K.D.Prasangika. Personal Details. : Hikgahawatta, Kaduruduwa, Wanchawala, Galle, :Sri Lankan

Size: px
Start display at page:

Download "K.D.Prasangika. Personal Details. : Hikgahawatta, Kaduruduwa, Wanchawala, Galle, :Sri Lankan"

Transcription

1 K.D.Prasangika Hikgahawatta, Kaduruduwa, Wanchawala, Galle, Mbile: +94(0) Persnal Details Name Permanent address Office ddress : Kalahe Diwelwattage Prasangika. : Hikgahawatta, Kaduruduwa, Wanchawala, Galle, : Department f Mathematics, University f Ruhuna Matara, Sri Lanka Date f irth : Civil status :Married Sex :Female Natinality :Sri Lankan Religin :uddhist Page 1 f 7

2 Educatinal Qualificatins Undergraduate: Sc (Special) Degree in Mathematics with First Class (Hns), University f Ruhuna, Matara, (2005)..Sc. (General) Degree Part I Mathematics pplicable Mathematics Physics.Sc. (General) Degree Part II Mathematics pplicable Mathematics Physics.Sc. (Special) Degree Part I lgebra Statistics Numerical nalysis nalysis.sc. (Special) Degree Part II Statistics lgebra nalysis Cmputatinal Fluid Dynamics General Curse ssessment Page 2 f 7

3 Schl Educatin: 1999 G.C.E. dvance Level, Suth lands Cllege, Galle. Pure Mathematics (C), pplied Mathematics (C), Chemistry (C), Physics (C) 1995 G.C.E. Ordinary Level, G/Kalahe Sri Sumangalldaya M.V. Science (C), Mathematics (D), Scial Study (C), Sinhala (D), Hme Science (C), uddhism (D), English (C), Music (C) I have passed the Final Examinatin f Dhamma Schl in Wrkshps /Seminars I have participated in the CIMP-IMMIS-VIETNM schl n Mathematical Finance held at the Institute f Mathematics Vietnamese cademy f Science and Technlgy, Hani, Vietnam frm 23 rd pril 2007 t 4 th May 2007 rganized by the Institute f Mathematics Vietnamese cademy f Science and Technlgy, Hani, Vietnam. I have participated in the Ruhuna Internatinal Schl n Cmputatinal and Mathematical Physics (RISCMP) held at the Department f Mathematics, University f Ruhuna, Sri Lanka in December The fllwing curses had been ffered at the Schl. Cmputatinal Physics, IT Security, Grids and Security, Objects, Cmpnents and Grids, Cmputatinal Chemistry, Densitymatrix renrmalizatin, Mdeling traffic and ther cmplex systems, Large Deviatin techniques fr disrdered systems. I have participated in the Stat Day 2003, rganized by the Stat Circle f the University f Clmb. Page 3 f 7

4 Emplyment Presently I am wrking at the Department f Mathematics, University f Ruhuna, as a prbatinary Lecturer since 1 st July I wrked as a Temprary ssistant Lecturer in Mathematics, Department f Mathematics, Faculty f Science, University f Ruhuna frm 09 th February 2006 t 1 st July I wrked as a Temprary Tutrial Instructr in Mathematics, Department f Mathematics, Faculty f Science, University f Ruhuna frm 08 th ugust 2005 t February 07 th I wrked as a Temprary Student Tutrial Instructr in Mathematics, Department f Mathematics, Faculty f Science, University f Ruhuna frm 4 th Octber 2004 t 3 rd January Cmputer Skills Gd Knwledge f the cmmnly used cmputer applicatins-ms Wrd, Excel, Pwer Pint, Knwledge f C language, Mathematica and Linux perating systems. I am very cnversant with Mathematica and C prgramming; Specially in prblem slutin statistical and applied Mathematics. Successfully cmpleted Certificate Curse f Infrmatin Technlgy (f tw year duratin) cnducted by the Department f Cmputer Science, University f Ruhuna, Matara, The fllwing curses had been ffered at the abve curse. Cmputer asis, DOS perating system, Cmputer prgramming (PSCL), Windws Envirnment, MS Wrd 7.0, MS Excel 7.0, ccess 7.0, Fxfr 2.5, System analysis and design, Unix perating system and X Windws, Netwrked cmputer systems, Internet Tls. Supervise undergraduate students research prjects and instruct undergraduates at cmputer practical classes. Page 4 f 7

