Cache CPI and DFAs and NFAs. CS230 Tutorial 10
|
|
- Frederica Lindsey
- 5 years ago
- Views:
Transcription
1 Cche CPI nd DFAs nd NFAs CS230 Tutoril 10
2 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 L2 cche miss 2
3 Multi-Level Cche: Clculting CPI We hve three possibilities, find the formul for ech: L1 Cche Hit Cost This is considered prt of norml execution. CPI cost is zero. L2 Cche Hit Cost We missed L1 cche but found the dt in the L2 cche This is L1-miss-penlty L1-miss-chnce L2 Cche Miss Cost We missed L1 nd L2 cche, hve to go ll the wy to memory This is min-memory-ccess-time globl-cche-miss-chnce globl-cche-miss-chnce could be clculted s L1-miss-chnce L2-miss-chnce CPI Formul is: bse-cpi + L2-cche-hit-CPI + L2-cche-miss-CPI 3
4 Multi-Level Cche: CPI Exmple Consider processor running t 5GHz with Bse-CPI of 2.4. It hs two-level cche. The L1 miss penlty is 2ns. The min memory ccess time is 60ns. The L1 cche hit rte is 85%. The L2 cche hit rte is 90%. Clculte the totl CPI of this processor with nd without n L2 cche. Wht is the speedup gined by hving n L2 cche? 4
5 Multi-Level Cche: CPI Exmple Solution A 5GHz processor hs cycle time of 0.2ns. L2 cche hit CPI = 2ns * 0.15 = 0.3ns = 1.5 CPI L2 cche miss CPI = 60ns * 0.15 * 0.10 = 0.9ns = 4.5 CPI So the effective CPI of this processor is: = 8.4 CPI If there ws no L2 cche this would be: L1 cche miss = 60ns * 0.15 = 9ns = 45 CPI So the effective CPI of this processor would hve been: = 47.4 CPI The speedup is therefore: 47.4/8.4 = ~5.6 times fster! 5
6 Multi-Level Cche: CPI Prctice Consider processor running t 2GHz with Bse-CPI of 1.4. It hs two-level cche. The L1 miss penlty is 10ns. The min memory ccess time is 40ns. The L1 cche hit rte is 90%. The globl cche miss rte with n L2 cche is 2%. Clculte the totl CPI of this processor with nd without n L2 cche. Wht is the speedup gined by hving n L2 cche? 6
7 Multi-Level Cche: CPI Prctice Solution A 2GHz processor hs cycle time of 500ps. L2 cche hit CPI = 10ns * 0.1 = 1ns = 1000ps = 2 CPI L2 cche miss CPI = 40ns * 0.02 = 0.8ns = 800ps = 1.6 CPI So the effective CPI of this processor is: = 5 CPI If there ws no L2 cche this would be: L1 cche miss = 40ns * 0.1 = 4ns = 4000ps = 8 CPI So the effective CPI of this processor would hve been: = 9.4 CPI The speedup is therefore: 9.4/5 = 1.88 times fster! 7
8 Regulr Lnguges There re vrious types of Forml Lnguges. Right now we cre bout Regulr Lnguges subset the of forml lnguges. Regulr lnguges cn be represented in vrious wys: DFAs NFAs Regulr Expressions (not covered yet) 8
9 Deterministic Finite Automt (DFAs) DFAs re grph (in the discrete mth sense) where there is set of sttes nd trnsitions between those sttes tht occur upon consuming chrcter from the input. The ide is tht you cn tke string (i.e. list of chrcters) nd check if it is ccepted by (i.e. fits in) the lnguge by seeing if you cn follow long the flow of the DFA nd rech the ccept stte (denoted by double circle) t the end of the string. If you mke it to n ccept stte t the end of the string, then the string is in the regulr lnguge defined by tht DFA. If you re not in n ccept stte t the end of the string, then the the string is not in the regulr lnguge defined by tht DFA. It is possible to enter n ccept stte nd then leve it gin. The string must end in n ccept stte to be ccepted. 9
10 DFA Exmples Drw DFA for the lnguge of ny combintion of the letters nd b. Drw DFA for the lnguge of t lest one, following by zero or more numeric digits followed by nd x nd then n optionl y. 10
11 DFA Exmple Solution 1 Drw DFA for the lnguge of ny combintion of the letters nd b. Solution:, b strt q 0 11
12 DFA Exmple Solution 2 Drw DFA for the lnguge represented by t lest one, following by zero or more numeric digits followed by nd x nd then n optionl y. 0-9 Solution: q 3 strt q 0 q x x y q 4 q 5 12
13 DFA Prctice Drw DFA for the lnguge of ny number of s, followed by n even number of c s. Drw DFA for the lnguge of two or three b s followed by zero or more c s followed by n or d. Drw DFA for the lnguge of t lest two s followed by n odd number of d s followed by two s or one c. 13
14 DFA Prctice Solution 1 Drw DFA for the lnguge of ny number of s, followed by n even number of c s. Solution: strt c q 0 c q 1 c q 2 14
15 DFA Prctice Solution 2 Drw DFA for the lnguge of two or three b s followed by zero or more c s followed by n or d. Solution: c strt b b b,c,d q 0 q 1 q 2 q 3 q 4,d 15
