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School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms October 14, 2012 Solutions to Problem 1 of Homework 6 (10 points) Name: Chang Wang Due: Wednesday, October 17 According to Josephus account of the siege of Yodfat, he and his n comrade soldiers were trapped in a
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms September 17, 2012 Solutions to Problem 1 of Homework 2 (10 points) Name: Hongnian Li Due: Tuesday, September 18 (a) (6 points) Suppose you have some procedure FASTMERGE that given two sorted lists of length m e
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms October 7, 2012 Solutions to Problem 1 of Homework 5 (10 points) Name: Chang Wang Due: Tuesday, October 9 (a) (4 points) Suppose we want to sort an array A of n elements from the set cfw_1, 2, . . . , (log n)log
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms September 24, 2012 Solutions to Problem 1 of Homework 3 (20 points) Name: Haoxiang Zuo Due: Tuesday, September 25 The sequence cfw_Fn | n 0 are dened as follows: F0 = 1, F1 = 1, F2 = 2 and, for i > 1, dene Fi :=
School: NYU
Course: Adb
Fast Calculations of Simple Primitives in Time Series Dennis Shasha Department of Computer Science Courant Institute of Mathematical Sciences New York university Joint work with Richard Cole, Xiaojian Zhao (correlation), Zhihua Wang (humming), Yunyue Zhu
School: NYU
School: NYU
Course: Nlp
Lecture Questions 1 1.1 Language Model 1 Question (time: 6:17) Say we have a vocabulary V = cfw_the and a constant N 1. For any x1 . . . xn such that xi V for i = 1 . . . (n 1) and xn = STOP, we 1 if n N N dene p(x1 , . . . , xn ) = 0 otherwise Is this a
School: NYU
Course: Introduction To Finance
H28 Option Profit/Payoffs on Expiration: Sample Tables W.L. Silber 1. Long Call with E =100 (1) S 150 140 130 120 110 100 90 80 70 60 50 (2) Payoff Max[0,S-E] 50 40 30 20 10 0 0 0 0 0 0 (3) Premium 10 10 10 10 10 10 10 10 10 10 10 (4) Profit (2) - (3) 40
School: NYU
Course: Introduction To Finance
H29 6. Evaluate what happens at the end if S > E and S # E S>E Exercise long C Deliver against short S Receive proceeds of investment Net Cash Flow -$100 ! +$106.18 +$6.18 S# E (e.g., S = 98) Leave call unexercised Buy S in market Deliver S against short
School: NYU
Course: Introduction To Finance
H25 With P 2 = 92.49 and tR2 = .0398 one-year investors are also indifferent between both of their strategies, as shown in the following 2 possibilities: 1. Buy tR1 and earn .02 2. Buy tR2 and sell after one year. The expected selling price of tR2 after o
School: NYU
Course: Introduction To Finance
H22 3. Yield to Maturity = internal rate of return. Implicitly includes all effects of P, C, and F on yields. a) Annual pay bonds IRR using number of periods = number of years b) Semi-annual pay bonds Double IRR using number of periods = twice the number
School: NYU
Course: Introduction To Finance
H31 Cash-Futures Arbitrage, B01.2311 Prof. Stijn Van Nieuwerburgh 1 Terminology If an investor enters in a long future position, he assumes the obligation to take delivery of the underlying at settlement date (date T ) at a price agreed upon when he enter
School: NYU
Course: Adb
Fast Calculations of Simple Primitives in Time Series Dennis Shasha Department of Computer Science Courant Institute of Mathematical Sciences New York university Joint work with Richard Cole, Xiaojian Zhao (correlation), Zhihua Wang (humming), Yunyue Zhu
School: NYU
Course: Fundamental Algorithms
5 SELECT customer.c_first| ' ' |customer.c_last as name, nvl(TO_CHAR(shipment_line.sl_date_received), 'NOT YET ARRIVED') as arrive_date FROM customer NATURAL JOIN orders NATURAL JOIN order_line NATURAL JOIN inventory NATURAL JOIN shipment_line WHERE order
School: NYU
Course: Fundamental Algorithms
BBC6521 Project Preliminary Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@bupt.com Android app fo
School: NYU
Course: Fundamental Algorithms
Project Specification Form School Name International School HAO Taiyan Programme BUPT student no Project No. IP_3135 Project Title Scope Android app for location recording using GPS Implementation Phone apps E-Commerce (H6NF) 10213135 Class QM stud
School: NYU
Course: Fundamental Algorithms
Mid Term Check Form School International School Programme E-Commerce (H6NF) Name HAO Taiyan BUPT student no 10213135 Project No. Implementation QM student no. 106672219 Android app for location recording using GPS Scope 2010215113 IP_3135 Project Titl
School: NYU
Course: Fundamental Algorithms
BBC6521 Project [Preliminary/Early-term Progress] Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@b
School: NYU
Course: Open Sou
Question 1: I: B C II: A D III: A D Question 2: A. B. C. D. E. sed '/\([0-9].*\)\cfw_3,\/s/[0-9]/g' sed 's/"[^"\]*"/"/g' sed 's/^\([^,]*\),\(.*\)$/\2,\1/' grep '^\(.\)*$' grep '[0-9]' | grep '[a-z]' Question 3: A. B. C. D. E. cut -d';' -f1 | tr a-z A-Z |
School: NYU
Course: Open Sou
Sample Questions 1. Under what circumstances can two processes have the same a - process id? b - parent process id? c - process group id? 2. Why should you never specify setuid permissions and write permissions for other on the same file? Please give an e
School: NYU
Course: Open Sou