5 Research My undergraduate research tpics: On the cmputatin f definite integral by the Mnte Carl Methd (Evaluatin f the definite integral and Evaluatin f the definite duble integral.), using the C language. Slving system f Linear equatins by the Mnte Carl Methd, using the C language. mdel in Ecnmics, Slving Samuelsn s Investment Mdel. With Mathematica. Investigatin f the Central Limit Therem and Nrmal pprximatin fr the inmial Distributin, with Mathematica. Thery and sme applicatins fr analyzing Ordinal data in educatinal based research. (Test f Hmgeniety between tw ppulatins.) During my undergraduate career, I believe that I have been very successful in establishing a firm fundatin fr advanced research in almst any area in Mathematics r Mathematical Statistics. Hwever, I als believe that I am s cnversant in cmputatinal aspects f thse areas due t the slid backgrund I gained via Mathematical Cmputing curse. s such, fllwing are sme f the areas that I have a high preference in my future research: Future research bjectives: Fluctuatins f the stck price in the stck market The stck market is an imprtant institutin fr capitalist cuntries because it encurages investment in cperate securities, prviding capital fr new business and incme fr investrs. The stck market is a stchastic prcess. Therefre stck price f the stck market is fluctuating accrding t the stchastic differential equatin. S I am quite interesting t study the behavir f stchastic differential equatins. I expect t study this stchastic nature using the tls available t the mathematician, namely Measure Thery, Stchastic Prcess and the Numerical Techniques. Page 5 f 7

6 ayesian technique in nnparametric regressin. In this aspect, I am quite interesting in t investigate the relevance f neural netwrk appraches used t slve the same prblem as in nnparametric regressin. (Fr e.g. ayesian Nnparametric via Neural Netwrks, Herbert K.H.Lee) Cmputatinal Statistics: I als have a great interest t study/investigate Mnte Carl apprach used in Statistics. E.g. Statistical mdel testing / validatin etc. uilding Statistical/stchastic mdels in medicinal and bilgical systems. The behavirs f almst all bilgical systems (e.g. Cmmunicatin f plants, cellular system, cell signaling, etc) are Stchastic. S in rder t make useful frecasts, it is essential t understand the stchastic behavir f these systems and cme-up with mdels that are capable f explaining the bserved behavir f these systems. chievements Rnie De Mel Gld Medal awarded by the Rnie De Mel Trust Fund t the graduate wh btained a First class Hnurs with the highest aggregate marks in the Final Examinatins leading t the Degree f achelr f Science held in 2004, 2005 Cnvcatin, University f Ruhuna, Wasantha Mhtti Memrial Gld Medal awarded by Dr. J.E. Mhtti t the graduate wh btained a First class Hnurs with the highest aggregate marks in the achelr f Science Special Degree Examinatin in Physical Science held in 2004, 2005 Cnvcatin, University f Ruhuna, Page 6 f 7

7 Prfessr Isabelle ttali, CIMP (Internatinal Centre fr Pure and pplied Mathematics, France) Gld Medal fr the utstanding graduate wh scred the highest aggregate at the achelr f Science Special Degree Examinatin in Mathematics with First Class Hnurs, in the year 2004, awarded by the Educatinal Supprt Fundatin-Ruhuna Mathematics, 2005 Cnvcatin, University f Ruhuna, Financial supprt frm CIMP (Internatinal Centre fr Pure and pplied Mathematics) t participate in CIMP-IMMIS-Vietnam Schl n Mathematical Finance in Hani Vietnam (pril/may 2007). Referees 1 Dr. J.R. Wedagedara Senir Lecturer, Dept f Mathematics, University f Ruhuna, Matara, Tel: (Office) janak@maths.ruh.ac.lk 2. ss. Prf. L..L.W.Jayasekara, Dept f Mathematics, University f Ruhuna, Matara, Tel: (Office) leslie@maths.ruh.ac.lk Page 7 f 7

Models of Curriculum Development for Spatial Thinking across the College Curriculum USC

Models of Curriculum Development for Spatial Thinking across the College Curriculum USC Mdels f Curriculum Develpment fr Spatial Thinking acrss the Cllege Curriculum Spatial @ USC Jhn P. Wilsn Spatial Thinking acrss the Cllege Curriculum Specialist Meeting University f Califrnia at Santa

More information

EASTERN ARIZONA COLLEGE Introduction to Statistics

EASTERN ARIZONA COLLEGE Introduction to Statistics EASTERN ARIZONA COLLEGE Intrductin t Statistics Curse Design 2014-2015 Curse Infrmatin Divisin Scial Sciences Curse Number PSY 220 Title Intrductin t Statistics Credits 3 Develped by Adam Stinchcmbe Lecture/Lab

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

Math Foundations 20 Work Plan

Math Foundations 20 Work Plan Math Fundatins 20 Wrk Plan Units / Tpics 20.8 Demnstrate understanding f systems f linear inequalities in tw variables. Time Frame December 1-3 weeks 6-10 Majr Learning Indicatrs Identify situatins relevant

More information

Interdisciplinary Physics Example Cognate Plans

Interdisciplinary Physics Example Cognate Plans Interdisciplinary Physics Example Cgnate Plans The Interdisciplinary Physics cncentratin allws students substantial flexibility t define the thematic fcus f their study. This flexibility cmes with a respnsibility;

More information

Accreditation Information

Accreditation Information Accreditatin Infrmatin The ISSP urges members wh have achieved significant success in the field t apply fr higher levels f membership in rder t enjy the fllwing benefits: - Bth Prfessinal members and Fellws

More information

History the Hood Way. Amy Shell-Gellasch Betty Mayfield Hood College. MD-DC-VA Section October 27, 2012