16 DFA Prctice Solution 3 Drw DFA for the lnguge of t lest two s followed by n odd number of d s followed by two s or one c. Solution: strt d d q 0 q 1 q 2 q 3 c d q 4 q 5 q 6 16
17 Non-Deterministic Finite Automt (NFAs) With DFAs, there is no mbiguity t ll, nd only one possible pth between sttes for given string. With NFAs, there re situtions where there could be more thn one choice for move from stte to stte while prsing string. NFAs cn hve (which DFAs do not hve): epsilon trnsitions (stte trnsitions on the empty chrcter) multiple trnsitions on the sme stte for prticulr chrcter (for exmple, on one stte, there could be 2 rrows out on the sme chrcter) NFAs cn lwys be trnslted to n equivlent DFA. 17
18 NFA Exmple Let s look t one of the exmples we ve been looking t so fr: Drw NFA for the lnguge of t lest one, following by zero or more numeric digits followed by nd x nd then n optionl y. 0-9 Recll tht the DFA is s follows: q 3 strt q 0 q x x y q 4 q 5 18
19 NFA Exmple Solution Exmple: Drw NFA for the lnguge of t lest one, following by zero or more numeric digits followed by nd x nd then n optionl y. Solution: Note: there re more thn one possible NFA for this problem, nd I m purposely showing mny NFA fetures strt nd not going for optiml NFA construction. q 3 ε x q 0 q 1 x 0-9 ε, y q 4 q 5 19
20 NFA Prctice Drw n NFA for the lnguge of ny number of s, followed by ny number of repetitions of bb. Drw n NFA for the lnguge of two or three b s followed by ny number of c s followed by n or d. Drw n NFA for the lnguge of one or more d s followed by ny number of c s followed by cd, or c, or nothing. 20
21 NFA Prctice Solution 1 Drw n NFA for the lnguge of ny number of s, followed by ny number of repetitions of bb. Solution: q 3 b strt q 0 b q 2 q 1 21
22 NFA Prctice Solution 2 Drw n NFA for the lnguge of two or three b s followed by zero or more c s followed by n or d. Solution: c strt b b b,ε,d q 0 q 1 q 2 q 3 q 4 22
23 NFA Prctice Solution 3 Drw n NFA for the lnguge of one or more d s followed by ny number of c s followed by cd, or c, or nothing. Solution: d c ε strt d ε c d q 0 q 1 q 2 q 3 q 5 c q 4 23
24 Assignment reminders Submit.txt XOR.pdf for ech question Do not submit both for the sme question! You my submit.pdf for one question nd.txt for different question Mke sure your digrms nd tbles re cler nd esy to red Mke sure to leve enough spce 24
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 information3/1/2016. 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 informationArithmetic 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 informationPushdown 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 informationDrawing 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 informationAccess your online resources today at
978--07-670- - CmbridgeMths: NSW Syllbus for the Austrlin Curriculum: Yer 0: Stte./. Access your online resources tody t www.cmbridge.edu.u/go. Log in to your existing Cmbridge GO user ccount or crete
More informationA 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 informationAddition 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 informationName 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 informationCH 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(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 informationWhat 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 informationchecks 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 information164 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 informationUNIT 7 SINGLE SAMPLING PLANS
UNIT 7 SINGLE SAMPLING PLANS Structure 7. Introduction Objectives 7. Single Smpling Pln 7.3 Operting Chrcteristics (OC) Curve 7.4 Producer s Risk nd Consumer s Risk 7.5 Averge Outgoing Qulity (AOQ) 7.6
More informationRoadmap 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 information3: 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 informationINF 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 informationBurrows-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 informationReinforcement 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 informationOutline. 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 informationChapter55. 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 informationSolutions to Exercises, Set 3
