Symplectic Semifield Planes and Z4–Linear Codes 1
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A Characterisation of Tangent Subplanes of PG(2,Q
A Characterisation of Tangent Subplanes of PG(2,q3) S.G. Barwick and Wen-Ai Jackson School of Mathematics, University of Adelaide Adelaide 5005, Australia June 23, 2018 Corresponding Author: Dr Susan Barwick, University of Adelaide, Adelaide 5005, Aus- tralia. Phone: +61 8 8303 3983, Fax: +61 8 8303 3696, email: [email protected] Keywords: Bruck-Bose representation, PG(2, q3), order q subplanes AMS code: 51E20 Abstract In [2], the authors determine the representation of order-q-subplanes and order- q-sublines of PG(2,q3) in the Bruck-Bose representation in PG(6,q). In particular, they showed that an order-q-subplane of PG(2,q3) corresponds to a certain ruled arXiv:1204.4953v1 [math.CO] 23 Apr 2012 surface in PG(6,q). In this article we show that the converse holds, namely that any ruled surface satisfying the required properties corresponds to a tangent order- q-subplane of PG(2,q3). 1 Introduction We begin with a brief introduction to 2-spreads in PG(5, q), and the Bruck-Bose repre- sentation of PG(2, q3) in PG(6, q), and introduce the notation we will use. A 2-spread of PG(5, q) is a set of q3 + 1 planes that partition PG(5, q). The following construction of a regular 2-spread of PG(5, q) will be needed. Embed PG(5, q) in PG(5, q3) and let g be a line of PG(5, q3) disjoint from PG(5, q). The Frobenius automorphism of 2 GF(q3) where x 7→ xq induces a collineation of PG(5, q3). -
A Characterisation of Translation Ovals in Finite Even Order Planes
A characterisation of translation ovals in finite even order planes S.G. Barwick and Wen-Ai Jackson School of Mathematics, University of Adelaide Adelaide 5005, Australia Abstract In this article we consider a set C of points in PG(4,q), q even, satisfying cer- tain combinatorial properties with respect to the planes of PG(4,q). We show that there is a regular spread in the hyperplane at infinity, such that in the correspond- ing Bruck-Bose plane PG(2,q2), the points corresponding to C form a translation hyperoval, and conversely. 1 Introduction In this article we first consider a non-degenerate conic in PG(2, q2), q even. We look at the corresponding point set in the Bruck-Bose representation in PG(4, q), and study its combinatorial properties (details of the Bruck-Bose representation are given in Section 2). Some properties of this set were investigated in [4]. In this article we are interested in combinatorial properties relating to planes of PG(4, q). We consider a set of points in PG(4, q) satisfying certain of these combinatorial properties and find that the points 2 arXiv:1305.6673v1 [math.CO] 29 May 2013 correspond to a translation oval in the Bruck-Bose plane PG(2, q ). In [3], the case when q is odd is considered, and we show that given a set of points in PG(4, q) satisfying the following combinatorial properties, we can reconstruct the conic in PG(2, q2). We use the following terminology in PG(4, q): if the hyperplane at infinity is denoted Σ∞, then we call the points of PG(4, q) \ Σ∞ affine points. -
Littlewood–Richardson Coefficients and Birational Combinatorics
Littlewood{Richardson coefficients and birational combinatorics Darij Grinberg 28 August 2020 [corrected version] Algebraic and Combinatorial Perspectives in the Mathematical Sciences slides: http: //www.cip.ifi.lmu.de/~grinberg/algebra/acpms2020.pdf paper: arXiv:2008.06128 aka http: //www.cip.ifi.lmu.de/~grinberg/algebra/lrhspr.pdf 1 / 43 The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Chapter 1 Chapter 1 Littlewood{Richardson coefficients References (among many): Richard Stanley, Enumerative Combinatorics, vol. 2, Chapter 7. Darij Grinberg, Victor Reiner, Hopf Algebras in Combinatorics, arXiv:1409.8356. -
Kernel Methods for Knowledge Structures
Kernel Methods for Knowledge Structures Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) von der Fakultät für Wirtschaftswissenschaften der Universität Karlsruhe (TH) genehmigte DISSERTATION von Dipl.-Inform.