History the Hood Way. Amy Shell-Gellasch Betty Mayfield Hood College. MD-DC-VA Section October 27, 2012 Histry the Hd Way Amy Shell-Gellasch Betty Mayfield Hd Cllege MD-DC-VA Sectin Octber 27, 2012 Weaving histry int the majr Mathematics as part f the liberal arts Frm the Department s Missin Statement: Students

More information

Competency Statements for Wm. E. Hay Mathematics for grades 7 through 12:

Competency Statements for Wm. E. Hay Mathematics for grades 7 through 12: Cmpetency Statements fr Wm. E. Hay Mathematics fr grades 7 thrugh 12: Upn cmpletin f grade 12 a student will have develped a cmbinatin f sme/all f the fllwing cmpetencies depending upn the stream f math

More information

KHAIR ZAMAN Ph.D Department of Chemistry, Abdul Wali Khan University, Mardan Cont # and

KHAIR ZAMAN Ph.D Department of Chemistry, Abdul Wali Khan University, Mardan Cont # and 1 PERSONALS Father s Name: Natinality: KHAIR ZAMAN Ph.D Department f Chemistry, Abdul Wali Khan University, Mardan Cnt # 0937-929122 E-mail: kzaman80@yah.ca and kzaman80@awkum.edu.pk Nawrz Khan Pakistani

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

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

A Correlation of. to the. South Carolina Academic Standards for Mathematics Precalculus

A Correlation of. to the. South Carolina Academic Standards for Mathematics Precalculus A Crrelatin f Suth Carlina Academic Standards fr Mathematics Precalculus INTRODUCTION This dcument demnstrates hw Precalculus (Blitzer), 4 th Editin 010, meets the indicatrs f the. Crrelatin page references

More information

8 th Grade Math: Pre-Algebra

8 th Grade Math: Pre-Algebra Hardin Cunty Middle Schl (2013-2014) 1 8 th Grade Math: Pre-Algebra Curse Descriptin The purpse f this curse is t enhance student understanding, participatin, and real-life applicatin f middle-schl mathematics

More information

PHY The Physics of Musical Sound

PHY The Physics of Musical Sound AS-2556-167-GE, PHY 1050 Physics f Musical Sunds 3 PHY - 1050 - The Physics f Musical Sund C. Curse - New General Educatin* Updated General Catalg Infrmatin Cllege/Department Physics and Astrnmy Semester

More information

NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION

NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION NUROP Chinese Pinyin T Chinese Character Cnversin NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION CHIA LI SHI 1 AND LUA KIM TENG 2 Schl f Cmputing, Natinal University f Singapre 3 Science

More information

Building research leadership consortia for Quantum Technology Research Hubs. Call type: Expression of Interest

Building research leadership consortia for Quantum Technology Research Hubs. Call type: Expression of Interest Building research leadership cnsrtia fr Quantum Technlgy Research Hubs Call type: Expressin f Interest Clsing date: 17:00, 07 August 2018 Hw t apply: Expressin f Interest (EI) fr research leaders t attend

More information

Design and Simulation of Dc-Dc Voltage Converters Using Matlab/Simulink

Design and Simulation of Dc-Dc Voltage Converters Using Matlab/Simulink American Jurnal f Engineering Research (AJER) 016 American Jurnal f Engineering Research (AJER) e-issn: 30-0847 p-issn : 30-0936 Vlume-5, Issue-, pp-9-36 www.ajer.rg Research Paper Open Access Design and

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

Internship Programme of German Business for the Countries of Western Balkans. How to complete your application?

Internship Programme of German Business for the Countries of Western Balkans. How to complete your application? Befre applying please make sure that yu fulfil the precnditins: Undergraduate students wh are enrlled in the 5th semester r higher when applying, Master r PhD students and yung graduates wh graduated after

More information

MODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards:

MODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards: MODULE FOUR This mdule addresses functins SC Academic Standards: EA-3.1 Classify a relatinship as being either a functin r nt a functin when given data as a table, set f rdered pairs, r graph. EA-3.2 Use

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

ANALYTIC RESEARCH FOUNDATIONS FOR THE NEXT GENERATION ELECTRIC GRID

ANALYTIC RESEARCH FOUNDATIONS FOR THE NEXT GENERATION ELECTRIC GRID ANALYTIC RESEARCH FOUNDATIONS FOR THE NEXT GENERATION ELECTRIC GRID A Natinal Research Cuncil Wrkshp BOARD ON MATHEMATICAL SCIENCES AND THEIR APPLICATIONS COMMITTEE ON ANALYTIC RESEARCH FOUNDATIONS FOR

More information

Mathematics and Computer Sciences Department. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses

Mathematics and Computer Sciences Department. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses SECTION A - Curse Infrmatin 1. Curse ID: 2. Curse Title: 3. Divisin: 4. Department: 5. Subject: 6. Shrt Curse Title: 7. Effective Term:: MATH 70S Integrated Intermediate Algebra Natural Sciences Divisin

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

Mathematics in H2020. ICT Proposers' Day. Anni Hellman DG CONNECT European Commission

