Math Homotopy Theory Additional notes

Size: px
Start display at page:

Download "Math Homotopy Theory Additional notes"

Transcription

1 Math Homotopy Theory Addtonal notes Martn Frankland February 4, 2013 The category Top s not Cartesan closed. problem. In these notes, we explan how to remedy that 1 Compactly generated spaces Ths secton and the next are essentally taken from [3, 1,2]. 1.1 Basc defntons and propertes Defnton 1.1. Let X be a topologcal space. A subset A X s called k-closed n X f for any compact Hausdorff space K and contnuous map u: K X, the premage u 1 (A) K s closed n K. The collecton of k-closed subsets of X forms a topology, whch contans the orgnal topology of X (.e. closed subsets are always k-closed). Notaton 1.2. Let kx denote the space whose underlyng set s that of X, but equpped wth the topology of k-closed subsets of X. Because the k-topology contans the orgnal topology on X, the dentty functon d: kx X s contnuous. Defnton 1.3. A space X s compactly generated (CG), sometmes called a k-space, f kx X s a homeomorphsm. In other words, every k-closed subset of X s closed n X. Example 1.4. Every locally compact space s CG. Example 1.5. Every frst-countable space s CG. More generally, every sequental space s CG. Example 1.6. Every CW-complex s CG. Notaton 1.7. Let CG denote the full subcategory of Top consstng of compactly generated spaces. Notaton 1.8. The constructon of kx defnes a functor k : Top CG, called the k-fcaton functor. Proposton 1.9. Let X be a CG space and Y an arbtrary space. Then a functon f : X Y s contnuous f and only f for every compact Hausdorff space K and contnuous map u: K X, the composte fu : K Y s contnuous. 1

2 Proposton For any space X, we have k 2 X = kx, so that kx s always compactly generated. Proposton Let X be a CG space and Y an arbtrary space. Then a functon f : X Y s contnuous f and only t s contnuous when vewed as a functon f : X ky. Proposton 1.11 can be reformulated n the followng more suggestve way, as a unversal property. For any CG space X and contnuous map f : X Y, there exsts a unque contnuous map f : X ky satsfyng f = d f,.e. makng the dagram f ky d Y X f commute. Note that f has the same underlyng functon as f. Ths exhbts ky Y as the closest approxmaton of Y by a CG space. Corollary The k-fcaton functor k : Top CG s rght adjont to the ncluson ι: CG Top. In other words, CG s a coreflectve subcategory of Top. The dentty functon ιkx X s the count of the adjuncton, whereas the unt W kιw s the dentty map for any CG space W. Proposton The category CG s complete. Lmts n CG are obtaned by applyng k to the lmt n Top. 2. The category CG s cocomplete. Colmts n CG are computed n Top. Proof. Let I be a small category and F : I CG an I-dagram. Let us wrte X := F () and, by abuse of notaton, lm X := lm I F. (1) Vewng the CG spaces X as spaces ιx, we can compute the lmt of the dagram ιf snce Top s complete (c.f. Homework 4 Problem 2). Applyng k yelds the CG space k(lm ιx ). For any CG space W, we have a natural somorphsm Hom CG (W, k(lm ιx )) = Hom Top (ιw, lm ιx ) = lm Hom Top (ιw, ιx ) = lm Hom CG (W, X ) where the last equalty comes from the fact that CG s a full subcategory of Top. Ths proves k(lm ιx ) = lm X. (2) We can compute the colmt X = colm ιx of the dagram ιf snce Top s cocomplete (c.f. Homework 4 Problem 2 and Remark afterwards). Snce X s a quotent of a coproduct of CG spaces ιx, X s also CG, by [3, Prop. 2.1, Prop. 2.2]. Moreover t s the desred colmt 2

