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Golden West - BIOL 200 - BIOL 200
Pharmacology Exam 3 Review (Units 6-8) Unit 6 Drugs of the CNS Memorize the Continum Chart Understand the 2 types of tolerance Factors contributing to drug dependence CNS Stimulants how do they work? What are the main types we talked about in class the ma
Golden West - BIOL 200 - BIOL 200
EXAM 2 REVIEW Unit 4 Drugs of the Peripheral Nervous System What are preganglionic and postganglionic neurons and where are they found? How many efferent neurons in the SNS, PNS, voluntary portion of the nervous system What are the main receptors we talke
Golden West - BIOL 200 - BIOL 200
Outline of Central Nervous System DrugsDirections: MEMORIZE THIS A. CNS Stimulants - increase activity of ALL parts of the CNS 1. Amphetamines and related compounds 2. Xanthines (including caffeine, theobromine, and theophylline) 3. Cocaine and related l
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 13 Convergence analysis of the barrier method complexity analysis of the barrier method convergence analysis of Newtons method choice of update parameter bound on the total number of Newton iterations initialization131Comp
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 14 Primal-dual interior-point methods primal-dual path-following Mehrotras corrector step computing the search directions141Central path and complementary slacknesss + Ax b = 0 AT z + c = 0 zisi = 1/t, z 0, i = 1, . . . ,
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 15 Self-dual formulations initialization and infeasibility detection skew-symmetric LPs homogeneous self-dual formulation self-dual formulation151Solution of an LPgiven a pair of primal and dual LPs minimize cT x subject
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 16 Large-scale linear programming cutting-plane method Benders decomposition delayed column generation Dantzig-Wolfe decomposition161Cutting-plane methodminimize cT x subject to Ax b A Rmn, m n general idea: solve sequenc
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 17 Integer linear programming integer linear programming, 0-1 linear programming a few basic facts branch-and-bound171Denitioninteger linear program (ILP) minimize cT x subject to Ax b, x ZncGx = dmixed integer linear
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 1 Introduction and overview linear programming example from optimal control example from combinatorial optimization history course topics software11Linear program (LP)nminimizej =1 ncj xj aij xj bi,j =1 nsubject toi
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 2 Linear inequalities vectors inner products and norms linear equalities and hyperplanes linear inequalities and halfspaces polyhedra21Vectors(column) vector x Rn: x1 x x = .2 . xn xi R: ith component or element of x al
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 3 Geometry of linear programming subspaces and ane sets, independent vectors matrices, range and nullspace, rank, inverse polyhedron in inequality form extreme points the optimal set of a linear program31SubspacesS Rn (S
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 4 The linear programming problem variants of the linear programming problem LP feasibility problem examples and some general applications linear-fractional programming41Variants of the linear programming problemgeneral fo
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 5 Structural optimization minimum weight truss design truss topology design limit analysis design with minimum number of bars51Truss m bars with lengths li and cross-sectional areas xi N nodes; nodes 1, . . . , n are free
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 6 FIR lter design FIR lters linear phase lter design magnitude lter design equalizer design61FIR ltersnite impulse response (FIR) lter:n1y (t) = =0h u(t ),tZ u : Z R is input signal ; y : Z R is output signal hi R a
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 7 Applications in control optimal input design robust optimal input design pole placement (with low-authority control)71Linear dynamical systemy (t) = h0u(t) + h1u(t 1) + h2u(t 2) + single input/single output: input u(t)
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 8 Network optimization network ows minimum cost network ow problem extreme ows shortest path problem bipartite matching81Networksnetwork (directed graph): m nodes connected by n directed arcs arcs are ordered pairs (i, j
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 9 Duality (part 1) the dual of an LP in inequality form weak duality examples optimality conditions and complementary slackness Farkas lemma and theorems of alternatives proof of strong duality91The dual of an LP in inequa
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 10 Duality (part 2) duality in algorithms sensitivity analysis via duality duality for general LPs examples mechanics interpretation circuits interpretation two-person zero-sum games101Duality in algorithmsmany algorithms
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 11 The simplex method extreme points adjacent extreme points one iteration of the simplex method degeneracy initialization numerical implementation111Idea of the simplex methodmove from one extreme point to an adjacent ex
Monmouth IL - CAAM - 236
EE236A (Fall 2007-08)Lecture 12 The barrier method brief history of interior-point methods Newtons method for smooth unconstrained minimization logarithmic barrier function central points, the central path the barrier method121The ellipsoid method 19
Monmouth IL - MATH - MATH427
MT 3802 NUMERICAL ANALYSIS2008/2009Dr Clare E Parnell and Dr Stphane Rgnier e eOctober 1, 2008Chapter 0Handout0.1 NotationThroughout this course we will be using scalars, vectors and matrices. It is essential that you know what they are and can tel
Monmouth IL - MATH - MATH427
MT 3802 NUMERICAL ANALYSIS2008/2009Dr Clare E Parnell and Dr Stphane Rgnier e eOctober 15, 2008Contents0 Handout 0.1 0.2 0.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . .