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(
More informationChapter 3: The Reinforcement Learning Problem. The Agent'Environment Interface. Getting the Degree of Abstraction Right. The Agent Learns a Policy
Chpter 3: The Reinforcement Lerning Problem The Agent'Environment Interfce Objectives of this chpter: describe the RL problem we will be studying for the reminder of the course present idelized form of
More informationProblem 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 informationComplete 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 informationFinancial Mathematics 3: Depreciation
Finncil Mthemtics 3: Deprecition Student Book - Series M-1 25% p.. over 8 yers Mthletics Instnt Workooks Copyright Finncil mthemtics 3: Deprecition Student Book - Series M Contents Topics Topic 1 - Modelling
More informationA Closer Look at Bond Risk: Duration
W E B E X T E S I O 4C A Closer Look t Bond Risk: Durtion This Extension explins how to mnge the risk of bond portfolio using the concept of durtion. BOD RISK In our discussion of bond vlution in Chpter
More informationGridworld Values V* Gridworld: Q*
CS 188: Artificil Intelligence Mrkov Deciion Procee II Intructor: Dn Klein nd Pieter Abbeel --- Univerity of Cliforni, Berkeley [Thee lide were creted by Dn Klein nd Pieter Abbeel for CS188 Intro to AI
More informationMARKET 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 informationTechnical 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 informationToday 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 informationCS 188 Introduction to Artificial Intelligence Fall 2018 Note 4
CS 188 Introduction to Artificil Intelligence Fll 2018 Note 4 These lecture notes re hevily bsed on notes originlly written by Nikhil Shrm. Non-Deterministic Serch Picture runner, coming to the end of
More information3. 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 informationDYNAMIC 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 informationThe 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 informationFirst 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 informationTrigonometry - 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 informationGet 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 informationRecap: MDPs. CS 188: Artificial Intelligence Fall Optimal Utilities. The Bellman Equations. Value Estimates. Practice: Computing Actions
CS 188: Artificil Intelligence Fll 2008 Lecture 10: MDP 9/30/2008 Dn Klein UC Berkeley Recp: MDP Mrkov deciion procee: Stte S Action A Trnition P(,) (or T(,, )) Rewrd R(,, ) (nd dicount γ) Strt tte 0 Quntitie:
More informationControlling 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 informationMath 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 informationTHE 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 informationA Fuzzy Inventory Model With Lot Size Dependent Carrying / Holding Cost
IOSR Journl of Mthemtics (IOSR-JM e-issn: 78-578,p-ISSN: 9-765X, Volume 7, Issue 6 (Sep. - Oct. 0, PP 06-0 www.iosrournls.org A Fuzzy Inventory Model With Lot Size Dependent Crrying / olding Cost P. Prvthi,
More informationUNIVERSITY OF NOTTINGHAM. Discussion Papers in Economics BERTRAND VS. COURNOT COMPETITION IN ASYMMETRIC DUOPOLY: THE ROLE OF LICENSING
UNIVERSITY OF NOTTINGHAM Discussion Ppers in Economics Discussion Pper No. 0/0 BERTRAND VS. COURNOT COMPETITION IN ASYMMETRIC DUOPOLY: THE ROLE OF LICENSING by Arijit Mukherjee April 00 DP 0/0 ISSN 160-48
More informationReleased 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 informationThe Market Approach to Valuing Businesses (Second Edition)
BV: Cse Anlysis Completed Trnsction & Guideline Public Comprble MARKET APPROACH The Mrket Approch to Vluing Businesses (Second Edition) Shnnon P. Prtt This mteril is reproduced from The Mrket Approch to
More informationProblem 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 informationAnnouncements. CS 188: Artificial Intelligence Fall Recap: MDPs. Recap: Optimal Utilities. Practice: Computing Actions. Recap: Bellman Equations
CS 188: Artificil Intelligence Fll 2009 Lecture 10: MDP 9/29/2009 Announcement P2: Due Wednedy P3: MDP nd Reinforcement Lerning i up! W2: Out lte thi week Dn Klein UC Berkeley Mny lide over the coure dpted
More informationContinuous Optimal Timing