3 n CG. For any CG space Y, we have a natural somorphsm Hom CG (X, Y ) = Hom Top (ιx, ιy ) = Hom Top (ι colm ιx, ιy ) = Hom Top (colm ιx, ιy ) = lm Hom Top (ιx, ιy ) whch proves X = colm X. = lm Hom CG (X, Y ) In partcular, products n CG may not agree wth the usual product n Top. Notaton For CG spaces X and Y, wrte X 0 Y = ιx ιy for ther usual product n Top, and wrte X Y = k(x 0 Y ) for ther product n CG. 1.2 Mappng spaces Defnton Let X and Y be CG spaces. For any compact Hausdorff space K, contnuous map u: K X, and open subset U Y, consder the set W (u, K, U) := {f : X Y contnuous fu(k) U}. Denote by C 0 (X, Y ) the set of contnuous maps from X to Y, equpped wth the topology generated by all such subsets W (u, K, U). Ths topology s called the compact-open topology. Note that C 0 (X, Y ) need not be CG. Wrte Map(X, Y ) := kc 0 (X, Y ). Theorem For any CG spaces X, Y, and Z, the natural map s a homeomorphsm. ϕ: Map(X Y, X) Map(X, Map(Y, Z)) (1) The fact that ϕ s bjectve tells us that CG s Cartesan closed, n the unenrched sense. The theorem s even better: CG s Cartesan closed, n the enrched sense. Note that CG s enrched n tself, gven that the composton map s contnuous. Map(X, Y ) Map(Y, Z) Map(X, Z) Remark The exponental object Map(X, Y ) s often denoted Y X. The somorphsm (1), whch can be wrtten as Z X Y = (Z Y ) X s often called the exponental law. 3

4 2 Weakly Hausdorff spaces The category CG would be good enough to work wth, but we can also mpose a separaton axom to our spaces. Defnton 2.1. A topologcal space X s weakly Hausdorff (WH) f for every compact Hausdorff space K and every contnuous map u: K X, the mage u(k) X s closed n X. Remark 2.2. Hausdorff spaces are weakly Hausdorff, snce u(k) s compact and thus closed n X f X s Hausdorff. Ths justfes the termnology. Moreover, weakly Hausdorff spaces are T 1, snce the sngle pont space s compact Hausdorff. Thus we have mplcatons Hausdorff weakly Hausdorff T 1. Example 2.3. Every CW-complex s Hausdorff, hence n partcular WH. Proposton 2.4. If X s a WH space, then any larger topology on X s stll WH. In partcular, kx s stll WH. Proof. Let X be the set X equpped wth a topology contanng the orgnal topology,.e. the dentty functon d: X X s contnuous. For any compact Hausdorff space K and contnuous map u: K X, the composte du: K X s contnuous and so ts mage du(k) X s closed n X. Thus u(k) = d 1 du(k) s closed n X. Proposton 2.5. Any subspace of a WH space s WH. Proof. Let X be a WH space and : A X the ncluson of a subspace. For any compact Hausdorff space K and contnuous map u: K A, the composte u: K X s contnuous and so ts mage u(k) X s closed n X, and thus n A as well. Notaton 2.6. Let CGWH denote the full subcategory of CG consstng of compactly generated weakly Hausdorff spaces. Defnton 2.7. For any CG space X, let hx be the quotent of X by the smallest closed equvalence relaton on X (see [3, Prop. 2.22]). Then hx s stll CG snce t s a quotent of a CG space [3, Prop. 2.1], and t s WH snce we quotented out a closed equvalence relaton on X [3, Cor. 2.21]. Ths defnes a functor h: CG CGWH called weak Hausdorfffcaton By constructon, the quotent map q : X hx satsfes the followng unversal property. For any CGWH space Y and contnuous map f : X Y, there exsts a unque contnuous map f : hx Y satsfyng f = fq,.e. makng the dagram X q hx f f Y commute. Ths exhbts X hx as the closest approxmaton of X by a CGWH space. 4

