dependent and wr i t er i ndependent on-l i ne cur s i ve handwr i t i ng r ecogni t i on. Thi s
|
|
|
- Melinda Carroll
- 5 years ago
- Views:
Transcription
1 5 CONCLUSIONS In this pper we hve presented the NPen ++ s ys t em, neur l r ecogni zer for writer dependent nd wr i t er i ndependent on-l i ne cur s i ve hndwr i t i ng r ecogni t i on. Thi s systemcombi nes r obus t i nput r epr es enttion, whi ch pr es er ves t he dynm informt i on, wi t h neur l net work i ntegrti ng recogni ti on single frmework. Thi s r chi t ect ur e hs been s tempor l s equences s pr ovi ded by t Evl uti on of the systemon to 20, 000 words i ndep
2 writer dependent writer independent % correct () size of vocbulry (b) Fi gur e 3: ( ) Di erent writing styles in the dtbse: (mi ddl e) nd mixture of both (bottom) t he vocbul r y s i ze cur s i ve ( t op), hnd- pr i nted (b) Recognitionresults withrespec For the writer dependentevlution, the systemws tri ned on 400 word vocbul ry, written by s i ngl e writer of ptterns fromthe sme writer. In t consisted of 4,000pttern 60 di erentwr writ Al
3 w i, i.e. logp( x T 0 jw i) mx q T 0 TX t=1 mx q T 0 logp ( x t+d t0d jq t;w i )+logp ( q t jq t01 ; w i ) TX t=1 logp ( q t jx t+d t0d ) 0 logp ( q t)+logp ( q t jq t01 ; w i ) : (2) Here, the mxi mumi s over l l pos s i bl e s equence s of s t t es q T 0 = q 0 : : : q T word model, p ( q t jxt0d t+d ) refers to the output of the sttes lyer p ( q t ) is the prior probbilityof observingstte 3.3 TRAINING OF THE RECOGNIZER Duri ng tri ni ng the gol i s to determi ne the posterior probbilityp ( w to mke tht m syst
4 bility ble bord bound bout bove zoom b l e... b o u t... b c x y z Sttes Lyer Hidden Lyer Input Lyer time Fi gur e 2: The Multi-Stte TDNNrchi tecture, consi sti ng of 3-l yer TDNNto estimte the posteriori probbilities of the chrcter sttes combi ned wi thwor uni t s, whose scores re deri ved f romthe word model s by Vi t er bi p of the likelihoods p ( x T 0 jw i). units, 40 units in the hidden lyer, nd 78 stte out time del ys bot h i n t he i nput nd hi dde The s of t mx norml i zed out put of the probbilities o ech ti m
5 inspce but globl intime. Tht mens, ech poi nt of the trjectoryis visible from ech ot her poi nt of t he t r j ect or y i n s ml l nei ghbour hood. By us i ng t hes e con bi t mps i n ddi ti on to the l ocl f etures, i mportnt i nf or mti on of the trjectory, whichreinlimi t ed nei ghbour ho 3 THE NPe n ++ RECOGNI ZE The NPen ++ recogni zer i ntegrtes re singlenetwork rchitectu TDNN). The MS- TDN recognition
6 time 0 T finl input representtion () context bitmps normlized coordinte sequence: context bitmps () x/y (b) writing direction curvture writing direction/ curvture (b) t-2 t-1 t t+1 t+2 x(t-2),y(t-2) x(t-1),y(t-1) α x(t),y(t) x(t+2),y(t+2) x(t+1),y(t+1) x(t-2),y(t-2) x(t-1),y(t-1) β x(t),y(t) x(t+2),y(t+2) x(t+1),y(t+1) Fi gure 1: Feture extrction for the norml i zed word \bl e". The nl i nput r epresenttion is derived by cl cul ti ng 15- di mensi onl f eture vector f or ech poi nt, whi ch cons i s t s of context bitmp () nd informti on bo nd wr i t i ng di r ect i on ( b). spce. Thi s r es mpl ed trjectory i s smoothed u order to remove s mpling noise. In representtion of the tr scling of t
7 1 I NTRODUCTI ON Sever l pr epr oce s s i ng nd r ecogni t i on ppr oches f or on- l i ne hndwriting recogni t i on hve been devel oped dur i ng t he ps t yer s. The mi n dvntge of on- l hndwritingrecognitionincompri son to opti cl chrcter recogni tempor l i nf or mti on of hndwriting, whi ch cn be r e t i on. I n gener l t hi s dynmi cwriting info coor di nt es ) i s not vilb ppe r we pr e s writin
8 G. Tesuro, D. Touretzky, nd J. Alspector (Eds.) Advnces in Neurl Informtion Processing Systems 7 MIT Press, Cmbridge MA The Use of Dynmic Wri ti ng Informtion i n Connecti oni st On-Li ne Cursi ve Hndwri ti ng Recogni ti on System StefnMnke Michel Finke AlexWibel Uni ver s i t y of Kr l s r uhe Comput er Sci ence Deprtment D Kr l s r uhe, Germny mnke@i r. uk. de, nkem@i r.uk. de Crnegi e MellonUni ver s i t y School of Comput er Sci ence Pittsburgh, PA , U. S.A. wi cmu. edu Abs tr c t In this pper we present NPen ++, connectionist systemf wr i t er i ndependent, lrge vocbulry on-line cursive recognition. This systemcombi nes r obus t whi ch pr es e r ves t he dynmi c wri net work rchi tecture, Network (MS- TD ttion i
P a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
% correct 1K 5K 20K. N-best. word recognitionrate. 98. References. Figure 5: Recogni ti on results wi th respect to the dictionary. sentences.
