdf

More information

Buckling of Stiffened Panels 1 overall buckling vs plate buckling PCCB Panel Collapse Combined Buckling

Buckling of Stiffened Panels 1 overall buckling vs plate buckling PCCB Panel Collapse Combined Buckling Buckling of Stiffened Pnels overll uckling vs plte uckling PCCB Pnel Collpse Comined Buckling Vrious estimtes hve een developed to determine the minimum size stiffener to insure the plte uckles while the

More information

E-Merge Process Table (Version 1)

E-Merge Process Table (Version 1) E-Merge Proess Tle (Version 1) I 1 Indiies Trgets Stte Energy Environmentl ESTABLISHING GOALS & BASELINES Identify gols from stte energy offie perspetive Identify stte puli helth nd welfre gols ffeted

More information

Released Assessment Questions, 2017 QUESTIONS

Released Assessment Questions, 2017 QUESTIONS Relese Assessment Questions, 2017 QUESTIONS Gre 9 Assessment of Mthemtis Applie Re the instrutions elow. Along with this ooklet, mke sure you hve the Answer Booklet n the Formul Sheet. You my use ny spe

More information

Today s Outline. One More Operation. Priority Queues. New Operation: Merge. Leftist Heaps. Priority Queues. Admin: Priority Queues

Today s Outline. One More Operation. Priority Queues. New Operation: Merge. Leftist Heaps. Priority Queues. Admin: Priority Queues Tody s Outline Priority Queues CSE Dt Structures & Algorithms Ruth Anderson Spring 4// Admin: HW # due this Thursdy / t :9pm Printouts due Fridy in lecture. Priority Queues Leftist Heps Skew Heps 4// One

More information

What do I have? What item are you looking for? What do I need?

What do I have? What item are you looking for? What do I need? Wht do I hve? quntity desription item 1 5200 series strike ody 1 Trim enhner 1 3! Wht item re you looking for? 1 2 3 Cution Before onneting eletri strike t the instlltion site verify input voltge using

More information

Exhibit A Covered Employee Notification of Rights Materials Regarding Allied Managed Care Incorporated Allied Managed Care MPN MPN ID # 2360

Exhibit A Covered Employee Notification of Rights Materials Regarding Allied Managed Care Incorporated Allied Managed Care MPN MPN ID # 2360 Covered Notifiction of Rights Mterils Regrding Allied Mnged Cre Incorported Allied Mnged Cre MPN This pmphlet contins importnt informtion bout your medicl cre in cse of workrelted injmy or illness You

More information

Problem Set 2 Suggested Solutions

Problem Set 2 Suggested Solutions 4.472 Prolem Set 2 Suggested Solutions Reecc Zrutskie Question : First find the chnge in the cpitl stock, k, tht will occur when the OLG economy moves to the new stedy stte fter the government imposes

More information

Maximizing Profit: Chapter 9: Weekly Revenue and Cost Data for a Gold Miner. Marginal Revenue:

Maximizing Profit: Chapter 9: Weekly Revenue and Cost Data for a Gold Miner. Marginal Revenue: Chpter : Mximizing rofit: rofit = Totl Revenue - Totl Cost Mrginl Revenue: It mesures the hnge in totl revenue generted y one dditionl unit of goods or servies. Totl Revenue (TR) = Averge Revenue (AR)

More information

checks are tax current income.

checks are tax current income. Humn Short Term Disbility Pln Wht is Disbility Insurnce? An esy explntion is; Disbility Insurnce is protection for your pycheck. Imgine if you were suddenly disbled, unble to work, due to n ccident or

More information

Chapter55. Algebraic expansion and simplification

Chapter55. Algebraic expansion and simplification Chpter55 Algebric expnsion nd simplifiction Contents: A The distributive lw B The product ( + b)(c + d) C Difference of two squres D Perfect squres expnsion E Further expnsion F The binomil expnsion 88

More information

P roceedings of the Third International Colloquium on Grammatical Inference, ICGI'96 Lecture Notes in Artificial Intelligence Vol