Mathematics in H2020. ICT Proposers' Day. Anni Hellman DG CONNECT European Commission Mathematics in H2020 ICT Prpsers' Day Anni Hellman DG CONNECT Eurpean Cmmissin The cnclusins frm ur cnsultatin n mathematics fr H2020 Why mathematics is imprtant in prpsals Messages frm mathematicians

More information

Broadcast Program Generation for Unordered Queries with Data Replication

Broadcast Program Generation for Unordered Queries with Data Replication Bradcast Prgram Generatin fr Unrdered Queries with Data Replicatin Jiun-Lng Huang and Ming-Syan Chen Department f Electrical Engineering Natinal Taiwan University Taipei, Taiwan, ROC E-mail: jlhuang@arbr.ee.ntu.edu.tw,

More information

Optimization Programming Problems For Control And Management Of Bacterial Disease With Two Stage Growth/Spread Among Plants

Optimization Programming Problems For Control And Management Of Bacterial Disease With Two Stage Growth/Spread Among Plants Internatinal Jurnal f Engineering Science Inventin ISSN (Online): 9 67, ISSN (Print): 9 676 www.ijesi.rg Vlume 5 Issue 8 ugust 06 PP.0-07 Optimizatin Prgramming Prblems Fr Cntrl nd Management Of Bacterial

More information

Competition and Invasion in a Microcosmic Setting

Competition and Invasion in a Microcosmic Setting University f Tennessee, Knxville Trace: Tennessee Research and Creative Exchange University f Tennessee Hnrs Thesis Prjects University f Tennessee Hnrs Prgram 5-2004 Cmpetitin and Invasin in a Micrcsmic

More information

INSTRUMENTAL VARIABLES

INSTRUMENTAL VARIABLES INSTRUMENTAL VARIABLES Technical Track Sessin IV Sergi Urzua University f Maryland Instrumental Variables and IE Tw main uses f IV in impact evaluatin: 1. Crrect fr difference between assignment f treatment

More information

Hypothesis Tests for One Population Mean

Hypothesis Tests for One Population Mean Hypthesis Tests fr One Ppulatin Mean Chapter 9 Ala Abdelbaki Objective Objective: T estimate the value f ne ppulatin mean Inferential statistics using statistics in rder t estimate parameters We will be

More information

UG Course Outline EC2203: Quantitative Methods II 2017/18

UG Course Outline EC2203: Quantitative Methods II 2017/18 UG Curse Outline EC2203: Quantitative Methds II 2017/18 Autumn: Instructr: Pierre0-Olivier Frtin Office: Hrtn H214 Phne: +44 (0) 1784 276474 E-mail: pierre-livier.frtin@rhul.ac.uk Office hurs: Tuesdays

More information

How T o Start A n Objective Evaluation O f Your Training Program

How T o Start A n Objective Evaluation O f Your Training Program J O U R N A L Hw T Start A n Objective Evaluatin O f Yur Training Prgram DONALD L. KIRKPATRICK, Ph.D. Assistant Prfessr, Industrial Management Institute University f Wiscnsin Mst training m e n agree that

More information

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised

More information

initially lcated away frm the data set never win the cmpetitin, resulting in a nnptimal nal cdebk, [2] [3] [4] and [5]. Khnen's Self Organizing Featur

initially lcated away frm the data set never win the cmpetitin, resulting in a nnptimal nal cdebk, [2] [3] [4] and [5]. Khnen's Self Organizing Featur Cdewrd Distributin fr Frequency Sensitive Cmpetitive Learning with One Dimensinal Input Data Aristides S. Galanpuls and Stanley C. Ahalt Department f Electrical Engineering The Ohi State University Abstract

More information

MODULE ONE. This module addresses the foundational concepts and skills that support all of the Elementary Algebra academic standards.

MODULE ONE. This module addresses the foundational concepts and skills that support all of the Elementary Algebra academic standards. Mdule Fundatinal Tpics MODULE ONE This mdule addresses the fundatinal cncepts and skills that supprt all f the Elementary Algebra academic standards. SC Academic Elementary Algebra Indicatrs included in

More information

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised

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

Revision: August 19, E Main Suite D Pullman, WA (509) Voice and Fax

Revision: August 19, E Main Suite D Pullman, WA (509) Voice and Fax .7.4: Direct frequency dmain circuit analysis Revisin: August 9, 00 5 E Main Suite D Pullman, WA 9963 (509) 334 6306 ice and Fax Overview n chapter.7., we determined the steadystate respnse f electrical

More information

SAMPLE ASSESSMENT TASKS MATHEMATICS SPECIALIST ATAR YEAR 11

SAMPLE ASSESSMENT TASKS MATHEMATICS SPECIALIST ATAR YEAR 11 SAMPLE ASSESSMENT TASKS MATHEMATICS SPECIALIST ATAR YEAR Cpyright Schl Curriculum and Standards Authrity, 08 This dcument apart frm any third party cpyright material cntained in it may be freely cpied,

More information

Inflow Control on Expressway Considering Traffic Equilibria

Inflow Control on Expressway Considering Traffic Equilibria Memirs f the Schl f Engineering, Okayama University Vl. 20, N.2, February 1986 Inflw Cntrl n Expressway Cnsidering Traffic Equilibria Hirshi INOUYE* (Received February 14, 1986) SYNOPSIS When expressway

