100 Pages

r50pooled

Course: CS 431, Fall 2009
School: Rutgers
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Word Count: 3903

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Header version5.1 DateThu Begin Oct 03 18:34:50 EDT 2002 Image FileC:\My Documents\Array Scans\10-3\r50pooled.tif Page0 Page Name InvertedFALSE Begin Field Dimensions FieldMetarowsMetacolsRowsCols A221615 End Field Dimensions Begin Measurement parameters Segmentation Methodauto Signal Low0 Signal High0 Background Low0 Background High0 Background Buffer3 Background Width3 End Measurement parameters Begin Alerts...

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Header version5.1 DateThu Begin Oct 03 18:34:50 EDT 2002 Image FileC:\My Documents\Array Scans\10-3\r50pooled.tif Page0 Page Name InvertedFALSE Begin Field Dimensions FieldMetarowsMetacolsRowsCols A221615 End Field Dimensions Begin Measurement parameters Segmentation Methodauto Signal Low0 Signal High0 Background Low0 Background High0 Background Buffer3 Background Width3 End Measurement parameters Begin Alerts Control TypeMinimum thresholdIf testedPercentage allowedIf failedMaximum thresholdIf testedPercentage allowedIf failedCV thresholdIf testedIf failed End Alerts Begin Quality settings Empty SpotsTRUEThreshold:0.24 Poor SpotsTRUE Begin Poor Spots Parameters Background contamination flagFALSEThreshold:0.995 Background tested against subgrid data onlyTRUE Signal contamination flagFALSEThreshold:0.995 Signal contamination test connected to background contamination thresholdFALSE Ignored percentage flagFALSEThreshold:25 Open perimeter flagFALSEThreshold:40 Shape regularity flagFALSEThreshold:0.7 Area To Perimeter Ratio flagFALSEThreshold:0.7 Offset flagFALSEThreshold:60 End Poor Spots Parameters Negative SpotsTRUE End Quality settings End Header Begin Raw Data FieldMeta RowMeta ColumnRowColumnGene IDFlagSignal MeanBackground MeanSignal MedianBackground MedianSignal ModeBackground ModeSignal AreaBackground AreaSignal TotalBackground TotalSignal StdevBackground StdevShape RegularityIgnored AreaSpot AreaIgnored MedianArea To PerimeterOpen PerimeterXCoordYCoordDiameterPosition offsetOffset XOffset YExpected XExpected YCM-XCM-YCM OffsetCM Offset-XCM Offset-YMin DiamMax DiamControlFailed ControlBackground contamination presentSignal contamination presentIgnored % failedOpen perimeter failedShape regularity failedPerim-to-area failedOffset failedEmpty spotNegative spot 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Rutgers - CS - 431
Cols 15Cols 15If failedIf failedIf testedPercentage allowedGreen Single Pooled Gene ID 17D041A 17D044A 17D047A 17D049A 17D052A 17D055A 17D057A 17D059A 17D089A 17D091A 17D097A 17D101A 17D103A 17D111A 17D114A 17D122A 17D156A 17D163A 17D166A
Rutgers - CS - 431
Microarray Analysis Procedure 1. Save quantified pixel intensity results from Imagene analysis software. Creates 2 txt files, in a user specified named file. One containing information from the red scan, the other from the green scan. 2. The files co
St. Edwards - SOCI - 4347
2 5 Q u e st i o n s & A n swe r s o n H e a lt h a n d H u m a n R i g h t sSection 1: Health & Human Rights Norms and StandardsQ.1What are human rights?WHO/PAHOHuman Rights:(1) Are guaranteed by international standards; Are legally prot
Purdue - MA - 16200
Ground Rules for MA 162, Summer 2009 Homework: There are 38 online homework assignments using WebAssign (https:/www.math.purdue.edu/academic/webassign). There is one or more homework assignments due at 11:55 pm on every class day except the rst day o
Arizona - SECTION - 505
