Churn Prediction using Dynamic RFMAugmented node2vec


 Claude Simpson
 2 years ago
 Views:
Transcription
1 Churn Predictin using Dynamic RFMAugmented nde2vec Sandra Mitrvić, Jchen de Weerdt, Bart Baesens & Wilfried Lemahieu Department f Decisin Sciences and Infrmatin Management, KU Leuven 18 September 2017, DyN Wrkshp, ECML 2017 Skpje, Macednia
2 Outline Intrductin Mtivatin Methdlgy Experimental evaluatin Results Cnclusin Future wrk 2 Churn Predictin using Dynamic RFMAugmented nde2vec
3 Intrductin Churn predictin (CP) Predict which custmers are ging t leave cmpany s services Still cnsidered as tpmst challenge fr Telcs (FCC reprt, 2009) Due t acquisitin/retentin cst imbalance Different types f data used fr CP Subscriptin, scidemgraphic, custmer cmplaints etc. Mre recently: Call Detail Recrds (CDRs) CDRs > call graphs 3 Churn Predictin using Dynamic RFMAugmented nde2vec
4 Call graph featurizatin Extracting infrmative features frm (call) graphs An intricate prcess, due t: Cmplex structure / different types f infrmatin Tplgybased (structural) Interactinbased (as part f custmer behavir) Edge weights quantifying custmer behavir Dynamic aspect Call graph are timeevlving Bth ndes and edges vlatile Churn = lack f activity 4 Churn Predictin using Dynamic RFMAugmented nde2vec
5 Mtivatin Prblems identified (w.r.t. current literature) Nt many studies accunt fr dynamic aspects f call netwrks Our gal Especially nt jintly with interactin and structural features Structural features are underexplited Due t high cmputatinal time in large graphs (e.g. betweenness centrality) And withut using adhc handcrafted features N featurizatin methdlgy Dataset dependent Perfrming hlistic featurizatin f call graphs Incrprating bth interactin and structural infrmatin Aviding/reducing feature handcrafting While als capturing the dynamic aspect f the netwrk 5 Churn Predictin using Dynamic RFMAugmented nde2vec
6 Methdlgy Hw d we address these gals? G1: Incrprating bth interactin and structural infrmatin Devise different peratinalizatins f RFM features and nvel RFMaugmented call graph architectures G2: Aviding/reducing feature handcrafting Opt fr representatin learning G3: Capturing the dynamic aspect f the netwrk Slice riginal netwrk int weekly snapshts 6 Churn Predictin using Dynamic RFMAugmented nde2vec
7 Integrating interactin and structural infrmatin Interactins (current literature) Interactins (this wrk) Usually delineated with RFM (Recency,Frequency,Mnetary) variables Benefits: Simple Yet still with gd predictive pwer Many different peratinalizatins Different dimensins Different granularities Summary RFM (RFM s ) Detailed RFM (RFM d ) Directin & destinatin sliced: X ut_h, X ut_, X in, X {R,F,M} Churn RFM (RFM ch ) Only w.r.t. churners 7 Churn Predictin using Dynamic RFMAugmented nde2vec
8 RFMAugmented netwrks Original tplgy extended By intrducing artificial ndes based n RFM Structural infrmatin partially preserved Each f R, F, M partitined int 5 quantiles One artificial nde assigned t each quantile Interactin inf embedded thrugh extended tplgy RFM features Netwrk tplgy 4 augmented netwrks RFM s RFM s RFM ch RFM d + RFM d RFM ch AG s AG s+ch AG d AG d+ch 8 Churn Predictin using Dynamic RFMAugmented nde2vec
9 Representatin learning Nde2vec Idea: Bring the representatins f the wrds frm the same cntext C clse (brrwed frm SkipGram) Learn f, f: V > R d, d<< V s.t. max Σ v in V lg Pr(C v f(v)) Definitin f cntext in graph setting? Neighbrhds/Randm walks Of which rder? Hw t perfrm a walk? Flexible walks using additinal parameters Return parameter p Inut parameter q Cming frm i, prbability t transitin w jk, if d ik = 1 frm j t k is: w jk /p, if d ik = 0 w jk /q, if d ik = 2 9 Figure surce: Grver & Leskvec, 2016 Churn Predictin using Dynamic RFMAugmented nde2vec