5 Corollary 2.8. The functor h: CG CGWH s left adjont to the ncluson functor ι: CG CGWH. In other words, CGWH s a reflectve subcategory of CG. The quotent map q : X hx s the unt of the adjuncton, whereas the count hιw W s the dentty map for any CGWH space W. Proposton 2.9. n CG. 1. The category CGWH s complete. Lmts n CGWH are computed 2. The category CGWH s cocomplete. Colmts n CGWH are obtaned by applyng h to the colmt n CG. Proof. Let I be a small category and F : I CGWH an I-dagram. (1) The lmt X = lm ιx computed n CG, whch exsts snce CG s complete (by 1.13), s stll WH. Indeed, an arbtrary product n CG of CGWH spaces s stll WH [3, Cor. 2.16], and so s an equalzer n CG of two maps (by 2.5 and 2.4). Therefore X s also the lmt n CGWH, by the same argument as 1.13 (2). (2) We have h(colm ιx ) = colm X n CGWH by the same argument as 1.13 (1). To summarze the stuaton, there are two adjont pars as follows: ι CG Top k h ι CGWH Proposton If X s a CG space and Y s a CGWH space, then Map(X, Y ) s CGWH. Consequently, the category CGWH s enrched n tself. Note that t s also Cartesan closed (n the enrched sense). Indeed, for any X, Y, and Z n CGWH, the natural map s a homeomorphsm. ϕ: Map(X Y, X) Map(X, Map(Y, Z)) 5

6 3 A convenent category of spaces In ths secton, we explan n what sense t s preferable to work wth the category CGWH nstead of Top. We follow the treatment n [1], tself nspred by [2]. Defnton 3.1. A convenent category of topologcal spaces s a full replete (meanng closed under somorphsms of objects) subcategory C of Top satsfyng the followng condtons. 1. All CW-complexes are objects of C. 2. C s complete and cocomplete. 3. C s Cartesan closed. Note that CG and CGWH are replete full subcategores of Top, snce both condtons of beng CG or WH are nvarant under homeomorphsm. Let us summarze the dscusson as follows. Proposton 3.2. The categores CG and CGWH are convenent. In fact, there are other desrable propertes for a convenent category of spaces. For nstance, one would lke that closed subspaces of objects n C also be n C. Both CG and CGWH satsfy ths addtonal condton. Proposton 3.3. Consder nclusons of spaces A B X. If A s k-closed n B and B s k-closed n X, then A s k-closed n X. Proof. Let K be a compact Hausdorff space and u: K X a contnuous map. Then the premage u 1 (B) s closed n K, hence compact (and also Hausdorff). Consder the restrcton u u 1 (B) : u 1 (B) B. Snce A s k-closed n B, the premage u 1 u 1 (B) (A) = u 1 (A) s closed n u 1 (B) and thus n K as well. Corollary 3.4. A closed subspace of a CG space s also CG. Proof. Let X be a CG space and B X a closed subspace. Let A B be a k-closed subset of B. Then A s k-closed n X (snce B s closed n X), hence closed n X (snce X s CG). Therefore A s closed n B. Corollary 3.5. A closed subspace of a CGWH space s also CGWH. 6

7 4 Some applcatons Here s a toy example llustratng the use of the exponental law. Proposton 4.1. For any spaces X and Y, the natural map [X, Y ] bjecton. Proof. Snce the map Map(X I, Y ) Map(I, Map(X, Y )) = π 0 Map(X, Y ) s a s a bjecton, two maps f, g : X Y are homotopc f and only f they are connected by a (contnuous) path n Map(X, Y ). Here s another beneft of workng n the category CG. Proposton 4.2. If X and Y are CW-complexes, then X Y nherts a CW-structure, where a p-cell of X and a q-cell of Y produce a (p + q)-cell of X Y. Here we do mean the product X Y s CG, not n Top. A pror, the product X 0 Y n Top could fal to be a CW-complex. See Hatcher (A.6) for detals. References [1] Ronald Brown, nlab: Convenent category of topologcal spaces (2012), avalable at org/nlab/show/convenent+category+of+topologcal+spaces. [2] N. E. Steenrod, A convenent category of topologcal spaces, Mchgan Math. J. 14 (1967), [3] Nel Strckland, The category of CGWH spaces (2009), avalable at shef.ac.uk/courses/homotopy/cgwh.pdf. 7

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

b * -Open Sets in Bispaces

b * -Open Sets in Bispaces Internatonal Journal of Mathematcs and Statstcs Inventon (IJMSI) E-ISSN: 2321 4767 P-ISSN: 2321-4759 wwwjmsorg Volume 4 Issue 6 August 2016 PP- 39-43 b * -Open Sets n Bspaces Amar Kumar Banerjee 1 and