full y- 100 board andtothe recognitionofsentences using language model s to achievefurther i mprovements of the word recognitionrate. 98 References % correct 96 [1] S. Manke and U. 94 Bodenhausen, \A Connecti
P a g e 3 6 of R e p o r t P B 4 / 0 9
P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J
176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s
A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps
T h e C S E T I P r o j e c t
T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T
A L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
( dg. ) 2 dt. + dt. dt j + dh. + dt. r(t) dt. Comparing this equation with the one listed above for the length of see that
Arc Length of Curves in Three Dimensionl Spce If the vector function r(t) f(t) i + g(t) j + h(t) k trces out the curve C s t vries, we cn mesure distnces long C using formul nerly identicl to one tht we
Animals and Behaviors. Templeton Biology
Animals and Behaviors Templeton Biology Nueral- Nami Expl ai n t he behavi or s you see i n t he vi deo. Woul d t hi s be a gr oup or i ndi vi dual f eedi ng behavi or why? Video of Prairie Dog Communication
Software Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode
Unit 2 : Software Process O b j ec t i ve This unit introduces software systems engineering through a discussion of software processes and their principal characteristics. In order to achieve the desireable
PROBLEM 3 AN INVESTIGATION OF DIE DESIGNS FOR TUBE DRAWING. This problan was suggested to the Mathemati os+lrr-lndus tr-y Study Group by
28 PROBLEM 3 AN INVESTIGATION OF DIE DESIGNS FOR TUBE DRAWING 1. OUTLINE OF PROBLEM This problan was suggested to the Mathemati os+lrr-lndus tr-y Study Group by Metal Manufacturers who produce, among other
The Nature of Engineering
The Nature of Engineering Dedication This book is dedicated to the increasing number of you ng women who a r e studying f or a degree in engineering. They are intelligent bei ngs who will not be put of
Chapter 14. Matrix Representations of Linear Transformations
Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn
Reinforcement learning II
CS 1675 Introduction to Mchine Lerning Lecture 26 Reinforcement lerning II Milos Huskrecht milos@cs.pitt.edu 5329 Sennott Squre Reinforcement lerning Bsics: Input x Lerner Output Reinforcement r Critic
The Fundamental Theorem of Calculus Part 2, The Evaluation Part
AP Clculus AB 6.4 Funmentl Theorem of Clculus The Funmentl Theorem of Clculus hs two prts. These two prts tie together the concept of integrtion n ifferentition n is regre by some to by the most importnt
DEFINITION OF ASSOCIATIVE OR DIRECT PRODUCT AND ROTATION OF VECTORS
3 DEFINITION OF ASSOCIATIVE OR DIRECT PRODUCT AND ROTATION OF VECTORS This chpter summrizes few properties of Cli ord Algebr nd describe its usefulness in e ecting vector rottions. 3.1 De nition of Associtive
CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A. Ήχος Πα. to os se e e na aș te e e slă ă ă vi i i i i
CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A Ήχος α H ris to os s n ș t slă ă ă vi i i i i ți'l Hris to o os di in c ru u uri, în tâm pi i n ți i'l Hris
GROWMARK, INC 2200 Sout h Ave nue, P. O. Box 587, Counc i l Bl uf f s, I A
UPDATED: J ul y 8, 2004 MATERIAL SAFETY DATA SHEET GROWMARK, INC 2200 Sout h Ave nue, P. O. Box 587, Counc i l Bl uf f s, I A 51502-0587 EMERGENCY TELEPHONE NUMBER: 1- ( 800) - 798-6457 ******************
I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o
I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o u l d a l w a y s b e t a k e n, i n c l u d f o l
