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Course: CIS 07, Fall 2008
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Economics Networked Internet Life CSE 112 Spring 2007 Prof. Michael Kearns Modern Networks are Economic Systems (whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives Users may subvert/improvise for their own purposes Regulatory environments for networking technology for privacy and security concerns in the Internet need more knobs for...

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Economics Networked Internet Life CSE 112 Spring 2007 Prof. Michael Kearns Modern Networks are Economic Systems (whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives Users may subvert/improvise for their own purposes Regulatory environments for networking technology for privacy and security concerns in the Internet need more knobs for society-technology interface network growth; peering agreements and SLAs free-riding for shared resources (e.g. in peer-to-peer networks) spam and DDoS as economic problems allocation of scarce resources; conflicting incentives Can Economic Principles Provide Guidance? Markets for the exchange of standardized resources goods & services prices encode exchange rates, compress info efficiency and equilibrium notions for performance measurement strategic behavior and the management of competing incentives behavioral and agent-based approaches Game theory, competitive and cooperative Learning and adaptation in economic systems Certain nontraditional topics in economic thought Active research at the CS-economics boundary The Internet: What is It? The Internet is a massive network of connected but decentralized computers Began as an experimental research NW of the DoD (ARPAnet) in the 1970s All aspects (protocols, services, hardware, software) evolved over many years Many individuals and organizations contributed Designed to be open, flexible, and general from the start Completely unlike prior centralized, managed NWs e.g. the AT&T telephone switching network Internet Basics Can divide all computers on the Internet into two types: computers and devices at the edge your desktop and laptop machines big compute servers like Eniac your web-browsing cell phone, your Internet-enabled toaster, etc. these are called routers they are very fast and highly specialized; basically are big switches computers in the core Every machine has a unique Internet (IP) address IP addresses are how everything finds everything else! Note: the Internet and the Web are not the same! the Web is one of many services that run on the Internet IP = Internet Protocol like phone numbers and physical addresses, IP addresses of nearby computers are often very similar your IP address may vary with your location, but its still unique Internet Packet Routing At the lowest level, all data is transmitted as packets small units of data with addressing and other important info if you have large amounts of data to send (e.g. a web page with lots of graphics), it must be broken into many small packets somebody will have to reassemble them at the other end forward packet to the next router on path to destination they only forward to routers they are physically connected to how do they know which neighboring router is next? giant look-up tables for each possible IP address, indicates which router is next need to make use of subnet addressing (similar to zip codes) distributed maintenance of table consistency is complex e.g. route addresses of form 128.8.*.* to neighbor router A route 128.7.2.* to neighbor router B, etc. All routers do is receive and forward packets Routing tables: Handy programs: traceroute, ping and nslookup must avoid (e.g.) cycles in routing requires distributed communication/coordination among routers The IP (Internet Protocol) There are many possible conventions or protocols routers could use to address issues such as: what to do if a router is down? who worries about lost packets? what if someone wants their packets to move faster? However, they all use a single, simple protocol: IP IP offers only one service: best effort packet delivery Higher-level protocols are layered on top of IP: with no guarantee of delivery with no levels of service with no notification of lost or delayed packets nothing knows about the applications generating/receiving packets this simplicity is its great strength: provides robustness and speed TCP: for building connections, resending lost packets, etc. http: for the sending and receiving of web pages ssh: for remote access to edge computers etc. etc. etc. Commercial Relationships in Internet Routing Customer-Provider Peer-peer customer pays to send and receive traffic provider transits traffic to the rest of Internet settlement free, under near-even traffic exchanges transit traffic to and from their respective customers These are existing economic realities They create specific economic incentives that must co-exist with technology, routing protocols, etc. Sprint AT&T UUNET Economic Incentives for Peering Customer B How to select peers? need to reach some other part of the Internet improve end-to-end customer performance avoid payments to upstream providers early-exit routing Provider B multiple peering points How to route the traffic? today: early-exit routing to use less bandwidth tomorrow: negotiate for lower total resource usage? Provider A Customer A Case Study: Selfish Routing Standard Internet routing: route your traffic follows entirely determined by routing tables out of your control generally based on shortest paths, not current congestion! you specify in the packet header the exact sequence of routers better be a legitimate path! in principle, can choose path to avoid congested routers traffic desiring to go from A to B (a flow) viewed as a player actions available to a flow: all the possible routes through the NW penalty to a flow following a particular route: latency in delivery rationality: if flow can get lower latency on a different route, it will! number of actions = number of routes (huge) number of players = number of flows (huge) Source routing: Source routing as a game: Main Result: Under certain reasonable assumptions, the Price of Anarchy is at most 1/3 no matter how big or complex the network! For more detail see T. Roughgardens excellent slides on the topic i.e. total latency at most 33% higher than under optimal, centralized (and impossible) planning Case Study: QoS QoS = Quality of Service many varying services and demands on the Internet varying QoS guarantees required e...

