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#4: Exercise Cross-Tabs and The General Social Survey (GSS) Puzzle #1: Gun Control 1) Do Americans favor or oppose gun control? 2) Are specific groups of the population more or less likely to support gun control? (e. g., men? educated? blacks? rich? republicans? liberals? Baptists? gun owners?) 3) Explain why these groups might support or oppose? What additional data would you require to test this argument? Puzzle #2: Abortion 1) Do Americans favor or oppose abortion? 2) Are specific groups of the population more or less likely to support the right to have an abortion? (e. g., men? educated? blacks? rich? republicans? liberals? religious people?) 3) Explain why these groups might support or oppose? What additional data would you require to test this argument? Puzzle #3: Affirmative Action 1) Do Americans favor or oppose affirmative action? 2) Are specific groups of the population more or less likely to support gun control? (e. g., men? educated? blacks? rich? republicans? liberals? religious people? social progressives?) 3) Explain why these groups might support or oppose? What additional data would you require to test this argument? Technical Instructions: A) Label the variable categories for the variables of interest by using the code book and the "Data"--"Label" command from pull the down menu. For example, you label the "sex" variable by setting value=1, label =male and value=2, label =female. B) Probe the data by constructing a wide range of one-way and two way tables (i.e., a one-way table for question (1) and two-way tables for question (2)). Identify the two or three most interesting relationships. C) Copy and Paste the Cross-Tabs into Microsoft Word document. D) Present your findings to the class as a whole. You are going to tell them to do a cross tab on you're their machines and then you are going to explain "why" this relationship is interesting. Hint: If the categorical data has too many categories, you can reduce the number (e.g., income or education). a) Go to the "Data" Window. b) Select "Data""Transform""Let" from the pull down menu. c) Create a new variable and name it something like "EDUC2" d) Select "Function Type" "Groups and Intervals" e) Select "Function" "CUT". (i.e., cut the data up). f) Specify the variable name and the break points between categories. So to cut the EDUC variable which ranges from 0 to 20 into three categories specify: CUT(EDUC, 12, 16, 20). This creates four categories: 1= high school; 2=college graduate; 3=graduate school; 4=don't know or missing. g) Click O.K. You should have created a new variable.
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UPenn >> PSCI >> 150 (Fall, 2009)
Political Science 150: International Relations In Theory and Practice. 10. International Organizations and Law: Ethnic Conflict, State Building, & Military Intervention. #24: Week 14: Wednesday, December 4: 2nd DEBATE: Should the UN have intervened i...
UPenn >> PSCI >> 150 (Fall, 2009)
This is a two part article: PART I: Copyright 1994 The Washington Post The Washington Post View Related Topics January 30, 1994, Sunday, Final Edition SECTION: FIRST SECTION; PAGE A1 LENGTH: 3302 words HEADLINE: The Raid That Went Wrong; How an Elite...
UPenn >> PSCI >> 295 (Fall, 2009)
User-agent: * Disallow: / ...
UPenn >> VR >> 71403 (Fall, 2009)
V20429 \'HEAD 91 WAGES \' TLOC= 262- 267 1992 Head\'s Income from Wages and Salaries in 1991 (Questions G13 and G24) % nonzero = 68.3 ...
UPenn >> VR >> 71403 (Fall, 2009)
High Dimensional Polynomial Interpolation on Sparse Grids: Smolyaks Algorithm Dirk Krueger University of Pennsylvania and NBER Felix Kubler Stanford University Victors Quantitative Macroeconomics Class University of Pennsylvania, November 25, 2003 ...
UPenn >> ECON >> 10204 (Fall, 2009)
7 Convergence and the World Income Distribution The Convergence Hypothesis Fact: Enormous variation in incomes per worker across countries Question: Do poor countries eventually catch up? Convergence hypothesis: They do, in the right sense! ...