P roceedings of the Third International Colloquium on Grammatical Inference, ICGI'96 Lecture Notes in Artificial Intelligence Vol P roeedings of the Third Interntionl Colloquium on Grmmtil Inferene, ICGI'96 Leture Notes in Artifiil Intelligene Vol. 1147. Springer 1996 Lerning liner grmmrs from struturl inform tion.? Jose M. Sempere

More information

Lecture 5 March 1, 2012

Lecture 5 March 1, 2012 6.851: Advned Dt Strutures Sring 2012 Prof. Erik Demine Leture 5 Mrh 1, 2012 1 Overview In the next two letures we study the question of dynmi otimlity, or whether there exists inry serh tree lgorithm

More information

A ppendix to. I soquants. Producing at Least Cost. Chapter

A ppendix to. I soquants. Producing at Least Cost. Chapter A ppendix to Chpter 0 Producing t est Cost This ppendix descries set of useful tools for studying firm s long-run production nd costs. The tools re isoqunts nd isocost lines. I soqunts FIGURE A0. SHOWS

More information

Chapter 02: Determination of Interest Rates

Chapter 02: Determination of Interest Rates Finnil Mrkets nd Institutions 12th Edition Mdur TEST BANK Full ler downlod (no formtting errors) t: https://testnkrel.om/downlod/finnil-mrkets-institutions-12th-edition-mdur-testnk/ Finnil Mrkets nd Institutions

More information

First Assignment, Federal Income Tax, Spring 2019 O Reilly. For Monday, January 14th, please complete problem set one (attached).

First Assignment, Federal Income Tax, Spring 2019 O Reilly. For Monday, January 14th, please complete problem set one (attached). First Assignment, Federl Income Tx, Spring 2019 O Reilly For Mondy, Jnury 14th, plese complete problem set one (ttched). Federl Income Tx Spring 2019 Problem Set One Suppose tht in 2018, Greene is 32,

More information

Chapter 2: Relational Model. Chapter 2: Relational Model

Chapter 2: Relational Model. Chapter 2: Relational Model Chpter : Reltionl Model Dtbse System Concepts, 5 th Ed. See www.db-book.com for conditions on re-use Chpter : Reltionl Model Structure of Reltionl Dtbses Fundmentl Reltionl-Algebr-Opertions Additionl Reltionl-Algebr-Opertions

More information

Small Binary Voting Trees

Small Binary Voting Trees Smll Binry Voting Trees Mihel Trik Astrt Sophistite voting on inry tree is ommon form of voting struture, s exemplifie y, for exmple, menment proeures. The prolem of hrterizing voting rules tht n e the

More information

International Monopoly under Uncertainty

International Monopoly under Uncertainty Interntionl Monopoly under Uncertinty Henry Ary University of Grnd Astrct A domestic monopolistic firm hs the option to service foreign mrket through export or y setting up plnt in the host country under

More information

THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA.

THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA. THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA. I would like to thnk Aris for his mthemticl contriutions nd his swet which hs enled deeper understnding of the turnover formul to emerge. His contriution

More information

DYNAMIC PROGRAMMING REINFORCEMENT LEARNING. COGS 202 : Week 7 Presentation

DYNAMIC PROGRAMMING REINFORCEMENT LEARNING. COGS 202 : Week 7 Presentation DYNAMIC PROGRAMMING REINFORCEMENT LEARNING COGS 202 : Week 7 Preenttion OUTLINE Recp (Stte Vlue nd Action Vlue function) Computtion in MDP Dynmic Progrmming (DP) Policy Evlution Policy Improvement Policy

More information

Appendix C Time Value of Money

Appendix C Time Value of Money REVIEW QUESTIONS Appendix C Time Vlue of Money Question C-1 (LO C-1) is the ost of orrowing money. Simple interest is interest we ern on the initil investment only. Compound interest is the interest we

More information

Ricardian Model. Mercantilism: 17 th and 18 th Century. Adam Smith s Absolute Income Hypothesis, End of 18 th Century: Major Shift in Paradigm