Empirical T=0 equations of state Near the zero-pressure volume, the Salpeter equation of state fails miserably. This is of course the region of interest for the interiors of planets where the central pressure is < 1 a.u.p. (true for all terrestrial p
University of Louisiana at Lafayette - KXK - 4695
A parametric bootstrap solution to the MANOVA under heteroscedasticityK. KRISHNAMOORTHY1and FEI LUDepartment of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70504-1010, USAIn this article, we consider the problem of compar
Nevada - FIN - 430
Nevada - FIN - 430
Applied Portfolio Management FIN 493 Exam IAli Nejadmalayeri March 1, 2006NAME:ID: R 0 0 0 _ _ _ _ _ _ _ _ _Please be precise yet concise. Avoid run-on stories. Manage your time prudently.1.In his other book, The Roaring 2000's Investor:
Nevada - FIN - 430
GUIDELINE FOR THE MID-TERM EXAM:From Stock Market Rules by Michael D. Sheimo:1. Insider Trading and Institutional Ownership (Ch. 9, 11) Is Insider Trading a Good Signal? What Does Institutional Ownership Means for Stock Price? 2. When and What to
Iowa State - ECON - 1943
Long name,Short name,Class,District,ConferenceAckley,Ackley,1A,1,1Algona,Algona,1A,1,1Atlantic,Atlantic,1A,1,1Bedford,Bedford,1A,1,1Burlington,Burlington,1A,1,1Carroll,Carroll,1A,1,1Cedar Falls,Cedar Falls,1A,1,1Cedar Falls NU,Cedar Falls NU,
Nevada - FIN - 742
Sampling, Estimation, & TestingQuantitativeMethodsAli NejadmalayeriPoint and Interval EstimatesEstimator: e.g., Formula for sample meanEstimate: e.g., Sample means from a set ofobservationsPointEstimates One sample mean from one sa
Nevada - FIN - 742
ResearchMethods BADM742Exam1AliNejadmalayeri February9,2006NAME:ID: R 0 0 0 _ _ _ _ _ _ _ _ _Dueon7:00p.m.,Wednesday,February22,2006. Ifyouemail,usealiala@unr.edu.Ifyoufax,use7757841769.PleaseUsetheINDUS.xlsfilefordata.AttachAllSpreadsheets.
UVA - ASTR - 553
RRgVc2 = R g 2 = g Rg Lz = R2 = const = (R, z ) ; r R 2 = ; R R z zd (Lz ) = 0 ; dt z= z=0z =0z= 2 z z2 z =0z =02 = z z2= 2 zz =0 2 = ( 2 / z 2 )z=0 z (t) = Z cos(t + 0 ) x(t) = X cos(t + 0 ) 2g y (t) = X s
UVA - ASTR - 553
R = R 2 R e R= RwhereeL2 z = (R, z ) + 2R2 e R=0=Rg R Rg 2 = gRg RVc2 Rg Rg e R=x= R 2 e x R2 Rg 2 e = x R2 Rg= 2 xRg=2 2 e R2=Rg R R3L2 + 4z = Rg Rgd 2 R + 42 dRRgLz Lz = 2 = R (Rg + x
UVA - ASTR - 553
2g X (t) = g t sin(t + 0 ) RgA=d Vc 1 Vc 2R dRd 1 = R 2 dR R0R0B=1 Vc d Vc + 2R dRd 2 1 + = R 2 dR R R0R00 = A B = 27 km/s/kpc2 = 4B (A B ) = 4B 0 ; 0 = 37 km/s/kpc 00 =2 0B = 1.3 AB
University of Texas - M - 348
M348 Homework 10Due 4/18/08 in class1. (10 points) BF 5.11 # 1(b). (Use a C+ code) 2. (20 points) Solve BF 5.11 # 1(b) by implicit Euler method, and compare results with Problem 1. (Use a C+ code) 3. (20 points) Solve BF 5.11 # 1(b) by implicit Tr
UVA - ASTR - 553
2 = 2 2G|k | + k 2 Vs 2 = 2 2G|k | F (, k, R )(Gas) (Stars)(r) = GV(r ) 3 dr |r r| r r (r ) d3 r |r r|3F(r) = (r) = GV F(r) = 4G(r)2 2(r) = 4G(r) (r) = 04GM = 4GV(r) d3 r F(r) d3 r =A 3=VF(r) d2 S | |2 d3 rV1
University of Texas - M - 348
M348 Homework 11Due 4/25/08 in class1. (20 points) BF 6.2 # 6. 2. (10 points) BF 6.2 # 9(a). 3. (10 points) BF 6.2 # 15(a). 4. (40 points) BF 6.3 # 1. (If a matrix is nonsingular, use a C+ code to compute its inverse.) 5. (20 points) BF 6.4 # 7. W
University of Texas - M - 348
M348 Homework 2Due 2/1/08 in class1. BF 2.1 # 3. (Please show the sequence of p s you got. Do not solve it by a computer program.) 2. Write a C+ code to solve BF 2.1 # 5(c). You may use the code posted on the HW page as reference. 3. BF 2.1 # 15 4
University of Texas - M - 348
M348 Homework 3Due 2/8/08 in class1. BF 2.2 # 4. 2. BF 2.2 # 12(a). (In part (ii), perform the calculations by hand and show the sequence {pn }.) 3. BF 2.2 # 12(c). (In part (ii), solve this by a C+ code. Attach both the code and the results.) 4.