More information

ALEKSANDROV URYSOHN COMPACTNESS CRITERION ON INTUITIONISTIC FUZZY S * STRUCTURE SPACE

ALEKSANDROV URYSOHN COMPACTNESS CRITERION ON INTUITIONISTIC FUZZY S * STRUCTURE SPACE mercan Journal of Mathematcs and cences Vol. 5, No., (January-December, 206) Copyrght Mnd Reader Publcatons IN No: 2250-302 www.journalshub.com LEKNDROV URYOHN COMPCTNE CRITERION ON INTUITIONITIC FUZZY

More information

Semi - - Connectedness in Bitopological Spaces

Semi - - Connectedness in Bitopological Spaces Journal of AL-Qadsyah for computer scence an mathematcs A specal Issue Researches of the fourth Internatonal scentfc Conference/Second صفحة 45-53 Sem - - Connectedness n Btopologcal Spaces By Qays Hatem

More information

BASIC DIFFERENTIABLE MANIFOLDS AND APPLICATIONS MAPS WITH TRANSLATORS TANGENT AND COTANGENT SPACES

BASIC DIFFERENTIABLE MANIFOLDS AND APPLICATIONS MAPS WITH TRANSLATORS TANGENT AND COTANGENT SPACES IJMMS, Vol., No. -2, (January-December 205): 25-36 Serals Publcatons ISSN: 0973-3329 BASIC DIFFERENTIABLE MANIFOLDS AND APPLICATIONS MAPS WITH TRANSLATORS TANGENT AND COTANGENT SPACES Mohamed M. Osman*

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

Some kinds of fuzzy connected and fuzzy continuous functions

Some kinds of fuzzy connected and fuzzy continuous functions Journal of Babylon Unversty/Pure and Appled Scences/ No(9)/ Vol(): 4 Some knds of fuzzy connected and fuzzy contnuous functons Hanan Al Hussen Deptof Math College of Educaton for Grls Kufa Unversty Hananahussen@uokafaq

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

FLOW POLYTOPES OF PARTITIONS

FLOW POLYTOPES OF PARTITIONS FLOW POLYTOPES OF PARTITIONS KAROLA MÉSZÁROS, CONNOR SIMPSON, AND ZOE WELLNER Abstract. Recent progress on flow polytopes ndcates many nterestng famles wth product formulas for ther volume. These product

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

A NOTE ON FUZZY CLOSURE OF A FUZZY SET

A NOTE ON FUZZY CLOSURE OF A FUZZY SET (JPMNT) Journal of Process Management New Technologes, Internatonal A NOTE ON FUZZY CLOSURE OF A FUZZY SET Bhmraj Basumatary Department of Mathematcal Scences, Bodoland Unversty, Kokrajhar, Assam, Inda,

More information

On the diameter of random planar graphs

On the diameter of random planar graphs On the dameter of random planar graphs Gullaume Chapuy, CNRS & LIAFA, Pars jont work wth Érc Fusy, Pars, Omer Gménez, ex-barcelona, Marc Noy, Barcelona. Probablty and Graphs, Eurandom, Endhoven, 04. Planar

More information

Ramsey numbers of cubes versus cliques

Ramsey numbers of cubes versus cliques Ramsey numbers of cubes versus clques Davd Conlon Jacob Fox Choongbum Lee Benny Sudakov Abstract The cube graph Q n s the skeleton of the n-dmensonal cube. It s an n-regular graph on 2 n vertces. The Ramsey

More information

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables

More information

UNIT 2 : INEQUALITIES AND CONVEX SETS

UNIT 2 : INEQUALITIES AND CONVEX SETS UNT 2 : NEQUALTES AND CONVEX SETS ' Structure 2. ntroducton Objectves, nequaltes and ther Graphs Convex Sets and ther Geometry Noton of Convex Sets Extreme Ponts of Convex Set Hyper Planes and Half Spaces

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law) Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes

More information

Lecture 5: Probability Distributions. Random Variables

Lecture 5: Probability Distributions. Random Variables Lecture 5: Probablty Dstrbutons Random Varables Probablty Dstrbutons Dscrete Random Varables Contnuous Random Varables and ther Dstrbutons Dscrete Jont Dstrbutons Contnuous Jont Dstrbutons Independent