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UPenn - CIS - 2
News and Notes 3/18 Two readings in game theory assigned Short lecture today due to 10 AM fire drill HW 2 handed back today, midterm handed back Tuesday No MK OHs todayIntroduction to Game TheoryNetworked Life CSE 112 Spring 2004 Prof. Michael
UPenn - SEAS - 06
S T A N F O R DBayesian Estimation for Autonomous Object Manipulation Based on Tactile PerceptionAnna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. NgEmail: anya@cs.stanford.edu Website: http:/cs.stanford.edu/~anyaMotivationToday rob
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S T A N F O R DBayesian Estimation for Autonomous Object Manipulation Based on Tactile PerceptionAnna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. NgEmail: anya@cs.stanford.edu Website: http:/cs.stanford.edu/~anyaMotivationToday rob
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Strategies for improving face recognition from videoDeborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. FlynnComputer Vision Research Lab, University of Notre Dame (http:/www.nd.edu/~cvrl)Goals Improve performance of face recogniti
UPenn - WIML - 06
Strategies for improving face recognition from videoDeborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. FlynnComputer Vision Research Lab, University of Notre Dame (http:/www.nd.edu/~cvrl)Goals Improve performance of face recogniti
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DISTRIBUTED DATA MINING ON ASTRONOMY CATALOGSCross Matching : Alignment of Astronomy Catalogs Tuple ID P1 P2 P3 Astronomy Sky Surveys (SDSS , 2MASS) Observes Galaxies, Quasars, Stars Serendipity Objects Raw Data from Telescope is pre-processed H
UPenn - WIML - 06
DISTRIBUTED DATA MINING ON ASTRONOMY CATALOGSCross Matching : Alignment of Astronomy Catalogs Tuple ID P1 P2 P3 Astronomy Sky Surveys (SDSS , 2MASS) Observes Galaxies, Quasars, Stars Serendipity Objects Raw Data from Telescope is pre-processed H
UPenn - CIS - 05
Course Overview and IntroductionNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsWhat do the following questions How does Google find what you want? How do tolerant populations become segregated? How many friends between you and Kevin
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The Networked Nature of SocietyNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsWhat is a Network? A collection of individual or atomic entities Referred to as nodes or vertices Collection of links or edges between vertices Links repr
UPenn - CIS - 05
Agenda: Tuesday, Jan 25Reports from the Field: Updates on course web page TA office hours New readings Friendster, Love and Money : Monday NY Times (Katy Keenan) Thats Soooo High School : Monday MSNBC (Jake Wiseman) BitTorrent: Today
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Agenda: Thursday, Feb 3 Midterm date: Thursday, March 3 New readings in Watts Our navigation experiment: some analysis Brief introduction to graph theoryNews and Notes: Tuesday Feb 8 From the Field: NY Times article 2/8 on hate groups on Or
UPenn - CIS - 05
The Web as NetworkNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsThe Web as Network Consider the web as a network vertices: individual (html) pages edges: hyperlinks between pages will view as both a directed and undirected graph Wha
UPenn - CIS - 2
Introduction to Game TheoryNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsGame Theory A mathematical theory designed to model: how rational individuals should behave when individual outcomes are determined by collective behavior strate
UPenn - CIS - 05
Interdependent Security Games and NetworksNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsGame Theory: Whirlwind Review Matrix (normal form) games, mixed strategies, Nash equil. Repeated matrix games Correlated equilibria the basic obje
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Exchange Economies and NetworksNetworked Life CSE 112 Spring 2005 Prof. Michael Kearns Suppose there are a bunch of different goods wheat, rice, paper, raccoon pelts, matches, grain alcohol, no differences or distinctions within a good: rice is
UPenn - CIS - 05
Pre-Play Preparation:Network Exchange Experiments: Tuesday, April 12Round 1 Play: 1. Check in with the TAs to discover whether you will be a buyer or a seller today. You will then be issued 2 envelopes. 2. On the outside of each envelope,
UPenn - CIS - 05
Behavioral Game Theory: A Brief IntroductionNetworked Life CSE 112 Spring 2005 Prof. Michael Kearns Supplementary slides courtesy of Colin Camerer, CalTechBehavioral Game Theory and Game Practice Game theory: how rational individuals should behav