10 Nde2vec > scalable nde2vec Nde2vec Accunts bth fr previus and current nde Additinal parameters (p,q) T make walks efficient, requires precmputatin f prbability transitins: On nde level (1 st time) Scalable nde2vec Accunts nly fr current nde N additinal parameters Requires precmputatin f prbability transitins nly n nde level Alias sampling retained On edge level (successive) Alias sampling used fr efficient sampling reduces O(n) t O(1) Therefre, scales well even n large graphs! Hwever, des nt scale well n large graphs! (ur case ~ 40M edges) 10 Churn Predictin using Dynamic RFMAugmented nde2vec
11 Dynamic graphs Different definitins (current literature) G = (V, E, T) G = (V, E, T, ΔT) G = (V, E, T, σ, ΔT) Standard apprach Cnsider several static snapshts f a dynamic graph Our setting Mnthly call graph G = (V, E) > Fur tempral graphs G i = (V i, E i, w i ), i =1,..,4 11 Churn Predictin using Dynamic RFMAugmented nde2vec
12 Methdlgy Graphical verview 12 Churn Predictin using Dynamic RFMAugmented nde2vec
13 Experimental Evaluatin (1/2) One prepaid, ne pstpaid dataset 4 mnths data (nly CDRs) Undirected netwrks Mdel Lgistic regressin with L 2 regul. (10fld CV fr tuning hyperparam.) Evaluatin AUC, lift (0.5%) Parameter Scalable nde2vec # walks 10 walk length 30 cntext size 10 # dimen. 128 # iteratins 5 13 Churn Predictin using Dynamic RFMAugmented nde2vec
14 Experimental Evaluatin (2/2) Research questins RQ1: D features taking int accunt dynamic aspects perfrm better than static nes? RQ2: D RFMaugmented netwrk cnstructins imprve predictive perfrmance? RQ3: Des the granularity f interactin infrmatin (summary, summary +churn, detailed, detailed+churn) influence the predictive perfrmance? Experiments RFM s stat. vs. RFM s dyn. vs. AG s stat. vs. AG s dyn. > summary RFM s+ch stat. vs. RFM s+ch dyn. vs. AG s+ch stat. vs. AG s+ch dyn. > summary+churn RFM d stat. vs. RFM d dyn. vs. AG d stat. vs. AG d dyn. > detailed RFM d+ch stat. vs. RFM d+ch dyn. vs. AG d+ch stat. vs. Ag d+ch dyn. > detailed+churn 14 Churn Predictin using Dynamic RFMAugmented nde2vec
15 Experimental results (1/2) Prepaid RQ1 Answer: Dynamic better than static! RQ2 Answer: RFMaugmented netwrks imprve predictive perfrmance RQ3 Answer: Best perfrming interactin granularity is: summary+churn Secnd best: detailed+churn 15 Churn Predictin using Dynamic RFMAugmented nde2vec
16 Experimental results (2/2) Pstpaid RQ1 Answer: Dynamic better than static! RQ2 Answer: RFMaugmented netwrks imprve predictive perfrmance RQ3 Answer: Best perfrming interactin granularity is summary+churn Secnd best: summary 16 Churn Predictin using Dynamic RFMAugmented nde2vec
17 Cnclusin We design RFMaugmentatins f riginal graphs Enable cnjining interactin and structural infrmatin We devise a scalable adaptin f the riginal nde2vec apprach Relaxing randm walk generatin and aviding grid search tuning fr tw additinal parameters Cnducted experiments shwcase the perfrmance benefits which stem frm taking int accunt the dynamic aspect Nvelty: Als frm expliting RFMaugmented netwrks and learning nde representatins frm these First wrk bth in using (dynamic) nde representatins in CDR graphs fr churn predictin and First wrk in applying the RFM framewrk tgether with unsupervised and dynamic learning f nde representatins 17 Churn Predictin using Dynamic RFMAugmented nde2vec
18 Future research Attempt capturing call dynamics in a mre sphisticated manner (e.g. the rdering f calls, their interevent time distributin) Investigate the effect f different time granularities Explre whether priritizing mre recent dynamic netwrks imprves perfrmance 18 Churn Predictin using Dynamic RFMAugmented nde2vec