More information

Math Foundations 10 Work Plan

Math Foundations 10 Work Plan Math Fundatins 10 Wrk Plan Units / Tpics 10.1 Demnstrate understanding f factrs f whle numbers by: Prime factrs Greatest Cmmn Factrs (GCF) Least Cmmn Multiple (LCM) Principal square rt Cube rt Time Frame

More information

Distributions, spatial statistics and a Bayesian perspective

Distributions, spatial statistics and a Bayesian perspective Distributins, spatial statistics and a Bayesian perspective Dug Nychka Natinal Center fr Atmspheric Research Distributins and densities Cnditinal distributins and Bayes Thm Bivariate nrmal Spatial statistics

More information

CCRI Department of Engineering and Technology INST and 103 Introduction to Instrumentation Technology Spring Semester 2016

CCRI Department of Engineering and Technology INST and 103 Introduction to Instrumentation Technology Spring Semester 2016 CCRI Department f Engineering and Technlgy INST 1010-001 and 103 Intrductin t Instrumentatin Technlgy Spring Semester 2016 Instructr: Office: Telephne: Office hurs: e-mail: Classrm: Labratry: Cmmunicatin:

More information

Application for Admission

Application for Admission Applicatin fr Admissin LL.M. in Intellectual Prperty Law American University Washingtn Cllege f Law INSTRUCTIONS If yu have any questins please cntact us at llm@wcl.american.edu befre cmpleting the applicatin

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

Evaluating enterprise support: state of the art and future challenges. Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany

Evaluating enterprise support: state of the art and future challenges. Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany Evaluating enterprise supprt: state f the art and future challenges Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany Intrductin During the last decade, mircecnmetric ecnmetric cunterfactual

More information

On Huntsberger Type Shrinkage Estimator for the Mean of Normal Distribution ABSTRACT INTRODUCTION

On Huntsberger Type Shrinkage Estimator for the Mean of Normal Distribution ABSTRACT INTRODUCTION Malaysian Jurnal f Mathematical Sciences 4(): 7-4 () On Huntsberger Type Shrinkage Estimatr fr the Mean f Nrmal Distributin Department f Mathematical and Physical Sciences, University f Nizwa, Sultanate

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

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

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came. MATH 1342 Ch. 24 April 25 and 27, 2013 Page 1 f 5 CHAPTER 24: INFERENCE IN REGRESSION Chapters 4 and 5: Relatinships between tw quantitative variables. Be able t Make a graph (scatterplt) Summarize the

More information

Code: MATH 151 Title: INTERMEDIATE ALGEBRA

Code: MATH 151 Title: INTERMEDIATE ALGEBRA Cde: MATH 151 Title: INTERMEDIATE ALGEBRA Divisin: MATHEMATICS Department: MATHEMATICS Curse Descriptin: This curse prepares students fr curses that require algebraic skills beynd thse taught in Elementary

More information

Computational modeling techniques

Computational modeling techniques Cmputatinal mdeling techniques Lecture 2: Mdeling change. In Petre Department f IT, Åb Akademi http://users.ab.fi/ipetre/cmpmd/ Cntent f the lecture Basic paradigm f mdeling change Examples Linear dynamical

More information

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal

More information

Bayesian nonparametric modeling approaches for quantile regression

Bayesian nonparametric modeling approaches for quantile regression Bayesian nnparametric mdeling appraches fr quantile regressin Athanasis Kttas Department f Applied Mathematics and Statistics University f Califrnia, Santa Cruz Department f Statistics Athens University

More information

[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y )

[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y ) (Abut the final) [COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t m a k e s u r e y u a r e r e a d y ) The department writes the final exam s I dn't really knw what's n it and I can't very well

More information

Evaluation of Temperature Monitoring System of Cold Chain at all Urban Health Centres (UHCs) of Ahmedabad Municipal Corporation (AMC) area.

Evaluation of Temperature Monitoring System of Cold Chain at all Urban Health Centres (UHCs) of Ahmedabad Municipal Corporation (AMC) area. Original Article Healthline Jurnal Vlume 6 Issue 1 (January - June 2015) Evaluatin f Mnitring System f Cld Chain at all Urban Health Centres (UHCs) f Ahmedabad Municipal Crpratin (AMC) area. 1 2 Kapil

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

Purpose: Use this reference guide to effectively communicate the new process customers will use for creating a TWC ID. Mobile Manager Call History

Purpose: Use this reference guide to effectively communicate the new process customers will use for creating a TWC ID. Mobile Manager Call History Purpse: Use this reference guide t effectively cmmunicate the new prcess custmers will use fr creating a TWC ID. Overview Beginning n January 28, 2014 (Refer t yur Knwledge Management System fr specific

More information

Humanities and Social Sciences Division. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses

Humanities and Social Sciences Division. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses SECTION A - Curse Infrmatin 1. Curse ID: 2. Curse Title: 3. Divisin: 4. Department: 5. Subject: 6. Shrt Curse Title: 7. Effective Term:: PHIL 3 Intrductin t Lgic Humanities and Scial Sciences Divisin Scilgy,

More information

Admissibility Conditions and Asymptotic Behavior of Strongly Regular Graphs

Admissibility Conditions and Asymptotic Behavior of Strongly Regular Graphs Admissibility Cnditins and Asympttic Behavir f Strngly Regular Graphs VASCO MOÇO MANO Department f Mathematics University f Prt Oprt PORTUGAL vascmcman@gmailcm LUÍS ANTÓNIO DE ALMEIDA VIEIRA Department

More information

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method.