30. a. Al(H 2 O) 6 + H 2 O Co(H 2 O) 5 (OH) 2+ + H 3 O + acid base conjugate base conjugate acid
Chpter 14 : xx,3,40,4,xx,xx,47,50,54,55,xx,64,xx,83,85,91,93,95,10,11,134,138 xx.. HC H 3 O (q) H O(l) C H 3 O (q) H 3 O (q) = [H O ][C H O ] 3 3 [HC H O ] 3 b. Co(H O) 6 3 (q) H O(l) Co(H O) 5 (OH) (q)
Statistical Machine Learning from Data
January 17, 2006 Samy Bengio Statistical Machine Learning from Data 1 Statistical Machine Learning from Data Other Artificial Neural Networks Samy Bengio IDIAP Research Institute, Martigny, Switzerland,
The First Fundamental Theorem of Calculus. If f(x) is continuous on [a, b] and F (x) is any antiderivative. f(x) dx = F (b) F (a).
![The First Fundamental Theorem of Calculus. If f(x) is continuous on [a, b] and F (x) is any antiderivative. f(x) dx = F (b) F (a).](/thumbs/78/77467690.jpg "The First Fundamental Theorem of Calculus. If f(x) is continuous on [a, b] and F (x) is any antiderivative. f(x) dx = F (b) F (a).")
The Fundmentl Theorems of Clculus Mth 4, Section 0, Spring 009 We now know enough bout definite integrls to give precise formultions of the Fundmentl Theorems of Clculus. We will lso look t some bsic emples
Hybrid Bonded Wheel
Hybrid Bonded Wheel 13. 06.. 1. Hybr i d wheel I nt r oduce P Hybrid bonds are manufactured with a mixture of Resin & Metallic Bond METAL + RESIN DIA / CBN *GOOD FORM HOLDING M *LONGER TOOL LIFE E *HIGH
For convenience, we rewrite m2 s m2 = m m m ; where m is repeted m times. Since xyz = m m m nd jxyj»m, we hve tht the string y is substring of the fir
CSCI 2400 Models of Computtion, Section 3 Solutions to Homework 4 Problem 1. ll the solutions below refer to the Pumping Lemm of Theorem 4.8, pge 119. () L = f n b l k : k n + lg Let's ssume for contrdiction
Section 5.1 #7, 10, 16, 21, 25; Section 5.2 #8, 9, 15, 20, 27, 30; Section 5.3 #4, 6, 9, 13, 16, 28, 31; Section 5.4 #7, 18, 21, 23, 25, 29, 40
Mth B Prof. Audrey Terrs HW # Solutions by Alex Eustis Due Tuesdy, Oct. 9 Section 5. #7,, 6,, 5; Section 5. #8, 9, 5,, 7, 3; Section 5.3 #4, 6, 9, 3, 6, 8, 3; Section 5.4 #7, 8,, 3, 5, 9, 4 5..7 Since
Hidden Markov Models
Hidden Mrkov Models Huptseminr Mchine Lerning 18.11.2003 Referent: Nikols Dörfler 1 Overview Mrkov Models Hidden Mrkov Models Types of Hidden Mrkov Models Applictions using HMMs Three centrl problems:
Section 14.3 Arc Length and Curvature
Section 4.3 Arc Length nd Curvture Clculus on Curves in Spce In this section, we ly the foundtions for describing the movement of n object in spce.. Vector Function Bsics In Clc, formul for rc length in
CS 188: Artificial Intelligence Fall 2010
CS 188: Artificil Intelligence Fll 2010 Lecture 18: Decision Digrms 10/28/2010 Dn Klein C Berkeley Vlue of Informtion 1 Decision Networks ME: choose the ction which mximizes the expected utility given
Gen ova/ Pavi a/ Ro ma Ti m i ng Count er st at Sep t. 2004
Ti m i ng Count er st at us @ Sep t. 2004 1 Ti m i n g Cou n t er act i vi t i es Ti m i n g r esol u t i on : 100 p s FWHM h ave b een ach i eved. PM s ch ar act er ised i n t h e COBRA m ag n et f or
Equations and Inequalities
Equtions nd Inequlities Equtions nd Inequlities Curriculum Redy ACMNA: 4, 5, 6, 7, 40 www.mthletics.com Equtions EQUATIONS & Inequlities & INEQUALITIES Sometimes just writing vribles or pronumerls in
Get Started on CreateSpace
{paperback self publishing process} Account Set Up Go to CreateSpace.com Click Sign up Fill out Create Account info fields * Under What type of media you are considering punlishing?, you may want to choose
Where did dynamic programming come from?
