Contents. Introduction: what is CDFT and why use it? Theoretical basis of CDFT in brief. CDFT implementation in CP2K. Summary

Size: px
Start display at page:

Download "Contents. Introduction: what is CDFT and why use it? Theoretical basis of CDFT in brief. CDFT implementation in CP2K. Summary"

Transcription

1 Cntents Intrductin: what is CDFT and why use it? Theretical basis f CDFT in brief CDFT implementatin in CP2K Algrithmic framewrk Overview f features using examples Summary

2 Intrductin CDFT allws creatin f charge and spin lcalized states Why are such states needed? Charge transfer phenmena Electrnic cuplings (key rle in charge transfer kinetics) Treating self-interactin errr due t spurius electrn delcalizatin Parametrizing mdel Hamiltnians (e.g. Heisenberg spin Hamiltnian) And mre [1] CDFT in CP2K [2] requires versin 5.1 r newer 1. Kaduk, B.; Kwalczyk, T.; van Vrhis, T., Chem. Rev., 2012, 112, Hlmberg, N.; Laasnen, K., J. Chem. Thery Cmput., 2017, 13,

3 Generatin f cnstrained states Enfrce density lcalizatin in atm-centered regins with cnstraint ptential(s) [3,4] EE CDFT λλ, ρρ = max λλ min ρρ EE KS ρρ + cc λλ cc ii=, Weight functin defines the type f cnstraint Lagrange multiplier (ptential strength) Ttal density cnstraint (ρρ + ρρ ): ww = ww = ww ww cc ii rr ρρ ii rr ddrr NN cc Magnetizatin density cnstraint (ρρ ρρ ): ww = ww = ww Spin specific cnstraint (ρρ ): ww = ww, ww = 0 Weight functin Target value 3. Wu, Q.; van Vrhis, T., Phys. Rev. A: At., Ml., Opt. Phys., 2005, 72, Wu, Q.; van Vrhis, T., J. Chem. Thery Cmput., 2006, 2,

4 CDFT weight functin Cnstructed as sum f nrmalized atmic weight functins ww cc ii rr = cc ii PP ii (rr) PP ii (rr) ii CC CP2K uses Becke partitining Smthed Vrni-like scheme Atmic sizes can be taken int accunt (recmmended) E.g. xygen has psitive charge in water withut adjustment NN ii 5

5 Optimizatin f the CDFT energy (1/2) Cnstraints are satisfied when cc λλ = ww ii 1 rr ρρ ii rr ddrr NN 1 ii=, = 00 In practice, λλ iteratively ptimized until mmaaaa cc λλ εε Uses rt-finding algrithms, e.g., Newtn s methd λλ nn+1 = λλ nn ααjj nn 1 cc λλ nn Step size [ 1, 0) JJ iiii = cc ii cc ii λλ + δδ ii λλ ii Jacbian matrix, apprximated by finite differences δδ ii cc ii (λλ) 6

6 Optimizatin f the CDFT energy (2/2) Input Cnstraints cnverged r max steps reached? CDFT lp Yes N Build Jacbian (ptinal) N/Dne New guess fr λλ Optimize step size? Yes Reduce step size Standard CP2K SCF Stre Yes data fr mixed CDFT Output 7

7 Defining cnstraints (1/2) &QS... &CDFT TYPE_OF_CONSTRAINT BECKE &OUTER_SCF ON TYPE BECKE_CONSTRAINT EXTRAPOLATION_ORDER 2 MAX_SCF 10! Cnvergence threshld EPS_SCF 1.0E-3! Optimizer selectin: nw Newtn's methd with backtracking line search OPTIMIZER NEWTON_LS! Optimizer step size STEP_SIZE -1.0! Line search settings MAX_LS 5 CONTINUE_LS FACTOR_LS 0.5! Finite difference settings fr calculatin f Jacbian matrix JACOBIAN_STEP 1.0E-2 JACOBIAN_FREQ 1 1 JACOBIAN_TYPE FD1 JACOBIAN_RESTART FALSE &END OUTER_SCF &END CDFT Full example files at 7

8 Defining cnstraints (2/2) &QS! Cnstraint definitins...! Each repetitin defines a cnstraint &CDFT &ATOM_GROUP... ATOMS 1 &END CDFT COEFF 1 &BECKE_CONSTRAINT CONSTRAINT_TYPE CHARGE! Take atmic radii int accunt? &END ATOM_GROUP ADJUST_SIZE FALSE Use e.g. cvalent radii! N cnstraint but calculate charges ATOMIC_RADII &DUMMY_ATOMS! Cmpute Becke charges? ATOMS 2 ATOMIC_CHARGES TRUE &END DUMMY_ATOMS! Cnstraint strength and target values! Print inf abut CDFT calculatin! Give ne value per cnstraint &PROGRAM_RUN_INFO ON STRENGTH ${BECKE_STR} &EACH TARGET ${BECKE_TARGET} QS_SCF 1! Cutff scheme &END EACH CUTOFF_TYPE ELEMENT COMMON_ITERATION_LEVELS 2 ELEMENT_CUTOFF 7.0 ADD_LAST NUMERIC! Perfrm Becke partitining nly within the space FILENAME./${NAME}! spanned by cnstraint atm centered spherical Gaussians &END PROGRAM_RUN_INFO! (reduces cst fr slvated systems) &END BECKE_CONSTRAINT CAVITY_CONFINE TRUE &END QS CAVITY_SHAPE VDW EPS_CAVITY 1.0E-7 IN_MEMORY TRUE SHOULD_SKIP TRUE

9 Example: ZZnn 22 + (1/2) When RR ZZnn ZZnn grws, charge shuld lcalize nt ne Zn atm Standard GGA/hybrid functinals place +0.5 charge n bth atms Frce charge lcalizatin n first atm! Set initial cnstraint strength t 0 (restarting frm DFT) STRENGTH 0.0! Cnstraint target is the number f valence electrns 1 TARGET 11.0 &ATOM_GROUP ATOMS 1 COEFF 1 CONSTRAINT_TYPE CHARGE &END ATOM_GROUP 9

