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University of Texas - M - 08
Eigenvalues of hyperbolic elements in Kleinian groupsD. D. Long & A. W. Reid November 4, 20081IntroductionLet be a torsion-free Kleinian group, so that M = H3 / is an orientable hyperbolic 3-manifold. The non-trivial elements of are classied
University of Texas - CO - 2
Impact of CO2 Snow Cleaning on HET Primary Mirror Segment Reflectivityprepared by Franois PichHobby*Eberly Telescope McDonald ObservatoryAugust 17, 1999HET Mirror Cleaning1. IntroductionRegular mirror cleaning is an important component of k
University of Texas - CVRC - 08
Observe-and-Explain: A New Approach for Multiple Hypotheses Tracking of Humans and ObjectsM. S. Ryoo and J. K. Aggarwal Computer & Vision Research Center / Department of ECE The University of Texas at Austin{mryoo, aggarwaljk}@mail.utexas.eduAbst
University of Texas - CVRC - 08
WMVC 2008IEEE Workshop on Motion and Video Computing Copper Mountain, CO, January 2008 (WMVC '08)WMVC 2008Recognition of High-level Group Activities Based on Activities of Individual MembersM. S. Ryoo and J. K. Aggarwal Computer & Vision Resea
University of Texas - CVRC - 08
WACV 2008IEEE 2008 Workshops on Applications of Computer Vision Copper, Colorado, Jan. 2008 (WACV 08)WACV 2008Tracking and Segmentation of Highway Vehicles in Cluttered and Crowded ScenesGoo Jun J. K. Aggarwal Dept. of Electrical and Computer
University of Texas - CC - 3467
Chia-Chih ChenTel: +1.512.584.1708 Email: ccchen@mail.utexas.edu OBJECTIVE To obtain an internship position in summer 2009. EDUCATION PhD student, Electrical and Computer Engineering, Fall 2006-present Communications, Networks, and Systems, The Uni
University of Texas - PGE - 102
We Have Full time jobs I am a Recruiter who specializes in working with clients in this industry. If you have the experience I have the client. If you are a Client I have the people.Apply to: Annalisa.Emm@gmail.comRole #1 Geologist The candidates
University of Texas - DSEMCLIGHT - 120508
DESMC Lighting ProjectAs of 12/05/08Phase 1 BldgsBUILDINGSTART DATE# of DAYSREMARKSACE6/18/084completedAHG6/3/083CompleteBIO5/30/084completeBUR5/31/086completeBWY5/20/081completeECJ6/27/0812c
University of Texas - LBJ - 98
Lyndon B. Johnson School of Public Affairs The University of Texas at Austin Number 135 Spring 1998Shaping and Managing the Publics Businessby Edwin Dorn, Dean LBJ School of Public AffairsINSIDEFEATURES Doom Is Optional Social Security chief Ke
University of Texas - M - 2
USING THE FIND NORMALIZING TRANSFORMATIONS FEATURE (Corresponds to Section 13.2 in textbook) Considerations to take into account: 1. The values found by the software are just estimates so it is silly to try to get too precise. Example: The software
University of Texas - M - 01
Algebra - hw # 1fall 2008Some general comments - A few things to keep in mind Some of you seem to have good ideas but some trouble expressing them. Welcome to graduate school: its time to improve your style. Keep in mind that the grader is only a
University of Texas - M - 02
Algebra - Hw # 209-12-08A few general comments If the action of a group on a set G X is say 3-transitive, that means (as you know) that you can send any triplet to any other triplet you want. It does NOT imply anything at all over the other of th
University of Texas - M - 03
Algebra - hw # 3fall 2008 If you want to use that for a group S you cant have S Keep in mind that: 1) Cardinality arguments only work when S is nite. 2) If you say that certain map cannot be surjective or injective, I need to see a proof, or at leas
University of Texas - M - 04
Algebra - hw # 4fall 2008 Remember when you rst started to like math? Remember how simple things were? Lets try to keep it that way. Some of you complicate things way too much. Lets try to work on keeping thins simple, as much as possible. Quote o
University of Texas - M - 05
Algebra - hw # 5fall 2008 About the "uniqueness" issue . 1. Problem 3.b The novelty here was to prove that the group was inded unique (up to isomoprhism, always). 2. Problem 3.b When you talk about a semidirect product A B you are implicitely referr