Where did dynmic progrmming come from? String lgorithms Dvid Kuchk cs302 Spring 2012 Richrd ellmn On the irth of Dynmic Progrmming Sturt Dreyfus http://www.eng.tu.c.il/~mi/cd/ or50/1526-5463-2002-50-01-0048.pdf
PH12b 2010 Solutions HW#3
PH 00 Solutions HW#3. The Hmiltonin of this two level system is where E g < E e The experimentlist sis is H E g jgi hgj + E e jei hej j+i p (jgi + jei) j i p (jgi jei) ) At t 0 the stte is j (0)i j+i,
ES.182A Topic 32 Notes Jeremy Orloff
ES.8A Topic 3 Notes Jerem Orloff 3 Polr coordintes nd double integrls 3. Polr Coordintes (, ) = (r cos(θ), r sin(θ)) r θ Stndrd,, r, θ tringle Polr coordintes re just stndrd trigonometric reltions. In
More on automata. Michael George. March 24 April 7, 2014
More on utomt Michel George Mrch 24 April 7, 2014 1 Automt constructions Now tht we hve forml model of mchine, it is useful to mke some generl constructions. 1.1 DFA Union / Product construction Suppose
Decision Networks. CS 188: Artificial Intelligence. Decision Networks. Decision Networks. Decision Networks and Value of Information
CS 188: Artificil Intelligence nd Vlue of Informtion Instructors: Dn Klein nd Pieter Abbeel niversity of Cliforni, Berkeley [These slides were creted by Dn Klein nd Pieter Abbeel for CS188 Intro to AI
The Fut ur e of Assessment How wi l l AI, Aut omat i on, and Mac hi ne Lear ni ng Change How We Devel op and Del i ver Assessment s?
AI ML The Fut ur e of Assessment How wi l l AI, Aut omat i on, and Mac hi ne Lear ni ng Change How We Devel op and Del i ver Assessment s? Nat han Thomps on, PhD I nt er nat i onal Conf er ence on Educat
Detection and Estimation Theory
ESE 54 Detection nd Estimtion heory Joseph A. O Sullivn Smuel C. Schs Professor Electronic Systems nd Signls Reserch Lbortory Electricl nd Systems Engineering Wshington University Urbuer Hll 34-935-473
Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D
Lesson eight What are characteristics of chemical reactions? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading
A B= ( ) because from A to B is 3 right, 2 down.
8. Vectors nd vector nottion Questions re trgeted t the grdes indicted Remember: mgnitude mens size. The vector ( ) mens move left nd up. On Resource sheet 8. drw ccurtely nd lbel the following vectors.
