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Course: CS 2, Fall 2009School: Carnegie Mellon
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ttyp6 eli Wed May 31 11:35 still logged in socks1.watson.ibm.com ftp ftp Mon May 29 07:33 - 07:33 (00:00) cr383271-a.lndn1.on.wave.home.com ftp ftp Sun May 28 20:22 - 20:22 (00:00) 213.8.0.167 ftp ftp Tue May 23 04:37 - 04:37 (00:00) ip68.los-angeles35.ca.pub-ip.psi.net wtmp begins May Tue 23 04:37:28 2000 eli ttyp6...
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ttyp6 eli Wed May 31 11:35 still logged in socks1.watson.ibm.com
ftp ftp Mon May 29 07:33 - 07:33 (00:00) cr383271-a.lndn1.on.wave.home.com
ftp ftp Sun May 28 20:22 - 20:22 (00:00) 213.8.0.167
ftp ftp Tue May 23 04:37 - 04:37 (00:00) ip68.los-angeles35.ca.pub-ip.psi.net
wtmp begins May Tue 23 04:37:28 2000
eli ttyp6 socks1.watson.ib Wed May 31 11:35 still logged in
ftp ftp 100.55.13.64 Mon May 29 07:33 - 07:33 (00:00)
ftp ftp 100.55.13.64 Su...
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Carnegie Mellon - PUB - 4
Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary DetectionAndrew N. Stein Thomas S. Stepleton Martial Hebert The Robotics Institute, Carnegie Mellon University Pittsburgh, Pennsylvania 15213, USAanstein@cmu.
Carnegie Mellon - PUB - 4
A Hierarchical Object Based Representation for Simultaneous Localization and MappingChieh-Chih WangARC Centre of Excellence for Autonomous Systems University of Sydney NSW, Australia Email: bobwang@ieee.orgCharles ThorpeQatar Campus Carnegie Mel
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Agent cloning Onn Shehory, Katia Sycara, Prasad Chalasani, and Somesh Jha The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213, U.S.A. onn,katia,chal,sjha@cs.cmu.eduAbstractMulti-agent systems are subject to performance bottlene
Carnegie Mellon - PUB - 1
WebMate : A Personal Agent for Browsing and SearchingLiren Chen and Katia Sycara The Robotics Institute Carnegie Mellon University Pittsburgh, PA. 15213 lchen@cs.cmu.edu, katia@cs.cmu.edu September 30, 1997AbstractThe World-Wide Web is developing
Carnegie Mellon - TR - 744
Outlier Detection and False Discovery Rates for Whole-genome DNA MatchingTzeng, Jung-Ying , Byerley, W. , Devlin, B. , Roeder, Kathryn and Wasserman, Larry Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 Depa
Carnegie Mellon - TR - 860
On the 1- q Regularized RegressionHan Liu1,2 , Jian Zhang3 1 Machine Learning Department, 2 Statistics Department Carnegie Mellon University, Pittsburgh, PA, 15213 3 Department of Statistics Purdue University, West Lafayette, IN, 47907 February 1, 2
Carnegie Mellon - WEEK - 724
36-724 Spring 2006: EDA, Prediction, Classification, ModelingBrian Junker March 22A, 2006 Choice of Loss in the Expected Prediction Error (EPE) Discrete Classification and the Bayes Classifier The Curse of Dimensionality MSE and the Bias-Varianc
Carnegie Mellon - MARYLAND - 2006
Using On-line Tutoring Records to Predict End-of-Year Exam Scores: Experience with the ASSISTments Project and MCAS 8th Grade MathematicsBrian W. Junker Department of Statistics Carnegie Mellon University Pittsburgh PA 15213 brian@stat.cmu.edu Decem
Carnegie Mellon - STAT - 726
Some Introductory Notes on Writing 1 IntroductionIn your career as a graduate student of statistics, and later as a professional statistician, you will do lots of technical writing. Examples of some forms you can look forward to (or have already tri
Carnegie Mellon - WEEK - 724
The mclust PackageJanuary 18, 2005Version 2.1-8 Author C. Fraley and A.E. Raftery, Dept. of Statistics, University of Washington. Title Model-based cluster analysis Description Model-based cluster analysis: the 2002 version of MCLUST Depends R (>=