Srlnd University Computer Science, Srbrücken, Germny My 6, 205 Outline Motivtion Preliminries Existing Algorithms Our Algorithm Empiricl Evlution Conclusion Motivtion Probbilistic models unrelible/unpredictble
More informationEarning 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 informationInterest. 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 informationOPEN BUDGET QUESTIONNAIRE SOUTH AFRICA
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE SOUTH AFRICA September 28, 2007 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC
More information1. Detailed information about the Appellant s and Respondent s personal information including mobile no. and -id are to be furnished.
Revised Form 36 nd Form 36A for filing ppel nd cross objection respectively before income tx ppellte tribunl (Notifiction No. 72 dted 23.10.2018) Bckground CBDT issued drft notifiction vide press relese
More informationChapter 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 informationproduction for Community & Culture Project Reference e 2 design episodes Bogotá: Building a Sustainable City and Affordable Green Housing.
Community & Culture Project Reference e 2 design episodes Bogotá: Building Sustinble City nd Affordble Green Housing. 1) Red the bckground essy nd discussion questions for e 2 design episodes Bogotá: Building
More informationAnnouncements. Maximizing Expected Utility. Preferences. Rational Preferences. Rational Preferences. Introduction to Artificial Intelligence
Introduction to Artificil Intelligence V22.0472-001 Fll 2009 Lecture 8: Utilitie Announcement Will hve Aignment 1 grded by Wed. Aignment 2 i up on webpge Due on Mon 19 th October (2 week) Rob Fergu Dept
More informationOutline. CS 188: Artificial Intelligence Spring Speeding Up Game Tree Search. Minimax Example. Alpha-Beta Pruning. Pruning
CS 188: Artificil Intelligence Spring 2011 Lecture 8: Gme, MDP 2/14/2010 Pieter Abbeel UC Berkeley Mny lide dpted from Dn Klein Outline Zero-um determinitic two plyer gme Minimx Evlution function for non-terminl
More informationStatic Fully Observable Stochastic What action next? Instantaneous Perfect
CS 188: Ar)ficil Intelligence Mrkov Deciion Procee K+1 Intructor: Dn Klein nd Pieter Abbeel - - - Univerity of Cliforni, Berkeley [Thee lide were creted by Dn Klein nd Pieter Abbeel for CS188 Intro to
More informationThe Okun curve is non-linear
Economics Letters 70 (00) 53 57 www.elsevier.com/ locte/ econbse The Okun curve is non-liner Mtti Viren * Deprtment of Economics, 004 University of Turku, Turku, Finlnd Received 5 My 999; ccepted 0 April
More informationJFE Online Appendix: The QUAD Method
JFE Online Appendix: The QUAD Method Prt of the QUAD technique is the use of qudrture for numericl solution of option pricing problems. Andricopoulos et l. (00, 007 use qudrture s the only computtionl
More informationa v p a v p a (60, 000) (1.05) ( )(2.743) (1.05) ( )(9.6612) 15, 065 Pa P a v p a v p a
1. Dimos Disbility Insurnce Compny uses the Stnr Sickness-Deth Moel with i 0.05 to price n reserve its isbility income policies. Dimos sells 10 yer isbility income policy. The policy pys premiums continuously
More informationOPEN BUDGET QUESTIONNAIRE
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE SOUTH KOREA September 28, 2007 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC 20002
More informationBuckling 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 informationOPEN BUDGET QUESTIONNAIRE UKRAINE
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE UKRAINE September 28, 2007 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC 20002
More informationMaximum Expected Utility. CS 188: Artificial Intelligence Fall Preferences. MEU Principle. Rational Preferences. Utilities: Uncertain Outcomes
CS 188: Artificil Intelligence Fll 2011 Mximum Expected Utility Why hould we verge utilitie? Why not minimx? Lecture 8: Utilitie / MDP 9/20/2011 Dn Klein UC Berkeley Principle of mximum expected utility:
More informationNon-Deterministic Search. CS 188: Artificial Intelligence Markov Decision Processes. Grid World Actions. Example: Grid World
CS 188: Artificil Intelligence Mrkov Deciion Procee Non-Determinitic Serch Dn Klein, Pieter Abbeel Univerity of Cliforni, Berkeley Exmple: Grid World Grid World Action A mze-like problem The gent live
More informationAnnouncements. CS 188: Artificial Intelligence Fall Reinforcement Learning. Markov Decision Processes. Example Optimal Policies.