10 Example: ZZnn 22 + (2/2) The default utput file cntains the CDFT SCF iteratins Each iteratin crrespnds t standard CP2K energy ptimizatin Uses ptimized slutin frm line search as restart if available The fllwing files are created with (quasi-)newtn ptimizers *.LineSearch.ut: Electrnic structure SCF and ptimizatin f step size *.cdftlg: Summary f CDFT parameters and cmputed partial charges *.JacbianInf.ut: Calculatin f Jacbian matrix with perturbed λλ *.inversejacbian: Restart file fr inverse Jacbian matrix 10

11 Standard CP2K SCF with fixed values f cnstraint strength and step size Restarted frm cnverged density btained during line search CDFT SCF iteratin infrmatin Cnstraint infrmatin

12 Fragment cnstraints (1/2) Number f valence electrns per mlecule nt necessarily well defined Case fr verlapping, strngly interacting mlecules Hw t set cnstraint target value? ρρ A (rr) ρρ B (rr) Use a fragment based cnstraint Only single-pint calculatins Cube read/write accelerated with MPI I/O since r18131 NN cc = ii=, ww ii rr ρρ A ii rr + ρρ B ii rr ddrr

13 Fragment cnstraints (2/2) Charge transfer energies f strngly interacting cmplexes ΔEE CT = EE CDFT EE DFT Energy f system with charge transfer prevented BW: BW+A: FBB+A: Becke Becke with atmic size adjustments Fragment Becke with atmic size adjustments Magnitude f charge transferred, Δqq, verestimated by nnfragment cnstraints 13

14 Cmbining multiple CDFT states Additinal prperties can be cmputed frm the interactins between CDFT states Charge transfer kinetics (Marcus thery) kk ab = 2ππ ħ HH ab 2 TT 4ππkkππξξ exp ξξ + ΔAA 2 4ππkkππξξ Cnfiguratin interactin within the basis f CDFT states Electrnic cupling Slvent rerganizatin energy Reactin free energy Apprximate electrnic cupling with CDFT Khn-Sham determinants after rthgnalizatin [5] i j HH ij ΦΦ CDFT HH KS ΦΦ CDFT = EE i j CDFT + EE CDFT i λλ i SS 2 iiii ΦΦ cc ww i j j cc (rr) + λλ ccwwcc(rr) CDFT 2 cc j ΦΦ CDFT 5. Wu, Q.; van Vrhis, T., J. Chem. Phys., 2006, 125,

15 The mixed CDFT mdule &MULTIPLE_FORCE_EVALS # Zn+ Zn FORCE_EVAL_ORDER 2 3 &FORCE_EVAL MULTIPLE_SUBSYS WFN_FILE ${WFN_FILE_1} RESTART ${RESTART_1} NAME ${PROJECT_NAME}-state1 METHOD BECKE_TARGET ${BECKE_TARGET_1} BECKE_STR ${BECKE_STR_1} MIXING_TYPE MIXED_CDFT METHOD QS NGROUPS ${DFT_FILE} &MIXED_CDFT &END FORCE_EVAL! Calculate mixed CDFT prperties every COUPLING step # Zn Zn+ COUPLING 1 &FORCE_EVAL! Settings determining hw frces are WFN_FILE ${WFN_FILE_2} FORCE_STATES 1 RESTART ${RESTART_2} LAMBDA NAME ${PROJECT_NAME}-state2! Orthgnalize CDFT states with Lwdin's BECKE_TARGET ${BECKE_TARGET_2} LOWDIN BECKE_STR ${BECKE_STR_2}! Cnfiguratin interactin? METHOD QS CI ${DFT_FILE} &PRINT &END FORCE_EVAL &PROGRAM_RUN_INFO ON &END &END PRINT &END MIXED_CDFT &END subsys.inc &END FORCE_EVAL Additinal settings available and explained in the manual 15

16 Electrnic cupling in ZZnn 22 + Zn + Zn HH ZnZn + Different rthgnalizatin algrithms 16

17 Electrnic cupling in ZZnn 22 + Zn + Zn HH ZnZn + Agrees with 5.49 mhartree estimate frm mre expensive wavefunctin based methd CASSCF/MRCI+Q Different rthgnalizatin algrithms 17

18 CDFT in slvated systems Cmputatinal efficiency f GPW/OT allws study f slvated charge transfer prcesses at full DFT level Evaluating intramlecular charge transfer kinetics in QTTFQ 258 water, 12 ps (0.5 fs timestep) MPI cres (~120k cre hurs)

19 List f CDFT capabilities in CP2K GPW and GAPW (n fragment cnstraint) Full DFT r QM/MM Primarily fr OT, diagnalizatin is supprted but difficult t cnverge Energies and frces fr an unlimited number f cnstraints (any type) Mixed CDFT mdule supprts Electrnic cuplings with varius rthgnalizatin methds Cnfiguratin interactin Remval f linearly-dependent MOs via SVD decmpsitin Electrnic cupling reliability metrics

20 Summary CDFT is a tl t study charge transfer phenmena Available in latest release versin Tutrial at that cmplements regtests Summaries f CDFT thery and the CP2K implementatin Walk thrughs f example calculatins Help prvided n Ggle grups if yu encunter issues with CDFT features

21 Questins?

Chapter 3: Cluster Analysis

Chapter 3: Cluster Analysis Chapter 3: Cluster Analysis } 3.1 Basic Cncepts f Clustering 3.1.1 Cluster Analysis 3.1. Clustering Categries } 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA

More information

GENESIS Structural Optimization for ANSYS Mechanical

GENESIS Structural Optimization for ANSYS Mechanical P3 STRUCTURAL OPTIMIZATION (Vl. II) GENESIS Structural Optimizatin fr ANSYS Mechanical An Integrated Extensin that adds Structural Optimizatin t ANSYS Envirnment New Features and Enhancements Release 2017.03