19 Thank yu! Questins?
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. ElSharkawi, 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 informationEric 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 informationAdmin. MDP Search Trees. Optimal Quantities. Reinforcement Learning
Admin Reinfrcement Learning Cntent adapted frm Berkeley CS188 MDP Search Trees Each MDP state prjects an expectimaxlike search tree Optimal Quantities The value (utility) f a state s: V*(s) = expected
More informationResampling Methods. Crossvalidation, Bootstrapping. Marek Petrik 2/21/2017
Resampling Methds Crssvalidatin, 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 informationDo big losses in judgmental adjustments affect experts behaviour? Fotios Petropoulos, Robert Fildes and Paul Goodwin
D big lsses in judgmental adjustments affect experts behaviur? Ftis Petrpuls, Rbert Fildes and Paul Gdwin This material has been created and cpyrighted by Lancaster Centre fr Frecasting, Lancaster University
More informationLecture 2: Supervised vs. unsupervised learning, biasvariance tradeoff
Lecture 2: Supervised vs. unsupervised learning, biasvariance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationA Scalable Recurrent Neural Network Framework for Modelfree
A Scalable Recurrent Neural Netwrk Framewrk fr Mdelfree POMDPs April 3, 2007 Zhenzhen Liu, Itamar Elhanany Machine Intelligence Lab Department f Electrical and Cmputer Engineering The University f Tennessee
More informationPSU 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 informationLecture 2: Supervised vs. unsupervised learning, biasvariance tradeoff
Lecture 2: Supervised vs. unsupervised learning, biasvariance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationHypothesis 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 informationMultiple Source Multiple. using Network Coding
Multiple Surce Multiple Destinatin Tplgy Inference using Netwrk Cding Pegah Sattari EECS, UC Irvine Jint wrk with Athina Markpulu, at UCI, Christina Fraguli, at EPFL, Lausanne Outline Netwrk Tmgraphy Gal,
More informationComputational 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 informationCHAPTER 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 informationENSC Discrete Time Systems. Project Outline. Semester
ENSC 49  iscrete Time Systems Prject Outline Semester 0061. 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 informationNUROP 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 informationFive Whys How To Do It Better
Five Whys Definitin. As explained in the previus article, we define rt cause as simply the uncvering f hw the current prblem came int being. Fr a simple causal chain, it is the entire chain. Fr a cmplex
More informationCS 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 informationGreen economic transformation in Europe: territorial performance, potentials and implications
ESPON Wrkshp: Green Ecnmy in Eurpean Regins? Green ecnmic transfrmatin in Eurpe: territrial perfrmance, ptentials and implicatins Rasmus Ole Rasmussen, NORDREGIO 29 September 2014, Brussels Green Grwth:
More informationChapter 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.. KMeans Methd 3..3 KMedids Methd 3..4 CLARA
More informationResampling Methods. Chapter 5. Chapter 5 1 / 52
Resampling Methds Chapter 5 Chapter 5 1 / 52 1 51 Validatin set apprach 2 52 Crss validatin 3 53 Btstrap Chapter 5 2 / 52 Abut Resampling An imprtant statistical tl Pretending the data as ppulatin and
More informationEnhancing Performance of MLP/RBF Neural Classifiers via an Multivariate Data Distribution Scheme
Enhancing Perfrmance f / Neural Classifiers via an Multivariate Data Distributin Scheme Halis Altun, Gökhan Gelen Nigde University, Electrical and Electrnics Engineering Department Nigde, Turkey haltun@nigde.edu.tr