Midterm Information The midterm exam is October 29, 2012 (in class). The topics covered on the exam will include everything we have discussed in class to date. You may bring the recommended texts for the course, the lecture slides, and personal notes. You
School: NYU
Course: PnP
CS 445 Negative-weight cycles Recall: If a graph G = (V, E) contains a negativeweight cycle, then some shortest paths may not exist. Example: <0 Shortest Paths in Graphs Bellman-Ford Algorithm Slides courtesy of Erik Demaine and Carola Wenk u u v v Bellma
School: NYU
Course: Computer Systems Organization
Computer Systems Organization V22.0201 Fall 2009 Sample Midterm Exam ANSWERS 1. True/False. Circle the appropriate choice. (a) T At most one operand of an x86 assembly instruction can be an memory address (b) F At most one operand of an x86 assembly instr
School: NYU
Course: Computer Systems Organization
Computer Systems Organization V22.0201 Fall 2009 Sample Midterm Exam 1. True/False. Circle the appropriate choice. (a) T F (b) T F At most one operand of an x86 assembly instruction can be an memory address At most one operand of an x86 assembly instructi
School: NYU
Course: Open Sou
Open Source Tools Assignment 2 Due: Thursday, October 10, 2013 at 11:59PM PartI:Sed 1.Theinternetmoviedatabase(www.imdb.com)containsalistofthetop250moviesofalltimeinrank order.Inthisquestion,wewillwriteasinglesedscriptorapipelineofsedcommandstoconvertthe
School: NYU
Course: Open Sou
Assignment 0 Due Thursday, September 10, 2013 before class For those who won't have their accounts created in time, it's OK to turn in the assignment late after your account is available. Just make sure you've sent off your request to the department admin
School: NYU
Course: Open Sou
Open Source Tools Assignment 1 Due: Wednesday, September 25, 2013 at 11:59pm Overview Inthisassignment,we'llusesomeoftheUNIXtoolswehaveseensofartoanalyzesomerealworld data.Specifically,willlookatthemostpopularnamesgiventobabies.DatafromtheSocialSecuritywe
School: NYU
Course: Open Sou
Open Source Tools Assignment 3 Due: November 4th, 2013 at 11:59PM Shell Scripting Forthisassignment,yourtaskistoscriptanewUNIXtoolcalledabc(ABetterCompressor).abchas similarfunctionalitytogzip;itsinputisafiletocompress,anditsoutputisaversionofthefilethati
School: NYU
MekyasMoges 11/23/13 DataStructures BSTandSkipListAnalysis Note:UnfortunatelySpeedTest.javawouldnotcompilecorrectly.Thereforetherewas notenoughsufficientdatatodrawanaccurateconclusiononthetwodatastructuresrun time. The following report is strictly a theor
School: NYU
I was so high I did not recognize, The fire burining in her eyes, the chaos that controlled my mind. Whispered goodbye as she got on a plane never to return again, a promise in my heart. that this love has taken its toll on me. She said goodbye to many t
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
Course: Adb
Advanced Database Systems, CSCI-GA.2434-001 New York University, Fall 2011 instructor: Dennis Shasha shasha@cs.nyu.edu 212-998-3086 Courant Institute New York University 251 Mercer Street NY, NY 10012 USA Oce Hours: 9 PM on Tuesdays or 5 PM Thursdays by a
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms October 14, 2012 Solutions to Problem 1 of Homework 6 (10 points) Name: Chang Wang Due: Wednesday, October 17 According to Josephus account of the siege of Yodfat, he and his n comrade soldiers were trapped in a
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms September 17, 2012 Solutions to Problem 1 of Homework 2 (10 points) Name: Hongnian Li Due: Tuesday, September 18 (a) (6 points) Suppose you have some procedure FASTMERGE that given two sorted lists of length m e
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms October 7, 2012 Solutions to Problem 1 of Homework 5 (10 points) Name: Chang Wang Due: Tuesday, October 9 (a) (4 points) Suppose we want to sort an array A of n elements from the set cfw_1, 2, . . . , (log n)log
School: NYU
Course: Fundamental Algorithms
CSCI-GA.1170-001/002 Fundamental Algorithms September 24, 2012 Solutions to Problem 1 of Homework 3 (20 points) Name: Haoxiang Zuo Due: Tuesday, September 25 The sequence cfw_Fn | n 0 are dened as follows: F0 = 1, F1 = 1, F2 = 2 and, for i > 1, dene Fi :=
School: NYU
Course: Adb
Fast Calculations of Simple Primitives in Time Series Dennis Shasha Department of Computer Science Courant Institute of Mathematical Sciences New York university Joint work with Richard Cole, Xiaojian Zhao (correlation), Zhihua Wang (humming), Yunyue Zhu
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
Course: Adb
AQuery A Database System for Order Dennis Shasha Joint work with Alberto Lerner lerner@cs.nyu.edu shasha@cs.nyu.edu Motivation The need for ordered data Queries in Finance, Biology, and Network Management depend on order. SQL 99 has extensions the OLAP a
School: NYU
Course: Fundamental Algorithms
5 SELECT customer.c_first| ' ' |customer.c_last as name, nvl(TO_CHAR(shipment_line.sl_date_received), 'NOT YET ARRIVED') as arrive_date FROM customer NATURAL JOIN orders NATURAL JOIN order_line NATURAL JOIN inventory NATURAL JOIN shipment_line WHERE order
School: NYU
Course: Fundamental Algorithms
BBC6521 Project Preliminary Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@bupt.com Android app fo
School: NYU
Course: Fundamental Algorithms