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method. Lessn Plan Reach: Ask the students if they ever ppped a bag f micrwave ppcrn and nticed hw many kernels were unppped at the bttm f the bag which made yu wnder if ther brands pp better than the ne yu are

More information

SIZE BIAS IN LINE TRANSECT SAMPLING: A FIELD TEST. Mark C. Otto Statistics Research Division, Bureau of the Census Washington, D.C , U.S.A.

SIZE BIAS IN LINE TRANSECT SAMPLING: A FIELD TEST. Mark C. Otto Statistics Research Division, Bureau of the Census Washington, D.C , U.S.A. SIZE BIAS IN LINE TRANSECT SAMPLING: A FIELD TEST Mark C. Ott Statistics Research Divisin, Bureau f the Census Washingtn, D.C. 20233, U.S.A. and Kenneth H. Pllck Department f Statistics, Nrth Carlina State

More information

Collocation Map for Overcoming Data Sparseness

Collocation Map for Overcoming Data Sparseness Cllcatin Map fr Overcming Data Sparseness Mnj Kim, Yung S. Han, and Key-Sun Chi Department f Cmputer Science Krea Advanced Institute f Science and Technlgy Taejn, 305-701, Krea mj0712~eve.kaist.ac.kr,

More information

Salem International University School of Business Program Review Final Year Review. August 2016 Submitted by. Dr.

Salem International University School of Business Program Review Final Year Review. August 2016 Submitted by. Dr. Salem Internatinal University Schl f Business Prgram Review 2015-2016 Final Year Review August 2016 Submitted by Dr. Marc Getty, Dean Academic Year 2015-2016 Changes 2015-2016 Cmplete preliminary questinnaire

More information

EASTERN ARIZONA COLLEGE Precalculus Trigonometry

EASTERN ARIZONA COLLEGE Precalculus Trigonometry EASTERN ARIZONA COLLEGE Precalculus Trignmetry Curse Design 2017-2018 Curse Infrmatin Divisin Mathematics Curse Number MAT 181 Title Precalculus Trignmetry Credits 3 Develped by Gary Rth Lecture/Lab Rati

More information

The steps of the engineering design process are to:

The steps of the engineering design process are to: The engineering design prcess is a series f steps that engineers fllw t cme up with a slutin t a prblem. Many times the slutin invlves designing a prduct (like a machine r cmputer cde) that meets certain

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

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9. Sectin 7 Mdel Assessment This sectin is based n Stck and Watsn s Chapter 9. Internal vs. external validity Internal validity refers t whether the analysis is valid fr the ppulatin and sample being studied.

More information

INTERNATIONAL BIRD STRIKE COMMITTEE IBSC27/WP X-3 Athens, May 2005 BIRD AVOIDANCE MODELS VS. REALTIME BIRDSTRIKE WARNING SYSTEMS A COMPARISON

INTERNATIONAL BIRD STRIKE COMMITTEE IBSC27/WP X-3 Athens, May 2005 BIRD AVOIDANCE MODELS VS. REALTIME BIRDSTRIKE WARNING SYSTEMS A COMPARISON INTERNATIONAL BIRD STRIKE COMMITTEE IBSC27/WP X-3 Athens, 23-27 May 2005 BIRD AVOIDANCE MODELS VS. REALTIME BIRDSTRIKE WARNING SYSTEMS A COMPARISON Wilhelm Ruhe, Dipl.Met., M.Sc. Bundeswehr Ge Infrmatin

More information

LECTURE NOTES. Chapter 3: Classical Macroeconomics: Output and Employment. 1. The starting point

LECTURE NOTES. Chapter 3: Classical Macroeconomics: Output and Employment. 1. The starting point LECTURE NOTES Chapter 3: Classical Macrecnmics: Output and Emplyment 1. The starting pint The Keynesian revlutin was against classical ecnmics (rthdx ecnmics) Keynes refer t all ecnmists befre 1936 as

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

Bachelor (BSc) i teknisk videnskab (mekatronik) Bachelor of Science (BSc) in Engineering (Mechatronics)

Bachelor (BSc) i teknisk videnskab (mekatronik) Bachelor of Science (BSc) in Engineering (Mechatronics) Chapter 9 Prgramme specific part f the curriculum fr Bachelr (BSc) i teknisk videnskab (mekatrnik) Bachelr f Science (BSc) in Engineering (Mechatrnics) Applicable t students admitted September 2015 nwards

More information

Comprehensive Exam Guidelines Department of Chemical and Biomolecular Engineering, Ohio University