More informationProfessional Development. Implementing the NGSS: High School Physics
Prfessinal Develpment Implementing the NGSS: High Schl Physics This is a dem. The 30min vide webinar is available in the full PD. Get it here. Tday s Learning Objectives NGSS key cncepts why this is different
More informationTHE LIFE OF AN OBJECT IT SYSTEMS
THE LIFE OF AN OBJECT IT SYSTEMS Persns, bjects, r cncepts frm the real wrld, which we mdel as bjects in the IT system, have "lives". Actually, they have tw lives; the riginal in the real wrld has a life,
More informationknearest 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 MemryBased Learning InstanceBased Learning Kearest eighbr Inductive Assumptin Similar inputs map t similar utputs If nt true => learning is impssible If true => learning reduces t
More informationLand Information New Zealand Topographic Strategy DRAFT (for discussion)
Land Infrmatin New Zealand Tpgraphic Strategy DRAFT (fr discussin) Natinal Tpgraphic Office Intrductin The Land Infrmatin New Zealand Tpgraphic Strategy will prvide directin fr the cllectin and maintenance
More informationCAUSAL 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 informationModelling of Clock Behaviour. Don Percival. Applied Physics Laboratory University of Washington Seattle, Washington, USA
Mdelling f Clck Behaviur Dn Percival Applied Physics Labratry University f Washingtn Seattle, Washingtn, USA verheads and paper fr talk available at http://faculty.washingtn.edu/dbp/talks.html 1 Overview
More informationIntelligent 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 CdrniuBusiness
More informationChecking 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 04, 017 BulgneBillancurt,
More informationMODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards:
MODULE FOUR This mdule addresses functins SC Academic Standards: EA3.1 Classify a relatinship as being either a functin r nt a functin when given data as a table, set f rdered pairs, r graph. EA3.2 Use
More informationPattern 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 informationTuring Machines. Humanaware Robotics. 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Announcement:
Turing Machines Humanaware 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/tmii.pdf
More informationx 1 Outline IAML: Logistic Regression Decision Boundaries Example Data
Outline IAML: Lgistic Regressin Charles Suttn and Victr Lavrenk Schl f Infrmatics Semester Lgistic functin Lgistic regressin Learning lgistic regressin Optimizatin The pwer f nnlinear basis functins Leastsquares
More informationAutonomic Power Management Schemes for Internet Servers and Data Centers. L. Mastroleon, N. Bambos, C. Kozyrakis, D. Economou
IEEE GLOBECOM 2005 Autnmic Pwer Management Schemes fr Internet Servers and Data Centers L. Mastrlen, N. Bambs, C. Kzyrakis, D. Ecnmu December 2005 St Luis, MO Outline Mtivatin The System Mdel and the DP
More informationINSTRUMENTAL 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 informationWe can see from the graph above that the intersection is, i.e., [ ).
MTH 111 Cllege Algebra Lecture Ntes July 2, 2014 Functin Arithmetic: With nt t much difficulty, we ntice that inputs f functins are numbers, and utputs f functins are numbers. S whatever we can d with
More informationThermodynamics 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[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 informationSections 15.1 to 15.12, 16.1 and 16.2 of the textbook (RobbinsMiller) cover the materials required for this topic.
Tpic : AC Fundamentals, Sinusidal Wavefrm, and Phasrs Sectins 5. t 5., 6. and 6. f the textbk (RbbinsMiller) cver the materials required fr this tpic.. Wavefrms in electrical systems are current r vltage
More informationNAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling?