UPenn - CIS - 05
Game Theory and the InternetNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsModern Networks are Economic Systems(whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives Us
UPenn - CIS - 06
Course Introduction and OverviewNetworked Life CSE 112 Spring 2006 Prof. Michael Kearns A purely technological network? Points are physical machines Links are physical wires Interaction is electronic What more is there to say?Internet, Rout
UPenn - CIS - 06
News and Notes, 1/12 Please give your completed handout from Tue to Jenn now Reminder: Mandatory out-of-class experiments 1/24 and 1/25 likely time: either 5-7PM or 6-8 PM both sessions are required if you are registered and cannot make one or b
UPenn - CIS - 06
Contagion and Tipping in NetworksNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsGladwell, page 7:The Tipping Point is the biography of the ideathat the best way to understand the emergence of fashion trends, the ebb and flow of crime wa
UPenn - CIS - 06
Social Network TheoryNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsNatural Networks and Universality Consider the many kinds of networks we have examined: These networks tend to share certain informal properties: large scale; conti
UPenn - CIS - 06
The Web as NetworkNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsThe Web as Network Consider the web as a network vertices: individual (html) pages edges: hyperlinks between pages will view as both a directed and undirected graph Wha
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Exchange Economies on NetworksNetworked Life CSE 112 Spring 2006 Prof. Michael Kearns Suppose there are a bunch of different goodsExchange Economies wheat, rice, paper, raccoon pelts, matches, grain alcohol, no differences or distinctions wit
UPenn - CIS - 2
Behavioral Graph ColoringMichael Kearns Computer and Information Science University of Pennsylvania Collaborators: Nick Montfort Siddharth Suri Special Thanks: Colin Camerer, Duncan Watts, Huanlei NiBackground and Motivation Network Structure Inf
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Economics and the InternetNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsModern Networks are Economic Systems(whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives User
UPenn - SEAS - 06
Some recent advances in near-neighbor learningMaya R. GuptaUniversity of WashingtonEric Garcia Univ. Washington William Mortenson Univ. Washington Andrey Stroilov GoogleMichael Friedlander Univ. British Columbia Richard Olshen Stanford Robert Gr
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Statistical Relational Learning: Theory & ApplicationsLise Getoor University of Maryland, College ParkWhy SRL?Traditional statistical machine learning approaches assume:A random sample of homogeneous objects from single relationTradition
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Learning User Preferences for Sets of ObjectsMarie desJardinsUniversity of Maryland Baltimore County Workshop on Machine Learning: Theory, Applications, Experience October 4, 2006Joint work with Eric Eaton and Kiri WagstaffThis work was support
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Language and Popular Culture: LING057First things firstFocus of this course:Contrasting A with B:A: What we know about language how it works, how we acquire it, what is its structure, and how it works in society (a.k.a.) Sociolinguistics B: Popu
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What is Quality?Workshop on Quality Assurance and Quality Measurement for Language and Speech ResourcesChristopher Cieri Linguistic Data Consortium {ccieri}@ldc.upenn.edus LREC2006: The 5th Language Resource and Evaluation Conference, Genoa, May
UPenn - PAPERS - 2006
More Data and Tools for More Languages and Research Areas:A Progress Report on LDC ActivitiesChristopher Cieri, Mark Liberman Linguistic Data Consortium {ccieri|myl}@ldc.upenn.edus LREC2006: The 5th Language Resource and Evaluation Conference, Ge
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The Mixer and Transcript Reading Corpora: Resources for Multilingual, Crosschannel Speaker Recognition Research*Christopher Cieri1, Walt Andrews2, Joseph P. Campbell3, George Doddington4, Jack Godfrey2, Shudong Huang1, Mark Liberman1, Alvin Martin4,
UPenn - PAPERS - 2006
Corpora Development and PublicationStephanie Strassel Andrew W. Cole University of Pennsylvania, Linguistic Data Consortium strassel@ldc.upenn.edu andrew.cole@ldc.upenn.edu www.ldc.upenn.eduNOTES 2. Each publication, release, updated version, etc.