More information

1 The limitations of Hartree Fock approximation

1 The limitations of Hartree Fock approximation Chapter: Pst-Hartree Fck Methds - I The limitatins f Hartree Fck apprximatin The n electrn single determinant Hartree Fck wave functin is the variatinal best amng all pssible n electrn single determinants

More information

Checking the resolved resonance region in EXFOR database

Checking the resolved resonance region in EXFOR database Checking the reslved resnance regin in EXFOR database Gttfried Bertn Sciété de Calcul Mathématique (SCM) Oscar Cabells OECD/NEA Data Bank JEFF Meetings - Sessin JEFF Experiments Nvember 0-4, 017 Bulgne-Billancurt,

More information

Computational modeling techniques

Computational modeling techniques Cmputatinal mdeling techniques Lecture 4: Mdel checing fr ODE mdels In Petre Department f IT, Åb Aademi http://www.users.ab.fi/ipetre/cmpmd/ Cntent Stichimetric matrix Calculating the mass cnservatin relatins

More information

Support-Vector Machines

Support-Vector Machines Supprt-Vectr Machines Intrductin Supprt vectr machine is a linear machine with sme very nice prperties. Haykin chapter 6. See Alpaydin chapter 13 fr similar cntent. Nte: Part f this lecture drew material

More information

COMP 551 Applied Machine Learning Lecture 11: Support Vector Machines

COMP 551 Applied Machine Learning Lecture 11: Support Vector Machines COMP 551 Applied Machine Learning Lecture 11: Supprt Vectr Machines Instructr: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted fr this curse

More information

Pattern Recognition 2014 Support Vector Machines

Pattern Recognition 2014 Support Vector Machines Pattern Recgnitin 2014 Supprt Vectr Machines Ad Feelders Universiteit Utrecht Ad Feelders ( Universiteit Utrecht ) Pattern Recgnitin 1 / 55 Overview 1 Separable Case 2 Kernel Functins 3 Allwing Errrs (Sft

More information

[ ] [ ] [ ] [ ] [ ] [ J] dt x x hard to solve in general solve it numerically. If there is no convection. is in the absence of reaction n

[ ] [ ] [ ] [ ] [ ] [ J] dt x x hard to solve in general solve it numerically. If there is no convection. is in the absence of reaction n .3 The material balance equatin Net change f [J] due t diffusin, cnvectin, and reactin [ ] [ ] [ ] d J J J n = D v k [ J ] fr n - th reactin dt x x hard t slve in general slve it numerically If there is

More information

Lecture 18 Title : Fine Structure : multi-electron atoms

Lecture 18 Title : Fine Structure : multi-electron atoms Lecture 8 Title : Fine Structure : multi-electrn atms Page-0 In this lecture we will cncentrate n the fine structure f the multielectrn atms. As discussed in the previus lecture that the fine structure

More information

A Quick Overview of the. Framework for K 12 Science Education

A Quick Overview of the. Framework for K 12 Science Education A Quick Overview f the NGSS EQuIP MODULE 1 Framewrk fr K 12 Science Educatin Mdule 1: A Quick Overview f the Framewrk fr K 12 Science Educatin This mdule prvides a brief backgrund n the Framewrk fr K-12

More information

Supporting information

Supporting information Electrnic Supplementary Material (ESI) fr Physical Chemistry Chemical Physics This jurnal is The wner Scieties 01 ydrgen perxide electrchemistry n platinum: twards understanding the xygen reductin reactin

More information

A Mechanistic Approach to Bond Formation in H 2

A Mechanistic Approach to Bond Formation in H 2 A Mechanistic Apprach t Bnd Frmatin in H Frank Riux Department f Chemistry Cllege f Saint Benedict Saint Jhnʹs University St. Jseph, MN 5674 Intrductin Ruedenbergʹs innvative analysis f the cvalent bnd

More information

ALE 21. Gibbs Free Energy. At what temperature does the spontaneity of a reaction change?

ALE 21. Gibbs Free Energy. At what temperature does the spontaneity of a reaction change? Name Chem 163 Sectin: Team Number: ALE 21. Gibbs Free Energy (Reference: 20.3 Silberberg 5 th editin) At what temperature des the spntaneity f a reactin change? The Mdel: The Definitin f Free Energy S

More information

NUMBERS, MATHEMATICS AND EQUATIONS

NUMBERS, MATHEMATICS AND EQUATIONS AUSTRALIAN CURRICULUM PHYSICS GETTING STARTED WITH PHYSICS NUMBERS, MATHEMATICS AND EQUATIONS An integral part t the understanding f ur physical wrld is the use f mathematical mdels which can be used t

More information

EDA Engineering Design & Analysis Ltd

EDA Engineering Design & Analysis Ltd EDA Engineering Design & Analysis Ltd THE FINITE ELEMENT METHOD A shrt tutrial giving an verview f the histry, thery and applicatin f the finite element methd. Intrductin Value f FEM Applicatins Elements

More information

LCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical).

LCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical). Principles f Organic Chemistry lecture 5, page LCAO APPROIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (catin, anin r radical).. Draw mlecule and set up determinant. 2 3 0 3 C C 2 = 0 C 2 3 0 = -

More information

Physical Nature of the Covalent Bond Appendix H + H > H 2 ( ) ( )

Physical Nature of the Covalent Bond Appendix H + H > H 2 ( ) ( ) Physical Nature f the Cvalent Bn Appeni his stuy f the nature f the H cvalent bn frms a mlecular rbital as a linear cmbinatin f scale hyrgenic rbitals, LCAO-MO. he quantum mechanical integrals necessary

More information

0νββ decay NMEs in large shell model space with the generator-coordinate method

0νββ decay NMEs in large shell model space with the generator-coordinate method νββ decay NMEs in large shell mdel space with the generatr-crdinate methd Changfeng Jia Department f Physics Central Michigan University June st @ INT 7-a Generatr Crdinate Methd (). Intrductin... Crrelatins

More information

Fall 2013 Physics 172 Recitation 3 Momentum and Springs

Fall 2013 Physics 172 Recitation 3 Momentum and Springs Fall 03 Physics 7 Recitatin 3 Mmentum and Springs Purpse: The purpse f this recitatin is t give yu experience wrking with mmentum and the mmentum update frmula. Readings: Chapter.3-.5 Learning Objectives:.3.