CS4445 ata Mining and Kwledge iscery in atabases. B Term 2014 Exam 1 Nember 24, 2014 Prf. Carlina Ruiz epartment f Cmputer Science Wrcester Plytechnic Institute NAME: Prf. Ruiz Prblem I: Prblem II: Prblem
More informationYou need to be able to define the following terms and answer basic questions about them:
CS440/ECE448 Sectin Q Fall 2017 Midterm Review Yu need t be able t define the fllwing terms and answer basic questins abut them: Intr t AI, agents and envirnments Pssible definitins f AI, prs and cns f
More informationMACHINE LEARNING FOR CLUSTER GALAXY CLASSIFICATION
MACHINE LEARNING FOR CLUSTER GALAXY CLASSIFICATION Silvia de Castr García Directres: Dr. Ricard Pérez Martínez, Dra. Ana María Pérez García 16/03/2018 Machine Learning fr clustergalaxy classificatin
More information, which yields. where z1. and z2
The Gaussian r Nrmal PDF, Page 1 The Gaussian r Nrmal Prbability Density Functin Authr: Jhn M Cimbala, Penn State University Latest revisin: 11 September 13 The Gaussian r Nrmal Prbability Density Functin
More informationNUMBERS, 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 informationExam #1. A. Answer any 1 of the following 2 questions. CEE 371 October 8, Please grade the following questions: 1 or 2
CEE 371 Octber 8, 2009 Exam #1 Clsed Bk, ne sheet f ntes allwed Please answer ne questin frm the first tw, ne frm the secnd tw and ne frm the last three. The ttal ptential number f pints is 100. Shw all
More informationBiochemistry Summer Packet
Bichemistry Summer Packet Science Basics Metric Cnversins All measurements in chemistry are made using the metric system. In using the metric system yu must be able t cnvert between ne value and anther.
More informationFall 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 informationUN Committee of Experts on Environmental Accounting New York, June Peter Cosier Wentworth Group of Concerned Scientists.
UN Cmmittee f Experts n Envirnmental Accunting New Yrk, June 2011 Peter Csier Wentwrth Grup f Cncerned Scientists Speaking Ntes Peter Csier: Directr f the Wentwrth Grup Cncerned Scientists based in Sydney,
More informationEmphases in Common Core Standards for Mathematical Content Kindergarten High School
Emphases in Cmmn Cre Standards fr Mathematical Cntent Kindergarten High Schl Cntent Emphases by Cluster March 12, 2012 Describes cntent emphases in the standards at the cluster level fr each grade. These
More informationCOMP 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 informationNETSYN : a connectionist approach to synthesis knowledge acquisition and use
Carnegie Melln University Research Shwcase @ CMU Department f Electrical and Cmputer Engineering Carnegie Institute f Technlgy 1992 NETSYN : a cnnectinist apprach t synthesis knwledge acquisitin and use
More informationELE Final Exam  Dec. 2018
ELE 509 Final Exam Dec 2018 1 Cnsider tw Gaussian randm sequences X[n] and Y[n] Assume that they are independent f each ther with means and autcvariances μ ' 3 μ * 4 C ' [m] 1 2 1 3 and C * [m] 3 1 10
More informationTechnical Bulletin. Generation Interconnection Procedures. Revisions to Cluster 4, Phase 1 Study Methodology
Technical Bulletin Generatin Intercnnectin Prcedures Revisins t Cluster 4, Phase 1 Study Methdlgy Release Date: Octber 20, 2011 (Finalizatin f the Draft Technical Bulletin released n September 19, 2011)
More informationWe respond to each of ORR s specific consultation questions in Annex A to this letter.