Carnegie Mellon - CMU - 303
36-303 Thursday, 3/22/07Last time: Critiqued Sleep Questionnaire, Estimation Example, Estimating Differences Today: Analyzing the Data / Introduction to R Next Time: Stratified SamplingIndividual: Group:Your Intro/Methods Draft is due today. HW
Carnegie Mellon - MSE - 2006
Plans Proposal Version 1.1 February 7, 2006Theoractice TeamKangWoon Hong HyungChoul Kim SangJin HanTheoractice Plans Proposal Document Revisions Revision 0.1 1.0 1.1 Date 10/17/05 12/08/05 02/07/06 Contributor Kang-Woon Hong Kang-Woon Hong Hyun
Wisconsin - ENGR - 447
CEE447 Concrete Structuresfocus for Chapters 1 and 2 1. 2. 3. What are the characteristics of concrete and steel (reinforcing) that make them compatible and desirable to use in combination? A single basic rule governs all of reinforced concrete desi
Wisconsin - ENGR - 951
I decided to copy the outline of my PPT presentation, It does not contain graphs and is missing some text, but I think the structure is more prevalent than the imprompt version you received today. I will attempt to put a compressed version on the web
Carnegie Mellon - GNHM - 06
The Utility of Affect Expression in Natural Language Interactions in Joint Human-Robot TasksMatthias ScheutzA.I. & Robotics Lab University of Notre Dame Notre Dame, IN 46556, USAPaul SchermerhornA.I. & Robotics Lab University of Notre Dame Notre
Wisconsin - ENGR - 160
John MurphyThursday 2:25 - 5:25SA: Travis ZSA: Tyler L
Wisconsin - ENGR - 160
Engineering to Enable or Alternative Purpose for EngineeringJay K. Martin John Murphy October 17, 2007Traditional Role of EngineeringIn her book Retooling, Rosalind Williams, historian and former Dean of Students at MIT, claims that for most
Wisconsin - ENGR - 160
InterEgr 160: Introduction to Engineering Design Spring 2008Eman Zaki & John Murphy Course CoordinatorsInterEgr 160: Introduction to Engineering DesignEman Zaki zaki@engr.wisc.edu (608) 265-8268 1150 Engineering HallJohn Murphy jmurphy@engr.wis
Wisconsin - ENGR - 160
InterEgr 160: Final LectureEman Zaki & John Murphy Course Coordinatorszaki@engr.wisc.edu (608) 265-8268 jmurphy@engr.wisc.edu (608) 265-4186InterEgr 160 Class expectations {seminar format} Worked Lab sections Real World engineering project
Wisconsin - ENGR - 160
InterEgr 160 Fall 2007 FINAL PRESENTATIONS PRACTICE SCHEDULE(This is the official schedule. You are welcome to make other arrangements on your own.)SA's select one half hour for your team.Thursday, December 6 3:00-7:30 1800 E Hall 10:00-10:30 1
Wisconsin - ENGR - 160
Name Jennifer Binzley Dan Goeser Jennifer Gerschke John Murphy Eman Zaki Dave Huth Sarah Treu Alan Gomez Tim Knapp Adam Steen Nick Hichon Hallie Kreitlow Amit Nimunkar Joshua Kundert Silas Bernardoni Jon Christian Ken Walz Ramsey Kropp , , , kropp@wi
Wisconsin - ME - 349
The Cutting EdgeUpdate PresentationME349 Nick Hoel 3 Jeff Neis 1 Scott Neis 2 Brad Schaller 4 April 14, 2004Project ReviewCombine functions of a string trimmer and a riding lawnmower deckDesign Goals Compatible with variety of mower decks Won
Carnegie Mellon - DP - 21
Flynn Jones 4/25/2006 15-394 Assignment 6NYC Webcam Report Part 3AbstractLow resolution camera already installed throughout the United States can be used as a way to better resolve irregular traffic flow within a given area. This could possibly b
Carnegie Mellon - DP - 15
Verifying Patterns from Stopped Traffic at IntersectionsCiera Christopher Institute for Software Research, International Carnegie Mellon University cchristo@cs.cmu.edu Abstract This paper examine where a driver stops relative to the white stop line.