Ricardian Model. Mercantilism: 17 th and 18 th Century. Adam Smith s Absolute Income Hypothesis, End of 18 th Century: Major Shift in Paradigm Mercntilism: th nd th Century Ricrdin Model lesson in Comprtive dvntge Trde ws considered s Zero-Sum Gme It ws viewed mens to ccumulte Gold & Silver Exports were encourged Imports were discourged End of

More information

Class Overview. Database Design. Database Design. Database Design Process. Entity / Relationship Diagrams. Introduction to Database Systems CSE 414

Class Overview. Database Design. Database Design. Database Design Process. Entity / Relationship Diagrams. Introduction to Database Systems CSE 414 Introution to Dtse Systems CSE 44 Leture 9: E/R Digrms Clss Overview Unit : Intro Unit : Reltionl Dt Moels n Query Lnguges Unit : Non-reltionl t Unit 4: RDMBS internls n query optimiztion Unit 5: Prllel

More information

Ratio and Proportion Long-Term Memory Review Day 1 - Review

Ratio and Proportion Long-Term Memory Review Day 1 - Review Rtio nd Proportion Dy 1 - Review 1. Provide omplete response to eh of the following: ) A rtio ompres two. ) A proportion sets two rtios to eh other. ) Wht re similr figures? 2. Drw two similr figures.

More information

GRAPH: A network of NODES (or VERTICES) and ARCS (or EDGES) joining the nodes with each other

GRAPH: A network of NODES (or VERTICES) and ARCS (or EDGES) joining the nodes with each other GRPH: network of NS (or VRTIS) and RS (or GS) joining the nodes with eah other IGRPH: graph where the ars have an RINTTIN (or IRTIN). a Graph a igraph d e d HIN etween two nodes is a sequene of ars where

More information

Math 205 Elementary Algebra Fall 2010 Final Exam Study Guide

Math 205 Elementary Algebra Fall 2010 Final Exam Study Guide Mth 0 Elementr Algebr Fll 00 Finl Em Stud Guide The em is on Tuesd, December th from :0m :0m. You re llowed scientific clcultor nd " b " inde crd for notes. On our inde crd be sure to write n formuls ou

More information

What is Monte Carlo Simulation? Monte Carlo Simulation

What is Monte Carlo Simulation? Monte Carlo Simulation Wht is Monte Crlo Simultion? Monte Crlo methods re widely used clss of computtionl lgorithms for simulting the ehvior of vrious physicl nd mthemticl systems, nd for other computtions. Monte Crlo lgorithm

More information

Dry Matter Intake Decreases Shortly After Initiation of Feeding Zilmax During the Summer

Dry Matter Intake Decreases Shortly After Initiation of Feeding Zilmax During the Summer Dry Mtter Intke Dereses Shortly After Initition of Feeding Zilmx During the C.D. Reinhrdt, C.I. Vhl, nd B.E. Depenush Introdution Sine Zilmx (zilpterol hydrohloride, ZIL; Merk Animl Helth, Summit, NJ)

More information

Problem Set for Chapter 3: Simple Regression Analysis ECO382 Econometrics Queens College K.Matsuda

Problem Set for Chapter 3: Simple Regression Analysis ECO382 Econometrics Queens College K.Matsuda Problem Set for Chpter 3 Simple Regression Anlysis ECO382 Econometrics Queens College K.Mtsud Excel Assignments You re required to hnd in these Excel Assignments by the due Mtsud specifies. Legibility

More information

Important Information when acting as sponsor for foreign visitor permits

Important Information when acting as sponsor for foreign visitor permits Importnt Informtion when ting s sponsor for foreign visitor permits The fee is 20 per visitor, with mximum fee of 100 for groups of 5-20 visitors. As it is dul form, if the visitor is ringing into the

More information

Name Date. Find the LCM of the numbers using the two methods shown above.