Je Quill Office f Rail Regulatin One Kemble Street Lndn, WC2B 4AN Hannah Devesn Regulatry Refrm Specialist Netwrk Rail Kings Place, 90 Yrk Way Lndn, N1 9AG Email:hannah.devesn@netwrkrail.c.uk Telephne:
More informationGetting Involved O. Responsibilities of a Member. People Are Depending On You. Participation Is Important. Think It Through
f Getting Invlved O Literature Circles can be fun. It is exciting t be part f a grup that shares smething. S get invlved, read, think, and talk abut bks! Respnsibilities f a Member Remember a Literature
More informationSection 5.8 Notes Page Exponential Growth and Decay Models; Newton s Law
Sectin 5.8 Ntes Page 1 5.8 Expnential Grwth and Decay Mdels; Newtn s Law There are many applicatins t expnential functins that we will fcus n in this sectin. First let s lk at the expnential mdel. Expnential
More informationYour appetizing introduction should consist of three important ingredients:
The Delicius Structure f an Essay I. Intrductry Paragraph: The Scintillating Appetizer Make yur readers salivate with desire t learn mre Yur appetizing intrductin shuld cnsist f three imprtant ingredients:
More informationIEEE Int. Conf. Evolutionary Computation, Nagoya, Japan, May 1996, pp. 366{ Evolutionary Planner/Navigator: Operator Performance and
IEEE Int. Cnf. Evlutinary Cmputatin, Nagya, Japan, May 1996, pp. 366{371. 1 Evlutinary Planner/Navigatr: Operatr Perfrmance and SelfTuning Jing Xia, Zbigniew Michalewicz, and Lixin Zhang Cmputer Science
More informationCONSTRUCTING STATECHART DIAGRAMS
CONSTRUCTING STATECHART DIAGRAMS The fllwing checklist shws the necessary steps fr cnstructing the statechart diagrams f a class. Subsequently, we will explain the individual steps further. Checklist 4.6
More informationBuilding to Transformations on Coordinate Axis Grade 5: Geometry Graph points on the coordinate plane to solve realworld and mathematical problems.
Building t Transfrmatins n Crdinate Axis Grade 5: Gemetry Graph pints n the crdinate plane t slve realwrld and mathematical prblems. 5.G.1. Use a pair f perpendicular number lines, called axes, t define
More informationPhys. 344 Ch 7 Lecture 8 Fri., April. 10 th,
Phys. 344 Ch 7 Lecture 8 Fri., April. 0 th, 009 Fri. 4/0 8. Ising Mdel f Ferrmagnets HW30 66, 74 Mn. 4/3 Review Sat. 4/8 3pm Exam 3 HW Mnday: Review fr est 3. See nline practice test lectureprep is t
More informationExcessive Social Imbalances and the Performance of Welfare States in the EU. Frank Vandenbroucke, Ron Diris and Gerlinde Verbist
Excessive Scial Imbalances and the Perfrmance f Welfare States in the EU Frank Vandenbrucke, Rn Diris and Gerlinde Verbist Child pverty in the Eurzne, SILC 2008 35.00 30.00 25.00 20.00 15.00 10.00 5.00.00
More informationDistributions, 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 informationAP Statistics Practice Test Unit Three Exploring Relationships Between Variables. Name Period Date
AP Statistics Practice Test Unit Three Explring Relatinships Between Variables Name Perid Date True r False: 1. Crrelatin and regressin require explanatry and respnse variables. 1. 2. Every least squares
More informationB. Definition of an exponential
Expnents and Lgarithms Chapter IV  Expnents and Lgarithms A. Intrductin Starting with additin and defining the ntatins fr subtractin, multiplicatin and divisin, we discvered negative numbers and fractins.