Project Specification Form School Name International School HAO Taiyan Programme BUPT student no Project No. IP_3135 Project Title Scope Android app for location recording using GPS Implementation Phone apps E-Commerce (H6NF) 10213135 Class QM stud
School: NYU
Course: Fundamental Algorithms
Mid Term Check Form School International School Programme E-Commerce (H6NF) Name HAO Taiyan BUPT student no 10213135 Project No. Implementation QM student no. 106672219 Android app for location recording using GPS Scope 2010215113 IP_3135 Project Titl
School: NYU
Course: Fundamental Algorithms
BBC6521 Project [Preliminary/Early-term Progress] Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@b
School: NYU
Course: Fundamental Algorithms
Fundamental Algorithms, Problem Set 2 Due Thursday, February 12 in Recitation He who learns but does not think is lost. He who thinks but does not learn is in great danger. Confucius 1. Illustrate the operation of PARTITION(A,1,12) on the array A = (13, 1
School: NYU
Course: Fundamental Algorithms
Fundamental Algorithms, Problem Set 1 Due Thursday, Feb 5, in Recitation The world can be divided into those who love New York City and those who dont. Those who love New York tend to be unusually lively people. They have to be. Characteristically, they a
School: NYU
Course: Fundamental Algorithms
ciww 829 Off Hor Tu3-5 spencer@cims.nyu.edu heap chap6 Ta mea378@nyu.edu office hours Thurs 6-8 13 fl laugh 1 83 2 58 3 4 4 9 10 83 semisorted array 5 6 7 8 heap A: heap size[A] (often n) length[A] the amount of data -increasing,decreasing buzzwords: dyna
School: NYU
Course: Adb
Designing Concurrent Search Structure Algorithms Dennis Shasha What is a Search Structure? Data structure (typically a B tree, hash structure, R-tree, etc.) that supports a dictionary. Operations are insert key-value pair, delete key-value pair, and sear
School: NYU
Course: Adb
StrangerDB -Safe Data Management with Untrusted Servers Dennis Shasha (shasha@cs.nyu.edu) Joint work (past and present): David Mazieres and Radu Sion 1 Goals Store private data in a public database: backup, concurrency control, and query processing Make
School: NYU
Course: Adb
PARTITIONING IN LARGE DATA SYSTEMS (IF IT IS NOT DYNAMIC, WHAT'S THE POINT?) NYU Advanced Databases Class, Invited Lecture, Oct/2009 alberto.lerner@gmail.com MoKvaKon c c c User Data Data par=oning as a way to scale If only one could "stretch" a
School: NYU
Course: Adb
Pig Latin: A Not-So-Foreign Language For Data Processing Chris Olston Benjamin Reed Utkarsh Srivastava Ravi Kumar Andrew Tomkins Research Data Processing Renaissance Internet companies swimming in data E.g. TBs/day at Yahoo! Data analysis is "inner loo
School: NYU
Course: Adb
Rapid Detection of Significant Spatial Clusters Daniel B. Neill Andrew W. Moore The Auton Lab Carnegie Mellon University School of Computer Science E-mail: cfw_neill, awm@cs.cmu.edu Introduction Goals of data mining: Discover patterns in data. Distingui
School: NYU
School: NYU
School: NYU
Course: Analytical Methods In Computer Science
Homework 1 Due 2011/2/25 Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions Writeup: You must do the writeup alone. For each question, cite all references used (or write none) and colla
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 6+7 Due on Mon Dec 14, after the class Collaboration is allowed, but you must write your own solutions. Absolutely no extensions. For all problems, the alphabet = cfw_0, 1. Problem 1 Solve Problem 4 from
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 4+5 Due on Monday Nov 16, after the class Collaboration is allowed, but you must write your own solutions. Problem 1 1. Solve: [Kleinberg Tardos] Chapter 3, problem 2, page 107. 2. Solve: [Kleinberg Tard
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 2+3 Due on Wed, Oct 21, after the class Collaboration is allowed, but you must write your own solutions. Problem 1 Suppose G(V, E ) is a connected graph and h : E R is an assignment of costs to its edges
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 1 Due on Wed, Sept 30, after the class Collaboration is allowed, but you must write your own solutions. Not all problems need divideand-conquer approach. Problem 1 Design an O(n) time algorithm that give
School: NYU
Course: Adb
Advanced Database Systems - CSCI-GA.2434 - Fall 2011 Professor: Dennis Shasha Homework 2 - Due: Tuesday, November 22, 2011 In this assignment, you should do EITHER problems 1 and 2 (worth 20 points each) OR problem 3 (worth 40 points). If you do both, the
School: NYU
Course: Adb
Advanced Database Systems - CSCI-GA.2434-001 - Fall 2011 Professor: Dennis Shasha Homework 1 - Due: Tuesday, October 11, 2011 Please send to radheshg@nyu.edu Each question is worth 10 points. You may work with one partner and sign both of your names to yo
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Homework 2 Due 2011/3/18 Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. Dictatorship test with perfect completeness: Prove that there is no 3-query dictatorship test t
School: NYU
Course: Analytical Methods In Computer Science
Homework 3 Due 2011/4/8 Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. The Nisan-Szegedy bound [2]: Let f : cfw_0, 1n R be a nonzero function of degree at most d (i.e.