University of Texas - M - 348
M348 Homework 5Due 2/29/08 in class1. BF 3.3 # 1(a). (Solve it by the method taught in class. Not by Theorem 3.9 or Algorithm 3.3) 2. BF 3.4 # 3(c). 3. BF 3.4 # 11. 4. BF 3.4 # 14. 5. Derive by Taylor series an approximation of f (x) using three p
UVA - ASTR - 553
% 8 G & # % & 7 & 6 & C 3 & % 8 S & 4 9 & I 7 0 9 65" T0 & C & % 0 2 & T0 D # 1" 8 # i 1" 3 0 C 3 8 2 G 1 8" 2 % & I2 " 9 63" H 85 & T 5 0 % 3 1 & H & C 3 # 1" 2 0 & % H 1" 6 4 I 8 85 @H 0 4 9 & & S & C 3 3 $ H p G h t P 2 & 5 4 & & s g % & @ " % 3
UVA - ASTR - 553
UVA - ASTR - 553
UVA - ASTR - 553
UVA - ASTR - 553
UVA - ASTR - 553
Topic 6 Homework1. Epicyclic Motion: Theory a) Derive the radial oscillation frequency, , for a star perturbed from a circular orbit in an arbitrary axisymmetric potential (R). Express your result first in terms of the angular velocity, (R), and the
UVA - ASTR - 553
Topic 6 Homework4. Estimating Pattern Speeds. Express all frequencies in km/s/kpc, and in Myr-1 a) For a galaxy with a flat rotation curve V = 250 km/s, whats the epicyclic frequency at 7 kpc? b) If corotation is at 6 kpc, whats this galaxy's patter
UVA - ASTR - 553
Circular Galactic Rotation. Derivation of Oorts constants: A & BS l rV(Ro) V(R) l.o.s. V = V(Ro) V(R) R dV/dR r cos l V(Ro) Ro R V(R)l90 l l.o.s.Fig 2 shows the velocity vectors of the sun and star (red) and their relative veloci
UVA - ASTR - 553
UVA - ASTR - 553
UVA - ASTR - 553
UVA - ASTR - 553
Washington University in St. Louis - MATH - 1322
%!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: Exam2sol.dvi %Pages: 3 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentPaperSizes: Letter %EndComments %DVIPSCommandLine: dvips Exam2sol.dvi -o Exam2sol.ps
Washington University in St. Louis - MATH - 1322
%!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: final.dvi %Pages: 2 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentPaperSizes: Letter %EndComments %DVIPSCommandLine: dvips final.dvi -o final.ps %DVIPSPar
Washington University in St. Louis - MATH - 1322
%!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: final.dvi %Pages: 22 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentPaperSizes: Letter %EndComments %DVIPSCommandLine: dvips final.dvi -o final.ps %DVIPSPa
Drexel - CS - 576
1/3/2005CS576: Dependable Software Systemsjust true f tg/stores/d communit rate-say cust-reccavorite-lis nm/justsay GETitemnoWhat will we cover in CS576? Course Web Page: www.cs.drexel.edu/~souter/cs576/w05 The Syllabus Intro to Software Tes
Drexel - CS - 576
1/10/2005Software TestingTerm Project Part 1 due: Jan 24th in class 1. Find an open source application junit test suite build.xml java source codeJanuary 10, 2005Topics: GUI Testing Test case generation Test oracles Coverage criteriaCS576
Drexel - CS - 576
"! |w| wv xwv vw }|z vw z w x| vw w } w }w| v w |w| v z w } ~ z w z w x } r5tSz5r5r7#ccrv#5iV5rr7Xi0r5cv |r5h# v x55| vPv$ppBHQ0 w| w w | }| |w| | w z| } v x ww }| w vw w z ~ ~v x zv
Drexel - CS - 576
An Empirical Study of Regression Test Selection TechniquesTODD L. GRAVES Los Alamos National Laboratory MARY JEAN HARROLD Georgia Institute of Technology JUNG-MIN KIM and ADAM PORTER University of Maryland and GREGG ROTHERMEL Oregon State University
Drexel - CS - 576
A CM SIGSOFTSoftware Engineering Notes vol 24 no 1January 1999 Page 69An Overview of Regression TestingYuejian Li Nancy J. Wahl Middle Tennessee State Universitywahl@mtsu.edu AbstractR egression testing is an important part of the software d
Drexel - CS - 576
A Scalable Approach to User-session based Testing of Web Applications through Concept AnalysisSreedevi Sampath CIS University of Delaware Newark, DE 19716 sampath@cis.udel.edu AbstractThe continuous use of the web for daily operations by businesses
Drexel - CS - 576
Drexel - CS - 576
1/31/2005CS576Midterm Review HandoutJanuary 31, 2005Topics: Paper: Improving Web Application Testing with User Session Data. Sebastian Elbaum, Srikanth Karre, Gregg Rothermel. ICSE 2003. Scalable User session testing through the use of Concep
Drexel - CS - 576