More informationFloating Point Method for Solving Transportation. Problems with Additional Constraints
Internatinal Mathematical Frum, Vl. 6, 20, n. 40, 983992 Flating Pint Methd fr Slving Transprtatin Prblems with Additinal Cnstraints P. Pandian and D. Anuradha Department f Mathematics, Schl f Advanced
More information4F5 : Performance of an Ideal Gas Cycle 10 pts
4F5 : Perfrmance f an Cycle 0 pts An ideal gas, initially at 0 C and 00 kpa, underges an internally reversible, cyclic prcess in a clsed system. The gas is first cmpressed adiabatically t 500 kpa, then
More informationThis project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement number
This prject has received funding frm the Eurpean Unin s Hrizn 2020 research and innvatin prgramme under grant agreement number 727524. Credit t & http://www.h3uni.rg/ https://ec.eurpa.eu/jrc/en/publicatin/eurscientificandtechnicalresearchreprts/behaviuralinsightsappliedplicyeurpeanreprt2016
More informationNGSS High School Physics Domain Model
NGSS High Schl Physics Dmain Mdel Mtin and Stability: Frces and Interactins HSPS21: Students will be able t analyze data t supprt the claim that Newtn s secnd law f mtin describes the mathematical relatinship
More informationFabrication Thermal Test. Methodology for a Safe Cask Thermal Performance
ENSA (Grup SEPI) Fabricatin Thermal Test. Methdlgy fr a Safe Cask Thermal Perfrmance IAEA Internatinal Cnference n the Management f Spent Fuel frm Nuclear Pwer Reactrs An Integrated Apprach t the BackEnd
More informationhttps://goo.gl/eaqvfo SUMMER REV: HalfLife DUE DATE: JULY 2 nd
NAME: DUE DATE: JULY 2 nd AP Chemistry SUMMER REV: HalfLife Why? Every radiistpe has a characteristic rate f decay measured by its halflife. Halflives can be as shrt as a fractin f a secnd r as lng
More informationThe KullbackLeibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition
The KullbackLeibler 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 information5 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 informationIncreasing Heat Transfer in Microchannels with Surface Acoustic Waves*
Increasing Heat Transfer in Micrchannels with Surface Acustic Waves* Shaun Berry 0/9/04 *This wrk was spnsred by the Department f the Air Frce under Air Frce Cntract #FA8705C000. Opinins, interpretatins,
More informationBootstrap 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 informationWRITING 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 informationDESIGN OPTIMIZATION OF HIGHLIFT CONFIGURATIONS USING A VISCOUS ADJOINTBASED METHOD
DESIGN OPTIMIZATION OF HIGHLIFT CONFIGURATIONS USING A VISCOUS ADJOINTBASED METHOD Sangh Kim Stanfrd University Juan J. Alns Stanfrd University Antny Jamesn Stanfrd University 40th AIAA Aerspace Sciences
More informationIMPROVEMENTS IN THE OPTIMIZATION OF FLEXIBLE MANUFACTURING CELLS MODELLED WITH DISCRETE EVENT DYNAMICS SYSTEMS: APPLICATION TO A REAL FACTORY PROBLEM
IMPROVEMENTS IN THE OPTIMIZATION OF FLEXIBLE MANUFACTURING CELLS MODELLED WITH DISCRETE EVENT DYNAMICS SYSTEMS: APPLICATION TO A REAL FACTORY PROBLEM Dieg R. Rdríguez (a), Mercedes Pérez (b) Juan Manuel
More informationData Mining: Concepts and Techniques. Classification and Prediction. Chapter February 8, 2007 CSE4412: Data Mining 1
Data Mining: Cncepts and Techniques Classificatin and Predictin Chapter 6.46 February 8, 2007 CSE4412: Data Mining 1 Chapter 6 Classificatin and Predictin 1. What is classificatin? What is predictin?
More informationSUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical model for microarray data analysis
SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical mdel fr micrarray data analysis David Rssell Department f Bistatistics M.D. Andersn Cancer Center, Hustn, TX 77030, USA rsselldavid@gmail.cm
More informationExam #1. A. Answer any 1 of the following 2 questions. CEE 371 March 10, Please grade the following questions: 1 or 2
CEE 371 March 10, 2009 Exam #1 Clsed Bk, ne sheet f ntes allwed Please answer ne questin frm the first tw, ne frm the secnd tw and ne frm the last three. The ttal ptential number f pints is 100. Shw all