Name Date. Find the LCM of the numbers using the two methods shown above. Lest Common Multiple Multiples tht re shred by two or more numbers re clled common multiples. The lest of the common multiples is clled the lest common multiple (LCM). There re severl different wys to

More information

3: Inventory management

3: Inventory management INSE6300 Ji Yun Yu 3: Inventory mngement Concordi Februry 9, 2016 Supply chin mngement is bout mking sequence of decisions over sequence of time steps, fter mking observtions t ech of these time steps.

More information

Properties of the nonsymmetric Robinson-Schensted-Knuth algorithm

Properties of the nonsymmetric Robinson-Schensted-Knuth algorithm Properties of the nonsymmetri Roinson-Shensted-Knuth algorithm James Haglund Department of Mathematis University of Pennsylvania, Philadelphia, PA 90. USA jhaglund@math.upenn.edu Sarah Mason Department

More information

The MA health reform and other issues

The MA health reform and other issues The MA helth reorm nd other issues Gruer: three key issues or ny reorm Poolin Need wy to ornize helth cre other thn need Otherwise -- dverse selection Prolem: current system leves out smll irms Aordility

More information

Academic. Grade 9 Assessment of Mathematics. Spring 2008 SAMPLE ASSESSMENT QUESTIONS

Academic. Grade 9 Assessment of Mathematics. Spring 2008 SAMPLE ASSESSMENT QUESTIONS Aemi Gre 9 Assessment of Mthemtis Spring 8 SAMPLE ASSESSMENT QUESTIONS Reor your nswers to the multiple-hoie questions on the lnk Stuent Answer Sheet (Spring 8, Aemi). Plese note: The formt of these ooklets

More information

Divide-and-Conquer. 1. Find the maximum and minimum. The problem: Given a list of unordered n elements, find max and min

Divide-and-Conquer. 1. Find the maximum and minimum. The problem: Given a list of unordered n elements, find max and min Geerl ide: Divide-d-oquer Divide prolem ito suprogrms of the sme id; solve suprogrms usig the sme pproh, d omie prtil solutio if eessry. Youg S 5 dv. lgo. opi: Divide d oquer. Fid the mximum d miimum he

More information

City of Greater Sudbury Greater Sudbury Event Centre Site Evaluation. June 2017

City of Greater Sudbury Greater Sudbury Event Centre Site Evaluation. June 2017 City of Greter Sudury Greter Sudury Event Centre Site Evlution Bkground The Event Centre Site Evlution Tem inluded the Speil Advisor to the CAO,, HDR CEI Arhiteture nd the following City Deprtments: Eonomi

More information

UNinhabited aerial vehicles (UAVs) are becoming increasingly

UNinhabited aerial vehicles (UAVs) are becoming increasingly A Process Algebr Genetic Algorithm Sertc Krmn Tl Shim Emilio Frzzoli Abstrct A genetic lgorithm tht utilizes process lgebr for coding of solution chromosomes nd for defining evolutionry bsed opertors is

More information

(a) by substituting u = x + 10 and applying the result on page 869 on the text, (b) integrating by parts with u = ln(x + 10), dv = dx, v = x, and

(a) by substituting u = x + 10 and applying the result on page 869 on the text, (b) integrating by parts with u = ln(x + 10), dv = dx, v = x, and Supplementry Questions for HP Chpter 5. Derive the formul ln( + 0) d = ( + 0) ln( + 0) + C in three wys: () by substituting u = + 0 nd pplying the result on pge 869 on the tet, (b) integrting by prts with

More information

Get Solution of These Packages & Learn by Video Tutorials on KEY CONCEPTS

Get Solution of These Packages & Learn by Video Tutorials on  KEY CONCEPTS FREE Downlod Study Pckge from wesite: www.tekoclsses.com & www.mthsbysuhg.com Get Solution of These Pckges & Lern y Video Tutorils on www.mthsbysuhg.com KEY CONCEPTS THINGS TO REMEMBER :. The re ounded

More information

ECON 105 Homework 2 KEY Open Economy Macroeconomics Due November 29

ECON 105 Homework 2 KEY Open Economy Macroeconomics Due November 29 Instructions: ECON 105 Homework 2 KEY Open Economy Mcroeconomics Due Novemer 29 The purpose of this ssignment it to integrte the explntions found in chpter 16 ok Kennedy with the D-S model nd the Money