More information

Tutorial 4: Parameter optimization

Tutorial 4: Parameter optimization SRM Curse 2013 Tutrial 4 Parameters Tutrial 4: Parameter ptimizatin The aim f this tutrial is t prvide yu with a feeling f hw a few f the parameters that can be set n a QQQ instrument affect SRM results.

More information

DESIGN OPTIMIZATION OF HIGH-LIFT CONFIGURATIONS USING A VISCOUS ADJOINT-BASED METHOD

DESIGN OPTIMIZATION OF HIGH-LIFT CONFIGURATIONS USING A VISCOUS ADJOINT-BASED METHOD DESIGN OPTIMIZATION OF HIGH-LIFT CONFIGURATIONS USING A VISCOUS ADJOINT-BASED METHOD Sangh Kim Stanfrd University Juan J. Alns Stanfrd University Antny Jamesn Stanfrd University 40th AIAA Aerspace Sciences

More information

Slide04 (supplemental) Haykin Chapter 4 (both 2nd and 3rd ed): Multi-Layer Perceptrons

Slide04 (supplemental) Haykin Chapter 4 (both 2nd and 3rd ed): Multi-Layer Perceptrons Slide04 supplemental) Haykin Chapter 4 bth 2nd and 3rd ed): Multi-Layer Perceptrns CPSC 636-600 Instructr: Ynsuck Che Heuristic fr Making Backprp Perfrm Better 1. Sequential vs. batch update: fr large

More information

University of Maryland Department of Physics, College Park, MD Physics 441,Topics in Particle and Nuclear Physics, Fall 2017

University of Maryland Department of Physics, College Park, MD Physics 441,Topics in Particle and Nuclear Physics, Fall 2017 University f Maryland Department f Physics, Cllege Park, MD 20742 Physics 441,Tpics in Particle and Nuclear Physics, Fall 2017 Instructr: Prf. Thmas Chen (I prefer t be addressed as Tm) Office: 3158 Physical

More information

UNIT 5: ATOMIC THEORY & THE PERIODIC TABLE CHEMISTRY 215, DUFFEY, CHAPTER 4 & SECTION 6.1

UNIT 5: ATOMIC THEORY & THE PERIODIC TABLE CHEMISTRY 215, DUFFEY, CHAPTER 4 & SECTION 6.1 UNIT 5: ATOMIC THEORY & THE PERIODIC TABLE CHEMISTRY 215, DUFFEY, CHAPTER 4 & SECTION 6.1 BIG IDEAS (we will tuch n small parts f Chp.5 as well) 4.1 Early Ideas Abut Matter 4.2 Defining the Atm 4.3 Hw

More information

5.60 Thermodynamics & Kinetics Spring 2008

5.60 Thermodynamics & Kinetics Spring 2008 MIT OpenCurseWare http://cw.mit.edu 5.60 Thermdynamics & Kinetics Spring 2008 Fr infrmatin abut citing these materials r ur Terms f Use, visit: http://cw.mit.edu/terms. 5.60 Spring 2008 Lecture #17 page

More information

DEFENSE OCCUPATIONAL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE

DEFENSE OCCUPATIONAL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE DEFENSE OCCUPATIOL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE 20 JUNE 2017 V1.0 i TABLE OF CONTENTS 1 INTRODUCTION... 1 2 CONCEPT

More information

ATOMIC ORBITAL MODEL OF THE ATOM Be able to draw rough sketches of s, p and d orbitals with different principal quantum numbers

ATOMIC ORBITAL MODEL OF THE ATOM Be able to draw rough sketches of s, p and d orbitals with different principal quantum numbers Chapter 7 Atmic Structure and Peridicity ATOMIC ORBITAL MODEL OF THE ATOM Be able t draw rugh sketches f s, p and d rbitals with different principal quantum numbers ELECTRONIC CONFIGURATIONS Knw the difference

More information

k-nearest Neighbor How to choose k Average of k points more reliable when: Large k: noise in attributes +o o noise in class labels

k-nearest Neighbor How to choose k Average of k points more reliable when: Large k: noise in attributes +o o noise in class labels Mtivating Example Memry-Based Learning Instance-Based Learning K-earest eighbr Inductive Assumptin Similar inputs map t similar utputs If nt true => learning is impssible If true => learning reduces t

More information

ENSC Discrete Time Systems. Project Outline. Semester

ENSC Discrete Time Systems. Project Outline. Semester ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding

More information

Phys102 Second Major-102 Zero Version Coordinator: Al-Shukri Thursday, May 05, 2011 Page: 1

Phys102 Second Major-102 Zero Version Coordinator: Al-Shukri Thursday, May 05, 2011 Page: 1 Crdinatr: Al-Shukri Thursday, May 05, 2011 Page: 1 1. Particles A and B are electrically neutral and are separated by 5.0 μm. If 5.0 x 10 6 electrns are transferred frm particle A t particle B, the magnitude

More information

Theoretical study of third virial coefficient with Kihara potential

Theoretical study of third virial coefficient with Kihara potential Theretical study f third virial cefficient with Kihara ptential Jurnal: Manuscript ID cjp-017-0705.r Manuscript Type: Article Date Submitted by the Authr: 6-Dec-017 Cmplete List f Authrs: Smuncu E.; Giresun

More information

Lecture 24: Flory-Huggins Theory

Lecture 24: Flory-Huggins Theory Lecture 24: 12.07.05 Flry-Huggins Thery Tday: LAST TIME...2 Lattice Mdels f Slutins...2 ENTROPY OF MIXING IN THE FLORY-HUGGINS MODEL...3 CONFIGURATIONS OF A SINGLE CHAIN...3 COUNTING CONFIGURATIONS FOR

More information

Part 3 Introduction to statistical classification techniques

Part 3 Introduction to statistical classification techniques Part 3 Intrductin t statistical classificatin techniques Machine Learning, Part 3, March 07 Fabi Rli Preamble ØIn Part we have seen that if we knw: Psterir prbabilities P(ω i / ) Or the equivalent terms

More information

TRAINING GUIDE. Overview of Lucity Spatial

TRAINING GUIDE. Overview of Lucity Spatial TRAINING GUIDE Overview f Lucity Spatial Overview f Lucity Spatial In this sessin, we ll cver the key cmpnents f Lucity Spatial. Table f Cntents Lucity Spatial... 2 Requirements... 2 Supprted Mdules...

