Scalability Evaluation of Big Data Processing Services in Clouds
|
|
- Martha Owen
- 5 years ago
- Views:
Transcription
1 Bench 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, Ministry f Educatin, Hangzhu ,China 2 Schl f Cmputer Science and Technlgy Hangzhu Dianzi University, Hangzhu , China 3 Department f Sftware Engineering, Zhejiang University f Science and Technlgy, Hangzhu, China 2018/12/29 1
2 Outline Intrductin Related Wrk Experiment and Analysis Implicatins
3 Intrductin Typical examples f clud-based big data prcessing services include Amazn EMR, Micrsft Azure HDInsight, and AliClud E-MapReduce. Amng varius clud-based data prcessing services, hw t scale the system is still challenging. Hw t evaluate the scalability f a big data prcessing system? Given a grup f wrklad, shuld user scale-up r scale-ut their deplyed cluster? i.e., hw t select the cluster cnfiguratin r rent a pre-cnfigured big data prcessing platfrm fr better perfrmance?
4 Related Wrk Big data benchmark: CludSuite BigDataBench HiBench Sme research effrts have been dne fr evaluating big data system Cmparisn f scalability f different service prviders is still missing.
5 Our Wrk We prpsed evaluatin mdel fr the scalability f big data prcessing system in cluds We evaluated the perfrmance f Hadp and Spark n AliClud and BaiduClud s big data prcessing platfrm in tw dimensins f scaleut and scale-up cnfiguratins
6 Evaluatin mdel Speedup measurement: S " represents the speed-up rati: S $ = M ' /M " (i.e., 1 nde ver multiple ndes) Scalability can be divided int three categries: 1. Linear acceleratin 2. Sub-linear acceleratin 3. Super linear acceleratin
7 Evaluatin mdel Acceleratin classificatin
8 Evaluatin mdel Fit the speed-up rati curve: S = f(p) Measure the scalability f the system by: Q = f p dp
9 Experiment and Analysis Platfrms: AliClud E-MapReduce Baidu Clud MRS Wrklads: Terasrt, WrdCunt System cnfiguratin fr the hst
10 Experiment and Analysis Scale-ut n AliClud(terasrt) AliClud Terasrt executin time AliClud Terasrt speed-up rati
11 Experiment and Analysis Scale-ut n AliClud (wrdcunt) WrdCunt executin time WrdCunt speed-up rati
12 Experiment and Analysis Scale-ut n Baidu MRS Terasrt executin time Terasrt speed-up rati
13 Experiment and Analysis Summary f Scale-ut cmparisn: 1. In the cmparisn f the speed-up rati n AliClud, (less than 8 ndes), scalability f Spark is better than Hadp, then Spark s scalability is wrse than Hadp(larger than 8 ndes). 2. When Hadp and Spark scale ut t 16 ndes, the scale-ut perfrmance is gd, and Hadp verall perfrmance(executin time) is better than the Spark in AliClud.
14 Experiment and Analysis Scale-up experiment(nly n AliClud)
15 Experiment and Analysis Executin time fr scale-up cnfig
16 Experiment and Analysis Cmparisn between scale-ut and scale-up
17 Implicatin #1 The scalability f Hadp and Spark are gd enugh n AliClud and Baidu Clud Hadp s scalability is slightly better than Spark n AliClud. Spark s speed is faster than Hadp n AliClud under WrdCunt wrklad The scalability f Hadp n Baidu Clud, is better than that n AliClud.
18 Implicatin #2 Fr Hadp, scale-up is better than scale-ut under the metric f prcessing perfrmance(executin time).hwever, it s nt true fr Spark. This means that scale-up the Spark cluster may nt achieve expected perfrmance imprvement. Here a dirty little secret is that scale-ut is nt mre expensive than scale-up. The results presented here can be suggestins fr Clud services prvider t design mre scalable big data prcessing services avid lss f custmers.