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Integrated Linguistic Resources for Language Exploitation TechnologiesStephanie Strassel, Christopher Cieri, Andy Cole, Denise DiPersio, Mark Liberman, Xiaoyi Ma, Mohamed Maamouri, Kazuaki Maeda {strassel, ccieri, acole2, dipersio, myl, xma, maamour
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Towards an Integrated Understanding of Speaking Rate in ConversationJiahong Yuan, Mark Liberman, Christopher CieriUniversity of Pennsylvania Sept. 18, 2006IntroductionFactors that affect speaking rate2 Demographic factors:slower speaking r
UPenn - PAPERS - 2004
The Mixer Corpus of Multilingual, Multichannel Speaker Recognition DataChristopher Cieri1, Joseph P. Campbell2, Hirotaka Nakasone3, David Miller1, Kevin Walker11University of Pennsylvania, Linguistic Data Consortium, Philadelphia, PA, USA 2 MIT L
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recent activities in resource creation and distribution and the development of tools and standards Christopher Cieri, Mark Liberman{ccieri,myl}@ldc.upenn.edu University of Pennsylvania Linguistic Data Consortium and Department of Linguistics 3600 Ma
UPenn - PAPERS - 2004
Dialectal Arabic Telephone Speech Corpus: Principles, Tool design, and Transcription ConventionsMohamed Maamouri, Tim Buckwalter, Christopher CieriLinguistic Data Consortium University of Pennsylvania maamouri@ldc.upenn.edu, timbuck2@ldc.upenn.edu,
UPenn - PAPERS - 2003
Robust Sociolinguistic Methodology:Tools, Data and Best PracticesChristopher Cieri, Stephanie Strassel {ccieri, strassel}@ldc.upenn.edu University of Pennsylvania Linguistic Data Consortium and Department of Linguistics 3600 Market Street, Philadel
UPenn - PAPERS - 2002
Resources for Arabic Natural Language ProcessingMohamed Maamouri, Christopher Cieri {maamouri,ccieri}@ldc.upenn.eduUniversity of Pennsylvania Linguistic Data Consortium and Department of Linguistics www.ldc.upenn.edus International Symposium on P
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Lecture 2 IntroRunway Problem, pg. 1Runway Prob., pg. 2Lect. Quest. 1: ramp prob.Lect. Quest. 1: ramp contd.Ramp Prob. Soln., pg. 1Ramp prob. Soln: time c-gLect. Quest. 2: vel. vs. timeLect. Quest. 2: choose graphsLect. Quest. 3: re
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Lecture 3 IntroDefinition of DynamicsNewtons Laws of MotionTypes of ForcesSpring ForcesNormal and friction forcesTensionNormal Forces explainedThe WeightWeight is Normal ForceFind Weight in an ElevatorTension ForcesConnect Que
UPenn - PHYS - 1
Lecture 4 IntroRadioactive DecayRadioactive Decay: statisticalRadioactive Decay: equationRadioactive Decay: exponentialHalf-Life definedNuclear Medicine definedConnect Quest: O15 minimumLect. Quest. 1: max. C leftLect. Quest. 2: 1/1
UPenn - PHYS - 1
Lecture 5 IntroIntro. To Uniform Circ. MotionDefinition of Uniform circ. Mot.Definition of tangential vel.Connect Quest.: Tangential Acc.Definition of RadiansRadians and degreesKinematics of circ. motionDefinition of period and ang. V
UPenn - PHYS - 1
Lecture 6 Intro PageOscillatory Motion - IntroConnection to Circular MotionOsc. And circles - use ScreenshotPhysical picture of diff. Eqs.Robert Hooke and his LawHookes Law and SpringsConnect Question: Eq. For Osc.Ang. Frequency for r
UPenn - PHYS - 1
Lecture 7 - Intro SlideConversations About ConservationDescartes Bold AssertionNewtons Bolder AssertionNewtonian SynthesisWhy A New Second Law?The Incomplete F = maNew Definition of MotionDefining the SystemDefining the System - col
UPenn - PHYS - 1
Lecture 8 IntroMore ConservationThere is More to Conserve!Kinetic Energy and ForcesThe Definition of WorkRefining the Work DefinitionStatement of Work-Energy TheoremExample 1: Work done on boxConnect Question 1: Centripetal Motion and
UPenn - PHYS - 1
Lecture 9 - Rotational MotionThe Universe and RotationRewind of Angular MotionRigid Body Angular MotionRigid Body Angular MotionRigid Body Angular DynamicsRadial Acceleration ReviewedWhy Use Angular Variables?Connect Question: calcula
UPenn - PHYS - 1
Lecture 10 - IntroductionRotations and The Universe continuedTranslation and RotationRotational and Linear Equations for motionMore Rotational and Linear EquationsPure Rolling MotionConnecting Rotation to TranslationWhat Is No Slipping?