University of Texas - PUBS - 410
BALANCE FORWARD CHECK*DEFINE ACCOUNT INFO / BUDGET GROUP - GB2 Year 03 04 Command: GB2 Account: 3012345678 Misc: _ Month: 13_ = ME -CHARLIES CHOCOLATE FACTORY Scr 1 of 9 Ca Ty Pl S Budget/Bud Adj BF/TR/DI/IN En/Sc/Sa/AL Free Balance - - - - - - - -1
University of Texas - PUBS - 410
BALANCE FORWARD RULESCA3 SCREEN *DEFINE BUDGET GROUP / ACCOUNT PROFILE - CA3 Year 03 04 Command: CA3 Account: 14123456 Misc: _ Month: SEP = Budget Group Titles: |Administrators: |Screen: 1 Short: AB - ABC DEPARTMENT_ | 123456789 DUCK, DONALD Long: D
University of Texas - PUBS - 410
ENCUMBRANCES Encumbrances are transactions that have an object class code of 0XXX. They have special balance forward procedures in order that departments may know exactly what purchase orders were brought forward from the previous year. Most encumb
University of Texas - PUBS - 410
SERVICE DATESService Dates Service dates influence whether a transaction will be rolled back or rolled forward. a. Service dates on transactions Service dates entered on the document cover sheet are official service dates.*DEFINE SERVICE PAYMENT RE
University of Texas - PUBS - 5
Command GG51Unit Codes Listings in Unit Code Order GG5 provides a listing of units in unit code order, as well as the unit title and administrator. Typing a 7-digit unit code number in the Misc field will bring up that code to the top of the list
University of Texas - PUBS - 6
VT6: IDT Correction of Charges When to Use a VT6 The VT6 document is used to make corrections only. The transaction must already exist in order for it to be corrected. Corrections can only be done by the creator of the original document or by the off
University of Texas - DE - 240
Example #1 Simple CaseBegin by typing SS1 in the Command line:> Select a document or a new action and press enter to continue < *DEFINE SCHOLARSHIP/FELLOWSHIP AWARD DOCUMENT - SS1 Year 05 06 Command: SS1 Misc: _ Account: _ == Status: - COVER SHEET
University of Texas - SS - 1
Example #1 Simple CaseBegin by typing SS1 in the Command line:> Select a document or a new action and press enter to continue < *DEFINE SCHOLARSHIP/FELLOWSHIP AWARD DOCUMENT - SS1 Year 05 06 Command: SS1 Misc: _ Account: _ == Status: - COVER SHEET
University of Texas - SS - 240
Example #1 Simple CaseBegin by typing SS1 in the Command line:> Select a document or a new action and press enter to continue < *DEFINE SCHOLARSHIP/FELLOWSHIP AWARD DOCUMENT - SS1 Year 05 06 Command: SS1 Misc: _ Account: _ == Status: - COVER SHEET
University of Texas - CE - 385
CE 385 D Water Resources Planning and ManagementIntroductionDaene C. McKinneyCourse Objectives Introduction to Water resource systems Planning, design, and operation Application of Economic principles (Cost Benefit and Microeconomic analy
University of Texas - CE - 385
WORLD WATER RESOURCESA NEW APPRAISAL AND ASSESSMENT FOR THE 21ST CENTURYA summaryof the monographWorldWaterResourcesprepared in the framework of the International Hydrological Programme bY Igor A. Shiklomanov Director State Hydrological
University of Texas - CE - 385
Staff Paper Prepared for the President's Commission to Study Capital Budgeting June 19, 1998 WATER RESOURCES DEVELOPMENT PROJECTS I. Description of Investment Water resources projects serve several purposes including navigation (deep water ports and
University of Texas - CE - 385
Economics Analysis of Water ResourcesLecture Notes CE 385D: Water Resources Planning and Management Daene C. McKinney Department of Civil Engineering The University of Texas at AustinContentsSection Page 1. Engineering Economics .. 1 1.1 Choosing
University of Texas - CE - 385
Homework #3CE 385D - McKinney1. Consider two alternative water resource projects, A and B. Project A will cost $2,533,000 and will return $1,000,000 at the end of 5 years and $4,000,000 at the end of 10 years. Project B will cost $4,000,000 and w
University of Texas - CE - 385
Mathematical ProgrammingLecture Notes CE 385D - McKinney Water Resources Planning and Management Department of Civil Engineering The University of Texas at Austin Section 1. Introduction 2. General Mathematical Programming Problem 3. Constraints 4.