+ x 2 dω 2 = c 2 dt 2 +a(t) [ 2 dr 2 + S 1 κx 2 /R0
Notes for Cosmology course, fll 2005 Cosmic Dynmics Prelude [ ds 2 = c 2 dt 2 +(t) 2 dx 2 ] + x 2 dω 2 = c 2 dt 2 +(t) [ 2 dr 2 + S 1 κx 2 /R0 2 κ (r) 2 dω 2] nd x = S κ (r) = r, R 0 sin(r/r 0 ), R 0 sinh(r/r
Lecture 3 Gaussian Probability Distribution
Introduction Lecture 3 Gussin Probbility Distribution Gussin probbility distribution is perhps the most used distribution in ll of science. lso clled bell shped curve or norml distribution Unlike the binomil
More on Construction of Surfaces
More on Construction of Surfces Weihu Hong Mingshen Wu Nthn Borchelt 3 Deprtment of Mthemtics Clyton Stte University Morrow GA 3060 Deprtment of Mthemtics Sttistics nd Computer Science University of Wisconsin-Stou
Senility Degree. Our machine derives APG waveform after 2 nd differential of arterial pulse wave in order to measure
Senility Degree Our machine derives APG waveform after 2 nd differential of arterial pulse wave in order to measure 1. Cardiac Eccentric Constriction Power 2. Arterial Elasticity 3. Remaining blood volume
MATH 144: Business Calculus Final Review
MATH 144: Business Clculus Finl Review 1 Skills 1. Clculte severl limits. 2. Find verticl nd horizontl symptotes for given rtionl function. 3. Clculte derivtive by definition. 4. Clculte severl derivtives
CSCI FOUNDATIONS OF COMPUTER SCIENCE
1 CSCI- 2200 FOUNDATIONS OF COMPUTER SCIENCE Spring 2015 My 7, 2015 2 Announcements Homework 9 is due now. Some finl exm review problems will be posted on the web site tody. These re prcqce problems not
dt. However, we might also be curious about dy
Section 0. The Clculus of Prmetric Curves Even though curve defined prmetricly my not be function, we cn still consider concepts such s rtes of chnge. However, the concepts will need specil tretment. For
Point Lattices: Bravais Lattices
Physics for Solid Stte Applictions Februry 18, 2004 Lecture 7: Periodic Structures (cont.) Outline Review 2D & 3D Periodic Crystl Structures: Mthemtics X-Ry Diffrction: Observing Reciprocl Spce Point Lttices:
Math Advanced Calculus II
Mth 452 - Advnced Clculus II Line Integrls nd Green s Theorem The min gol of this chpter is to prove Stoke s theorem, which is the multivrible version of the fundmentl theorem of clculus. We will be focused
SECTION 9-4 Translation of Axes
9-4 Trnsltion of Aes 639 Rdiotelescope For the receiving ntenn shown in the figure, the common focus F is locted 120 feet bove the verte of the prbol, nd focus F (for the hperbol) is 20 feet bove the verte.
Math 426: Probability Final Exam Practice
Mth 46: Probbility Finl Exm Prctice. Computtionl problems 4. Let T k (n) denote the number of prtitions of the set {,..., n} into k nonempty subsets, where k n. Argue tht T k (n) kt k (n ) + T k (n ) by
Data Assimilation. Alan O Neill Data Assimilation Research Centre University of Reading
Dt Assimiltion Aln O Neill Dt Assimiltion Reserch Centre University of Reding Contents Motivtion Univrite sclr dt ssimiltion Multivrite vector dt ssimiltion Optiml Interpoltion BLUE 3d-Vritionl Method
_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9
C C A 55NED n R 5 0 9 b c c \ { s AS EC 2 5? 9 Con 0 \ 0265 o + s ^! 4 y!! {! w Y n < R > s s = ~ C c [ + * c n j R c C / e A / = + j ) d /! Y 6 ] s v * ^ / ) v } > { ± n S = S w c s y c C { ~! > R = n
Come to Me, All Who Labor/ Vengan a Mí los Agobiados
12079-Z Come Me, All Who L/ J. Cortez SCTB $1.70 USA Vengn Mí, Abi Mtthew 11:28 30; John 14:3 6; John 6:40 Keyd Gm7/C Come Me, All Who L/ Vengn Mí Abi in loving memory of Ry Dubeu (1921 2002) pr Asmble,
TO: Next Year s AP Calculus Students
TO: Net Yer s AP Clculus Students As you probbly know, the students who tke AP Clculus AB nd pss the Advnced Plcement Test will plce out of one semester of college Clculus; those who tke AP Clculus BC
GG303 Lab 6 9/25/12. Components of cross product v2 x v1 N x N y N z. N=v2xv1. Plane trend ( ) Pole N. Plane. Pole N. plunge ( ) strike ( ) dip ( )
1 Lb 6 ROTATIONS (163 pts totl) Eercise 1: Apprent dip problem (24 points totl) 1) An pprent dip of 62 to the southwest is mesured for bedding plne in verticl cross section tht strikes 230 (cll this pprent
LECTURE NOTE #12 PROF. ALAN YUILLE
LECTURE NOTE #12 PROF. ALAN YUILLE 1. Clustering, K-mens, nd EM Tsk: set of unlbeled dt D = {x 1,..., x n } Decompose into clsses w 1,..., w M where M is unknown. Lern clss models p(x w)) Discovery of
MTH 4-16a Trigonometry