UPenn >> EC >> 70204 (Fall, 2009)
1 1.1 March 16-18 Lucas Tree Model We will study Lucas Tree Model (Lucas 19781 ) and look at the things that Finance people talk about. Lucas tree model is a simple but powerful model. 1.1.1 The Model Suppose there is a tree which produces random ...
UPenn >> VR >> 71403 (Fall, 2009)
@ This program reads and filters the citibase series @ library pgraph; #include c:\\Mydoc\\pigia\\courses\\econ984\\hp.txt; @ execute procedure for hp filter @ nobs=136; @ number of observations @ nser=4; l=1600; @ smoothing paramet...
UPenn >> CIT >> 597 (Fall, 2009)
The assert statement About the assert statement The purpose of the assert statement is to give you a way to catch program errors early The assert statement is new in Java 1.4 The assert statement uses a new keyword, assert The new keyword may br...
UPenn >> CIS >> 700 (Spring, 2006)
Rails and Ajax Apr 10, 2009 HTML Forms The <form arguments> . </form> tag encloses form elements (and usually includes other HTML as well) The arguments to form tell what to do with the user input action=\"URL\" method=\"get\" Specifies whe...
UPenn >> RTAS >> 05 (Fall, 2009)
Author Guidelines for 8.5 x 11-inch Proceedings Manuscripts Author(s) Name(s) Author Affiliation(s) E-mail Abstract The abstract is to be in fully-justified italicized text, at the top of the left-hand column as it is here, below the author informati...
UPenn >> CIS >> 610 (Fall, 2009)
Visualizing the Connection Among Convex Hull, Voronoi Diagram and Delaunay Triangulation John Fisher Department of Computer Science Michigan Technological University Houghton, MI 499311295, USA E-mail: jnfisher@mtu.edu Abstract The convex hull, Voro...
UPenn >> CIS >> 610 (Fall, 2009)
Mathematics 622 Assignment 1 (Shatz) Due Thursday, October 2, 2003 1 Part A (Easier problemsnot for discussion) AI. If V, W are ane varieties, we have dened a morphism from V to W (here, the sheaves of functions OV , OW are being suppressed in the n...
UPenn >> CIS >> 610 (Fall, 2009)
This is page 535 Printer: Opaque this References [1] Ralph Abraham and Jerrold E. Marsden. Foundations of Mechanics. Addison-Wesley, second edition, 1978. [2] Lars V. Ahlfors and Leo Sario. Riemann Surfaces. Princeton Math. Series, No. 2. Princeton...
UPenn >> CIS >> 610 (Fall, 2009)
Manifolds, Lie Groups, Lie Algebras, with Applications Kurt W.A.J.H.Y. Reillag (alias Jean Gallier) CIS610, Spring 2005 1 Motivations and Goals 1. Motivations Observation: Often, the set of all objects having some common properties has some topolog...
UPenn >> CIS >> 610 (Fall, 2009)
Tensors and Component Analysis Musawir Ali Tensor: Generalization of an n-dimensional array Vector: order-1 tensor Order-3 tensor Matrix: order-2 tensor Reshaping Tensors Matrix to vector \"Vectorizing\" a matrix = Reshaping Tensors z Order-3 ...
UPenn >> CIS >> 260 (Fall, 2008)
CIS 260 Recitations 3 Feb 6 Problem 1 (Complete proof of example 11 on page 91) Show that there exist irrational numbers x and y such that x y is rational. First, we remind ourselves what the definition of a rational/irrational number is: A real p nu...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700: Project Suresh Venkatasubramanian 1 Guidelines The idea of this project is to take on a more intricate problem involving computations on the GPU, and demonstrate a plausible, efficient implementation of a particular problem. The problem y...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700/010: Assignment 2 Suresh Venkatasubramanian 1 Problem 1: Matrix Operations (40) 1. Implement a GPU algorithm to add two n n matrices. Use the simple approach that runs in n/b passes. 2. Implement a GPU program for multiplying two dense n ...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700/010 Lecture IX Computational Geometry: Distance Fields Suresh Venkatasubramanian Scribed by IkkJin Ahn 1 Denition Given a set of objects, a distance eld is dened at each point by the smallest distance from the point to the objects. Each ob...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700/010: Matrix Operations I Suresh Venkatasubramanian Scribed by Kennedy Behrman March 3, 2005 1 Matrix operations There are three basic matrix operations that would be part of any GPU matrix toolkit: 1. The inner product of two vectors c = a...