School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
Course: Open Sou
Open Source Tools Assignment 2 Due: Thursday, October 10, 2013 at 11:59PM PartI:Sed 1.Theinternetmoviedatabase(www.imdb.com)containsalistofthetop250moviesofalltimeinrank order.Inthisquestion,wewillwriteasinglesedscriptorapipelineofsedcommandstoconvertthe
School: NYU
Course: Open Sou
Assignment 0 Due Thursday, September 10, 2013 before class For those who won't have their accounts created in time, it's OK to turn in the assignment late after your account is available. Just make sure you've sent off your request to the department admin
School: NYU
Course: Open Sou
Open Source Tools Assignment 1 Due: Wednesday, September 25, 2013 at 11:59pm Overview Inthisassignment,we'llusesomeoftheUNIXtoolswehaveseensofartoanalyzesomerealworld data.Specifically,willlookatthemostpopularnamesgiventobabies.DatafromtheSocialSecuritywe
School: NYU
Course: Open Sou
Open Source Tools Assignment 3 Due: November 4th, 2013 at 11:59PM Shell Scripting Forthisassignment,yourtaskistoscriptanewUNIXtoolcalledabc(ABetterCompressor).abchas similarfunctionalitytogzip;itsinputisafiletocompress,anditsoutputisaversionofthefilethati
School: NYU
MekyasMoges 11/23/13 DataStructures BSTandSkipListAnalysis Note:UnfortunatelySpeedTest.javawouldnotcompilecorrectly.Thereforetherewas notenoughsufficientdatatodrawanaccurateconclusiononthetwodatastructuresrun time. The following report is strictly a theor
School: NYU
I was so high I did not recognize, The fire burining in her eyes, the chaos that controlled my mind. Whispered goodbye as she got on a plane never to return again, a promise in my heart. that this love has taken its toll on me. She said goodbye to many t
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Homework 5 Due 2011/5/13 Instructions as before. 1. Stronger KKL theorem: Prove the following strengthening of the KKL theorem. There exists a c >
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Homework 4 Due 2011/4/29 Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. Learning juntas with queries: Show an algorithm for learning k-juntas in time poly(n, 2k ) usin
School: NYU
Course: Nlp
Lecture Questions 1 1.1 Language Model 1 Question (time: 6:17) Say we have a vocabulary V = cfw_the and a constant N 1. For any x1 . . . xn such that xi V for i = 1 . . . (n 1) and xn = STOP, we 1 if n N N dene p(x1 , . . . , xn ) = 0 otherwise Is this a
School: NYU
Course: Introduction To Finance
H28 Option Profit/Payoffs on Expiration: Sample Tables W.L. Silber 1. Long Call with E =100 (1) S 150 140 130 120 110 100 90 80 70 60 50 (2) Payoff Max[0,S-E] 50 40 30 20 10 0 0 0 0 0 0 (3) Premium 10 10 10 10 10 10 10 10 10 10 10 (4) Profit (2) - (3) 40
School: NYU
Course: Introduction To Finance
H29 6. Evaluate what happens at the end if S > E and S # E S>E Exercise long C Deliver against short S Receive proceeds of investment Net Cash Flow -$100 ! +$106.18 +$6.18 S# E (e.g., S = 98) Leave call unexercised Buy S in market Deliver S against short
School: NYU
Course: Introduction To Finance
H25 With P 2 = 92.49 and tR2 = .0398 one-year investors are also indifferent between both of their strategies, as shown in the following 2 possibilities: 1. Buy tR1 and earn .02 2. Buy tR2 and sell after one year. The expected selling price of tR2 after o
School: NYU
Course: Introduction To Finance
H22 3. Yield to Maturity = internal rate of return. Implicitly includes all effects of P, C, and F on yields. a) Annual pay bonds IRR using number of periods = number of years b) Semi-annual pay bonds Double IRR using number of periods = twice the number
School: NYU
Course: Introduction To Finance
H31 Cash-Futures Arbitrage, B01.2311 Prof. Stijn Van Nieuwerburgh 1 Terminology If an investor enters in a long future position, he assumes the obligation to take delivery of the underlying at settlement date (date T ) at a price agreed upon when he enter
School: NYU
Course: Introduction To Finance
H21 Calculating the Annual Return (Realized Compound Yield) on a Coupon Bond William L. Silber Objective: To show that the annual return actually earned on a coupon-bearing bond will equal its yield to maturity only if you can and do reinvest the coupons
School: NYU
Course: Introduction To Finance
H19 P0 = E1 (1 b ) k ROE b This gives us: (6) P0 1 b = E1 k ROE b Thus, the price-earnings ratio is determined by the market capitalization rate k, the plowback ratio b, and the return on equity ROE. When ROE = k , something interesting happens: P0 1 = E1
School: NYU
Course: Introduction To Finance
H27 Example of Immunization, B01.2311 Prof. Stijn Van Nieuwerburgh 1 The Problem The idea behind immunization is to eliminate interest rate risk. Most companies balance sheets are subject to interest rate risk because the duration of their assets and liab
School: NYU
Course: Introduction To Finance
H25 William L. Silber Foundations of Finance (B01.2311) Equilibrium Term Structure under the Expectations Theory PART I R1 = .02; and Expected R1 = .04 Given: t To Prove: Equilibrium tR2 = [(l .02)(1.04)]1/2 -1 = .02995 .03 Approach: Assume tR2 = .02995 =
School: NYU
Course: Introduction To Finance
H24 Note on Forward Rates Professors Otto Van Hemert and Stijn Van Nieuwerburgh Setting Suppose that A one-year zero has a YTM of y1 = 2% A two-year zero has a YTM of y2 = 3% What is the forward rate for period 2? Assuming a face value of $1000, the pri