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

PROPERTIES OF BIPOLAR FUZZY GRAPHS

PROPERTIES OF BIPOLAR FUZZY GRAPHS Internatonal ournal of Mechancal Engneerng and Technology (IMET) Volume 9, Issue 1, December 018, pp. 57 56, rtcle ID: IMET_09_1_056 valable onlne at http://www.a aeme.com/jmet/ssues.asp?typeimet&vtype

More information

such that is accepted of states in , where Finite Automata Lecture 2-1: Regular Languages be an FA. A string is the transition function,

such that is accepted of states in , where Finite Automata Lecture 2-1: Regular Languages be an FA. A string is the transition function, * Lecture - Regular Languages S Lecture - Fnte Automata where A fnte automaton s a -tuple s a fnte set called the states s a fnte set called the alphabet s the transton functon s the ntal state s the set

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Line geometry, according to the principles of Grassmann s theory of extensions. By E. Müller in Vienna.

Line geometry, according to the principles of Grassmann s theory of extensions. By E. Müller in Vienna. De Lnengeometre nach den Prnzpen der Grassmanschen Ausdehnungslehre, Monastshefte f. Mathematk u. Physk, II (89), 67-90. Lne geometry, accordng to the prncples of Grassmann s theory of extensons. By E.

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Clustering on antimatroids and convex geometries

Clustering on antimatroids and convex geometries Clusterng on antmatrods and convex geometres YULIA KEMPNER 1, ILYA MUCNIK 2 1 Department of Computer cence olon Academc Insttute of Technology 52 Golomb tr., P.O. Box 305, olon 58102 IRAEL 2 Department

More information

Notes on Organizing Java Code: Packages, Visibility, and Scope

Notes on Organizing Java Code: Packages, Visibility, and Scope Notes on Organzng Java Code: Packages, Vsblty, and Scope CS 112 Wayne Snyder Java programmng n large measure s a process of defnng enttes (.e., packages, classes, methods, or felds) by name and then usng

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola

More information

Harmonic Coordinates for Character Articulation PIXAR

Harmonic Coordinates for Character Articulation PIXAR Harmonc Coordnates for Character Artculaton PIXAR Pushkar Josh Mark Meyer Tony DeRose Bran Green Tom Sanock We have a complex source mesh nsde of a smpler cage mesh We want vertex deformatons appled to

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Intro. Iterators. 1. Access

Intro. Iterators. 1. Access Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy

More information

Any Pair of 2D Curves Is Consistent with a 3D Symmetric Interpretation

Any Pair of 2D Curves Is Consistent with a 3D Symmetric Interpretation Symmetry 2011, 3, 365-388; do:10.3390/sym3020365 OPEN ACCESS symmetry ISSN 2073-8994 www.mdp.com/journal/symmetry Artcle Any Par of 2D Curves Is Consstent wth a 3D Symmetrc Interpretaton Tadamasa Sawada

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

More information

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an On Central Spannng Trees of a Graph S. Bezrukov Unverstat-GH Paderborn FB Mathematk/Informatk Furstenallee 11 D{33102 Paderborn F. Kaderal, W. Poguntke FernUnverstat Hagen LG Kommunkatonssysteme Bergscher

More information

Introduction. Leslie Lamports Time, Clocks & the Ordering of Events in a Distributed System. Overview. Introduction Concepts: Time

Introduction. Leslie Lamports Time, Clocks & the Ordering of Events in a Distributed System. Overview. Introduction Concepts: Time Lesle Laports e, locks & the Orderng of Events n a Dstrbuted Syste Joseph Sprng Departent of oputer Scence Dstrbuted Systes and Securty Overvew Introducton he artal Orderng Logcal locks Orderng the Events

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Cordial and 3-Equitable Labeling for Some Star Related Graphs

Cordial and 3-Equitable Labeling for Some Star Related Graphs Internatonal Mathematcal Forum, 4, 009, no. 31, 1543-1553 Cordal and 3-Equtable Labelng for Some Star Related Graphs S. K. Vadya Department of Mathematcs, Saurashtra Unversty Rajkot - 360005, Gujarat,