19 Cnclusins Different big data prcessing systems have different scalability Users shuld chse scale-ut r scale-up wisely Clud services prvider can d mre t prvide mre scalable big data prcessing services
20 Thanks!
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 informationENSC 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 informationThe 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 informationPerformance Bounds for Detect and Avoid Signal Sensing
Perfrmance unds fr Detect and Avid Signal Sensing Sam Reisenfeld Real-ime Infrmatin etwrks, University f echnlgy, Sydney, radway, SW 007, Australia samr@uts.edu.au Abstract Detect and Avid (DAA) is a Cgnitive
More informationChurn 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 informationNGSS 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 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 informationWeb-based GIS Systems for Radionuclides Monitoring. Dr. Todd Pierce Locus Technologies
Web-based GIS Systems fr Radinuclides Mnitring Dr. Tdd Pierce Lcus Technlgies Lcus Technlgies 2014 Overview What is the prblem? Nuclear pwer plant peratrs need t mnitr radinuclides t safeguard the envirnment
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 informationSPECIMEN. Candidate Surname. Candidate Number
Candidate Frename General Certificate f Secndary Educatin Mdern Freign Languages Prtuguese - Writing Specimen Paper Candidates answer n the questin paper. Additinal materials: nne Centre Number Candidate
More informationLecture 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 informationDesign 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 informationEEO 401 Digital Signal Processing Prof. Mark Fowler
EEO 401 Digital Signal Prcessing Prf. Mark Fwler Intrductin Nte Set #1 ading Assignment: Ch. 1 f Prakis & Manlakis 1/13 Mdern systems generally DSP Scenari get a cntinuus-time signal frm a sensr a cnt.-time
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 informationLecture 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 informationLifting a Lion: Using Proportions
Overview Students will wrk in cperative grups t slve a real-wrd prblem by using the bk Hw D yu Lift a Lin? Using a ty lin and a lever, students will discver hw much wrk is needed t raise the ty lin. They
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 informationOptimization 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 informationAssessment 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 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 informationA 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 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 informationNumerical Simulation of the Flow Field in a Friction-Type Turbine (Tesla Turbine)
Numerical Simulatin f the Flw Field in a Frictin-Type Turbine (Tesla Turbine) Institute f Thermal Pwerplants Vienna niversity f Technlgy Getreidemarkt 9/313, A-6 Wien Andrés Felipe Rey Ladin Schl f Engineering,
More informationLCA14-206: 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 informationLab 1 The Scientific Method
INTRODUCTION The fllwing labratry exercise is designed t give yu, the student, an pprtunity t explre unknwn systems, r universes, and hypthesize pssible rules which may gvern the behavir within them. Scientific
More informationA 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 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 informationAPEX DYNAMICS, INC. Stainless. No. 10, Keyuan 3rd Rd., Situn District, Taichung City 40763, Taiwan (R.O.C.) APEX AE/AER Series - 1.
PEX DYNMIS, IN. HIGH PREISION PLNETRY GERBOX E / ER Series N. 0, Keyuan rd Rd., Situn District, Taichung ity 0, Taiwan (R.O..) PEX-0-09-E/ER Series -.0V Stainless High precisin high speed planetary gearbx
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 Back-End
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 nn-linear basis functins Least-squares
More informationAD / ADR / ADS Series
PEX DYMIS, I. HIGH PREISIO HIGH SPEED PLETRY GERBOX D / DR / DS Series PEX00D/DR/DS SERIES.0ETW Stainless PEX DYMIS, I. D Series Ordering de Ordering de D0 00 P MOTOR Gearbx Size: D0, D0, D090 D0, D0,
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 informationAPEX DYNAMICS, INC. Stainless
APE DYNAMICS, INC. HIGH PRECISION PLANETARY GEARBO AB / ABR Series Stainless High precisin planetary gearbx AB / ABR series Apex Dynamics, Inc. is the wrld s mst prductive manufacturer f servmtr drive
More informationCESAR Science Case The differential rotation of the Sun and its Chromosphere. Introduction. Material that is necessary during the laboratory
Teacher s guide CESAR Science Case The differential rtatin f the Sun and its Chrmsphere Material that is necessary during the labratry CESAR Astrnmical wrd list CESAR Bklet CESAR Frmula sheet CESAR Student
More informationInterdisciplinary 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 informationDrought damaged area
ESTIMATE OF THE AMOUNT OF GRAVEL CO~TENT IN THE SOIL BY A I R B O'RN EMS S D A T A Y. GOMI, H. YAMAMOTO, AND S. SATO ASIA AIR SURVEY CO., l d. KANAGAWA,JAPAN S.ISHIGURO HOKKAIDO TOKACHI UBPREFECTRAl OffICE
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 informationLinearization of the Output of a Wheatstone Bridge for Single Active Sensor. Madhu Mohan N., Geetha T., Sankaran P. and Jagadeesh Kumar V.