CS 188: Artificil Intelligence Fll 2008 Lecture 9: MDP 9/25/2008 Announcement Homework olution / review eion: Mondy 9/29, 7-9pm in 2050 Vlley LSB Tuedy 9/0, 6-8pm in 10 Evn Check web for detil Cover W1-2,
More informationInternational 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 informationChapter 4. Profit and Bayesian Optimality
Chpter 4 Profit nd Byesin Optimlity In this chpter we consider the objective of profit. The objective of profit mximiztion dds significnt new chllenge over the previously considered objective of socil
More informationA portfolio approach to the optimal funding of pensions
Economics Letters 69 (000) 01 06 www.elsevier.com/ locte/ econbse A portfolio pproch to the optiml funding of pensions Jysri Dutt, Sndeep Kpur *, J. Michel Orszg b, b Fculty of Economics University of
More informationFully Observable. Perfect
CS 188: Ar)ficil Intelligence Mrkov Deciion Procee II Stoch)c Plnning: MDP Sttic Environment Fully Obervble Perfect Wht ction next? Stochtic Intntneou Intructor: Dn Klein nd Pieter Abbeel - - - Univerity
More informationSTAT 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 informationRevision 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 informationECON 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 informationSmart Investment Strategies
Smrt Investment Strtegies Risk-Rewrd Rewrd Strtegy Quntifying Greed How to mke good Portfolio? Entrnce-Exit Exit Strtegy: When to buy? When to sell? 2 Risk vs.. Rewrd Strtegy here is certin mount of risk
More informationLecture 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 informationAsset 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 informationEconomics Department Fall 2013 Student Learning Outcomes (SLOs) Assessment Economics 4 (Principles of Microeconomics)
Jnury 2014 Economics Deprtment Fll 2013 Stuent Lerning Outcomes (SLOs) Assessment Economics 4 (Principles of Microeconomics) Lerning Outcome Sttement: In the Fll 2013 semester the Economics Deprtment engge
More informationFINANCIAL ANALYSIS I. INTRODUCTION AND METHODOLOGY
Dhk Wter Supply Network Improvement Project (RRP BAN 47254003) FINANCIAL ANALYSIS I. INTRODUCTION AND METHODOLOGY A. Introduction 1. The Asin Development Bnk (ADB) finncil nlysis of the proposed Dhk Wter
More informationOpen Space Allocation and Travel Costs
Open Spce Alloction nd Trvel Costs By Kent Kovcs Deprtment of Agriculturl nd Resource Economics University of Cliforni, Dvis kovcs@priml.ucdvis.edu Pper prepred for presenttion t the Americn Agriculturl
More informationMath 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 informationESL Helpful Handouts The Verb Have Page 1 of 7 Talking About Health Problems When someone has a health problem, the verb have is often used.
ESL Helpful Hndouts The Verb Hve Pge 1 of 7 When someone hs helth problem, the verb hve is often used. I hve n llergy. You hve n llergy. He hs n llergy. She hs n llergy. Helth Problems We hve n llergy.