More information

BEFORE THE ENVIRONMENT COURT

BEFORE THE ENVIRONMENT COURT BEFORE THE ENVIRONMENT COURT I MUA I TE KOOTI TAIAO O AOTEAROA ENV-2018-AKL-000078 Under the Resource Mngement Act 1991 (the Act) In the mtter of direct referrl of n ppliction for resource consent for

More information

Patents, R&D investments, and Competition

Patents, R&D investments, and Competition Ptents, R&D investments, nd Competition Tois Mrkepnd June 8, 2008 Astrct We nlyze the incentives of firm to crete sleeping ptents, i.e., to tke out ptents which re not exploited. We show tht sleeping ptents

More information

PSAS: Government transfers what you need to know

PSAS: Government transfers what you need to know PSAS: Government trnsfers wht you need to know Ferury 2018 Overview This summry will provide users with n understnding of the significnt recognition, presenttion nd disclosure requirements of the stndrd.

More information

MARKET POWER AND MISREPRESENTATION

MARKET POWER AND MISREPRESENTATION MARKET POWER AND MISREPRESENTATION MICROECONOMICS Principles nd Anlysis Frnk Cowell Note: the detil in slides mrked * cn only e seen if you run the slideshow July 2017 1 Introduction Presenttion concerns

More information

164 CHAPTER 2. VECTOR FUNCTIONS

164 CHAPTER 2. VECTOR FUNCTIONS 164 CHAPTER. VECTOR FUNCTIONS.4 Curvture.4.1 Definitions nd Exmples The notion of curvture mesures how shrply curve bends. We would expect the curvture to be 0 for stright line, to be very smll for curves

More information

3. Argumentation Frameworks

3. Argumentation Frameworks 3. Argumenttion Frmeworks Argumenttion current hot topic in AI. Historiclly more recent thn other pproches discussed here. Bsic ide: to construct cceptble set(s) of beliefs from given KB: 1 construct rguments

More information

Reaching Agreements. Reaching Agreements. Mechanisms, Protocols, and Strategies. Mechanism Design: Desirable Properties

Reaching Agreements. Reaching Agreements. Mechanisms, Protocols, and Strategies. Mechanism Design: Desirable Properties Rehing Agreements An Introdution to Multi-Agent Systems Mihel Wooldridge Rehing Agreements How do gents rehing greements when they re self interested? In n extreme se (zero sum enounter) no greement is

More information

Math-3 Lesson 2-5 Quadratic Formula

Math-3 Lesson 2-5 Quadratic Formula Mth- Lesson - Qudrti Formul Quiz 1. Complete the squre for: 10. Convert this perfet squre trinomil into the squre of inomil: 6 9. Solve ompleting the squre: 0 8 Your turn: Solve ftoring. 1.. 6 7 How would

More information

The High Price of Cheap Fashion p. 12

The High Price of Cheap Fashion p. 12 quiz 3 interntionl The High Prie of Chep Fshion p. 12 Uses: opy mhine, opque projetor, or trnspreny mster for overhe projetor. Sholsti In. grnts teher-susriers to The New York Times Upfront permission

More information

8.1 External solid plastering

8.1 External solid plastering 8.1 Externl solid plstering Aims nd ojectives At the end of these ctivity sheets, lerners should e le to: understnd the preprtion of ckgrounds identify the mterils used for externl plstering identify the

More information

An Estimation of the Size of Non-Compact Suffix Trees

An Estimation of the Size of Non-Compact Suffix Trees Ata Cyernetia 22 (2016) 823 832. An Estimation of the Size of Non-Compat Suffix Trees Bálint Vásárhelyi Astrat A suffix tree is a data struture used mainly for pattern mathing. It is known that the spae

More information

CERTIFICATE OF LIABILITY INSURANCE

CERTIFICATE OF LIABILITY INSURANCE UMBRELL LIB EXCESS LIB CLIMS-MDE DED RETENTION WORKERS COMPENSTION ND EMPLOYERS' LIBILITY NY PROPRIETOR/PRTNER/EXECUTIVE OFFICER/MEMBER EXCLUDED? (Mandatory in NH) If yes, describe under DESCRIPTION OF

More information

Complete the table below to show the fixed, variable and total costs. In the final column, calculate the profit or loss made by J Kane.