UPenn >> CIS >> 700 (Spring, 2006)
Graphics From a Systems Perspective Nick Triantos, April 2005 Agenda 1. 2. 3. PC System overview Driver components System challenges Copyright NVIDIA Corporation 2004 2005. All rights reserved. System Overview Pentium/AMD K7 Intel CPU RAM Co...
UPenn >> CIS >> 700 (Spring, 2006)
Fast Normalized Cut for Image Segmentation on the GPU Joseph Kider Liming Zhao University of Pennsylvania Abstract Recent advances in the speed and programmability of graphics hardware permit the GPU to grow as a powerful vector coprocessor to the CP...
UPenn >> CIS >> 700 (Spring, 2006)
The Cell Processor: Technological Breakthrough or Yet Another Over-hyped Chip? Prof. Milo Martin for CIS700 Agenda Cell overview PlayStation 2 review More on the Cell (from Peter Hofstee\'s HPCA slides) Programming the Cell (brief) Impact & Speculati...
UPenn >> CIS >> 700 (Spring, 2006)
Dense Matrix Algebra on the GPU dm Moravnszky NovodeX AG adam.moravanszky@novodex.com 1. Introduction Perhaps the most important innovation of the latest generation of programmable graphics processors (GPUs) is their capability to work with floating...
UPenn >> CIS >> 700 (Spring, 2006)
GPU as a Parallel Machine: Sorting on the GPU CIS 700/010: 3/17/05 Scribed by Joseph T. Kider Jr. 1 .Background Sorting is a fundamental algorithmic building block. One of the most studied problems in computer science is ordering a list of items eff...
UPenn >> CIS >> 700 (Spring, 2006)
Cache and Bandwidth Aware Matrix Multiplication on the GPU Jesse D. Hall Nathan A. Carr John C. Hart University of Illinois Abstract Recent advances in the speed and programmability of consumer level graphics hardware has sparked a flurry of researc...
UPenn >> CIS >> 700 (Spring, 2006)
Radiosity on the GPU Steve Crowe, Adam Micciulla 1 Introduction Ever since its proposal, Radiosity has been a widely used method in Global Illumination rendering. Considering the parallel nature of the problem and the recent advances in GPU techno...
UPenn >> CIS >> 700 (Spring, 2006)
CIS700/010:GPUProgrammingand Architecture SureshVenkatasubramanian (suvenkat@saul.cis.upenn.edu) http:/www.cis.upenn.edu/~suvenkat/700/ ATIAnimusicDemo http:/www.cis.upenn.edu/~suvenkat/700/ CPUperformancegrowthisslowing http://www.cis.upenn.edu...
UPenn >> CIS >> 700 (Spring, 2006)
CgShadingTutorial(OpenGL) CIS700/010 GPUProgrammingandArchitecture Instructor:Dr.SureshVenkatasubramanian TA:PaulKanyuk Overview WhatisCg? HowtoInstall/RunCgprograms AnatomyofaCgShader. CgExamples RenderTexture WhatisCg? Cgstandsf...
UPenn >> CIS >> 700 (Spring, 2006)
GPUPerformanceAnalysis http:/www.cis.upenn.edu/~suvenkat/700/ 3DAPI: OpenGLor Direct3D GPU Command& DataStream GPU FrontEnd Pretransformed Vertices 3DAPI Commands 3D Application OrGame CPUGPUBoundary(AGP/PCIe) Vertex Index Stream Primitive Assem...