-Wirt. Stephan Bloehdorn Tag der mündlichen Prüfung: 10. Juli 2008 Referent: Prof. Dr. Rudi Studer Koreferent: Prof. Dr. Dr. Lars Schmidt-Thieme 2008 Karlsruhe Abstract Kernel methods constitute a new and popular field of research in the area of machine learning. Kernel-based machine learning algorithms abandon the explicit represen- tation of data items in the vector space in which the sought-after patterns are to be detected. Instead, they implicitly mimic the geometry of the feature space by means of the kernel function, a similarity function which maintains a geometric interpreta- tion as the inner product of two vectors. Knowledge structures and ontologies allow to formally model domain knowledge which can constitute valuable complementary information for pattern discovery. For kernel-based machine learning algorithms, a good way to make such prior knowledge about the problem domain available to a machine learning technique is to incorporate it into the kernel function. This thesis studies the design of such kernel functions. First, this thesis provides a theoretical analysis of popular similarity functions for entities in taxonomic knowledge structures in terms of their suitability as kernel func- tions. It shows that, in a general setting, many taxonomic similarity functions can not be guaranteed to yield valid kernel functions and discusses the alternatives. Secondly, the thesis addresses the design of expressive kernel functions for text mining applications. A first group of kernel functions, Semantic Smoothing Kernels (SSKs) retain the Vector Space Models (VSMs) representation of textual data as vec- tors of term weights but employ linguistic background knowledge resources to bias the original inner product in such a way that cross-term similarities adequately con- tribute to the kernel result. -
148. Symplectic Translation Planes by Antoni
Lecture Notes of Seminario Interdisciplinare di Matematica Vol. 2 (2003), pp. 101 - 148. Symplectic translation planes by Antonio Maschietti Abstract1. A great deal of important work on symplectic translation planes has occurred in the last two decades, especially on those of even order, because of their link with non-linear codes. This link, which is the central theme of this paper, is based upon classical groups, particularly symplectic and orthogonal groups. Therefore I have included an Appendix, where standard notation and basic results are recalled. 1. Translation planes In this section we give an introductory account of translation planes. Compre- hensive textbooks are for example [17] and [3]. 1.1. Notation. We will use linear algebra to construct interesting geometrical structures from vector spaces, with special regard to vector spaces over finite fields. Any finite field has prime power order and for any prime power q there is, up to isomorphisms, a unique finite field of order q. This unique field is commonly de- noted by GF (q)(Galois Field); but also other symbols are usual, such as Fq.If q = pn with p a prime, the additive structure of F is that of an n dimensional q − vector space over Fp, which is the field of integers modulo p. The multiplicative group of Fq, denoted by Fq⇤, is cyclic. Finally, the automorphism group of the field Fq is cyclic of order n. Let A and B be sets. If f : A B is a map (or function or else mapping), then the image of x A will be denoted! by f(x)(functional notation). -
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference Sunday 30 May to Friday 4 June 2010 Index of Talks and Abstracts Main talks 1. Simeon Ball, On subsets of a finite vector space in which every subset of basis size is a basis 2. Simon Blackburn, Honeycomb arrays 3. G`abor Korchm`aros, Curves over finite fields, an approach from finite geometry 4. Cheryl Praeger, Basic pregeometries 5. Bernhard Schmidt, Finiteness of circulant weighing matrices of fixed weight 6. Douglas Stinson, Multicollision attacks on iterated hash functions Short talks 1. Mari´en Abreu, Adjacency matrices of polarity graphs and of other C4–free graphs of large size 2. Marco Buratti, Combinatorial designs via factorization of a group into subsets 3. Mike Burmester, Lightweight cryptographic mechanisms based on pseudorandom number generators 4. Philippe Cara, Loops, neardomains, nearfields and sets of permutations 5. Ilaria Cardinali, On the structure of Weyl modules for the symplectic group 6. Bill Cherowitzo, Parallelisms of quadrics 7. Jan De Beule, Large maximal partial ovoids of Q−(5, q) 8. Bart De Bruyn, On extensions of hyperplanes of dual polar spaces 1 9. Frank De Clerck, Intriguing sets of partial quadrangles 10. Alice Devillers, Symmetry properties of subdivision graphs 11. Dalibor Froncek, Decompositions of complete bipartite graphs into generalized prisms 12. Stelios Georgiou, Self-dual codes from circulant matrices 13. Robert Gilman, Cryptology of infinite groups 14. Otokar Groˇsek, The number of associative triples in a quasigroup 15. Christoph Hering, Latin squares, homologies and Euler’s conjecture 16. Leanne Holder, Bilinear star flocks of arbitrary cones 17. Robert Jajcay, On the geometry of cages 18. -
Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein
Advances in Mathematics AI1567 advances in mathematics 122, 49149 (1996) article no. 0057 Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Sergey Fomin* Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and Theory of Algorithms Laboratory, St. Petersburg Institute of Informatics, Russian Academy of Sciences, St. Petersburg, Russia and Andrei Zelevinsky- Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Received October 24, 1995; accepted November 27, 1995 Contents. 1. Introduction and main results. 2. Lusztig variety and Chamber Ansatz. 2.1. Semifields and subtraction-free rational expressions. 2.2. Lusztig variety. 2.3. Pseudo-line arrangements. 2.4. Minors and the nilTemperleyLieb algebra. 2.5. Chamber Ansatz. 2.6. Solutions of the 3-term relations. 2.7. An alternative description of the Lusztig variety. 2.8. Trans- 0 n n ition from n . 2.9. Polynomials T J and Za . 2.10. Formulas for T J . 2.11. Sym- metries of the Lusztig variety. 3. Total positivity criteria. 3.1. Factorization of unitriangular matrices. 3.2. General- izations of Fekete's criterion. 3.3. Polynomials TJ (x). 3.4. The action of the four- group on N >0. 3.5. The multi-filtration in the coordinate ring of N. 3.6. The S3 _ZÂ2Z symmetry. 3.7. Dual canonical basis and positivity theorem. 4. Piecewise-linear minimization formulas. 4.1. Polynomials over the tropical semi- field. 4.2. Multisegment duality. 4.3. Nested normal orderings. 4.4. Quivers and boundary pseudo-line arrangements. 5. The case of an arbitrary permutation. -
2.2 Kernel and Range of a Linear Transformation
2.2 Kernel and Range of a Linear Transformation Performance Criteria: 2. (c) Determine whether a given vector is in the kernel or range of a linear trans- formation. Describe the kernel and range of a linear transformation. (d) Determine whether a transformation is one-to-one; determine whether a transformation is onto. When working with transformations T : Rm → Rn in Math 341, you found that any linear transformation can be represented by multiplication by a matrix. At some point after that you were introduced to the concepts of the null space and column space of a matrix. In this section we present the analogous ideas for general vector spaces. Definition 2.4: Let V and W be vector spaces, and let T : V → W be a transformation. We will call V the domain of T , and W is the codomain of T . Definition 2.5: Let V and W be vector spaces, and let T : V → W be a linear transformation. • The set of all vectors v ∈ V for which T v = 0 is a subspace of V . It is called the kernel of T , And we will denote it by ker(T ). • The set of all vectors w ∈ W such that w = T v for some v ∈ V is called the range of T . It is a subspace of W , and is denoted ran(T ). It is worth making a few comments about the above: • The kernel and range “belong to” the transformation, not the vector spaces V and W . If we had another linear transformation S : V → W , it would most likely have a different kernel and range. -
23. Kernel, Rank, Range
23. Kernel, Rank, Range We now study linear transformations in more detail. First, we establish some important vocabulary. The range of a linear transformation f : V ! W is the set of vectors the linear transformation maps to. This set is also often called the image of f, written ran(f) = Im(f) = L(V ) = fL(v)jv 2 V g ⊂ W: The domain of a linear transformation is often called the pre-image of f. We can also talk about the pre-image of any subset of vectors U 2 W : L−1(U) = fv 2 V jL(v) 2 Ug ⊂ V: A linear transformation f is one-to-one if for any x 6= y 2 V , f(x) 6= f(y). In other words, different vector in V always map to different vectors in W . One-to-one transformations are also known as injective transformations. Notice that injectivity is a condition on the pre-image of f. A linear transformation f is onto if for every w 2 W , there exists an x 2 V such that f(x) = w. In other words, every vector in W is the image of some vector in V . An onto transformation is also known as an surjective transformation. Notice that surjectivity is a condition on the image of f. 1 Suppose L : V ! W is not injective. Then we can find v1 6= v2 such that Lv1 = Lv2. Then v1 − v2 6= 0, but L(v1 − v2) = 0: Definition Let L : V ! W be a linear transformation. The set of all vectors v such that Lv = 0W is called the kernel of L: ker L = fv 2 V jLv = 0g: 1 The notions of one-to-one and onto can be generalized to arbitrary functions on sets. -
LINEAR GROUPS AS RIGHT MULTIPLICATION GROUPS of QUASIFIELDS 3 Group T (Π) of Translations