More information

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme Mathematcal and Computatonal Applcatons Artcle A Fve-Pont Subdvson Scheme wth Two Parameters and a Four-Pont Shape-Preservng Scheme Jeqng Tan,2, Bo Wang, * and Jun Sh School of Mathematcs, Hefe Unversty

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Rough Neutrosophic Multisets Relation with Application in Marketing Strategy

Rough Neutrosophic Multisets Relation with Application in Marketing Strategy Neutrosophc Sets and Systems, Vol. 21, 2018 Unversty of New Mexco 36 Rough Neutrosophc Multsets Relaton wth Applcaton n Marketng Strategy Surana Alas 1, Daud Mohamad 2, and Adbah Shub 3 1 Faculty of Computer

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

Detection of an Object by using Principal Component Analysis

Detection of an Object by using Principal Component Analysis Detecton of an Object by usng Prncpal Component Analyss 1. G. Nagaven, 2. Dr. T. Sreenvasulu Reddy 1. M.Tech, Department of EEE, SVUCE, Trupath, Inda. 2. Assoc. Professor, Department of ECE, SVUCE, Trupath,

More information

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6)

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6) Harvard Unversty CS 101 Fall 2005, Shmon Schocken Assembler Elements of Computng Systems 1 Assembler (Ch. 6) Why care about assemblers? Because Assemblers employ some nfty trcks Assemblers are the frst

More information

Mercer Kernels for Object Recognition with Local Features

Mercer Kernels for Object Recognition with Local Features TR004-50, October 004, Department of Computer Scence, Dartmouth College Mercer Kernels for Object Recognton wth Local Features Swe Lyu Department of Computer Scence Dartmouth College Hanover NH 03755 A

More information

Computers and Mathematics with Applications. Discrete schemes for Gaussian curvature and their convergence

Computers and Mathematics with Applications. Discrete schemes for Gaussian curvature and their convergence Computers and Mathematcs wth Applcatons 57 (009) 87 95 Contents lsts avalable at ScenceDrect Computers and Mathematcs wth Applcatons journal homepage: www.elsever.com/locate/camwa Dscrete schemes for Gaussan

More information

EXTENDED FORMAL SPECIFICATIONS OF 3D SPATIAL DATA TYPES

EXTENDED FORMAL SPECIFICATIONS OF 3D SPATIAL DATA TYPES - 1 - EXTENDED FORMAL SPECIFICATIONS OF D SPATIAL DATA TYPES - TECHNICAL REPORT - André Borrann Coputaton Cvl Engneerng Technsche Unverstät München INTRODUCTION Startng pont for the developent of a spatal

More information

On Embedding and NP-Complete Problems of Equitable Labelings

On Embedding and NP-Complete Problems of Equitable Labelings IOSR Journal o Mathematcs (IOSR-JM) e-issn: 78-578, p-issn: 39-765X Volume, Issue Ver III (Jan - Feb 5), PP 8-85 wwwosrjournalsorg Ombeddng and NP-Complete Problems o Equtable Labelngs S K Vadya, C M Barasara

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

Reactors: A Data-Oriented Synchronous/Asynchronous Programming Model for Distributed Applications

Reactors: A Data-Oriented Synchronous/Asynchronous Programming Model for Distributed Applications Reactors: A Data-Orented Synchronous/Asynchronous Programmng Model for Dstrbuted Applcatons John Feld 1, Mara-Crstna Marnescu 1, and Chrstan Stefansen 2 1 IBM T.J. Watson Research Center {jfeld,maracm@us.bm.com

More information

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS - T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

Concurrent models of computation for embedded software

Concurrent models of computation for embedded software Concurrent models of computaton for embedded software and hardware! Researcher overvew what t looks lke semantcs what t means and how t relates desgnng an actor language actor propertes and how to represent

More information

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm Internatonal Journal of Advancements n Research & Technology, Volume, Issue, July- ISS - on-splt Restraned Domnatng Set of an Interval Graph Usng an Algorthm ABSTRACT Dr.A.Sudhakaraah *, E. Gnana Deepka,