Carnegie Mellon - TR - 814
Uniform Distributions on the Natural NumbersOliver SchirokauerDepartment of Mathematics Oberlin College Oberlin, OH 44074 U.S.AJoseph B. KadaneDepartment of Statistics Carnegie Mellon University Pittsburgh, PA 15213 U.S.AAbstract. We compare
Carnegie Mellon - TR - 868
Calculating RemittitursJoseph B. Kadane1Abstract This note reviews several methods proposed for calculating the amount of an appropriate remittitur using a sample of "comparable" cases. It recommends a simple quantile estimate.A remittitur is a p
Carnegie Mellon - STAT - 36
, '~"~ J@aing and Inference 4 (19$@45-54). @aN6ktii~kIb~MdAlb?is~ir$ Company -5.' ._c',(>' ' a+/ _ :-loF *?iixbiz- NUMBIER 6x, ,~`~,_,~; `c;RdlpHI v) II," ,`(3 _I &;:,ry"&)-.r .: I&3pmmeti, , ^,i' , .;. > ,."A , s\ JI i ' ./1":. ^c%f
Carnegie Mellon - STAT - 217
Handout 3 Supplements material in Sections 2.1 and 2.2 in L-GQuick Review of Set Theory Let be a set and denote the elements in by .The empty set contains no elements and is a subset of every set, . A subset of is a collection of elements of , and
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Golden Redux-1. Changes in the gridpoints of Golden when the endpoints of the search interval are changed.2. Remaking the Golden program to more efficiently use memory, with pointers.--1. Changes in the gridpoints of Golden when the en
Carnegie Mellon - LECTURE - 24
Gre The tical I de I n Com r S nce at ore as pute cie John Laffe rty Le cture24 Nove be 16, 2006 m r C 15-251 S Fall 2006 C gieMe Unive arne llon rsityGradeS chool Re visite d: How To Multiply Two Num rs be2X2= 5Thebe way is st ofte far from n o
Carnegie Mellon - LECTURE - 16
Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 16 Oct 19, 2006 CS 15-251 Fall 2006 Carnegie Mellon UniversityPolynomials, Secret Sharing, And Error-Correcting CodesP(X) = X3 +X2 +X1 +Polynomials in one variable over the re
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Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 1 Aug 29th, 2OO6 CS 15-251 Fall 2006 Carnegie Mellon UniversityPancakes With A Problem!Fall 06 Course StaffProfs: Anupam Gupta John Lafferty Albert Sheu Andreas Krause Todd Phillip
Carnegie Mellon - LECTURE - 03
Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 3 Sept 5, 2006 CS 15-251 Fall 2006 Carnegie Mellon UniversityInduction II: Inductive PicturesInductive Proof: "Standard" Induction "Least Counter-example" "All Previous" Induction a
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AREA FORECAST DISCUSSIONNATIONAL WEATHER SERVICE MILWAUKEE/SULLIVAN WI309 PM CST SUN FEB 23 2003FCST FOCUS ON DVLPG LGT SNOW TNGT.THEN TEMPS ESPCLY MON NGT.HAVE BEEN WATCHING LGT SNOW DVLP OVR IOWA AND SRN MN AND PROGS INGNRL AGRMNT WITH MOVG
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FXUS63 KMKX 240928AFDMKEAREA FORECAST DISCUSSIONNATIONAL WEATHER SERVICE MILWAUKEE/SULLIVAN WI328 AM CST MON FEB 24 2003FCST FOCUS ON END OF LGT SNOW TODAY.THEN EFFECTS OF ARCTIC AIRMASSSETTLING IN OVER REGION.LGT SNOW TAPERING OFF FROM WES
Wisconsin - CS - 701
Finding Additional Independent Instructions for Parallel IssueWe can extend the capabilities of processors: Out of order execution allows a processor to "search ahead" for independent instructions to launch.Reading AssignmentRead pp 367-386 o