LINEAR GROUPS AS RIGHT MULTIPLICATION GROUPS OF QUASIFIELDS GABOR´ P. NAGY Abstract. For quasifields, the concept of parastrophy is slightly weaker than isotopy. Parastrophic quasifields yield isomorphic translation planes but not conversely. We investigate the right multiplication groups of fi- nite quasifields. We classify all quasifields having an exceptional finite transitive linear group as right multiplication group. The classification is up to parastrophy, which turns out to be the same as up to the iso- morphism of the corresponding translation planes. 1. Introduction A translation plane is often represented by an algebraic structure called a quasifield. Many properties of the translation plane can be most easily understood by looking at the appropriate quasifield. However, isomorphic translation planes can be represented by nonisomorphic quasifields. Further- more, the collineations do not always have a nice representation in terms of operations in the quasifields. Let p be a prime number and (Q, +, ·) be a quasifield of finite order pn. Fn We identify (Q, +) with the vector group ( p , +). With respect to the multiplication, the set Q∗ of nonzero elements of Q form a loop. The right multiplication maps of Q are the maps Ra : Q → Q, xRa = x · a, where Fn a, x ∈ Q. By the right distributive law, Ra is a linear map of Q = p . Clearly, R0 is the zero map. If a 6= 0 then Ra ∈ GL(n,p). In geometric context, the set of right translations are also called the slope set or the spread set of the quasifield Q, cf. [6, Chapter 5]. The right multiplication group RMlt(Q) of the quasifield Q is the linear group generated by the nonzero right multiplication maps. -
Non Associative Linear Algebras
NON ASSOCIATIVE LINEAR ALGEBRAS W. B. Vasantha Kandasamy Florentin Smarandache ZIP PUBLISHING Ohio 2012 This book can be ordered from: Zip Publishing 1313 Chesapeake Ave. Columbus, Ohio 43212, USA Toll Free: (614) 485-0721 E-mail: [email protected] Website: www.zippublishing.com Copyright 2012 by Zip Publishing and the Authors Peer reviewers: Sukanto Bhattacharya, Deakin Graduate School of Business, Deakin University, Australia Kuldeep Kumar, School of Business, Bond University, Australia Professor Paul P. Wang, Ph D, Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA Many books can be downloaded from the following Digital Library of Science: http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htm ISBN-13: 978-1-59973-176-6 EAN: 9781599731766 Printed in the United States of America 2 CONTENTS Preface 5 Chapter One BASIC CONCEPTS 7 Chapter Two NON ASSOCIATIVE SEMILINEAR ALGEBRAS 13 Chapter Three NON ASSOCIATIVE LINEAR ALGEBRAS 83 Chapter Four GROUPOID VECTOR SPACES 111 3 Chapter Five APPLICATION OF NON ASSOCIATIVE VECTOR SPACES / LINEAR ALGEBRAS 161 Chapter Six SUGGESTED PROBLEMS 163 FURTHER READING 225 INDEX 229 ABOUT THE AUTHORS 231 4 PREFACE In this book authors for the first time introduce the notion of non associative vector spaces and non associative linear algebras over a field. We construct non associative space using loops and groupoids over fields. In general in all situations, which we come across to find solutions may not be associative; in such cases we can without any difficulty adopt these non associative vector spaces/linear algebras. Thus this research is a significant one. -
On Semifields of Type
I I G ◭◭ ◮◮ ◭ ◮ On semifields of type (q2n,qn,q2,q2,q), n odd page 1 / 19 ∗ go back Giuseppe Marino Olga Polverino Rocco Tromebtti full screen Abstract 2n n 2 2 close A semifield of type (q , q , q , q , q) (with n > 1) is a finite semi- field of order q2n (q a prime power) with left nucleus of order qn, right 2 quit and middle nuclei both of order q and center of order q. Semifields of type (q6, q3, q2, q2, q) have been completely classified by the authors and N. L. Johnson in [10]. In this paper we determine, up to isotopy, the form n n of any semifield of type (q2 , q , q2, q2, q) when n is an odd integer, proving n−1 that there exist 2 non isotopic potential families of semifields of this type. Also, we provide, with the aid of the computer, new examples of semifields of type (q14, q7, q2, q2, q), when q = 2. Keywords: semifield, isotopy, linear set MSC 2000: 51A40, 51E20, 12K10 1. Introduction A finite semifield S is a finite algebraic structure satisfying all the axioms for a skew field except (possibly) associativity. The subsets Nl = {a ∈ S | (ab)c = a(bc), ∀b, c ∈ S} , Nm = {b ∈ S | (ab)c = a(bc), ∀a, c ∈ S} , Nr = {c ∈ S | (ab)c = a(bc), ∀a, b ∈ S} and K = {a ∈ Nl ∩ Nm ∩ Nr | ab = ba, ∀b ∈ S} are fields and are known, respectively, as the left nucleus, the middle nucleus, the right nucleus and the center of the semifield.