Linearizatin f the Output f a Wheatstne Bridge fr Single Active Sensr Madhu Mhan N., Geetha T., Sankaran P. and Jagadeesh Kumar V. Dept. f Electrical Engineering, Indian Institute f Technlgy Madras, Chennai
More informationThe 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 informationEffect of Conductivity Between Fasteners and Aluminum Skin On Eddy Current Specimens. Abstract
Effect f Cnductivity Between Fasteners and Aluminum Skin n Eddy Current Specimens David G. Mre Sandia Natinal Labratries Federal Aviatin Administratin Airwrthiness Assurance ND Validatin Center Albuquerque,
More informationChapter 3 Digital Transmission Fundamentals
Chapter 3 Digital Transmissin Fundamentals Errr Detectin and Crrectin CSE 3213, Winter 2010 Instructr: Frhar Frzan Mdul-2 Arithmetic Mdul 2 arithmetic is perfrmed digit y digit n inary numers. Each digit
More informationAircraft Performance - Drag
Aircraft Perfrmance - Drag Classificatin f Drag Ntes: Drag Frce and Drag Cefficient Drag is the enemy f flight and its cst. One f the primary functins f aerdynamicists and aircraft designers is t reduce
More informationEngineering Approach to Modelling Metal THz Structures
Terahertz Science and Technlgy, ISSN 1941-7411 Vl.4, N.1, March 11 Invited Paper ngineering Apprach t Mdelling Metal THz Structures Stepan Lucyszyn * and Yun Zhu Department f, Imperial Cllege Lndn, xhibitin
More informationQTC Pisa up-date. STM & HPK Sensors received from : December 03-February. 04 Qualification Summary and preliminary acceptance Company data comparison
QTC Pisa up-date STM & HPK Sensrs received frm : December 03-February 04 Qualificatin Summary and preliminary acceptance Cmpany data cmparisn L.Brrell, D.Kartashv, A.Messine, G.Segneri, D.Sentenac March
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 informationApply Discovery Teaching Model to Instruct Engineering Drawing Course: Sketch a Regular Pentagon
Available nline at www.sciencedirect.cm Prcedia - Scial and Behaviral Sciences 6 ( 01 ) 7 66 INTERNATIONAL EDUCATIONAL TECHNOLOGY CONFERENCE IETC01 Apply Discvery Teaching Mdel t Instruct Engineering Drawing
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 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 informationA New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation
III-l 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 informationData Mining Techniques
Data Mining Techniques CS 6220 - Sectin 3 - Fall 2016 Lecture 11 Jan-Willem van de Meent (credit: Yijun Zha, Dave Blei) PROJECT GUIDELINES (updated) Prject Gals Select a dataset / predictin prblem Perfrm
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 informationEarly detection of mining truck failure by modelling its operation with neural networks classification algorithms
RU, Rand GOLOSINSKI, T.S. Early detectin f mining truck failure by mdelling its peratin with neural netwrks classificatin algrithms. Applicatin f Cmputers and Operatins Research ill the Minerals Industries,
More informationComparison 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 informationA Few Basic Facts About Isothermal Mass Transfer in a Binary Mixture
Few asic Facts but Isthermal Mass Transfer in a inary Miture David Keffer Department f Chemical Engineering University f Tennessee first begun: pril 22, 2004 last updated: January 13, 2006 dkeffer@utk.edu
More informationJapanese HPCI Open Call
SC-Asia 2018, SG-JP Jint sessin Japanese HPCI Open Call - HPCI : High Perfrmance Cmputing Infrastructure - Mti Okuda Research Organizatin fr Infrmatin Science & Technlgy What is HPCI? n Wrld tp class supercmputing
More informationTree Structured Classifier
Tree Structured Classifier Reference: Classificatin and Regressin Trees by L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stne, Chapman & Hall, 98. A Medical Eample (CART): Predict high risk patients
More informationA Frequency-Based Find Algorithm in Mobile Wireless Computing Systems
A Frequency-Based Find Algrithm in Mbile Wireless Cmputing Systems Seung-yun Kim and Waleed W Smari Department f Electrical Cmputer Engineering University f Daytn 300 Cllege Park Daytn, OH USA 45469 Abstract
More informationWriting Guidelines. (Updated: November 25, 2009) Forwards
Writing Guidelines (Updated: Nvember 25, 2009) Frwards I have fund in my review f the manuscripts frm ur students and research assciates, as well as thse submitted t varius jurnals by thers that the majr
More informationPerfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Key Wrds: Autregressive, Mving Average, Runs Tests, Shewhart Cntrl Chart
Perfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Sandy D. Balkin Dennis K. J. Lin y Pennsylvania State University, University Park, PA 16802 Sandy Balkin is a graduate student
More informationCPM plans: the short, the medium and the long
plans: the shrt, the medium and the lng Summary f recent prgress Next steps New Test sftware/firmware Slice test/beam test requirements Hw can we use current s? D we need mre new s? rductin testing requirements
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 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 information1996 Engineering Systems Design and Analysis Conference, Montpellier, France, July 1-4, 1996, Vol. 7, pp
THE POWER AND LIMIT OF NEURAL NETWORKS T. Y. Lin Department f Mathematics and Cmputer Science San Jse State University San Jse, Califrnia 959-003 tylin@cs.ssu.edu and Bereley Initiative in Sft Cmputing*
More informationBios 6648: Design & conduct of clinical research
Bis 6648: Design & cnduct f clinical research Sectin 3 - Essential principle 3.1 Masking (blinding) 3.2 Treatment allcatin (randmizatin) 3.3 Study quality cntrl : Interim decisin and grup sequential :
More informationPlease Stop Laughing at Me and Pay it Forward Final Writing Assignment
Kirk Please Stp Laughing at Me and Pay it Frward Final Writing Assignment Our fcus fr the past few mnths has been n bullying and hw we treat ther peple. We ve played sme games, read sme articles, read
More informationCity of Angels School Independent Study Los Angeles Unified School District
City f Angels Schl Independent Study Ls Angeles Unified Schl District INSTRUCTIONAL GUIDE Algebra 1B Curse ID #310302 (CCSS Versin- 06/15) This curse is the secnd semester f Algebra 1, fulfills ne half
More informationCOMP 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 informationProfessional 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 informationHow 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 informationLab #3: Pendulum Period and Proportionalities
Physics 144 Chwdary Hw Things Wrk Spring 2006 Name: Partners Name(s): Intrductin Lab #3: Pendulum Perid and Prprtinalities Smetimes, it is useful t knw the dependence f ne quantity n anther, like hw the
More informationDocument for ENES5 meeting
HARMONISATION OF EXPOSURE SCENARIO SHORT TITLES Dcument fr ENES5 meeting Paper jintly prepared by ECHA Cefic DUCC ESCOM ES Shrt Titles Grup 13 Nvember 2013 OBJECTIVES FOR ENES5 The bjective f this dcument
More informationDeep Captioning with Multimodal Recurrent Neural Networks (m-rnn)
Deep Captining with Multimdal Recurrent Neural Netwrks (m-rnn) http://www.stat.ucla.edu/~junhua.ma/m-rnn.html Junhua Ma 1,2, Wei Xu 1, Yi Yang 1, Jiang Wang 1, Zhiheng Huang 1, Alan Yuille 2 1 Baidu Research
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 informationConcurrent Error Detection for Reliable SHA-3 Design
5/18/2016 1 Cncurrent Errr Detectin fr Reliable SHA-3 Design Pei LUO 1 Cheng LI 2 Yunsi FEI 1 1. Nrtheastern Universit Energ-Efficient and Secure Sstems Lab http://nueess.ce.neu.edu Electrical & Cmputer
More informationGroup Analysis: Hands-On
Grup Analysis: Hands-On Gang Chen SSCC/NIMH/NIH/HHS 3/19/16 1 Make sure yu have the files!! Under directry grup_analysis_hands_n/! Slides: GrupAna_HO.pdf! Data: AFNI_data6/GrupAna_cases/! In case yu dn
More informationChapter 6 Fingerprints
Chapter 6 Fingerprints Vcabulary: Arch: a fingerprint pattern in which the ridge pattern riginates frm ne side f the print and leaves frm the ther side Cre: Delta: a triangular ridge pattern with ridges
More informationSpace Shuttle Ascent Mass vs. Time
Space Shuttle Ascent Mass vs. Time Backgrund This prblem is part f a series that applies algebraic principles in NASA s human spaceflight. The Space Shuttle Missin Cntrl Center (MCC) and the Internatinal
More informationFirst Survey. Carried out by IPR feedback
First Survey Carried ut by IPR feedback Hell my name is and I am calling frm IPR Feedback. IPR Feedback is a research center hired by Bccni University t study the pinins f the citizens f Arezz regarding
More information8.1. Review of experiments
VIII E V A L U A T I N VIII 1 E P E R I M E N T S Review f experiments As nted earlier,, the majr evaluatin tests f manual dexg systems which have been carried ut the last decade prvide the backgrund fr
More informationComputational 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 informationBiocomputers. [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 informationWhat is Statistical Learning?