More information

Lecture 23: Lattice Models of Materials; Modeling Polymer Solutions

Lecture 23: Lattice Models of Materials; Modeling Polymer Solutions Lecture 23: 12.05.05 Lattice Mdels f Materials; Mdeling Plymer Slutins Tday: LAST TIME...2 The Bltzmann Factr and Partitin Functin: systems at cnstant temperature...2 A better mdel: The Debye slid...3

More information

Biocomputers. [edit]scientific Background

Biocomputers. [edit]scientific Background Bicmputers Frm Wikipedia, the free encyclpedia Bicmputers use systems f bilgically derived mlecules, such as DNA and prteins, t perfrm cmputatinal calculatins invlving string, retrieving, and prcessing

More information

COMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d)

COMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d) COMP 551 Applied Machine Learning Lecture 9: Supprt Vectr Machines (cnt d) Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Class web page: www.cs.mcgill.ca/~hvanh2/cmp551 Unless therwise

More information

MATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank

MATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank MATCHING TECHNIQUES Technical Track Sessin VI Emanuela Galass The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Emanuela Galass fr the purpse f this wrkshp When can we use

More information

Eric Klein and Ning Sa

Eric Klein and Ning Sa Week 12. Statistical Appraches t Netwrks: p1 and p* Wasserman and Faust Chapter 15: Statistical Analysis f Single Relatinal Netwrks There are fur tasks in psitinal analysis: 1) Define Equivalence 2) Measure

More information

IAML: Support Vector Machines

IAML: Support Vector Machines 1 / 22 IAML: Supprt Vectr Machines Charles Suttn and Victr Lavrenk Schl f Infrmatics Semester 1 2 / 22 Outline Separating hyperplane with maimum margin Nn-separable training data Epanding the input int

More information

More Tutorial at

More Tutorial at Answer each questin in the space prvided; use back f page if extra space is needed. Answer questins s the grader can READILY understand yur wrk; nly wrk n the exam sheet will be cnsidered. Write answers,

More information

Determining the Accuracy of Modal Parameter Estimation Methods

Determining the Accuracy of Modal Parameter Estimation Methods Determining the Accuracy f Mdal Parameter Estimatin Methds by Michael Lee Ph.D., P.E. & Mar Richardsn Ph.D. Structural Measurement Systems Milpitas, CA Abstract The mst cmmn type f mdal testing system

More information

Feasibility Testing Report Muscle Optimization Stage I

Feasibility Testing Report Muscle Optimization Stage I Feasibility Testing Reprt Muscle Optimizatin Stage I Team: P15001: Active Ankle Ft Orthtic Engineer: Nah Schadt Mechanical Engineer Engineer: Tyler Leichtenberger Mechanical Engineer Test Date: 10/14/2014

More information

Find this material useful? You can help our team to keep this site up and bring you even more content consider donating via the link on our site.

Find this material useful? You can help our team to keep this site up and bring you even more content consider donating via the link on our site. Find this material useful? Yu can help ur team t keep this site up and bring yu even mre cntent cnsider dnating via the link n ur site. Still having truble understanding the material? Check ut ur Tutring

More information

NGSS High School Physics Domain Model

NGSS High School Physics Domain Model NGSS High Schl Physics Dmain Mdel Mtin and Stability: Frces and Interactins HS-PS2-1: Students will be able t analyze data t supprt the claim that Newtn s secnd law f mtin describes the mathematical relatinship

More information

MATCHING TECHNIQUES Technical Track Session VI Céline Ferré The World Bank

MATCHING TECHNIQUES Technical Track Session VI Céline Ferré The World Bank MATCHING TECHNIQUES Technical Track Sessin VI Céline Ferré The Wrld Bank When can we use matching? What if the assignment t the treatment is nt dne randmly r based n an eligibility index, but n the basis

More information

the results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must

the results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must M.E. Aggune, M.J. Dambrg, M.A. El-Sharkawi, R.J. Marks II and L.E. Atlas, "Dynamic and static security assessment f pwer systems using artificial neural netwrks", Prceedings f the NSF Wrkshp n Applicatins

More information

A Matrix Representation of Panel Data

A Matrix Representation of Panel Data web Extensin 6 Appendix 6.A A Matrix Representatin f Panel Data Panel data mdels cme in tw brad varieties, distinct intercept DGPs and errr cmpnent DGPs. his appendix presents matrix algebra representatins

More information

Floating Point Method for Solving Transportation. Problems with Additional Constraints

Floating Point Method for Solving Transportation. Problems with Additional Constraints Internatinal Mathematical Frum, Vl. 6, 20, n. 40, 983-992 Flating Pint Methd fr Slving Transprtatin Prblems with Additinal Cnstraints P. Pandian and D. Anuradha Department f Mathematics, Schl f Advanced

More information

Intelligent Pharma- Chemical and Oil & Gas Division Page 1 of 7. Global Business Centre Ave SE, Calgary, AB T2G 0K6, AB.