CS576 Midterm Review February 7, 2005 Midterm will consist of a series of fact questions and open ended questions. You may bring one piece of paper 8x11 to the exam. Topics covered on the exam.GUI Testing What aspects of GUIs make testing challengin
Drexel - CS - 576
WERST Proceedings/ACM SIGSOFT SENPage 1September 2004 Volume 29 Number 5The (Im)maturity Level of Software TestingAntonia BertolinoIstituto di Scienza e Tecnologie dellInformazione A. FaedoISTI-CNR, via Moruzzi, 1 Pisa, Italy +39 050 315 2914
Drexel - CS - 576
Proceedings of the International Conference on Software Engineering, Orlando, Florida, U.S.A., May 2002, ACM CopyrightVisualization of Test Information to Assist Fault LocalizationJames A. Jones, Mary Jean Harrold, John Stasko College of Computing
Drexel - CS - 576
Test Plan for Your Project Version Enter Ver/Build<your company> Testing DepartmentDocument Signoff: Product Manager Program Manager Development Lead Test Manager Test Lead Lead Data Analyst _ _Document Information: Revision: 1.0 Date Revised: E
Dickinson State - ECON - 201
Econ 201 - Lecture NotesMcConnell and Brue Chapter #20 - Supply & Demand: Elasticities and Government Set Prices I. Review Supply and Demand - Chapter #3 1. Demand (define) 2. Law of Demand 3. Demand Models (1) Narrative (2) Tabular (3) Graphic 4. R
Dickinson State - ECON - 201
Econ 201 - Lecture NotesChapter 21 - McConnell & Brue, 15th EditionConsumer Behavior and Utility MaximizationI. Law of Demand 1. An inverse relationship between price and quantity demanded. 2. Three reasons for Law of Demand. (1) Income Effect -
Dickinson State - ECON - 201
Econ 201 - Lecture NotesChapter #23 - Part I - Pure Competition McConnell & Brue, 15th Edition I. This Chapter Brings Together Costs and Revenues of a Purely Competitive Firm 1. Chapter #22 - Introduced inputs and their costs to produce products wit
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMarch 1, 2000A Brief Introduction to the CALSHP Computing SystemIn 572 we will be using SAS as provided on the CALSHP computing system. You can access this system from your own computer using a telnet connection or from
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMarch 1, 2000A Brief Introduction to the vi EditorThe vi editor is a powerful editor for entering and editing data. In 572 we will also use it to create and modify SAS programs. In some ways vi will seem like a word pro
Wisconsin - STAT - 572
Wisconsin - STAT - 572
Stat/For/Hart 572 - Final Exam, Spring 96 - Partial Solutions1. Note: that the randomized design might look something like:3.(4(4Source Row co1 A B AxB Error Totaldf 3 3 1 1 1 6 15(b) We treat this as a one-way ANOVA. The(b) land6.2.
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMay 16, 1997Final ExamName: Discussion Section #:Instructions: 1. This exam is open book. You may use textbooks, notebooks, and a calculator. 2. Do all your work in the spaces provided. If you need additional space, u
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMarch 18, 1997Midterm IName:Instructions: 1. This exam is open book. You may use textbooks, notebooks, and a calculator. 2. Do all your work in the spaces provided. If you need additional space, use the back of the pr
Wisconsin - STAT - 572
Stat/For/Hort 572 Midterm I, Spring 97 Solutions1. (a) SSPureError = 2+128+288+392+1152 = 1962 on 5 df. SSLack-of-Fit = SSErr SSPE = 2200 1962 = 238 on 3 df. The test statistic is then 238/3 F= = .202 1962/5 From the F tables, we get p-value > 0
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMay 12, 1998Final ExamInstructions: 1. This exam is open book. You may use textbooks, notebooks, and a calculator. 2. Do all your work in the spaces provided. If you need additional space, use the back of the preceding
Wisconsin - STAT - 572
Stat/For/Hort 572 Final Exam, Spring 98 Partial Solutions25A1 Present25A1 Absent21.(a) True. The ANOVA table for this experiment could be sketched out as: Source Blocks Protein Fiber PF Error Total df 7 1 2 2 35 4720 Response1 2Yel
Wisconsin - STAT - 572
Stat/For/Hort 572ClaytonMarch 19, 1998Midterm IFor graders use: Question 1 2 3 4 Total Possible Points 20 20 25 35 100 Score1. (a) The italicized comment below is either True or False. Indicate whether the statement is True or False, and giv