School: NYU
Course: Introduction To Finance
H19 Equity Valuation Formulas William L. Silber and Jessica Wachter I. The Dividend Discount Model Suppose a stock with price P0 pays dividend D1 one year from now, D2 two years from now, and so on, for the rest of time. P0 is then equal to the discounted
School: NYU
Course: Introduction To Finance
H18 Arbitraging Away Mis-Pricing How could you make an arbitrage prot if the coupon bond were trading at $100 instead of its fair value of $121.7? Because the coupon bond is undervalued, you will buy the coupon bond (buy low) and sell short the portfolio
School: NYU
Course: Introduction To Finance
H18 Arbitrage handout, B01.2311 Prof. Stijn Van Nieuwerburgh 1 Introduction Our working denition so far was: Arbitrage is the transaction of selling something at a high price and simultaneously buying that same thing at a low price, without cash outlay. T
School: NYU
Course: Introduction To Finance
H16 Cov[Ri , Rj ] = V ar[Ri ]. Now we are ready to attack our problem. Lets x the expected portfolio return to be a number r. Now nd the portfolio weights of the portfolio that has an expected return r and the lowest possible portfolio variance. That is,
School: NYU
Course: Introduction To Finance
H17 Excess Returns and Beta: Deriving the Security Market Line, B01.2311 Prof. William L. Silber and Prof. Stijn Van Nieuwerburgh 1 A First Reward-to-Risk Relationship We showed that market forces combined with a search by investors for ecient portfolios
School: NYU
Course: Introduction To Finance
H15 Portfolio Variance with Many Risky Securities William L Silber and Jessica A. Wachter Case 1: Unsystematic risk only. Recall that when the correlation between two securities equals zero, the portfolio variance is given by: 2 2 2 2 2 p = w1 1 + w2 2 A
School: NYU
Course: Introduction To Finance
H13 H14 Gains from Diversication: A Two-Security Example, B01.2311 Prof. William L. Silber and Prof. Stijn Van Nieuwerburgh The nice thing about diversication is that it almost always produces gains to a portfolio in the form of increased return that exce
School: NYU
Course: Introduction To Finance
H11 Zero (Very Low) Correlation In case 2(c) we see that the stock fund and the Russian bond fund do not move in a reliable relationship relative to their means across the different scenarios. In particular, in recession the stock fund is below its mean a
School: NYU
Course: Introduction To Finance
H10 Expected Value The expected value of R1 , E[R1 ] or 1 , tells you what the most likely outcome of the random variable R1 is. + 1 = E[R1 ] = R1 (s)f (R1 (s) ds s= Note the similarity with the denition of expected value for a discretely distributed rand
School: NYU
Course: Introduction To Finance
H12 From our Normal Distribution table: Prob(Z = 1.70) = Prob(Z = 1.70) = .4554 Thus the probability of an outcome between .7623 (the mean) and zero is .4554. Hence, the probability of losing money is only .0446 and the odds of making money are .9554/.044
School: NYU
Course: Introduction To Finance
H11 scenario, we calculate a mean return on the stock fund of .11. The mean return on the bond fund is .07. Table I also records the deviations from the mean for each scenario. Table I U.S. Stock Fund Scenario Recession Normal Boom Return -.07 .12 .28 Dev
School: NYU
Course: Introduction To Finance
H9 THE REGRESSION EFFECT Lets consider the above form of the fitted regression equation with regard to a prediction. We showed previously that a home of 2,500 square feet would be predicted to sell for $227,125. We obtained this value as 88,400 + 55.49 2,
School: NYU
Course: Introduction To Finance
H10 4 Multiple Variables Let R1 , R2 ,., RN be N dierent random variables with means 1 through N and standard deviations 1 through N . For concreteness, think of a portfolio with N = 20 dierent stocks; Rn is the (random) return on the nth stock. Your goal
School: NYU
Course: Introduction To Finance
H10 Expected Value The expected value of R1 , E[R1 ] or 1 , tells you what the most likely outcome of the random variable R1 is. The denition of an expected value is in equation (1): S 1 = E[R1 ] = R1 (s)p(s) (1) s=1 It is the value of the stock in each e
School: NYU
Course: Introduction To Finance
H9 RESIDUALS Suppose that we use this prediction method for a home that was part of our original data set. The home listed as the first data point had an area of 1,800 square feet. The fitted price would be 88,400 + 55.49 1,800 = 88,400 + 99,882 = 188,282
School: NYU
Course: Introduction To Finance
H9 REGRESSION In most statistical work with two variables, we move beyond the correlation concept to explore the related notion of regression. In the regression context we think of one of the variables as a possible influence on the other. The correlation
School: NYU
Course: Introduction To Finance
H9 Here is another positive contribution to the covariance, as both the area and the price are below average: 240000 Price 220000 200000 180000 160000 1500 2000 Area 18 2500 H9 Contributions to the covariance are negative when one of the variables is abov
School: NYU
Course: Introduction To Finance
H9 = 1 cfw_ 9 ( -137 ) ( -13,470 ) + ( -575 ) ( -23, 070 ) + . + ( 272 ) ( 27,230 ) Although you should do a sample calculation by hand to reinforce your understanding of the concept, actual calculations of this type should certainly be done by computer!