Intelligent Pharma- Chemical and Oil & Gas Division Page 1 of 7. Global Business Centre Ave SE, Calgary, AB T2G 0K6, AB. Intelligent Pharma- Chemical and Oil & Gas Divisin Page 1 f 7 Intelligent Pharma Chemical and Oil & Gas Divisin Glbal Business Centre. 120 8 Ave SE, Calgary, AB T2G 0K6, AB. Canada Dr. Edelsys Cdrniu-Business

More information

Bootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >

Bootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) > Btstrap Methd > # Purpse: understand hw btstrap methd wrks > bs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(bs) > mean(bs) [1] 21.64625 > # estimate f lambda > lambda = 1/mean(bs);

More information

Public Key Cryptography. Tim van der Horst & Kent Seamons

Public Key Cryptography. Tim van der Horst & Kent Seamons Public Key Cryptgraphy Tim van der Hrst & Kent Seamns Last Updated: Oct 5, 2017 Asymmetric Encryptin Why Public Key Crypt is Cl Has a linear slutin t the key distributin prblem Symmetric crypt has an expnential

More information

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007 CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is

More information

( ) kt. Solution. From kinetic theory (visualized in Figure 1Q9-1), 1 2 rms = 2. = 1368 m/s

( ) kt. Solution. From kinetic theory (visualized in Figure 1Q9-1), 1 2 rms = 2. = 1368 m/s .9 Kinetic Mlecular Thery Calculate the effective (rms) speeds f the He and Ne atms in the He-Ne gas laser tube at rm temperature (300 K). Slutin T find the rt mean square velcity (v rms ) f He atms at

More information

Turing Machines. Human-aware Robotics. 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Announcement:

Turing Machines. Human-aware Robotics. 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Announcement: Turing Machines Human-aware Rbtics 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Annuncement: q q q q Slides fr this lecture are here: http://www.public.asu.edu/~yzhan442/teaching/cse355/lectures/tm-ii.pdf

More information

Section 6-2: Simplex Method: Maximization with Problem Constraints of the Form ~

Section 6-2: Simplex Method: Maximization with Problem Constraints of the Form ~ Sectin 6-2: Simplex Methd: Maximizatin with Prblem Cnstraints f the Frm ~ Nte: This methd was develped by Gerge B. Dantzig in 1947 while n assignment t the U.S. Department f the Air Frce. Definitin: Standard

More information

Lower Limb Stiffness Estimation during Running: The Effect of Using Kinematic Constraints in Muscle Force Optimization Algorithms

Lower Limb Stiffness Estimation during Running: The Effect of Using Kinematic Constraints in Muscle Force Optimization Algorithms Lwer Limb Stiffness Estimatin during Running: The Effect f Using Kinematic Cnstraints in Muscle Frce Optimizatin Algrithms Rbert Brtlett 1, Enric Pagell 1, and Davide Pivesan 2 1 Intelligent Autnmus Systems

More information

Thermodynamics and Equilibrium

Thermodynamics and Equilibrium Thermdynamics and Equilibrium Thermdynamics Thermdynamics is the study f the relatinship between heat and ther frms f energy in a chemical r physical prcess. We intrduced the thermdynamic prperty f enthalpy,

More information

Homology groups of disks with holes

Homology groups of disks with holes Hmlgy grups f disks with hles THEOREM. Let p 1,, p k } be a sequence f distinct pints in the interir unit disk D n where n 2, and suppse that fr all j the sets E j Int D n are clsed, pairwise disjint subdisks.

More information

Lecture 13: Electrochemical Equilibria

Lecture 13: Electrochemical Equilibria 3.012 Fundamentals f Materials Science Fall 2005 Lecture 13: 10.21.05 Electrchemical Equilibria Tday: LAST TIME...2 An example calculatin...3 THE ELECTROCHEMICAL POTENTIAL...4 Electrstatic energy cntributins

More information

Lecture 17: Free Energy of Multi-phase Solutions at Equilibrium

Lecture 17: Free Energy of Multi-phase Solutions at Equilibrium Lecture 17: 11.07.05 Free Energy f Multi-phase Slutins at Equilibrium Tday: LAST TIME...2 FREE ENERGY DIAGRAMS OF MULTI-PHASE SOLUTIONS 1...3 The cmmn tangent cnstructin and the lever rule...3 Practical

More information

PSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa

PSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa There are tw parts t this lab. The first is intended t demnstrate hw t request and interpret the spatial diagnstics f a standard OLS regressin mdel using GeDa. The diagnstics prvide infrmatin abut the

More information

Methods for Determination of Mean Speckle Size in Simulated Speckle Pattern

Methods for Determination of Mean Speckle Size in Simulated Speckle Pattern 0.478/msr-04-004 MEASUREMENT SCENCE REVEW, Vlume 4, N. 3, 04 Methds fr Determinatin f Mean Speckle Size in Simulated Speckle Pattern. Hamarvá, P. Šmíd, P. Hrváth, M. Hrabvský nstitute f Physics f the Academy

More information

Dr M. BROUARD. 5. Thermodynamic formulation of Transition State Theory Entropy of activation. Thermochemical kinetics. CHEMICAL REACTION RATES

Dr M. BROUARD. 5. Thermodynamic formulation of Transition State Theory Entropy of activation. Thermochemical kinetics. CHEMICAL REACTION RATES CHEMICAL REACTION RATES Dr M. BROUARD Trinity Term 2003 A. Bimlecular Reactins 5 Lectures 1. Intrductin Simple cllisin thery. Ptential energy curves and surfaces. The reactin crdinate and barriers t reactin.