Monmouth IL - MATH - MATH427
Chapter 2Iterative Methods2.1 IntroductionIn this section, we will consider three different iterative methods for solving a sets of equations. First, we consider a series of examples to illustrate iterative methods. To construct an iterative method, we
Monmouth IL - MATH - MATH427
cep 2008/09MT3802 - Numerical AnalysisSOLUTIONS - Tutorial Sheet 1 1. Show that the following satisfy the requirements of a norm specified on a Vector Space: (i)bf =a|f (x)|dxwhere f (x) C[a, b] ; Proof that is satisfies Rule 1 (1.1) f = 0 & conti
Monmouth IL - MATH - MATH427
cep 2008/09MT3802 - Numerical AnalysisSOLUTIONS - Tutorial Sheet 2 1. For a sub-ordinate matrix norm and an invertible matrix A, where Ax = b, show that (i) As AssNUsing Rule 5 (1.9) of sub-ordinate matrix norms we have As A As-1 A2As-2 As-1A As
Monmouth IL - MATH - MATH427
Monmouth IL - MATH - MATH427
Monmouth IL - MATH - MATH427
cep 2008/09MT3802 - Numerical AnalysisTutorial Sheet 1 1. Show that the following satisfy the requirements of a norm specified on a Vector Space: (i)bf =a|f (x)|dxwhere f (x) C[a, b] ;(ii) x = max |xi |1inwhere x Rn .2. An inner product is defi
Monmouth IL - MATH - MATH427
cep 2008/09MT3802 - Numerical AnalysisTutorial Sheet 2 1. For a sub-ordinate matrix norm and an invertible matrix A, where Ax = b, show that (i) (ii) As AssN1 x A-1 A b2. Assume that (A + A)-1 is computed as an approximation to A-1 . Show that (A +
Monmouth IL - MATH - MATH427
Monmouth IL - MATH - MATH427
Monmouth IL - MATH - MATH427
cep 2008/09MT3802 - Numerical AnalysisTutorial Sheet 5 1. Let p1 (x) be the linear interpolant of f (x) at the points (x0 , f (x0 ) and (x1 , f (x1 ), where x1 - x0 = h. Show that for x [x0 , x1 ] f (x) - p1 (x) where f (x) M h2 8 M.Suppose ex on [-
Monmouth IL - CS - CS 399
ScriptEase: A Pattern Based Approach to ScriptingCurtis Onuczko Thursday, January 19, 2006Overview What is ScriptEase? Why use ScriptEase? When to use ScriptEase? How to use ScriptEase Tips and TricksWhat is ScriptEase?Why use ScriptEase?A tool to
Monmouth IL - CS - CS 399
Overview Game Creation ProcessCMPUT 299 Winter 2006 Thursday January 12 ! Making a computer game is a lengthy process, involving many creative people! High-profile productions often take 2-3 years ! Challenging games can involve > 100 people!! This lec
Monmouth IL - CS - CS 399
convexity (series) new modes: adding multiplayerGame DesignCMPUT 299Finnegan Southey XBox Live GamerTag: Aleafeatures qbilities, training, introducing abilities critical gaming save dev qa coverage/replay approachable doors & keys consuming items s
Monmouth IL - CS - CS 399
Lecture Review Sheet: Game Design Finnegan Southey CMPUT 299 Winter 2006 Terms and Definitions MDA n A model for game design that views the user experience as a three part process: mechanics, dynamics, and aesthetics Funativity n A model for types of "fun
Monmouth IL - CS - CS 399
Overview! AnnouncementsWhat have we learned thus far?CMPUT 299 Fall 2005 Tuesday, January 31! ! ! !Design Document next Thursday Meet with producer beforehand BioWare internships (CS only) Lab this week/lab exam! NWN Module Discussion ! Computation
Monmouth IL - CS - CS 399