School: NYU
Course: Introduction To Finance
H8 Relation IRR and annualized HPR The (annualized) holding period return (HPR) is dened for an investment that is unwound at one point in time. Specically the annualized HPR is given by 1/t Vt 1, annualized HPR = V0 V0 = begin value investment, Vt = end
School: NYU
Course: Introduction To Finance
H9 You might note that a different symbol, s2 rather than s2, is used for this situation. Also, the forms pi ( xi - m ) and computational strategies. 2 p i xi2 - m 2 represent two different As an example of the variance of a random variable, consider this
School: NYU
Course: Introduction To Finance
H9 EXAMPLE: Consider all American males between the ages of 21 and 30. What would be the standard deviation of their weights? We certainly cannot answer this question precisely without data, but its easy to make a plausible guess at the standard deviation
School: NYU
Course: Introduction To Finance
H7 Notice that project B is better (has a higher NPV) than project A when the cost of capital is above 10% (above 20% both have negative NPVs, but B is less bad), while project A is better when the cost of capital is below 8%. In fact, you can calculate t
School: NYU
Course: Introduction To Finance
H9 EXAMPLE: A sample was taken of 20 suburban families, and each sampled family was asked how many cars it owned. The data were these: 2 2 1 2 3 2 2 1 2 2 2 1 2 2 1 0 2 2 1 2 You can get x by simply adding these numbers and then dividing by 20. However, t
School: NYU
Course: Introduction To Finance
H9 n 1 written as n n xi = x i i =1 n i =1 . The symbol i is nothing but a counting convenience. You should note that n x = i i =1 n x j n x = u j =1 . u =1 In nearly every case youll encounter, the entire list of n values will be added, and its burdenso
School: NYU
Course: Introduction To Finance
H3 P .05 = $10 P= $10 = $200. .05 Investing $200 at 5 percent generates $10 in interest per year and continues to do so forever. Thus, if an annuity promises to pay $10 forever and the annual interest rate is 5 percent, the value of that infinite stream o
School: NYU
Course: Introduction To Finance
H7 NPV Versus IRR W.L. Silber I. Our favorite project A has the following cash flows: -1000 0 0 +300 +600 +900 0 1 2 3 4 5 We know that if the cost of capital is 18 percent we reject the project because the net present value is negative: - 1000 + 300 600
School: NYU
Course: Introduction To Finance
H5 We also know the YTM on this 9-year bond (to the people who bought the bond from you at the end of one year at the price of $480): YTM = (1000/480)^(1/9) 1 = .0850 Thus if you sell your bond after its yield to maturity has fallen (in this case from 8.6
School: NYU
Course: Introduction To Finance
H4 A note on EAR: It is quite straightforward to calculate the EAR if you are given a continuously compounded rate. We saw above that $1 compounded continuously at 6% produces 1.061836 at the end of one year: 1 e .06 = 1.061836 Subtracting one from the ri
School: NYU
Course: Introduction To Finance
H2 Numerical Example in Valuing Zero Coupon Bonds William L. Silber Year 0 1 3 5 10 Interest Rate a) 10% 1000.00 909.09 751.31 620.92 385.54 b) 7% 1000.00 934.58 816.30 712.99 508.35 NOTES: 1) Each entry in the table represents the value of a zero coupon
School: NYU
Course: Introduction To Finance
H6 (3) Rann = (526.176 / 435) -1 = .0998 Thus, we see that the arithmetic mean is bigger than the true annual average return because we know that .0998 is correct since we calculated it from first principles, that is, we calculated it using the proper def
School: NYU
Course: Introduction To Finance
H0 We can even put one summation inside of another one: 2 3 pi i=1 Rj = p1 (R1 + R2 + R3 ) + p2 (R1 + R2 + R3 ) j=1 The way to read this is: for every value of i (here there are 2 values), add up the inside sum over the values of j (there are 3 of them).
School: NYU
Course: Adb
Partitioning in Large Data Systems Alberto Lerner Motivation + achieving scalability through data partitioning is not new + but new desiderata: + not all systems are born large so expand as need be, just by adding nodes (disk+memory) + access pattern
School: NYU
Course: Adb
Fast Calculations of Simple Primitives in Time Series Dennis Shasha Department of Computer Science Courant Institute of Mathematical Sciences New York university Joint work with Richard Cole, Xiaojian Zhao (correlation), Zhihua Wang (humming), Yunyue Zhu
School: NYU
Course: Fundamental Algorithms
5 SELECT customer.c_first| ' ' |customer.c_last as name, nvl(TO_CHAR(shipment_line.sl_date_received), 'NOT YET ARRIVED') as arrive_date FROM customer NATURAL JOIN orders NATURAL JOIN order_line NATURAL JOIN inventory NATURAL JOIN shipment_line WHERE order
School: NYU
Course: Fundamental Algorithms
BBC6521 Project Preliminary Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@bupt.com Android app fo
School: NYU
Course: Fundamental Algorithms
Project Specification Form School Name International School HAO Taiyan Programme BUPT student no Project No. IP_3135 Project Title Scope Android app for location recording using GPS Implementation Phone apps E-Commerce (H6NF) 10213135 Class QM stud
School: NYU
Course: Fundamental Algorithms
Mid Term Check Form School International School Programme E-Commerce (H6NF) Name HAO Taiyan BUPT student no 10213135 Project No. Implementation QM student no. 106672219 Android app for location recording using GPS Scope 2010215113 IP_3135 Project Titl
School: NYU
Course: Fundamental Algorithms
BBC6521 Project [Preliminary/Early-term Progress] Report [/] School Student Name Project No. Project Title International School Hao Taiyan IP_3135 Programme BUPT Student No. Email E-Commerce 10213135 Class QM Student No. 2010215113 100672219 10213135@b
School: NYU