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

LECTURE : MANIFOLD LEARNING

LECTURE : MANIFOLD LEARNING LECTURE : MANIFOLD LEARNING Rta Osadchy Some sldes are due to L.Saul, V. C. Raykar, N. Verma Topcs PCA MDS IsoMap LLE EgenMaps Done! Dmensonalty Reducton Data representaton Inputs are real-valued vectors

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

Bridges and cut-vertices of Intuitionistic Fuzzy Graph Structure

Bridges and cut-vertices of Intuitionistic Fuzzy Graph Structure Internatonal Journal of Engneerng, Scence and Mathematcs (UGC Approved) Journal Homepage: http://www.jesm.co.n, Emal: jesmj@gmal.com Double-Blnd Peer Revewed Refereed Open Access Internatonal Journal -

More information

Fitting: Deformable contours April 26 th, 2018

Fitting: Deformable contours April 26 th, 2018 4/6/08 Fttng: Deformable contours Aprl 6 th, 08 Yong Jae Lee UC Davs Recap so far: Groupng and Fttng Goal: move from array of pxel values (or flter outputs) to a collecton of regons, objects, and shapes.

More information

Secure Index Coding: Existence and Construction

Secure Index Coding: Existence and Construction Secure Index Codng: Exstence and Constructon Lawrence Ong 1, Badr N. Vellamb 2, Phee Lep Yeoh 3, Jörg Klewer 2, and Jnhong Yuan 4 1 The Unversty of Newcastle, Australa; 2 New Jersey Insttute of Technology,

More information

Solving some Combinatorial Problems in grid n-ogons

Solving some Combinatorial Problems in grid n-ogons Solvng some Combnatoral Problems n grd n-ogons Antono L. Bajuelos, Santago Canales, Gregoro Hernández, Ana Mafalda Martns Abstract In ths paper we study some problems related to grd n-ogons. A grd n-ogon

More information

Oracle Database: SQL and PL/SQL Fundamentals Certification Course

Oracle Database: SQL and PL/SQL Fundamentals Certification Course Oracle Database: SQL and PL/SQL Fundamentals Certfcaton Course 1 Duraton: 5 Days (30 hours) What you wll learn: Ths Oracle Database: SQL and PL/SQL Fundamentals tranng delvers the fundamentals of SQL and

More information

A Note on Quasi-coincidence for Fuzzy Points of Fuzzy Topology on the Basis of Reference Function

A Note on Quasi-coincidence for Fuzzy Points of Fuzzy Topology on the Basis of Reference Function I.J. Mathematcal Scences and Computng, 2016, 3, 49-57 Publshed Onlne July 2016 n MECS (http://www.mecs-press.net) DOI: 10.5815/jmsc.2016.03.05 Avalable onlne at http://www.mecs-press.net/jmsc A Note on

More information

NETWORKS of dynamical systems appear in a variety of

NETWORKS of dynamical systems appear in a variety of Necessary and Suffcent Topologcal Condtons for Identfablty of Dynamcal Networks Henk J. van Waarde, Petro Tes, and M. Kanat Camlbel arxv:807.09v [math.oc] 2 Jul 208 Abstract Ths paper deals wth dynamcal

More information

Fuzzy soft -ring. E Blok Esenler, Istanbul, Turkey 2 Department of Mathematics, Marmara University, Istanbul, Turkey

Fuzzy soft -ring. E Blok Esenler, Istanbul, Turkey 2 Department of Mathematics, Marmara University, Istanbul, Turkey IJST (202) A4: 469-476 Irnn Journl of Scence & Technology http://wwwshrzucr/en Fuzzy soft -rng S Onr, B A Ersoy * nd U Tekr 2 Deprtment of Mthemtcs, Yıldız Techncl Unversty Dvutpş Kmpüsü E Blok 202 34220

More information

On 3D DDFV discretization of gradient and divergence operators. I. Meshing, operators and discrete duality.