More informationConditions for FlexiLink
Conditions for FlexiLink Your policy 1 Wht your policy covers FlexiLink is single-premium investment-linked pln designed to increse the vlue of your investment. Through this pln, you cn invest in one or
More informationThis 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 informationEdgeworth box. apples. F-f. A-a. trade. f f F. fig leaves
Chpters 9 nd 1 pples Edgeworth box 9.4.1 F-f trde A- A f f F fig leves pples Edgeworth box 9.4.1 F-f trde A- A Adm gets (f,) Eve gets (F-f, A-) f f F fig leves pples Edgeworth box 9.4.1 F-f endowment A-
More informationInternational Budget Partnership OPEN BUDGET QUESTIONNAIRE Slovenia, September 2009
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE Sloveni, September 2009 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street NE, Suite 510 Wshington, DC 20002 www.interntionlbudget.org
More informationThis 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 ZB (279 0092) BSc egrees n iploms for Grutes in Economics, Mngement, Finnce n the Socil Sciences, the iploms in Economics
More informationstyle type="text/css".wpb_animate_when_almost_visible { opacity: 1; }/style
style type="text/css".wpb_nimte_when_lmost_visible { opcity: 1; }/style Sign in How py bills with bnk meric crd How py bills with bnk meric crd mobile How py bills with bnk meric crd & online bnking ccess
More informationTechnical Report Global Leader Dry Bulk Derivatives. FIS Technical - Grains And Ferts. Highlights:
Technicl Report Technicl Anlyst FIS Technicl - Grins And Ferts Edwrd Hutn 442070901120 Edwrdh@freightinvesr.com Client Reltions Andrew Cullen 442070901120 Andrewc@freightinvesr.com Highlights: SOY remins
More informationOPEN BUDGET QUESTIONNAIRE CZECH REPUBLIC
Interntionl Budget Project OPEN BUDGET QUESTIONNAIRE CZECH REPUBLIC October 2005 Interntionl Budget Project Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC 20002 www.interntionlbudget.org
More informationCSCI 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 informationSiemens Investor Plan
Siemens Investor Pln Your complete guide to mking the most of your Siemens pension If you nee d dvice... IFA Pro motion cn pro indepe v ndent finnci ide contct d Visit th et l dvise eir web rs in yo ils
More informationInternational Budget Partnership OPEN BUDGET QUESTIONNAIRE POLAND
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE POLAND September 28, 2007 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC 20002
More informationWhy did French Savers buy Foreign Asset before 1914? Decomposition of the Diversification Benefit
Why did French Svers buy Foreign Asset before 1914? Decomposition of the Diversifiction Benefit Dvid Le Bris 1 First version: July 2008 This version: April 2009 Abstrct: The diversifiction effect is nlysed
More informationASYMMETRIC SWITCHING COSTS CAN IMPROVE THE PREDICTIVE POWER OF SHY S MODEL
Document de trvil 2012-14 / April 2012 ASYMMETRIC SWITCHIG COSTS CA IMPROVE THE PREDICTIVE POWER OF SHY S MODEL Evens Slies OFCE-Sciences-Po Asymmetric switching costs cn improve the predictive power of
More informationwhy who when best resource
g ds r o C chin Tow formnce A Mngers Guide to Coch nd Inspire Empoyees when At the beginning of new [stretch] ssignment. Yer round nd during performnce reviews. At the beginning of new stff/empoyee retionship.
More informationOPEN BUDGET QUESTIONNAIRE CZECH REPUBLIC
Interntionl Budget Prtnership OPEN BUDGET QUESTIONNAIRE CZECH REPUBLIC September 28, 2007 Interntionl Budget Prtnership Center on Budget nd Policy Priorities 820 First Street, NE Suite 510 Wshington, DC
More informationResearch Article Existence of Positive Solution to Second-Order Three-Point BVPs on Time Scales
Hindwi Publishing Corportion Boundry Vlue Problems Volume 2009, Article ID 685040, 6 pges doi:10.1155/2009/685040 Reserch Article Existence of Positive Solution to Second-Order hree-point BVPs on ime Scles
More information1 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