UPenn >> CIS >> 700 (Spring, 2006)
GPU Programming \"Languages\" http:/www.cis.upenn.edu/~suvenkat/700/ The Language Zoo Sh BrookGPU Renderman Rendertexture SlabOps OpenVidia HLSL Cg GLSL http:/www.cis.upenn.edu/~suvenkat/700/ Some History Cook and Perlin first to develop...
UPenn >> CIS >> 700 (Spring, 2006)
Techniques for Data Storage and Transfer in the Graphics Pipeline Shankar Krishnan AT&T Labs Research Graphics Hardware Pipeline Vertex Connectivity Vertices Vertex Transformation Transformed Vertices Primitive Assembly and Rasterization Frag...
UPenn >> CIS >> 700 (Spring, 2006)
The programmable pipeline Lecture 3 http:/www.cis.upenn.edu/~suvenkat/700/ 3D API: OpenGL or Direct3D GPU Command & Data Stream GPU Front End Pretransformed Vertices 3D API Commands Programmable pipeline 3D Application Or Game CPUGPU Boundary (A...
UPenn >> CIS >> 700 (Spring, 2006)
The Cell Processor: Technological Breakthrough or Yet Another Over-hyped Chip? Prof. Milo Martin for CIS700 Agenda Cell overview PlayStation 2 review More on the Cell (from Peter Hofstees HPCA slides) Programming the Cell (brief) Impact & Speculatio...
UPenn >> CIS >> 700 (Spring, 2006)
RenderTexture Tutorial http:/www.cis.upenn.edu/~suvenkat/700/ A simple example Render an increasing depth field. http:/www.cis.upenn.edu/~suvenkat/700/ Use this to modulate color of a torus oColor = iColor * depth field value http:/www.cis.upe...
UPenn >> CIS >> 700 (Spring, 2006)
Understandingthegraphicspipeline Lecture2 http:/www.cis.upenn.edu/~suvenkat/700/ LectureOutline Ahistoricalperspectiveonthegraphicspipeline Dimensionsofinnovation. Wherewearetoday Fixedfunctionvsprogrammablepipelines Acloserlookatthefixedfunct...
UPenn >> CIS >> 700 (Spring, 2006)
Global Illumination in Real-Time Project Proposal Abstract: Global illumination is a well known technique for producing realistic scenes and overcoming shortcomings of the local lighting model. Common algorithms for solving global illumination rely o...
UPenn >> CIS >> 700 (Spring, 2006)
Feng Zhang Kennedy Behrman Mark van Langeveld CIS 700-Spring 05 Final Project Proposal: Nonlinear Optimization for Imaged Based Modeling- Fitting Lafortune Model to BRDF Data on GPU/CPU Verification Project Summary: The 2003 SIGGRAPH paper: \"Nonline...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700/001 GPU Programming and Architecture Homework #1 Due: February 10, 2005. 1) Bezier Curves & Color Shaping (20 points) Background: The concept of color shaping was introduced in Lecture 4 as a demonstration of shading that exceeds the capabil...
UPenn >> CIS >> 700 (Spring, 2006)
Nikhil Haldar-Sinha Nick Rivera CIS 700-010 Project Proposal Volumetric Rendering in Real Time Our project will be a GPU implementation of various volumetric rendering techniques. Our main focus will be a real-time cloud simulation which will also in...
UPenn >> CIS >> 700 (Spring, 2006)
CIS 700/010: 2/15/05 Scribed by Mike Lehr 1) Diameter: For a set of points P in R2, the diameter (P) is the distance between the two furthest points. Formally, ( P ) = max max d ( p, q ) p q d(p,q) In general d(.,.) is the Euclid distance function:...
UPenn >> CIS >> 677 (Fall, 2008)
Duality, Arrangements Sudipto Guha February 2, 2003 1 The Duality Consider the canonical equation of a line in the Cartesian coordinate system. The equation of a non-vertical line is typically written as y = ax - b. Thus a line is specified by two...