More informationEDA 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 informationPurpose: 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 informationRevision: 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 informationCollocation Map for Overcoming Data Sparseness
Cllcatin Map fr Overcming Data Sparseness Mnj Kim, Yung S. Han, and KeySun Chi Department f Cmputer Science Krea Advanced Institute f Science and Technlgy Taejn, 305701, Krea mj0712~eve.kaist.ac.kr,
More informationDavid HORN and Irit OPHER. School of Physics and Astronomy. Raymond and Beverly Sackler Faculty of Exact Sciences
Cmplex Dynamics f Neurnal Threshlds David HORN and Irit OPHER Schl f Physics and Astrnmy Raymnd and Beverly Sackler Faculty f Exact Sciences Tel Aviv University, Tel Aviv 69978, Israel hrn@neurn.tau.ac.il
More informationChem 163 Section: Team Number: ALE 24. Voltaic Cells and Standard Cell Potentials. (Reference: 21.2 and 21.3 Silberberg 5 th edition)
Name Chem 163 Sectin: Team Number: ALE 24. Vltaic Cells and Standard Cell Ptentials (Reference: 21.2 and 21.3 Silberberg 5 th editin) What des a vltmeter reading tell us? The Mdel: Standard Reductin and
More informationFibrereinforced plastic composites Declaration of raw material characteristics Part 5: Additional requirements for core materials
CEN/TC 249 N494 Date: 201002 pren xxx5:2010 CEN/TC 249 Secretariat: NBN Fibrereinfrced plastic cmpsites Declaratin f raw material characteristics Part 5: Additinal requirements fr cre materials Einführendes
More informationScalability Evaluation of Big Data Processing Services in Clouds
Bench 2018@Seattle Scalability Evaluatin f Big Data Prcessing Services in Cluds Wei Huang 1,2, Cngfeng Jiang 1,2, Zujie Ren 1,2, Huayu Si 1,2, Jian Wan 3 1 Key Labratry f Cmplex Systems Mdeling and Simulatin,
More informationLCA14206: Scheduler tooling and benchmarking. Tue4Mar, 11:15am, Zoran Markovic, Vincent Guittot
LCA14206: Scheduler tling and benchmarking Tue4Mar, 11:15am, Zran Markvic, Vincent Guittt Scheduler Tls and Benchmarking Frm Energy Aware minisummit @ Ksummit 2013 extract frm [1]: Ing Mlnar came in
More informationIntroduction to Models and Properties
Intrductin t Mdels and Prperties Cmputer Science and Artificial Intelligence Labratry MIT Armand SlarLezama Nv 23, 2015 Nvember 23, 2015 1 Recap Prperties Prperties f variables Prperties at prgram pints
More informationDepartment of Electrical Engineering, University of Waterloo. Introduction
Sectin 4: Sequential Circuits Majr Tpics Types f sequential circuits Flipflps Analysis f clcked sequential circuits Mre and Mealy machines Design f clcked sequential circuits State transitin design methd
More informationIf (IV) is (increased, decreased, changed), then (DV) will (increase, decrease, change) because (reason based on prior research).
Science Fair Prject Set Up Instructins 1) Hypthesis Statement 2) Materials List 3) Prcedures 4) Safety Instructins 5) Data Table 1) Hw t write a HYPOTHESIS STATEMENT Use the fllwing frmat: If (IV) is (increased,
More informationA New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation
IIIl III. A New Evaluatin Measure J. Jiner and L. Werner Abstract The prblems f evaluatin and the needed criteria f evaluatin measures in the SMART system f infrmatin retrieval are reviewed and discussed.
More informationEvaluating 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 informationECE 2100 Circuit Analysis
ECE 2100 Circuit Analysis Lessn 25 Chapter 9 & App B: Passive circuit elements in the phasr representatin Daniel M. Litynski, Ph.D. http://hmepages.wmich.edu/~dlitynsk/ ECE 2100 Circuit Analysis Lessn
More informationLHS Mathematics Department Honors PreCalculus Final Exam 2002 Answers
LHS Mathematics Department Hnrs Prealculus Final Eam nswers Part Shrt Prblems The table at the right gives the ppulatin f Massachusetts ver the past several decades Using an epnential mdel, predict the
More informationSIZE 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 informationPart 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 informationCOMP9444 Neural Networks and Deep Learning 3. Backpropagation
COMP9444 Neural Netwrks and Deep Learning 3. Backprpagatin Tetbk, Sectins 4.3, 5.2, 6.5.2 COMP9444 17s2 Backprpagatin 1 Outline Supervised Learning Ockham s Razr (5.2) MultiLayer Netwrks Gradient Descent
More information