University of Texas - CE - 385
Homework #4 1. Find the maximum value of the following functionCE 385D - McKinney2. A vertical cylindrical steel tank of height h and inside diameter D is to be constructed. The tank is open at the top, and it is known that the bottom must be twi
University of Texas - CE - 385
INTRODUCTION TO GAMS1 Daene C. McKinney CE385D Water Resources Planning and Management The University of Texas at AustinTable of Contents1. 2. 3. 4. Introduction .. 2 GAMS Installation . 2 GAMS Operation .. 2 Examples . 11 4.1. Algebraic Equation
University of Texas - CE - 385
BASIC OPTIMIZATION MODELS FOR WATER AND ENERGY MANAGEMENTBy Daene C. McKinney And Andre G. SavitskyRevision 8 January 2006iContentsACKNOWLEDGEMENTS .. VI ABOUT THE AUTHORS .VII PART 1: RESOURCE MANAGEMENT MODELS..1 1. INTRODUCTION .1 1.1 1.2
University of Texas - CE - 385
Precipitation-runoff models Stochastic streamflow models Extending and filling in historic records Syr DaryaNaryn River Function (X) whose value (x) depends on the outcome of a cha
University of Texas - CE - 385
July 9, 200722:3spi-b465 Bridges Over Water9.75in x 6.5inch10FA110. THE USE OF RIVER BASIN MODELING AS A TOOL TO ASSESS CONFLICT AND POTENTIAL COOPERATIONObjectivesAfter reading this chapter, you should have a general understanding of
University of Texas - CE - 385
Homework #7CE 385D - McKinney1. (a) Compute the storage yield function for a single reservoir system by the modified sequentpeak methods given the following sequences of annual flows: (7, 3, 5, 1, 2, 5, 6, 3, 4). (b) Assume that each year has tw
University of Texas - CE - 385
Homework #8 (2007)CE 385D - McKinneyProblem 1. Assume that there are two sites along a stream, i = 1, 2, at which waste (BOD) is discharged. Currently, without any wastewater treatment, the quality (DO), q2 and q3, at each of sites 2 and 3 is les
University of Texas - CE - 385
Homework #9 (2008)CE 385D - McKinneyProblem 1 (Loucks and van Beek 9.2). Consider the allocation model you have been using in previous chapters involving three water users i. Allocations xi of water can be made from a given total amount Q to the
University of Texas - CE - 385
Homework #11 (2008)CE 385D - McKinneyProblem 1. This is an extension of the example presented in class to compute Expected Annual Flood Damage (EAD). In that example, the calculations produced the EAD for the withoutproject conditions. In this pr
University of Texas - CS - 07
Journal of Machine Learning Research 8 (2007) 2125-2167Submitted 11/06; Revised 4/07; Published 9/07Transfer Learning via Inter-Task Mappings for Temporal Difference LearningMatthew E. Taylor Peter Stone Yaxin LiuDepartment of Computer Sciences
University of Texas - CS - 07
To appear in Adaptive Behavior, 15(1), 2007.Empirical Studies in Action Selection with Reinforcement LearningShimon Whiteson Department of Computer Sciences The University of Texas at Austin 1 University Station C0500 Austin, TX 78712-1188 shimon@
University of Texas - CS - 08
In The Autonomous Agents and Multi-Agent Systems Conference (AAMAS-08), Estoril, Portugal, May 2008.Autonomous Transfer for Reinforcement LearningMatthew E. Taylor, Gregory Kuhlmann, and Peter Stone Department of Computer Sciences The University o
University of Texas - CS - 08
In The First Conference on Artificial General Intelligence (AGI-08), Memphis, Tennessee, March 2008.Transfer Learning and Intelligence: an Argument and ApproachMatthew E. TAYLOR, Gregory KUHLMANN, and Peter STONE Department of Computer Sciences Th
University of Texas - CS - 07
Cross-Domain Transfer for Reinforcement LearningMatthew E. Taylor Peter Stone Department of Computer Sciences, The University of Texas at AustinMTAYLOR@CS.UTEXAS.EDU PSTONE@CS.UTEXAS.EDUAbstractA typical goal for transfer learning algorithms is
University of Texas - CS - 07
Cross-Domain Transfer for Reinforcement LearningMatthew E. Taylor Peter Stone Department of Computer Sciences, The University of Texas at AustinMTAYLOR@CS.UTEXAS.EDU PSTONE@CS.UTEXAS.EDUAbstractA typical goal for transfer learning algorithms is
University of Texas - CS - 07