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'Fault Finding and Fixing' Combinations and Chance Tasks - Set #2 (solutions)Malcolm Swan Mathematics Education University of Nottingham Malcolm.Swan@nottingham.ac.uk Jim Ridgway School of Education University of Durham Jim.Ridgway@durham.ac.uk Comb
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'Fault Finding and Fixing' Percentages Tasks - Set #1Malcolm Swan Mathematics Education University of Nottingham Malcolm.Swan@nottingham.ac.uk Jim Ridgway School of Education University of Durham Jim.Ridgway@durham.ac.uk Percentages are a common sou
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MS&E 371 Problem Solving II VBA Naming Conventions Variables Data Type Boolean Byte Integer Long Single Double Currency Decimal Date String Variant Object Controls Control Form Command Button Label Text Box Option Button Check Box List Box Combo Box
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Carnegie Mellon - PS - 70208
70208 Regression Analysis Dan LiHomework 2Due May. 31, 05 Total points: 200 Important: You should turn in the project proposal together with this homework. Guideline for this homework:Empirical part of the problem set can be done in either R or e
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INSTALLATIONHow to install Mobal 3.0b (Beta release) for SunOS 4.1.* and X11 1. Select a directory that is to become Mobal's home directory.This should be an empty new directory in which the different Mobalsubdirectories will go.2. Set the env
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Package: Name: Summary: Version: Description:areas/learning/systems/oc1/ OC1 Oblique Classifier 1OC1 (Oblique Classifier 1) is a multivariate decision tree induction system designed for applications where the instances have numeric feature values
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Package: Name: Summary: Version: Description:areas/learning/systems/learn_pl/aq1/ AQ-Prolog Reimplementation of Michalski's AQ for attribute vectors 14-JAN-94This directory contains Jeffrey M. Becker's AQ-PROLOG, a reimplementation of Michalski's
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Package: Name: Summary: Version: Description:areas/learning/systems/learn_pl/arch1/ ARCH1 Winston's incremental learning procedure. 14-JAN-94This directory contains ARCH1, Stefan Wrobel's implementation of Winston's incremental learning procedure
Carnegie Mellon - FOIL - 2
/*FOIL.2.1-Summary of changes from FOIL.1:-* arbitrary number of constants* arbitrary constant names* incorporation of types and keys* modified calculation of encoding length* option to prohibit negated literals* some more risk-fr
Carnegie Mellon - OC - 1
- ANNOUNCEMENT OC1 is now available via Anonymous FTP.- OC1 (Oblique Classifier 1) is a multivariate decision treeinduction system designed for applications where the instances havenumeric feature values. O
Wisconsin - A - 636
O212.956MeUnknown A08 in CDCl3 Athena (Bruker-AC 250) 13C spectrum200198.123O CH3 O158.233150133.25210077.682 77.258 76.834 76.591 75.821CDCl3 ref'ed to 77.25ppm57.05850 PPM48.88737.518 35.031 33.232 28.339 27.893 25.997