Complete the table below to show the fixed, variable and total costs. In the final column, calculate the profit or loss made by J Kane. Tsk 1 J Kne sells mchinery to the frm industry. His fixed costs re 10,000 nd ech mchine costs him 400 to buy. He sells them t 600 nd is trying to work out his profit or loss t vrious levels of sles. He

More information

Math F412: Homework 4 Solutions February 20, κ I = s α κ α

Math F412: Homework 4 Solutions February 20, κ I = s α κ α All prts of this homework to be completed in Mple should be done in single worksheet. You cn submit either the worksheet by emil or printout of it with your homework. 1. Opre 1.4.1 Let α be not-necessrily

More information

STAT 472 Fall 2016 Test 2 November 8, 2016

STAT 472 Fall 2016 Test 2 November 8, 2016 STAT 47 Fll 016 Test November 8, 016 1. Anne who is (65) buys whole life policy with deth benefit of 00,000 pyble t the end of the yer of deth. The policy hs nnul premiums pyble for life. The premiums

More information

Burrows-Wheeler Transform and FM Index

Burrows-Wheeler Transform and FM Index Burrows-Wheeler Trnsform nd M Index Ben ngmed You re free to use these slides. If you do, plese sign the guestbook (www.lngmed-lb.org/teching-mterils), or emil me (ben.lngmed@gmil.com) nd tell me briefly

More information

KEY SKILLS INFORMATION TECHNOLOGY Level 3 Games [KSI3J4] Question Paper. 28 and 29 January 2004

KEY SKILLS INFORMATION TECHNOLOGY Level 3 Games [KSI3J4] Question Paper. 28 and 29 January 2004 KEY SKILLS INFORMATION TECHNOLOGY Level 3 Gmes [KSI3J4] Question Pper 28 n 29 Jnury 2004 WHAT YOU NEED This Question Pper An Answer Cover Sheet Aess to omputer, softwre n printer Aess to the t files to

More information

Earning Money. Earning Money. Curriculum Ready ACMNA: 189.

Earning Money. Earning Money. Curriculum Ready ACMNA: 189. Erning Money Curriculum Redy ACMNA: 189 www.mthletics.com Erning EARNING Money MONEY Different jos py different mounts of moneys in different wys. A slry isn t pid once in yer. It is pid in equl prts

More information

EVIDENCE OF PROPERTY INSURANCE

EVIDENCE OF PROPERTY INSURANCE THIS EVIDENCE OF PROPERTY INSURNCE IS ISSUED S MTTER OF INFORMTION ONLY ND CONFERS NO RIGHTS UPON THE DDITIONL INTEREST NMED BELOW. THIS EVIDENCE DOES NOT FFIRMTIVELY OR NEGTIVELY MEND, ETEND OR LTER THE

More information

Technical Appendix. The Behavior of Growth Mixture Models Under Nonnormality: A Monte Carlo Analysis

Technical Appendix. The Behavior of Growth Mixture Models Under Nonnormality: A Monte Carlo Analysis Monte Crlo Technicl Appendix 1 Technicl Appendix The Behvior of Growth Mixture Models Under Nonnormlity: A Monte Crlo Anlysis Dniel J. Buer & Ptrick J. Currn 10/11/2002 These results re presented s compnion

More information

Report by the Comptroller and. SesSIon June The performance and management of hospital PFI contracts

Report by the Comptroller and. SesSIon June The performance and management of hospital PFI contracts Report y the Comptroller nd Auditor Generl HC 68 SesSIon 2010 2011 17 June 2010 The performne nd mngement of hospitl PFI ontrts 4 Summry The performne nd mngement of hospitl PFI ontrts Summry The Privte