MTH 4-16 Trigonometry Level 4 [UNIT 5 REVISION SECTION ] I cn identify the opposite, djcent nd hypotenuse sides on right-ngled tringle. Identify the opposite, djcent nd hypotenuse in the following right-ngled
Ash Wednesday. First Introit thing. * Dómi- nos. di- di- nos, tú- ré- spi- Ps. ne. Dó- mi- Sál- vum. intra-vé-runt. Gló- ri-
sh Wdsdy 7 gn mult- tú- st Frst Intrt thng X-áud m. ns ní- m-sr-cór- Ps. -qu Ptr - m- Sál- vum m * usqu 1 d fc á-rum sp- m-sr-t- ó- num Gló- r- Fí- l- Sp-rí- : quó-n- m ntr-vé-runt á- n-mm c * m- quó-n-
CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)
CS 373, Spring 29. Solutions to Mock midterm (sed on first midterm in CS 273, Fll 28.) Prolem : Short nswer (8 points) The nswers to these prolems should e short nd not complicted. () If n NF M ccepts
5 Probability densities
5 Probbility densities 5. Continuous rndom vribles 5. The norml distribution 5.3 The norml pproimtion to the binomil distribution 5.5 The uniorm distribution 5. Joint distribution discrete nd continuous
Calculus 2: Integration. Differentiation. Integration
Clculus 2: Integrtion The reverse process to differentition is known s integrtion. Differentition f() f () Integrtion As it is the opposite of finding the derivtive, the function obtined b integrtion is
I N A C O M P L E X W O R L D
IS L A M I C E C O N O M I C S I N A C O M P L E X W O R L D E x p l o r a t i o n s i n A g-b eanste d S i m u l a t i o n S a m i A l-s u w a i l e m 1 4 2 9 H 2 0 0 8 I s l a m i c D e v e l o p m e
Multiple Integrals. Review of Single Integrals. Planar Area. Volume of Solid of Revolution
Multiple Integrls eview of Single Integrls eding Trim 7.1 eview Appliction of Integrls: Are 7. eview Appliction of Integrls: Volumes 7.3 eview Appliction of Integrls: Lengths of Curves Assignment web pge
Mathematics of Motion II Projectiles
Chmp+ Fll 2001 Dn Stump 1 Mthemtics of Motion II Projectiles Tble of vribles t time v velocity, v 0 initil velocity ccelertion D distnce x position coordinte, x 0 initil position x horizontl coordinte
Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D
Lesson seven What is a chemical reaction? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading Informational Text,
Canadian Graduate and Professional Student Survey (CGPSS) 2016
Ac a d e m i c S t u d e n t l i f e O v e r a l l Canadian Graduate and Professional Student Survey (CGPSS) Summary of Results Prepared by the Office of Institutional Analysis The CGPSS was administered
The Dirac distribution
A DIRAC DISTRIBUTION A The Dirc distribution A Definition of the Dirc distribution The Dirc distribution δx cn be introduced by three equivlent wys Dirc [] defined it by reltions δx dx, δx if x The distribution
Fr anchi s ee appl i cat i on for m
Other Fr anchi s ee appl i cat i on for m Kindly fill in all the applicable information in the spaces provided and submit to us before the stipulated deadline. The information you provide will be held
Solution for Assignment 1 : Intro to Probability and Statistics, PAC learning
Solution for Assignment 1 : Intro to Probbility nd Sttistics, PAC lerning 10-701/15-781: Mchine Lerning (Fll 004) Due: Sept. 30th 004, Thursdy, Strt of clss Question 1. Bsic Probbility ( 18 pts) 1.1 (
NOTES ON HILBERT SPACE
NOTES ON HILBERT SPACE 1 DEFINITION: by Prof C-I Tn Deprtment of Physics Brown University A Hilbert spce is n inner product spce which, s metric spce, is complete We will not present n exhustive mthemticl
13.4 Work done by Constant Forces
13.4 Work done by Constnt Forces We will begin our discussion of the concept of work by nlyzing the motion of n object in one dimension cted on by constnt forces. Let s consider the following exmple: push
In-Class Problems 2 and 3: Projectile Motion Solutions. In-Class Problem 2: Throwing a Stone Down a Hill
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Deprtment of Physics Physics 8T Fll Term 4 In-Clss Problems nd 3: Projectile Motion Solutions We would like ech group to pply the problem solving strtegy with the
Reversals of Signal-Posterior Monotonicity for Any Bounded Prior
Reversls of Signl-Posterior Monotonicity for Any Bounded Prior Christopher P. Chmbers Pul J. Hely Abstrct Pul Milgrom (The Bell Journl of Economics, 12(2): 380 391) showed tht if the strict monotone likelihood
CS 188 Introduction to Artificial Intelligence Fall 2018 Note 7