Course: Fundamental Algorithms
Fundamental Algorithms, Problem Set 2 Due Thursday, February 12 in Recitation He who learns but does not think is lost. He who thinks but does not learn is in great danger. Confucius 1. Illustrate the operation of PARTITION(A,1,12) on the array A = (13, 1
School: NYU
Course: Fundamental Algorithms
Fundamental Algorithms, Problem Set 1 Due Thursday, Feb 5, in Recitation The world can be divided into those who love New York City and those who dont. Those who love New York tend to be unusually lively people. They have to be. Characteristically, they a
School: NYU
Course: Fundamental Algorithms
ciww 829 Off Hor Tu3-5 spencer@cims.nyu.edu heap chap6 Ta mea378@nyu.edu office hours Thurs 6-8 13 fl laugh 1 83 2 58 3 4 4 9 10 83 semisorted array 5 6 7 8 heap A: heap size[A] (often n) length[A] the amount of data -increasing,decreasing buzzwords: dyna
School: NYU
Course: Adb
Designing Concurrent Search Structure Algorithms Dennis Shasha What is a Search Structure? Data structure (typically a B tree, hash structure, R-tree, etc.) that supports a dictionary. Operations are insert key-value pair, delete key-value pair, and sear
School: NYU
Course: Adb
StrangerDB -Safe Data Management with Untrusted Servers Dennis Shasha (shasha@cs.nyu.edu) Joint work (past and present): David Mazieres and Radu Sion 1 Goals Store private data in a public database: backup, concurrency control, and query processing Make
School: NYU
Course: Adb
PARTITIONING IN LARGE DATA SYSTEMS (IF IT IS NOT DYNAMIC, WHAT'S THE POINT?) NYU Advanced Databases Class, Invited Lecture, Oct/2009 alberto.lerner@gmail.com MoKvaKon c c c User Data Data par=oning as a way to scale If only one could "stretch" a
School: NYU
Course: Adb
Pig Latin: A Not-So-Foreign Language For Data Processing Chris Olston Benjamin Reed Utkarsh Srivastava Ravi Kumar Andrew Tomkins Research Data Processing Renaissance Internet companies swimming in data E.g. TBs/day at Yahoo! Data analysis is "inner loo
School: NYU
Course: Adb
Rapid Detection of Significant Spatial Clusters Daniel B. Neill Andrew W. Moore The Auton Lab Carnegie Mellon University School of Computer Science E-mail: cfw_neill, awm@cs.cmu.edu Introduction Goals of data mining: Discover patterns in data. Distingui
School: NYU
Course: Adb
AQuery A Database System for Order Dennis Shasha Joint work with Alberto Lerner lerner@cs.nyu.edu shasha@cs.nyu.edu Motivation The need for ordered data Queries in Finance, Biology, and Network Management depend on order. SQL 99 has extensions the OLAP a
School: NYU
Course: Open Sou
Question 1: I: B C II: A D III: A D Question 2: A. B. C. D. E. sed '/\([0-9].*\)\cfw_3,\/s/[0-9]/g' sed 's/"[^"\]*"/"/g' sed 's/^\([^,]*\),\(.*\)$/\2,\1/' grep '^\(.\)*$' grep '[0-9]' | grep '[a-z]' Question 3: A. B. C. D. E. cut -d';' -f1 | tr a-z A-Z |
School: NYU
Course: Open Sou
Sample Questions 1. Under what circumstances can two processes have the same a - process id? b - parent process id? c - process group id? 2. Why should you never specify setuid permissions and write permissions for other on the same file? Please give an e
School: NYU
Course: Open Sou
Midterm Information The midterm exam is October 29, 2012 (in class). The topics covered on the exam will include everything we have discussed in class to date. You may bring the recommended texts for the course, the lecture slides, and personal notes. You
School: NYU
Course: PnP
CS 445 Negative-weight cycles Recall: If a graph G = (V, E) contains a negativeweight cycle, then some shortest paths may not exist. Example: <0 Shortest Paths in Graphs Bellman-Ford Algorithm Slides courtesy of Erik Demaine and Carola Wenk u u v v Bellma
School: NYU
Course: Computer Systems Organization
Computer Systems Organization V22.0201 Fall 2009 Sample Midterm Exam ANSWERS 1. True/False. Circle the appropriate choice. (a) T At most one operand of an x86 assembly instruction can be an memory address (b) F At most one operand of an x86 assembly instr
School: NYU
Course: Computer Systems Organization
Computer Systems Organization V22.0201 Fall 2009 Sample Midterm Exam 1. True/False. Circle the appropriate choice. (a) T F (b) T F At most one operand of an x86 assembly instruction can be an memory address At most one operand of an x86 assembly instructi
School: NYU
Course: Computer Systems Organization
Computer Systems Organization V22.0201 Fall 2009 Midterm Exam Answers 1. True/False. Circle the appropriate choice. (a) F Registers and cache memory are the same thing. (b) T Putting #include "foo.h" in your C le is the equivalent of typing the contents o
School: NYU
Course: Computer Systems Organization
Name: Computer Systems Organization V22.0201 Fall 2009 Midterm Exam 1. True/False. Circle the appropriate choice. (a) T F Registers and cache memory are the same thing. (b) T F Putting #include "foo.h" in your C le is the equivalent of typing the contents
School: NYU
Course: Adb
G22.2434.01 fall 2009 Advanced Database Systems Report of Tuning Project By: Pratik Daga Student ID: N18669576 DEDICATED TO MY FAMILY, SYLPHY AND JACKEY Index Server Information . 2 Case 1: Remove cursor from procedure Procedure which calculate & store da
School: NYU
Name_ Computer Architecture V22.0436 Fall 2004 Mid-Term Exam SOLUTIONS Please answer question 1 on this paper and put all other answers in the blue book. 1. True/False. Please circle the correct response. a. F Falling edge-triggered sequential circu
School: NYU
Course: Open Sou
Open Source Tools Assignment 2 Due: Thursday, October 10, 2013 at 11:59PM PartI:Sed 1.Theinternetmoviedatabase(www.imdb.com)containsalistofthetop250moviesofalltimeinrank order.Inthisquestion,wewillwriteasinglesedscriptorapipelineofsedcommandstoconvertthe
School: NYU
Course: Open Sou