UPenn - PHYS - 10
Lecture 10 - IntroductionRotations and The Universe continuedTranslation and RotationRotational and Linear Equations for motionMore Rotational and Linear EquationsPure Rolling MotionConnecting Rotation to TranslationWhat Is No Slipping?
UPenn - PHYS - 1
Lecture 11 - IntroCentral Forces and GravitationOrbital ViewSidereal Not SynodicWhy Arent Orbits Simple?The Law of Universal GravitationWhy an Inverse Square Law?Remarks on the TheoryMore Remarks.And One Last RemarkWhy g is Consta
UPenn - PHYS - 11
Lecture 11 - IntroCentral Forces and GravitationOrbital ViewSidereal Not SynodicWhy Arent Orbits Simple?The Law of Universal GravitationWhy an Inverse Square Law?Remarks on the TheoryMore Remarks.And One Last RemarkWhy g is Consta
UPenn - CML - 3
Neighborhood Information SystemCreated by Cartographic Modeling Lab University of Pennsylvania Funded by William Penn Foundation The Pew Charitable Trusts University of PennsylvaniaCore Concept Work with City agencies to create an integrated parc
UPenn - LDC - 2004
Dialectal Arabic Telephone Speech Corpus: Principles, Tool design, and Transcription ConventionsMohamed Maamouri, Tim Buckwalter, Christopher CieriLinguistic Data Consortium University of Pennsylvania maamouri@ldc.upenn.edu, timbuck2@ldc.upenn.edu,
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CONFIDENTIALEducation for the Examination vs. Education for Holistic Development-The Transformation of Teacher Beliefs and Practices in Rural Northwest ChinaDocument Date Tanja Carmel Sargent University of Pennsylvania, Graduate School of Educat
UPenn - CIS - 03
DISE: A Programmable Macro Engine for Customizing ApplicationsMarc Corliss, E Lewis, Amir Roth University of PennsylvaniaCorliss, Lewis, + Roth ISCA-30Overview Application customization functions (ACFs) E.g., safety-checking, debugging, decomp
UPenn - CIS - 05
Low Overhead Debugging DISEwithMarc L. Corliss E Christopher Lewis Amir Roth Department of Computer and Information Science University of PennsylvaniaOverview Goal: Low overhead interactive debugging Solution: Implement efficient debugging pr
UPenn - CIS - 04
Using DISE to Protect Return Addresses from AttackMarc L. Corliss, E Christopher Lewis, Amir Roth University of PennsylvaniaOverview Prevent stack-smashing attacks Old approach, new implementation Dynamic Instruction Stream Editing (DISE)DISE
UPenn - WHARTONSBD - 100
Congr atu la tio ns to Phila delp hia 100 !Philadelphia has the third most companies in the 2003 Inc. 500, with 21, behind Washington, D.C. (41) & Boston (23)Outline Describe selection process andcriteria Describe the winners Recognize entre
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Lecture II Sept 8, 2003Lawrence Lessigs Four Modalities of Regulation & Cyberspace9/8/03CSE 100 - Lecture 21Lessig: Constitutions for Cyberspace By constitution, I dont mean a legal text. Rather, as the British understand when they speak o