CS 188 Introduction to Artificil Intelligence Fll 2018 Note 7 These lecture notes re hevily bsed on notes originlly written by Nikhil Shrm. Decision Networks In the third note, we lerned bout gme trees
12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2014
CS125 Lecture 12 Fll 2014 12.1 Nondeterminism The ide of nondeterministic computtions is to llow our lgorithms to mke guesses, nd only require tht they ccept when the guesses re correct. For exmple, simple
1. Extend QR downwards to meet the x-axis at U(6, 0). y
In the digrm, two stright lines re to be drwn through so tht the lines divide the figure OPQRST into pieces of equl re Find the sum of the slopes of the lines R(6, ) S(, ) T(, 0) Determine ll liner functions
Predict Global Earth Temperature using Linier Regression
Predict Globl Erth Temperture using Linier Regression Edwin Swndi Sijbt (23516012) Progrm Studi Mgister Informtik Sekolh Teknik Elektro dn Informtik ITB Jl. Gnesh 10 Bndung 40132, Indonesi 23516012@std.stei.itb.c.id
n=0 ( 1)n /(n + 1) converges, but not n=100 1/n2, is at most 1/100.
Mth 07H Topics since the second exm Note: The finl exm will cover everything from the first two topics sheets, s well. Absolute convergence nd lternting series A series n converges bsolutely if n converges.
Executive Committee and Officers ( )
Gifted and Talented International V o l u m e 2 4, N u m b e r 2, D e c e m b e r, 2 0 0 9. G i f t e d a n d T a l e n t e d I n t e r n a t i o n a2 l 4 ( 2), D e c e m b e r, 2 0 0 9. 1 T h e W o r
approaches as n becomes larger and larger. Since e > 1, the graph of the natural exponential function is as below
. Eponentil nd rithmic functions.1 Eponentil Functions A function of the form f() =, > 0, 1 is clled n eponentil function. Its domin is the set of ll rel f ( 1) numbers. For n eponentil function f we hve.
THIS PAGE DECLASSIFIED IAW EO 12958
L " ^ \ : / 4 a " G E G + : C 4 w i V T / J ` { } ( : f c : < J ; G L ( Y e < + a : v! { : [ y v : ; a G : : : S 4 ; l J / \ l " ` : 5 L " 7 F } ` " x l } l i > G < Y / : 7 7 \ a? / c = l L i L l / c f
MATH STUDENT BOOK. 10th Grade Unit 5
MATH STUDENT BOOK 10th Grde Unit 5 Unit 5 Similr Polygons MATH 1005 Similr Polygons INTRODUCTION 3 1. PRINCIPLES OF ALGEBRA 5 RATIOS AND PROPORTIONS 5 PROPERTIES OF PROPORTIONS 11 SELF TEST 1 16 2. SIMILARITY
CS 188: Artificial Intelligence
CS 188: Artificil Intelligence Lecture 19: Decision Digrms Pieter Abbeel --- C Berkeley Mny slides over this course dpted from Dn Klein, Sturt Russell, Andrew Moore Decision Networks ME: choose the ction
Prof. Anchordoqui. Problems set # 4 Physics 169 March 3, 2015
Prof. Anchordoui Problems set # 4 Physics 169 Mrch 3, 15 1. (i) Eight eul chrges re locted t corners of cube of side s, s shown in Fig. 1. Find electric potentil t one corner, tking zero potentil to be
Supplementary Information to The role of endogenous and exogenous mechanisms in the formation of R&D networks
Supplementry Informtion to The role of endogenous nd exogenous mechnisms in the formtion of R&D networks Mrio V. Tomsello 1, Nicol Perr 2, Cludio J. Tessone 1, Márton Krsi 3, nd Frnk Schweitzer 1 1 Chir
By Ken Standfield, Director Research & Development, KNOWCORP
1 THE NORMAL DISTRIBUTION METHOD ARTICLE NO.: 10080 By Ken Stndfield, Director Reserch & Development, KNOWCORP http://www.knowcorp.com Emil: ks@knowcorp.com INTRODUCTION The following methods hve been
Case (a): Ans Ans. Case (b): ; s 1 = 65(4) Ans. s 1 = pr t. = 1.04 ksi. Ans. s 2 = pr 2t ; s 2 = 65(4) = 520 psi
8 3. The thin-wlled cylinder cn be supported in one of two wys s shown. Determine the stte of stress in the wll of the cylinder for both cses if the piston P cuses the internl pressure to be 65 psi. The
Tests for the Ratio of Two Poisson Rates
Chpter 437 Tests for the Rtio of Two Poisson Rtes Introduction The Poisson probbility lw gives the probbility distribution of the number of events occurring in specified intervl of time or spce. The Poisson
Purpose of the experiment
Newton s Lws II PES 6 Advnced Physics Lb I Purpose of the experiment Exmine two cses using Newton s Lws. Sttic ( = 0) Dynmic ( 0) fyi fyi Did you know tht the longest recorded flight of chicken is thirteen
Chemistry 2 Exam Roane State Academic Festival. Name (print neatly) School
Name (print neatly) School There are fifteen question on this exam. Each question is weighted equally. n the answer sheet, write your name in the space provided and your answers in the blanks provided.