What is Statistical Learning? Sales 5 10 15 20 25 Sales 5 10 15 20 25 Sales 5 10 15 20 25 0 50 100 200 300 TV 0 10 20 30 40 50 Radi 0 20 40 60 80 100 Newspaper Shwn are Sales vs TV, Radi and Newspaper,
More informationUse a lens holder fabricated from SiC. SiC has a larger CTE than C-C, i.e. it is better matched to the SFL6.
Frm: Steve Sctt, Jinsek K, Syun ichi Shiraiwa T: MSE enthusiasts Re: MSE mem 101b: allwable thickness f Vitn sheet Nvember 25, 2008 Update frm MSE Mem 101b Let s assume: Vitn thickness = 1 mm Vitn mdulus
More informationDetermining 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 informationAppendix A: Mathematics Unit
Appendix A: Mathematics Unit 16 Delaware Mdel Unit Gallery Template This unit has been created as an exemplary mdel fr teachers in (re)design f curse curricula. An exemplary mdel unit has undergne a rigrus
More informationIAML: 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 informationNWC SAF ENTERING A NEW PHASE
NWC SAF ENTERING A NEW PHASE Pilar Fernández Agencia Estatal de Meterlgía (AEMET) Lenard Priet Castr, 8; 28040 Madrid, Spain Phne: +34 915 819 654, Fax: +34 915 819 767 E-mail: mafernandeza@aemet.es Abstract
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 informationName: Period: Date: BONDING NOTES ADVANCED CHEMISTRY
Name: Perid: Date: BONDING NOTES ADVANCED CHEMISTRY Directins: This packet will serve as yur ntes fr this chapter. Fllw alng with the PwerPint presentatin and fill in the missing infrmatin. Imprtant terms
More information8 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 informationTooth Surface Design for Variable Transmission Ratio Bevel Gearing
Applied Mathematics, 05, 6, 685-695 Published Online September 05 in SciRes. http://www.scirp.rg/jurnal/am http://dx.di.rg/0.436/am.05.6050 Tth Surface Design fr Variable Transmissin Rati Bevel Gearing
More informationAnalysis 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 informationIntroduction to Regression
Intrductin t Regressin Administrivia Hmewrk 6 psted later tnight. Due Friday after Break. 2 Statistical Mdeling Thus far we ve talked abut Descriptive Statistics: This is the way my sample is Inferential
More informationMath 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 information3.6 Condition number and RGA
g r qp q q q qp! the weight " #%$ s that mre emphasis is placed n utput &. We d this y increasing the andwidth requirement in utput channel & y a factr f (!( : *,+.-0/!1 &3254769:4;$=< >@?BAC D 6 E(F
More informationCollocation 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 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 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 informationCMU Noncooperative games 3: Price of anarchy. Teacher: Ariel Procaccia
CMU 15-896 Nncperative games 3: Price f anarchy Teacher: Ariel Prcaccia Back t prisn The nly Nash equilibrium in Prisner s dilemma is bad; but hw bad is it? Objective functin: scial cst = sum f csts NE
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 information