More information

5 th grade Common Core Standards

5 th grade Common Core Standards 5 th grade Cmmn Cre Standards In Grade 5, instructinal time shuld fcus n three critical areas: (1) develping fluency with additin and subtractin f fractins, and develping understanding f the multiplicatin

More information

Chapters 29 and 35 Thermochemistry and Chemical Thermodynamics

Chapters 29 and 35 Thermochemistry and Chemical Thermodynamics Chapters 9 and 35 Thermchemistry and Chemical Thermdynamics 1 Cpyright (c) 011 by Michael A. Janusa, PhD. All rights reserved. Thermchemistry Thermchemistry is the study f the energy effects that accmpany

More information

Online Model Racing based on Extreme Performance

Online Model Racing based on Extreme Performance Online Mdel Racing based n Extreme Perfrmance Tiantian Zhang, Michael Gergipuls, Gergis Anagnstpuls Electrical & Cmputer Engineering University f Central Flrida Overview Racing Algrithm Offline vs Online

More information

A Scalable Recurrent Neural Network Framework for Model-free

A Scalable Recurrent Neural Network Framework for Model-free A Scalable Recurrent Neural Netwrk Framewrk fr Mdel-free POMDPs April 3, 2007 Zhenzhen Liu, Itamar Elhanany Machine Intelligence Lab Department f Electrical and Cmputer Engineering The University f Tennessee

More information

LCA14-206: Scheduler tooling and benchmarking. Tue-4-Mar, 11:15am, Zoran Markovic, Vincent Guittot

LCA14-206: Scheduler tooling and benchmarking. Tue-4-Mar, 11:15am, Zoran Markovic, Vincent Guittot LCA14-206: Scheduler tling and benchmarking Tue-4-Mar, 11:15am, Zran Markvic, Vincent Guittt Scheduler Tls and Benchmarking Frm Energy Aware mini-summit @ Ksummit 2013 extract frm [1]: Ing Mlnar came in

More information

The standards are taught in the following sequence.

The standards are taught in the following sequence. B L U E V A L L E Y D I S T R I C T C U R R I C U L U M MATHEMATICS Third Grade In grade 3, instructinal time shuld fcus n fur critical areas: (1) develping understanding f multiplicatin and divisin and

More information

QCE Chemistry. Year 2015 Mark 0.00 Pages 20 Published Jan 31, Chemistry: Revision Notes. By Sophie (1 ATAR)

QCE Chemistry. Year 2015 Mark 0.00 Pages 20 Published Jan 31, Chemistry: Revision Notes. By Sophie (1 ATAR) QCE Chemistry Year 2015 Mark 0.00 Pages 20 Published Jan 31, 2017 11 Chemistry: Revisin Ntes By Sphie (1 ATAR) Pwered by TCPDF (www.tcpdf.rg) Yur ntes authr, Sphie. Sphie achieved an ATAR f 1 in 2016 while

More information

Comparison of hybrid ensemble-4dvar with EnKF and 4DVar for regional-scale data assimilation

Comparison of hybrid ensemble-4dvar with EnKF and 4DVar for regional-scale data assimilation Cmparisn f hybrid ensemble-4dvar with EnKF and 4DVar fr reginal-scale data assimilatin Jn Pterjy and Fuqing Zhang Department f Meterlgy The Pennsylvania State University Wednesday 18 th December, 2013

More information

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce

More information

Churn Prediction using Dynamic RFM-Augmented node2vec

Churn Prediction using Dynamic RFM-Augmented node2vec Churn Predictin using Dynamic RFM-Augmented nde2vec Sandra Mitrvić, Jchen de Weerdt, Bart Baesens & Wilfried Lemahieu Department f Decisin Sciences and Infrmatin Management, KU Leuven 18 September 2017,

More information

Modelling of NOLM Demultiplexers Employing Optical Soliton Control Pulse

Modelling of NOLM Demultiplexers Employing Optical Soliton Control Pulse Micwave and Optical Technlgy Letters, Vl. 1, N. 3, 1999. pp. 05-08 Mdelling f NOLM Demultiplexers Emplying Optical Slitn Cntrl Pulse Z. Ghassemly, C. Y. Cheung & A. K. Ray Electrnics Research Grup, Schl

More information

In SMV I. IAML: Support Vector Machines II. This Time. The SVM optimization problem. We saw:

In SMV I. IAML: Support Vector Machines II. This Time. The SVM optimization problem. We saw: In SMV I IAML: Supprt Vectr Machines II Nigel Gddard Schl f Infrmatics Semester 1 We sa: Ma margin trick Gemetry f the margin and h t cmpute it Finding the ma margin hyperplane using a cnstrained ptimizatin

More information

CHM112 Lab Graphing with Excel Grading Rubric

CHM112 Lab Graphing with Excel Grading Rubric Name CHM112 Lab Graphing with Excel Grading Rubric Criteria Pints pssible Pints earned Graphs crrectly pltted and adhere t all guidelines (including descriptive title, prperly frmatted axes, trendline

More information

Experiment #3. Graphing with Excel

Experiment #3. Graphing with Excel Experiment #3. Graphing with Excel Study the "Graphing with Excel" instructins that have been prvided. Additinal help with learning t use Excel can be fund n several web sites, including http://www.ncsu.edu/labwrite/res/gt/gt-

More information

Resampling Methods. Cross-validation, Bootstrapping. Marek Petrik 2/21/2017

Resampling Methods. Cross-validation, Bootstrapping. Marek Petrik 2/21/2017 Resampling Methds Crss-validatin, Btstrapping Marek Petrik 2/21/2017 Sme f the figures in this presentatin are taken frm An Intrductin t Statistical Learning, with applicatins in R (Springer, 2013) with

More information

Principles of Organic Chemistry lecture 5, page 1

Principles of Organic Chemistry lecture 5, page 1 Principles f Organic Chemistry lecture 5, page 1 Bnding Mdels Fact: electrns hld mlecules tgether. Theries: mre than ne way t cnceptualize bnding. Let s fllw Carrll in the cnsideratin f tw theries f bnding.

More information

3.1 Ground State Geometries of Surfaces

3.1 Ground State Geometries of Surfaces Chapter 3. Semicnductr Surface Studies Chapter 3. Semicnductr Surface Studies Spnsr Jint Services Electrnics Prgram (Cntracts DAAL3-86-K-2 and DAAL3-89-C-1) Academic and Research Staff Prfessr Jhn D. Jannpuls,

More information

February 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA

February 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA February 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA Mental Experiment regarding 1D randm walk Cnsider a cntainer f gas in thermal

More information

CHEM Thermodynamics. Change in Gibbs Free Energy, G. Review. Gibbs Free Energy, G. Review

CHEM Thermodynamics. Change in Gibbs Free Energy, G. Review. Gibbs Free Energy, G. Review Review Accrding t the nd law f Thermdynamics, a prcess is spntaneus if S universe = S system + S surrundings > 0 Even thugh S system

More information

Resumen de presentación

Resumen de presentación TÍTULO: Theretical study f the gemetrical, electrnic and catalytic prperties f metal clusters and nanparticles. AUTOR: Estefanía Fernández Villanueva, esfervi@dctr.upv.es PROGRAMA DE DOCTORADO: Química

More information

2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS

2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS 2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS 6. An electrchemical cell is cnstructed with an pen switch, as shwn in the diagram abve. A strip f Sn and a strip f an unknwn metal, X, are used as electrdes.