Chapter 5. , r = r 1 r 2 (1) µ = m 1 m 2. r, r 2 = R µ m 2. R(m 1 + m 2 ) + m 2 r = r 1. m 2. r = r 1. R + µ m 1
Tor Kjellsson Stockholm University Chpter 5 5. Strting with the following informtion: R = m r + m r m + m, r = r r we wnt to derive: µ = m m m + m r = R + µ m r, r = R µ m r 3 = µ m R + r, = µ m R r. 4
Recurrent Neural Network
Recurrent Neural Network The Deepest of All Deep Learning Slides by Chen Liang Deep Learning Deep Learning Deep learning works like the human brain? Demystify Deep Learning Deep Learning: Building Blocks
Intensity transformations
Intensity trnsformtions Stefno Ferrri Università degli Studi di Milno stefno.ferrri@unimi.it Methods for Imge Processing cdemic yer 2017 2018 Sptil domin The sptil domin of n imge is the plne tht contins
Local orthogonality: a multipartite principle for (quantum) correlations
Locl orthogonlity: multiprtite principle for (quntum) correltions Antonio Acín ICREA Professor t ICFO-Institut de Ciencies Fotoniques, Brcelon Cusl Structure in Quntum Theory, Bensque, Spin, June 2013
We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.
![We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.](/thumbs/93/114277851.jpg "We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.")
Mth 255 - Vector lculus II Notes 4.2 Pth nd Line Integrls We begin with discussion of pth integrls (the book clls them sclr line integrls). We will do this for function of two vribles, but these ides cn
Energy Bands Energy Bands and Band Gap. Phys463.nb Phenomenon
Phys463.nb 49 7 Energy Bnds Ref: textbook, Chpter 7 Q: Why re there insultors nd conductors? Q: Wht will hppen when n electron moves in crystl? In the previous chpter, we discussed free electron gses,
Math 116 Final Exam April 26, 2013
Mth 6 Finl Exm April 26, 23 Nme: EXAM SOLUTIONS Instructor: Section:. Do not open this exm until you re told to do so. 2. This exm hs 5 pges including this cover. There re problems. Note tht the problems
New Expansion and Infinite Series
Interntionl Mthemticl Forum, Vol. 9, 204, no. 22, 06-073 HIKARI Ltd, www.m-hikri.com http://dx.doi.org/0.2988/imf.204.4502 New Expnsion nd Infinite Series Diyun Zhng College of Computer Nnjing University
THIS PAGE DECLASSIFIED IAW EO 12958
THIS PAGE DECLASSIFIED IAW EO 2958 THIS PAGE DECLASSIFIED IAW EO 2958 THIS PAGE DECLASSIFIED IAW E0 2958 S T T T I R F R S T Exhb e 3 9 ( 66 h Bm dn ) c f o 6 8 b o d o L) B C = 6 h oup C L) TO d 8 f f
Robust Predictions in Games with Incomplete Information
Robust Predictions in Gmes with Incomplete Informtion Dirk Bergemnn nd Stephen Morris April 2011 Introduction in gmes of incomplete informtion, privte informtion represents informtion bout: pyo environment