UPenn >> CSE >> 110 (Fall, 2009)
CSE 110 Final Exam June 29, 2000 Name Email Recitation Section Code Word (for posting grades/ 4-8 letters and numbers) @seas Question 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Total Total Points 4 12 8 8 6 6 10 8 8 12 10 8 100 Points Awarded 1 1. ...
UPenn >> CSE >> 110 (Fall, 2009)
CSE 110 Final Exam Summer Session 1, 2001 name: e-mail: question total points points awarded 1 10 2 15 3 10 4 10 5 10 6 15 7 10 8 10 9 10 Total 100 1. 10 points A data structure is needed to hold the information for all of the musicians who were at ...
UPenn >> CSE >> 110 (Fall, 2009)
NOTE: This was an 1 1/2 hour exam, and your exam will also be 1 1/2 hours. - 1. The source code part1.c is translated into machine code, which is then linked with machine code from libraries to form an executable file a.out - 2. (I wouldn\'t ask ...
UPenn >> CSE >> 110 (Fall, 2009)
Grading Guidelines for hw3 = Problem 1: 50 points Note: The sample solution keeps separate totals for cheesesteaks, hoagies, etc., so if 2 cheesesteaks were chosen, and then 3 cheesesteaks were chosen, the total cost would be 3 times the cost of ...
UPenn >> CSE >> 110 (Fall, 2009)
1. a) function calls: printf(\"%d\ \", foo(1,2); (there are actually two function calls here - foo and also printf (remember, printf is just a function, one that happens to have already been written) b) function implementation: int foo(...
UPenn >> CSE >> 110 (Fall, 2009)
lab notes for 5/22: 1. log on 2. run netscape, look at 110 web page 3. basic usage: files on eniac accounts available as E: need to log into eniac account to compile and run from unix shell prompt. 4. to log into enia...
UPenn >> CSE >> 110 (Fall, 2009)
= Problem 1: 20 points Make sure it works for sequences in which the largest value is the first number, and also for cases in which it is not: 10,3,4,9,0 10,3,4,20,0 -10 if it only works for one of these cases. Doesn\'t matter what happens when...
UPenn >> CSE >> 110 (Fall, 2009)
Grading Guidelines for hw4 = Problem 1. 30 points 0 credit if they did it all in the main function, without writing a function ComputePay. No need to check if the user entered \'y\' or \'n\' in response to the the \"Is today a weekday?\" question. Al...
UPenn >> CSE >> 4 (Fall, 2009)
Grading Guidelines for hw4 = Problem 1. 30 points 0 credit if they did it all in the main function, without writing a function ComputePay. No need to check if the user entered \'y\' or \'n\' in response to the the \"Is today a weekday?\" question. Al...
UPenn >> CSE >> 110 (Fall, 2009)
Grading Guidelines for hw5 = Problem 1a. 10 points -5 if they put it all within main, without writing a DotProduct function. Test on a few cases, such as: % /html/courses/cse110/hw5p1a enter an integer n: 4 enter A[0] of vector A: 1 enter A[1] of...
UPenn >> RTAS >> 05 (Fall, 2009)
Final Technical Program - = Monday, March 7 = Workshops: FALSE-II: 2nd Workshop on High-Performance Fault-Adaptive Large-Scale Embedded Real-Time Systems Telegraph Hill A&B IWSSPS: International Workshop on Software Support for Portab...
UPenn >> MGMT >> 560 (Fall, 2009)
From http:/www.microsoft.com/billgates/speeches/2001/08-07aiconference.asp Remarks by Bill Gates International Joint Conference on Artificial Intelligence Seattle, Wash., August 7, 2001 Today, we can ...
UPenn >> CRTS >> 2008 (Fall, 2009)
- CALL FOR PAPERS - CRTS\'08 (co-located with RTSS\'08) - Workshop on Compositional Theory and Technology for Real-Time Embedded Systems (CRTS\'08) November 30, Barcelona, Spain CRT...