Comprehensive Exam Guidelines Department of Chemical and Biomolecular Engineering, Ohio University Cmprehensive Exam Guidelines Department f Chemical and Bimlecular Engineering, Ohi University Purpse In the Cmprehensive Exam, the student prepares an ral and a written research prpsal. The Cmprehensive

More information

Sample questions to support inquiry with students:

Sample questions to support inquiry with students: Area f Learning: Mathematics Calculus 12 Big Ideas Elabratins The cncept f a limit is fundatinal t calculus. cncept f a limit: Differentiatin and integratin are defined using limits. Sample questins t

More information

Report on experiences abroad. Study semester at the Higher School of Economics, Moscow, Russia

Report on experiences abroad. Study semester at the Higher School of Economics, Moscow, Russia Reprt n experiences abrad Study semester at the Higher Schl f Ecnmics, Mscw, Russia Summer term 2005 (March July 2005) By Nicle Petrick Idea The idea f spending ne semester in Russia develped already a

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

Verification of Quality Parameters of a Solar Panel and Modification in Formulae of its Series Resistance

Verification of Quality Parameters of a Solar Panel and Modification in Formulae of its Series Resistance Verificatin f Quality Parameters f a Slar Panel and Mdificatin in Frmulae f its Series Resistance Sanika Gawhane Pune-411037-India Onkar Hule Pune-411037- India Chinmy Kulkarni Pune-411037-India Ojas Pandav

More information

Assessment Primer: Writing Instructional Objectives

Assessment Primer: Writing Instructional Objectives Assessment Primer: Writing Instructinal Objectives (Based n Preparing Instructinal Objectives by Mager 1962 and Preparing Instructinal Objectives: A critical tl in the develpment f effective instructin

More information

EDUCATION PhD in Financial Mathematics ETH Zürich starting in 09/2018 Zürich, Switzerland

EDUCATION PhD in Financial Mathematics ETH Zürich starting in 09/2018 Zürich, Switzerland BALINT GERSEY bg368@alumni.cam.ac.uk (+44) 7802 703765 Avenue de Diane 7 bx 4, 1410 Waterl, Belgium https://github.cm/balintgersey https://www.researchgate.net/prfile/balint_gersey https://www.linkedin.cm/in/balintgersey/

More information

AC : A NEW APPROACH FOR TEACHING IN-PLANE PRINCIPAL STRESSES, PRINCIPAL DIRECTIONS AND MAXIMUM SHEAR STRESS FOR PLANE STRESS

AC : A NEW APPROACH FOR TEACHING IN-PLANE PRINCIPAL STRESSES, PRINCIPAL DIRECTIONS AND MAXIMUM SHEAR STRESS FOR PLANE STRESS AC 007-1374: A NEW APPROACH FOR TEACHING IN-PLANE PRINCIPAL STRESSES, PRINCIPAL DIRECTIONS AND MAXIMUM SHEAR STRESS FOR PLANE STRESS Karim Muci-Küchler, Suth Dakta Schl f Mines and Technlgy Dr. Karim Muci-Küchler

More information

Direct Monte Carlo Simulation of Time- Dependent Problems

Direct Monte Carlo Simulation of Time- Dependent Problems the Technlgy Interface/Fall 007 Direct Mnte Carl Simulatin f Time- Depent Prblems by Matthew. N. O. Sadiku, Cajetan M. Akujubi, Sarhan M. Musa, and Sudarshan R. Nelatury Center f Excellence fr Cmmunicatin

More information

Pure adaptive search for finite global optimization*

Pure adaptive search for finite global optimization* Mathematical Prgramming 69 (1995) 443-448 Pure adaptive search fr finite glbal ptimizatin* Z.B. Zabinskya.*, G.R. Wd b, M.A. Steel c, W.P. Baritmpa c a Industrial Engineering Prgram, FU-20. University

More information

Analysis on the Stability of Reservoir Soil Slope Based on Fuzzy Artificial Neural Network

Analysis on the Stability of Reservoir Soil Slope Based on Fuzzy Artificial Neural Network Research Jurnal f Applied Sciences, Engineering and Technlgy 5(2): 465-469, 2013 ISSN: 2040-7459; E-ISSN: 2040-7467 Maxwell Scientific Organizatin, 2013 Submitted: May 08, 2012 Accepted: May 29, 2012 Published:

More information

Elements of Machine Intelligence - I

Elements of Machine Intelligence - I ECE-175A Elements f Machine Intelligence - I Ken Kreutz-Delgad Nun Vascncels ECE Department, UCSD Winter 2011 The curse The curse will cver basic, but imprtant, aspects f machine learning and pattern recgnitin

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

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

Simulation of Line Outage Distribution Factors (L.O.D.F) Calculation for N-Buses System

Simulation of Line Outage Distribution Factors (L.O.D.F) Calculation for N-Buses System Simulatin f Line Outage Distributin Factrs (L.O.D.F) Calculatin fr N-Buses System Rashid H. AL-Rubayi Department f Electrical Engineering, University f Technlgy Afaneen A. Abd Department f Electrical Engineering,