More information

Controlling a population of identical MDP

Controlling a population of identical MDP Controlling popultion of identicl MDP Nthlie Bertrnd Inri Rennes ongoing work with Miheer Dewskr (CMI), Blise Genest (IRISA) nd Hugo Gimert (LBRI) Trends nd Chllenges in Quntittive Verifiction Mysore,

More information

Asset finance (US) Opportunity. Flexibility. Planning. Develop your capabilities using the latest equipment

Asset finance (US) Opportunity. Flexibility. Planning. Develop your capabilities using the latest equipment Asset finnce (US) Opportunity Develop your cpbilities using the ltest equipment Flexibility Mnge your cshflow nd ccess the technology you need Plnning Mnge your investment with predictble costs nd plnned

More information

p = 0.4 q = 0.6 Pr(X = 4) = 5 C 4 (0.4) 4 (0.6) 1 = = Pr(X = x) = n C x p x q n x a n = 4 x = 1 Pr(X = 2) = 4 1 b n = 4 x = 2

p = 0.4 q = 0.6 Pr(X = 4) = 5 C 4 (0.4) 4 (0.6) 1 = = Pr(X = x) = n C x p x q n x a n = 4 x = 1 Pr(X = 2) = 4 1 b n = 4 x = 2 Chpter The inomil distriution Eerise A The inomil distriution No, sine there re more thn possile outomes. Yes, sine there re independent trils nd two possile outomes, three or nonthree. No, sine there

More information

This paper is not to be removed from the Examination Halls

This paper is not to be removed from the Examination Halls This pper is not to be remove from the Exmintion Hlls UNIVESITY OF LONON FN3092 ZA (279 0092) BSc egrees n iploms for Grutes in Economics, Mngement, Finnce n the Socil Sciences, the iploms in Economics

More information

JOURNAL THE ERGODIC TM CANDLESTICK OSCILLATOR ROBERT KRAUSZ'S. Volume 1, Issue 7

JOURNAL THE ERGODIC TM CANDLESTICK OSCILLATOR ROBERT KRAUSZ'S. Volume 1, Issue 7 ROBERT KRUSZ'S JOURNL Volume 1, Issue 7 THE ERGODIC TM CNDLESTICK OSCILLTOR S ometimes we re lucky (due to our diligence) nd we find tool tht is useful nd does the jo etter thn previous tools, or nswers

More information

Australian Sports Commission. Ethics in Sport.

Australian Sports Commission. Ethics in Sport. Austrlin Sports Commission. Ethis in Sport. Prepred for Deie Simms, Austrlin Sports Commission. CB Contt Dvid Brue, Reserh Diretor Phone (02) 6249 8566 Emil Dvid.Brue@r.om.u Issue Dte April 2010 Projet

More information

International Economics. Topics in Comparative Advantage

International Economics. Topics in Comparative Advantage Lecture 3 Interntionl Economics: Lecture 4 Topics in Comprtive Advntge Armn Gbrielyn ATC, Februry 13, 2017 Armn Gbrielyn(ATC) Interntionl Economics 1 Misconceptions Misconceptions bout Ricrdo s Model Myth

More information

Compiler construction in4303 lecture 4. Overview. Bottom-up (LR) parsing. LR(0) parsing. LR(0) parsing. LR(0) parsing. Compiler construction lecture 4

Compiler construction in4303 lecture 4. Overview. Bottom-up (LR) parsing. LR(0) parsing. LR(0) parsing. LR(0) parsing. Compiler construction lecture 4 Compler constructon lecture Compler constructon n lecture Bottom-up prsng Chpter.. Overvew synt nlyss: tokens S lnguge grmmr prser genertor ottom-up prsng push-down utomton CION/GOO tles LR(), SLR(), LR(),

More information

Conditions for GrowthLink

Conditions for GrowthLink Importnt: This is smple of the policy document. To determine the precise terms, conditions nd exclusions of your cover, plese refer to the ctul policy nd ny endorsement issued to you. Conditions for GrowthLink

More information