Wisconsin - C - 636
Sample F41 (F:C-01), Spring 2003 Unknowns24 Mar 2003 D. R. Mowrey Name Code Solvent(s) Operator(s) Notes Acquisition Time (sec) File Name Nucleus Sweep Width (Hz) F41 CDCl3 Brian Popp None Class Spring 2003 Mass Spec. Synthesized by FW 215.0 [+23 Na
Wisconsin - N - 636
Unknown N06 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3O C Ph O CH2CH2 CH23.63.43.23.02.82.6PPMCH2CH3all expansions but 12.6ppm at 20Hz/cmN CH31.2 1.1 1.0 0.9 0.8 0.7 PPM1.85 1.80 1.75 1.70 1.65PPM1.96 3.265.50
Wisconsin - A - 11
Unknown A11 in CDCl3 INOVA-600 HSQCAD 1H-13C 1-bond hsqc spectrum1200x400 FT'd to 2048x1024 gaussian apodization How does one make the 2 and 3 assignments without this spectrum (and then confirm the chirality of 1 without the NOE data)?11 12 10 9 8
Wisconsin - P - 60
Unknown P60 in CDCl3 Athena (Bruker-AC 300) 1H spectrum10Hz/cm 10Hz/cmH3C NCH3+3.75 3.70 3.65 3.60 3.55 3.50 3.45 3.40 PPM 2.72 2.70 2.68 2.66 PPMHCH220Hz/cm 10Hz/cm2.12.01.91.81.7PPM5.047.357.307.257.207.15PPM
Wisconsin - B - 14
030124_21 59 (1.091) Cm (57:78-200:220) 100361.1B14 in methanol LCT S. Przybylinski699.1TOF MS ES+ 2.77e4%700.2 362.1 701.202002503003504004505005506006507007508008509009501000m/z030124_21 59 (1.091)
Wisconsin - A - 11
Unknown A11 in CDCl3 INOVA-600 gcosy 1H COSY spectrumF2 (ppm) 1.2 1.47a 7b-OH 10b 12b 6 12a 10a 11a 3a 11b15 3b 2a 2b161024x256 FT'd to 1024x1024 sinebell-squared apodization symmetrized11 12 10 9 8 7 61.6 1.8 2.0 2.2 2.4 2.6 2.8H3C3
Wisconsin - E - 636
E03 (E:A-03) in CDCl3 orginally labelled E05 Athena Automation run Jan 16, 2003 D. Mowrey8.07.87.67.47.27.06.8PPMExpansions are 20 Hz/cmCH3OCH3HOO2.22.12.01.91.8PPM4.40 1.994.34PPM 3.00 3.011.981.98 2
Wisconsin - C - 13
Unknown O13 in CDCl3 Athena (Bruker-AC 300) 1H spectrumO O CH3all expansions at 20Hz/cmOCH33.43.33.23.1PPM2.952.012.020.990.981.00 1.001.962.018.07.97.87.77.67.57.44.36 4.32 PPM 4.38 4.34 4.30 PPM2.
Wisconsin - C - 636
Unknown O13 in CDCl3 Athena (Bruker-AC 300) 1H spectrumO O CH3all expansions at 20Hz/cmOCH33.43.33.23.1PPM2.952.012.020.990.981.00 1.001.962.018.07.97.87.77.67.57.44.36 4.32 PPM 4.38 4.34 4.30 PPM2.
Wisconsin - C - 42
Unknown O42 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3 O OO2.25 2.20 2.15 2.10 2.05 2.00 1.95 1.90 PPM3.5 8.1 8.0 7.93.063.42.993.33.23.1PPM7.87.77.6PPM3.002.00all expansions at 20Hz/cm1.07 1.051.041.041.00
Wisconsin - C - 636
Unknown O42 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3 O OO2.25 2.20 2.15 2.10 2.05 2.00 1.95 1.90 PPM3.5 8.1 8.0 7.93.063.42.993.33.23.1PPM7.87.77.6PPM3.002.00all expansions at 20Hz/cm1.07 1.051.041.041.00
Wisconsin - C - 10
Unknown O10 in CDCl3 Athena (Bruker-AC 300) 13C spectrum180 160169.844CH2O O C140CH3120 100 80148.982 132.341 130.753 130.696 130.498 130.047 129.923 128.757 128.165 128.005 127.490 126.866 126.611 126.553 123.311 121.414 121.297 115.
Wisconsin - C - 636
Unknown O10 in CDCl3 Athena (Bruker-AC 300) 13C spectrum180 160169.844CH2O O C140CH3120 100 80148.982 132.341 130.753 130.696 130.498 130.047 129.923 128.757 128.165 128.005 127.490 126.866 126.611 126.553 123.311 121.414 121.297 115.