More information

Regents Chemistry Period Unit 3: Atomic Structure. Unit 3 Vocabulary..Due: Test Day

Regents Chemistry Period Unit 3: Atomic Structure. Unit 3 Vocabulary..Due: Test Day Name Skills: 1. Interpreting Mdels f the Atm 2. Determining the number f subatmic particles 3. Determine P, e-, n fr ins 4. Distinguish istpes frm ther atms/ins Regents Chemistry Perid Unit 3: Atmic Structure

More information

Sodium D-line doublet. Lectures 5-6: Magnetic dipole moments. Orbital magnetic dipole moments. Orbital magnetic dipole moments

Sodium D-line doublet. Lectures 5-6: Magnetic dipole moments. Orbital magnetic dipole moments. Orbital magnetic dipole moments Lectures 5-6: Magnetic diple mments Sdium D-line dublet Orbital diple mments. Orbital precessin. Grtrian diagram fr dublet states f neutral sdium shwing permitted transitins, including Na D-line transitin

More information

T Algorithmic methods for data mining. Slide set 6: dimensionality reduction

T Algorithmic methods for data mining. Slide set 6: dimensionality reduction T-61.5060 Algrithmic methds fr data mining Slide set 6: dimensinality reductin reading assignment LRU bk: 11.1 11.3 PCA tutrial in mycurses (ptinal) ptinal: An Elementary Prf f a Therem f Jhnsn and Lindenstrauss,

More information

**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!**

**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!** Tpics lists: UV-Vis Absrbance Spectrscpy Lab & ChemActivity 3-6 (nly thrugh 4) I. UV-Vis Absrbance Spectrscpy Lab Beer s law Relates cncentratin f a chemical species in a slutin and the absrbance f that

More information

Statistics, Numerical Models and Ensembles

Statistics, Numerical Models and Ensembles Statistics, Numerical Mdels and Ensembles Duglas Nychka, Reinhard Furrer,, Dan Cley Claudia Tebaldi, Linda Mearns, Jerry Meehl and Richard Smith (UNC). Spatial predictin and data assimilatin Precipitatin

More information

5 th Grade Goal Sheet

5 th Grade Goal Sheet 5 th Grade Gal Sheet Week f Nvember 26 th, 2018 Frm Ms. Simmns: Upcming dates: 11/26 Thanksgiving Break Packets are due 12/4 Prgress Reprts fr 2 nd Quarter 12/5 12/7 Benchmark Testing 12/11- Parent Partnership

More information

WRITING THE REPORT. Organizing the report. Title Page. Table of Contents

WRITING THE REPORT. Organizing the report. Title Page. Table of Contents WRITING THE REPORT Organizing the reprt Mst reprts shuld be rganized in the fllwing manner. Smetime there is a valid reasn t include extra chapters in within the bdy f the reprt. 1. Title page 2. Executive

More information

Chapter 8 Reduction and oxidation

Chapter 8 Reduction and oxidation Chapter 8 Reductin and xidatin Redx reactins and xidatin states Reductin ptentials and Gibbs energy Nernst equatin Disprprtinatin Ptential diagrams Frst-Ebswrth diagrams Ellingham diagrams Oxidatin refers

More information

Standard Title: Frequency Response and Frequency Bias Setting. Andrew Dressel Holly Hawkins Maureen Long Scott Miller

Standard Title: Frequency Response and Frequency Bias Setting. Andrew Dressel Holly Hawkins Maureen Long Scott Miller Template fr Quality Review f NERC Reliability Standard BAL-003-1 Frequency Respnse and Frequency Bias Setting Basic Infrmatin: Prject number: 2007-12 Standard number: BAL-003-1 Prject title: Frequency

More information

Study Group Report: Plate-fin Heat Exchangers: AEA Technology

Study Group Report: Plate-fin Heat Exchangers: AEA Technology Study Grup Reprt: Plate-fin Heat Exchangers: AEA Technlgy The prblem under study cncerned the apparent discrepancy between a series f experiments using a plate fin heat exchanger and the classical thery

More information

Admin. MDP Search Trees. Optimal Quantities. Reinforcement Learning

Admin. MDP Search Trees. Optimal Quantities. Reinforcement Learning Admin Reinfrcement Learning Cntent adapted frm Berkeley CS188 MDP Search Trees Each MDP state prjects an expectimax-like search tree Optimal Quantities The value (utility) f a state s: V*(s) = expected

More information

COMP 551 Applied Machine Learning Lecture 4: Linear classification

COMP 551 Applied Machine Learning Lecture 4: Linear classification COMP 551 Applied Machine Learning Lecture 4: Linear classificatin Instructr: Jelle Pineau (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted

More information

FEM for engineering applications (SE1025), 6 hp, Fall 2011

FEM for engineering applications (SE1025), 6 hp, Fall 2011 KTH Slid Mechanics SE1025 FEM. FEM fr engineering applicatins (SE1025), 6 hp, Fall 2011 FEM fr engineering applicatins (SE1025), 6 hp, Fall 2011 1. General infrmatin The curse gives an intrductin t energy

More information

The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition

The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition The Kullback-Leibler Kernel as a Framewrk fr Discriminant and Lcalized Representatins fr Visual Recgnitin Nun Vascncels Purdy H Pedr Mren ECE Department University f Califrnia, San Dieg HP Labs Cambridge

More information