Assignment 0 Due Thursday, September 10, 2013 before class For those who won't have their accounts created in time, it's OK to turn in the assignment late after your account is available. Just make sure you've sent off your request to the department admin
School: NYU
Course: Open Sou
Open Source Tools Assignment 1 Due: Wednesday, September 25, 2013 at 11:59pm Overview Inthisassignment,we'llusesomeoftheUNIXtoolswehaveseensofartoanalyzesomerealworld data.Specifically,willlookatthemostpopularnamesgiventobabies.DatafromtheSocialSecuritywe
School: NYU
Course: Open Sou
Open Source Tools Assignment 3 Due: November 4th, 2013 at 11:59PM Shell Scripting Forthisassignment,yourtaskistoscriptanewUNIXtoolcalledabc(ABetterCompressor).abchas similarfunctionalitytogzip;itsinputisafiletocompress,anditsoutputisaversionofthefilethati
School: NYU
MekyasMoges 11/23/13 DataStructures BSTandSkipListAnalysis Note:UnfortunatelySpeedTest.javawouldnotcompilecorrectly.Thereforetherewas notenoughsufficientdatatodrawanaccurateconclusiononthetwodatastructuresrun time. The following report is strictly a theor
School: NYU
I was so high I did not recognize, The fire burining in her eyes, the chaos that controlled my mind. Whispered goodbye as she got on a plane never to return again, a promise in my heart. that this love has taken its toll on me. She said goodbye to many t
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Homework 5 Due 2011/5/13 Instructions as before. 1. Stronger KKL theorem: Prove the following strengthening of the KKL theorem. There exists a c >
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Homework 4 Due 2011/4/29 Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. Learning juntas with queries: Show an algorithm for learning k-juntas in time poly(n, 2k ) usin
School: NYU
Course: Analytical Methods In Computer Science
Homework 3 Due 2011/4/8 Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. The Nisan-Szegedy bound [2]: Let f : cfw_0, 1n R be a nonzero function of degree at most d (i.e.
School: NYU
Course: Analytical Methods In Computer Science
Spring 2011 Analytical Methods in CS Homework 2 Due 2011/3/18 Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions as before. 1. Dictatorship test with perfect completeness: Prove that there is no 3-query dictatorship test t
School: NYU
Course: Analytical Methods In Computer Science
Homework 1 Due 2011/2/25 Spring 2011 Analytical Methods in CS Oded Regev Dept. of Computer Science Ecole normale sup rieure, Paris e Instructions Writeup: You must do the writeup alone. For each question, cite all references used (or write none) and colla
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 6+7 Due on Mon Dec 14, after the class Collaboration is allowed, but you must write your own solutions. Absolutely no extensions. For all problems, the alphabet = cfw_0, 1. Problem 1 Solve Problem 4 from
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 4+5 Due on Monday Nov 16, after the class Collaboration is allowed, but you must write your own solutions. Problem 1 1. Solve: [Kleinberg Tardos] Chapter 3, problem 2, page 107. 2. Solve: [Kleinberg Tard
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 2+3 Due on Wed, Oct 21, after the class Collaboration is allowed, but you must write your own solutions. Problem 1 Suppose G(V, E ) is a connected graph and h : E R is an assignment of costs to its edges
School: NYU
Course: Analysis Of Algorithms
G22.3520: Honors Analysis of Algorithms Problem Set 1 Due on Wed, Sept 30, after the class Collaboration is allowed, but you must write your own solutions. Not all problems need divideand-conquer approach. Problem 1 Design an O(n) time algorithm that give
School: NYU
Course: Adb
Advanced Database Systems - CSCI-GA.2434 - Fall 2011 Professor: Dennis Shasha Homework 2 - Due: Tuesday, November 22, 2011 In this assignment, you should do EITHER problems 1 and 2 (worth 20 points each) OR problem 3 (worth 40 points). If you do both, the
School: NYU
Course: Adb
Advanced Database Systems - CSCI-GA.2434-001 - Fall 2011 Professor: Dennis Shasha Homework 1 - Due: Tuesday, October 11, 2011 Please send to radheshg@nyu.edu Each question is worth 10 points. You may work with one partner and sign both of your names to yo
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Dynamic Web Development
The web warrior Guide to Web Programming, course.com, Review Questions Solutions
School: NYU
Course: Daa2
1. 22.1-2 Give an adjacency-list representation for a complete binary tree on 7 vertices. Give an equivalent adjacency-matrix representation. Assume that vertices are numbered from 1 to 7 as in a binary heap. 1 2 4 5 6 3 7 1 2 3 4 5 6 7 / / / / 2 4 6 3/ 5
School: NYU
S1007 - Introduction to Computer Science Homework #2 Due: 10 June 2003, 6:00 AM In this assignment you will use Javas control-ow constructs to write a a simple two-player game. The game you will be implementing is called Rock-Paper-Scissors. In each
School: NYU
G22.3350-001 Honors Theory of Computation March 25, 2004 Problem Set 4 Lecturer: Yevgeniy Dodis Due: Thursday, April 8 When showing polynomial-time (or log-space) mapping reduction A p B, make sure you follow the following order: (1) briey explain
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School: NYU
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School: NYU
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School: NYU
School: NYU
School: NYU
School: NYU
School: NYU
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School: NYU
School: NYU
Course: Adb
Advanced Database Systems, CSCI-GA.2434-001 New York University, Fall 2011 instructor: Dennis Shasha shasha@cs.nyu.edu 212-998-3086 Courant Institute New York University 251 Mercer Street NY, NY 10012 USA Oce Hours: 9 PM on Tuesdays or 5 PM Thursdays by a