In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), Vancouver, Canada, July 2007.Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical ComparisonMatthew E. Taylor, Shimon Whiteson,
University of Texas - CS - 07
In The Autonomous Agents and Multi-Agent Systems Conference (AAMAS-07), Honolulu, Hawaii, May 2007.Transfer via Inter-Task Mappings in Policy Search Reinforcement LearningMatthew E. Taylor, Shimon Whiteson, and Peter Stone Department of Computer S
University of Texas - CS - 07
In The Autonomous Agents and Multi-Agent Systems Conference (AAMAS-07), Honolulu, Hawaii, May 2007.Towards Reinforcement Learning Representation TransferMatthew E. Taylor and Peter Stone Department of Computer Sciences The University of Texas at A
University of Texas - CS - 07
In The 20th International FLAIRS Conference (FLAIRS-07), Key West, Forida, May 2007.Guiding Inference with Policy Search Reinforcement LearningMatthew E. TaylorDepartment of Computer Sciences The University of Texas at Austin Austin, TX 78712-118
University of Texas - CS - 07
In The 20th International FLAIRS Conference (FLAIRS), Key West, Florida, May 2007.Autonomous Classification of Knowledge into an OntologyMatthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrockmtaylor@cs.utexas.edu Department of Com
University of Texas - CS - 06
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 1321-1328, Seattle, WA, July 2006Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning DomainMatthew E. Taylor mtaylor@cs.utexas.e
University of Texas - CS - 06
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 1321-1328, Seattle, WA, July 2006Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning DomainMatthew E. Taylor mtaylor@cs.utexas.e
University of Texas - CS - 05
In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pp. 880-885, Pittsburgh, PA, July 2005.Value Functions for RL-Based Behavior Transfer: A Comparative StudyMatthew E. Taylor, Peter Stone, and Yaxin LiuDepar
University of Texas - CS - 05
In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-05), pp. 53-59, Utrecht, The Netherlands, July 2005.Behavior Transfer for Value-Function-Based Reinforcement LearningMatthew E. Taylor and Peter Stone
University of Texas - CS - 05
In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-05), pp. 53-59, Utrecht, The Netherlands, July 2005.Behavior Transfer for Value-Function-Based Reinforcement LearningMatthew E. Taylor and Peter Stone
University of Texas - CS - 08
Transferring Instances for Model-Based Reinforcement LearningMatthew E. Taylor, Nicholas K. Jong, and Peter Stone Department of Computer Sciences The University of Texas at Austin Austin, Texas 78712-1188 {mtaylor, nkj, pstone}@cs.utexas.edu ABSTRAC
University of Texas - CS - 08
Transferring Instances for Model-Based Reinforcement LearningMatthew E. Taylor, Nicholas K. Jong, and Peter Stone Department of Computer Sciences The University of Texas at Austin Austin, Texas 78712-1188 {mtaylor, nkj, pstone}@cs.utexas.edu ABSTRAC
University of Texas - CS - 07
In Machine Learning for Systems Problems (NIPS-07 Workshop), Whistler, British Columbia, Canada, December 2007.Policy Search Optimization for Spatial Path PlanningMatthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Doug Burger, and Kathryn S
University of Texas - CS - 07
In ICAPS-07 Workshop on AI Planning and Learning (AIPL-07), Providence, RI, September 2007.Accelerating Search with Transferred HeuristicsMatthew E. Taylor, Gregory Kuhlmann, and Peter StoneDepartment of Computer Sciences The University of Texas
University of Texas - CS - 07
In ICAPS-07 Workshop on AI Planning and Learning (AIPL-07), Providence, RI, September 2007.Accelerating Search with Transferred HeuristicsMatthew E. Taylor, Gregory Kuhlmann, and Peter StoneDepartment of Computer Sciences The University of Texas
University of Texas - CS - 07
In AAAI 2007 Fall Symposium on Computational Approaches to Representation Change during Learning and Development, Arlington, Virginia, November 2007.Representation Transfer for Reinforcement LearningMatthew E. Taylor and Peter StoneDepartment of