On 3D DDFV discretization of gradient and divergence operators. I. Meshing, operators and discrete duality. On 3D DDFV dscretzaton of gradent and dvergence operators. I. Meshng, operators and dscrete dualty. Bors Andreanov, Mostafa Bendahmane, Florence Hubert, Stella Krell To cte ths verson: Bors Andreanov,

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

Cell Count Method on a Network with SANET

Cell Count Method on a Network with SANET CSIS Dscusson Paper No.59 Cell Count Method on a Network wth SANET Atsuyuk Okabe* and Shno Shode** Center for Spatal Informaton Scence, Unversty of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

More information

Lecture 4: Principal components

Lecture 4: Principal components /3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness

More information

Universal Hinge Patterns for Folding Orthogonal Shapes

Universal Hinge Patterns for Folding Orthogonal Shapes Unversal Hnge Patterns for Foldng Orthogonal Shapes Nada M. Benbernou Erk D. Demane Martn L. Demane Avv Ovadya 1 Introducton An early result n computatonal orgam s that every polyhedral surface can be

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

(1) The control processes are too complex to analyze by conventional quantitative techniques.

(1) The control processes are too complex to analyze by conventional quantitative techniques. Chapter 0 Fuzzy Control and Fuzzy Expert Systems The fuzzy logc controller (FLC) s ntroduced n ths chapter. After ntroducng the archtecture of the FLC, we study ts components step by step and suggest a

More information

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface.

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface. IDC Herzlya Shmon Schocken Assembler Shmon Schocken Sprng 2005 Elements of Computng Systems 1 Assembler (Ch. 6) Where we are at: Human Thought Abstract desgn Chapters 9, 12 abstract nterface H.L. Language

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

More information

T. Background material: Topology

T. Background material: Topology MATH41071/MATH61071 Algebraic topology Autumn Semester 2017 2018 T. Background material: Topology For convenience this is an overview of basic topological ideas which will be used in the course. This material

More information

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Visual Curvature. 1. Introduction. y C. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2007

Visual Curvature. 1. Introduction. y C. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2007 IEEE onf. on omputer Vson and Pattern Recognton (VPR June 7 Vsual urvature HaRong Lu, Longn Jan Lateck, WenYu Lu, Xang Ba HuaZhong Unversty of Scence and Technology, P.R. hna Temple Unversty, US lhrbss@gmal.com,

More information

Stable Structures of Coalitions in Competitive and Altruistic Military Teams. M. Aurangzeb, D. Mikulski, G, Hudas, F.L. Lewis, and Edward Gu

Stable Structures of Coalitions in Competitive and Altruistic Military Teams. M. Aurangzeb, D. Mikulski, G, Hudas, F.L. Lewis, and Edward Gu Stable Structures of Coaltons n Compettve and Altrustc Mltary Teams M. Aurangzeb, D. Mkulsk,, Hudas, F.L. Lews, and Edward u M. Aurangzeb, The Unversty of Texas at Arlngton, Research Insttute, USA, aurangze@uta.edu

More information

Specifying Database Updates Using A Subschema

Specifying Database Updates Using A Subschema Specfyng Database Updates Usng A Subschema Sona Rstć, Pavle Mogn 2, Ivan Luovć 3 Busness College, V. Perća 4, 2000 Nov Sad, Yugoslava sdrstc@uns.ns.ac.yu 2 Vctora Unversty of Wellngton, School of Mathematcal

More information

A Unified, Integral Construction For Coordinates Over Closed Curves

A Unified, Integral Construction For Coordinates Over Closed Curves A Unfed, Integral Constructon For Coordnates Over Closed Curves Schaefer S., Ju T. and Warren J. Abstract We propose a smple generalzaton of Shephard s nterpolaton to pecewse smooth, convex closed curves

More information

Secure Index Coding: Existence and Construction

Secure Index Coding: Existence and Construction Secure Index Codng: Exstence and Constructon Lawrence Ong 1, Badr N. Vellamb 2, Phee Lep Yeoh 3, Jörg Klewer 2, and Jnhong Yuan 4 1 The Unversty of Newcastle, Australa; 2 New Jersey Insttute of Technology,

More information

treated this question with techniques permitting proofs. A synthesis of their

treated this question with techniques permitting proofs. A synthesis of their 588 MATHEMATICS: G. DEBREU PROC. N. A. S. 1 A. Borel, "Sur la cohomologe des espaces fbres prncpaux et des espaces homogbnes des groupes de Le compacts," Ann. Math., 57, No. 1, 115, 1953; "Homology and

More information