Management is Fundamentals of Project ManagementCMPUT 299 H. James Hoover Winter 2005 2005-09-22 Version 1.21 CMPUT 299 - Winter 2005 Project Management People Process Problem Solving2People Identify skills - have and missing Identify personalities
Monmouth IL - CS - CS 399
OpeningNarrative:Concepts of Narrative in Interactive New Media Art FormsLecture Goals for the next two classes1. Introduce concepts of traditional narrative, most of which apply to later technologies. 2. Discuss the application of these concepts to n
Monmouth IL - CS - CS 399
Meetings, Bloody Meetings How To Run MeetingsCMPUT 299 H. James Hoover Fall 2005 2005-09-23,27 Version 1.0CMPUT 299 - Fall 2005 How to Run Meetings Written by John Cleese and Antony Jay Starring John Cleese and Robert Hardy Video Arts, 1993 34 minMee
Monmouth IL - CS - CS 399
The Sims 2 More Post-Mortems, SharewareCMPUT 299 Winter 2005 February 16CMPUT 299 - Fall 2005 Game PostmortemsThe Sims 2! What went right! ! ! ! ! Prototyping Understanding the audience Kleenex testing Custom content and community SWAT TeamsCMPUT 29
Monmouth IL - CS - CS 399
Lecture Overview Artificial Intelligence Part ICMPUT 299 Winter 2006 February 28, 2006 ! Lab Exam! Course Evals! Design Issue Presentations ! Artificial Intelligence! Definition! Related concepts! Algorithm ! Time/Memory Cost! Finite State Machines
Monmouth IL - CS - CS 399
Lecture Overview Artificial Intelligence Part IICMPUT 299 Winter 2006 March 2, 2006 ! ! ! ! ! Lab Exam Pizza! Summary (Tuesday) Pathfinding Finite State MachinesCMPUT 299 - Fall 2005 Artificial IntelligenceLab Exam ScoresA+ A B CPizza!! Get together
Monmouth IL - CS - CS 399
The Early Days Graphics and GenresCMPUT 299 ! The very earliest video games (pre-1975) were custom built machines. ! Designed/built by engineers (like a TV).Tennis for Two(Brookhaven Labs,1958)Finnegan Southey XBox Live GamerTag: AleaCMPUT 299 - Fall
Monmouth IL - CS - CS 399
Lecture Review Sheet: Artificial Intelligence Nathan Sturtevant CMPUT 299 Winter 2006 Terms and Definitions Intelligence is usually said to involve mental capabilities such as the ability to reason, plan, solve problems, think abstractly, comprehend ideas
Monmouth IL - CS - CS 399
Lecture Review Sheet: The culture of Gaming Sean Gouglas CMPUT 299 Winter 2006Main Ideas and Concepts A. Introduction Culture describes social actors, the webs of significance that allow for social actors to communicate and understand each other, and the
Canterbury Christ Church University - ENGR - 102
Rochester College - CS - CSC400
THE DIGITAL MILLENNIUM COPYRIGHT ACT OF 1998U.S. Copyright Office SummaryDecember 1998INTRODUCTIONThe Digital Millennium Copyright Act (DMCA)1 was signed into law by President Clinton on October 28, 1998. The legislation implements two 1996 World Inte
National University of Juridical Sciences - ACC - 640M
National University Course Outline ACC640M Governmental and Non-profit Accounting Please Note: We will be using e-college. All handouts, and other class materials are available in e-college under DOC Sharing Tab at top of page. Make copies of your handout
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808
University of Florida - CHM - 0808