UPenn >> CIS >> 260 (Fall, 2008)
CIS260 Recitations, Jan 30 Problem 1. Show that disjunctive syllogism and simplification are valid rules of inference. Problem 2. Show that p->q, q, p is not a valid argument. Problem 3: 60a, page 30 Problem 4: 50, page 50 Problem 5: 12, page 47...
UPenn >> CIS >> 260 (Fall, 2008)
CIS 260 Homework 3 Due Feb 12 before 3pm 1) Problem 5, page 85 2) Problem 9, page 85 3) Problen 23, page 85 4) Problem 32, page 108 5) Problem 42, page 86 ...
UPenn >> CIS >> 629 (Fall, 2009)
SPECIFICATION OF A FILESYSTEM SYNCHRONIZER (Draft: Version 10) DEFINITION [Basic sets]: x,y : XX are FILE NAMES p,q,r : PP are PATHS where PP is the set of sequences of file names (empt...
UPenn >> FOOL >> 7 (Fall, 2009)
MessageorObject? - Origin and Future of Concurrent/Mobile Objects -Akinori Yonezawa University of Tokyo Invited Talk at FOOL 7 (Foundations of ObjectOriented Languages) in Boston, January. 22, 2000 Modular Actor Formalism for AI Manifesto/slogan pa...
UPenn >> CIS >> 629 (Fall, 2009)
Project 3 - [Note: It would be easier to get going with this assignment if you get the Hello example running. ] The purpose of the assignment is to gain familiarity with JAVA RMI. The specific goal is to exten...
UPenn >> CIS >> 260 (Fall, 2008)
CIS 260 Recitations, Feb 13 Examples 19, 20 and 21 on page 159/160. Problem 3, page 130 Problem 16, page 131 Problem 19, page 131 Problem 29, page 131 ...
UPenn >> CIT >> 594 (Fall, 2009)
Searching Searching an array of integers If an array is not sorted, there is no better algorithm than linear search for finding an element in it staticfinalintNONE=1;/notalegalindex staticintlinearSearch(inttarget,int[]a){ for(intp=0;p<a.length;p...
UPenn >> CIT >> 594 (Fall, 2009)
Heapsort Why study Heapsort? It is a well-known, traditional sorting algorithm you will be expected to know Heapsort is always O(n log n) Quicksort is usually O(n log n) but in the worst case slows to O(n2) Quicksort is generally faster, but Hea...
UPenn >> CIT >> 594 (Fall, 2009)
Comparable and Comparator Outline of the Student class import java.util.*; public class Student implements Comparable { public Student(String name, int score) {.} public int compareTo(Object o) throws ClassCastException {.} public static void main(S...
UPenn >> CIT >> 594 (Fall, 2009)
Huffman Encoding Entropy Entropy is a measure of information content: the number of bits actually required to store data. Entropy is sometimes called a measure of surprise A highly predictable sequence contains little actual information Example:...
UPenn >> CIT >> 591 (Fall, 2009)
All the Operators Precedence An operator with higher precedence is done earlier (prededes) one with lower precedence A higher precedence is indicated with a lower number; zero is the highest precedence Most of the time, operators with equal prec...
UPenn >> CIT >> 591 (Fall, 2009)
Simple Java I/O Part I General Principles Streams All modern I/O is stream-based A stream is a connection to a source of data or to a destination for data (sometimes both) An input stream may be associated with the keyboard An input stream or an...
UPenn >> CIT >> 591 (Fall, 2009)
Java GUI building with the AWT AWT (Abstract Window Toolkit) Present in all Java implementations Described in (almost) every Java textbook Adequate for many applications Uses the controls defined by your OS therefore it\'s \"least common denomina...
UPenn >> CIT >> 591 (Fall, 2009)
Methods Complexity The programmer\'s biggest adversary is complexity Primitive types and the basic statements (assignment, while, if, etc.) are theoretically enough to perform any computation Everything else in Java is designed to organize actions...
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