More information

Teacher s Training in the Czech Republic II

Teacher s Training in the Czech Republic II Teacher s Training in the Czech Republic II Marcela Grecvá, Zdeněk Hrdlička, Vernika Ppvá Institute f Chemical Technlgy Prague (Czech Republic) Zdenek.Hrdlicka@vscht.cz Abstract 518300-LLP-2011-IT-COMENIUS-CNW

More information

West Deptford Middle School 8th Grade Curriculum Unit 4 Investigate Bivariate Data

West Deptford Middle School 8th Grade Curriculum Unit 4 Investigate Bivariate Data West Deptfrd Middle Schl 8th Grade Curriculum Unit 4 Investigate Bivariate Data Office f Curriculum and Instructin West Deptfrd Middle Schl 675 Grve Rd, Paulsbr, NJ 08066 wdeptfrd.k12.nj.us (856) 848-1200

More information

Professional Development. Implementing the NGSS: High School Physics

Professional Development. Implementing the NGSS: High School Physics Prfessinal Develpment Implementing the NGSS: High Schl Physics This is a dem. The 30-min vide webinar is available in the full PD. Get it here. Tday s Learning Objectives NGSS key cncepts why this is different

More information

Computational modeling techniques

Computational modeling techniques Cmputatinal mdeling techniques Lecture 3: Mdeling change (2) Mdeling using prprtinality Mdeling using gemetric similarity In Petre Department f IT, Ab Akademi http://www.users.ab.fi/ipetre/cmpmd/ http://users.ab.fi/ipetre/cmpmd/

More information

KATE GLEASON COLLEGE OF ENGINEERING PATHWAYS TO STUDY ABROAD

KATE GLEASON COLLEGE OF ENGINEERING PATHWAYS TO STUDY ABROAD KTE GLESON COLLEGE OF ENGINEERING PTHWYS TO STUDY ROD imedical Engineering S Chemical Engineering S Cmputer Engineering S Cre Prgram in Electrical Engineering S Industrial Engineering S Mechanical Engineering

More information

Ecology 302 Lecture III. Exponential Growth (Gotelli, Chapter 1; Ricklefs, Chapter 11, pp )

Ecology 302 Lecture III. Exponential Growth (Gotelli, Chapter 1; Ricklefs, Chapter 11, pp ) Eclgy 302 Lecture III. Expnential Grwth (Gtelli, Chapter 1; Ricklefs, Chapter 11, pp. 222-227) Apcalypse nw. The Santa Ana Watershed Prject Authrity pulls n punches in prtraying its missin in apcalyptic

More information

COMP 551 Applied Machine Learning Lecture 5: Generative models for linear classification

COMP 551 Applied Machine Learning Lecture 5: Generative models for linear classification COMP 551 Applied Machine Learning Lecture 5: Generative mdels fr linear classificatin Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Jelle Pineau Class web page: www.cs.mcgill.ca/~hvanh2/cmp551

More information

Level Control in Horizontal Tank by Fuzzy-PID Cascade Controller

Level Control in Horizontal Tank by Fuzzy-PID Cascade Controller Wrld Academy f Science, Engineering and Technlgy 5 007 Level Cntrl in Hrizntal Tank by Fuzzy-PID Cascade Cntrller Satean Tunyasrirut, and Santi Wangnipparnt Abstract The paper describes the Fuzzy PID cascade

More information

Advice to 1968 Software Engineers

Advice to 1968 Software Engineers Advice t 1968 Sftware Engineers Sftware Evlutin Prgram Understanding Security - Daniel, Brad, Dave, Gerge Sftware Evlutin Sftware will have t g thrugh cntinual change in rder t adapt with its envirnment

More information

Explore Pollination. Cups Scissors Computers or tablets with internet access Poster board & markers

Explore Pollination. Cups Scissors Computers or tablets with internet access Poster board & markers Lessn Plan fr Grades: Middle Schl Length f Lessn: 90 minutes Authred by: UT Envirnmental Science Institute Date created: 05/10/2017 Subject area/curse: Science Materials: Pipe cleaners Tissue Paper Tape

More information

EXPERIMENTAL STUDY ON DISCHARGE COEFFICIENT OF OUTFLOW OPENING FOR PREDICTING CROSS-VENTILATION FLOW RATE

EXPERIMENTAL STUDY ON DISCHARGE COEFFICIENT OF OUTFLOW OPENING FOR PREDICTING CROSS-VENTILATION FLOW RATE EXPERIMENTAL STUD ON DISCHARGE COEFFICIENT OF OUTFLOW OPENING FOR PREDICTING CROSS-VENTILATION FLOW RATE Tmnbu Gt, Masaaki Ohba, Takashi Kurabuchi 2, Tmyuki End 3, shihik Akamine 4, and Tshihir Nnaka 2

More information

INSTRUCTIONAL PLAN Day 2

INSTRUCTIONAL PLAN Day 2 INSTRUCTIONAL PLAN Day 2 Subject: Trignmetry Tpic: Other Trignmetric Ratis, Relatinships between Trignmetric Ratis, and Inverses Target Learners: Cllege Students Objectives: At the end f the lessn, students

More information