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Sampling Signals on Graphs from Theory to Applications
1 Sampling Signals on Graphs From Theory to Applications Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, and Gene Cheung Abstract The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review current progress on sampling over graphs focusing on theory and potential applications. Although most methodologies used in graph signal sampling are designed to parallel those used in sampling for standard signals, sampling theory for graph signals significantly differs from the theory of Shannon–Nyquist and shift-invariant sampling. This is due in part to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. Throughout this review, we discuss similarities and differences between standard and graph signal sampling and highlight open problems and challenges. I. INTRODUCTION Sampling is one of the fundamental tenets of digital signal processing (see [1] and references therein). As such, it has been studied extensively for decades and continues to draw considerable research efforts. Standard sampling theory relies on concepts of frequency domain analysis, shift invariant (SI) signals, and bandlimitedness [1]. Sampling of time and spatial domain signals in shift-invariant spaces is one of the most important building blocks of digital signal processing systems. However, in the big data era, the signals we need to process often have other types of connections and structure, such as network signals described by graphs. -
Kissing Numbers: Surprises in Dimension Four Günter M
Kissing numbers: Surprises in Dimension Four Günter M. Ziegler, TU Berlin The “kissing number problem” is a basic geometric problem that got its name from billards: Two balls “kiss” if they touch. The kissing number problem asks how many other balls can touch one given ball at the same time. If you arrange the balls on a pool ta- ble, it is easy to see that the answer is exactly six: Six balls just perfectly surround a given ball. Graphic: Detlev Stalling, ZIB Berlin and at the same time Vladimir I. Levenstein˘ in Russia proved that the correct, exact maximal numbers for the kissing number prob- lem are 240 in dimension 8, and 196560 in dimension 24. This is amazing, because these are also the only two dimensions where one knows a precise answer. It depends on the fact that mathemati- cians know very remarkable configurations in dimensions 8 and 24, which they call the E8 lattice and the Leech lattice, respectively. If, however, you think about this as a three-dimensional problem, So the kissing number problem remained unsolved, in particular, the question “how many balls can touch a given ball at the same for the case of dimension four. The so-called 24-cell, a four- time” becomes much more interesting — and quite complicated. In dimensional “platonic solid” of remarkable beauty (next page), fact, The 17th century scientific genius Sir Isaac Newton and his yields a configuration of 24 balls that would touch a given one in colleague David Gregory had a controversy about this in 1694 — four-dimensional space. -
A Negative Result for Hearing the Shape of a Triangle: a Computer-Assisted Proof
AN ELECTRONIC JOURNAL OF THE SOCIETAT CATALANA DE MATEMATIQUES` A negative result for hearing the shape of a triangle: a computer-assisted proof ⇤Gerard Orriols Gim´enez Resum (CAT) ETH Z¨urich. En aquest article demostrem que existeixen dos triangles diferents pels quals [email protected] el primer, segon i quart valor propi del Laplaci`a amb condicions de Dirichlet coincideixen. Aix`o resol una conjectura proposada per Antunes i Freitas i Corresponding author ⇤ suggerida per la seva evid`encia num`erica. La prova ´esassistida per ordinador i utilitza una nova t`ecnica per tractar l’espectre d’un l’operador, que consisteix a combinar un M`etode d’Elements Finits per localitzar aproximadament els primers valors propis i controlar la seva posici´oa l’espectre, juntament amb el M`etode de Solucions Particulars per confinar aquests valors propis a un interval molt m´esprec´ıs. Abstract (ENG) We prove that there exist two distinct triangles for which the first, second and fourth eigenvalues of the Laplace operator with zero Dirichlet boundary conditions coincide. This solves a conjecture raised by Antunes and Freitas and suggested by their numerical evidence. We use a novel technique for a computer-assisted proof about the spectrum of an operator, which combines a Finite Element Method, to locate roughly the first eigenvalues keeping track of their position in the spectrum, and the Method of Particular Solutions, to get a much more precise bound of these eigenvalues. Keywords: computer-assisted proof, Laplace eigenvalues, spectral geome- Acknowledgement try, Finite Element Method, Method The author would like to thank Re- of Particular Solutions. -
Anna C. Gilbert
Anna C. Gilbert Dept. of Mathematics University of Michigan 530 Church Street Ann Arbor, MI 48109-1109 [email protected] 734-763-5728 (phone) http://www.math.lsa.umich.edu/~annacg 734-763-0937 (fax) Revision date: 30 October, 2017. Research • General interests: analysis, probability, signal processing, and algorithms. • Specialization: randomized algorithms with applications to massive datasets and signal processing. Education • Ph.D., Mathematics, Princeton University, June 1997, Multiresolution homogenization schemes for differential equations and applications, Professor Ingrid Daubechies, advisor. • S.B., Mathematics (with honors), University of Chicago, June 1993. Positions Held • Herman H. Goldstine Collegiate Professor. Department of Mathematics, University of Michigan, 2014{ present. • Full Professor (Courtesy appointment). Division of Electrical and Computer Engineering, University of Michigan, 2010{present. • Full Professor. Department of Mathematics, University of Michigan, 2010{present. • Associate Professor (Courtesy appointment). Division of Electrical and Computer Engineering, Uni- versity of Michigan, 2008{2010. • Associate Professor. Department of Mathematics, University of Michigan, 2007{2010. • Assistant Professor. Department of Mathematics, University of Michigan, 2004{2007. • Principal technical staff member. Internet and Network Systems Research Center, AT&T Labs-Research, 2002{2004. • Senior technical staff member. Information Sciences Research Center, AT&T Labs-Research, 1998{ 2002. • Visiting instructor. Department of Mathematics, Stanford University, Winter quarter 2000. • Postdoctoral research associate. Yale University and AT&T Labs-Research, 1997{1998. • Intern. Lucent Technologies Bell Laboratories, summer 1996. • Research assistant. Princeton University, 1994{1995. • Intern. AT&T Bell Laboratories, summers 1993{1995. Publications Refereed Journal Publications • F. J. Chung, A. C. Gilbert, J. G. Hoskins, and J. C. Schotland, \Optical tomography on graphs", Inverse Problems, Volume 33, Number 5, 2017. -
Gazette Des Mathématiciens
JUILLET 2018 – No 157 la Gazette des Mathématiciens • Autour du Prix Fermat • Mathématiques – Processus ponctuels déterminantaux • Raconte-moi... le champ libre gaussien • Tribune libre – Quelle(s) application(s) pour le plan Torossian-Villani la GazetteComité de rédaction des Mathématiciens Rédacteur en chef Sébastien Gouëzel Boris Adamczewski Université de Nantes Institut Camille Jordan, Lyon [email protected] [email protected] Sophie Grivaux Rédacteurs Université de Lille [email protected] Thomas Alazard École Normale Supérieure de Paris-Saclay [email protected] Fanny Kassel IHÉS Maxime Bourrigan [email protected] Lycée Saint-Geneviève, Versailles [email protected] Pauline Lafitte Christophe Eckès École Centrale, Paris Archives Henri Poincaré, Nancy [email protected] [email protected] Damien Gayet Romain Tessera Institut Fourier, Grenoble Université Paris-Sud [email protected] [email protected] Secrétariat de rédaction : smf – Claire Ropartz Institut Henri Poincaré 11 rue Pierre et Marie Curie 75231 Paris cedex 05 Tél. : 01 44 27 67 96 – Fax : 01 40 46 90 96 [email protected] – http://smf.emath.fr Directeur de la publication : Stéphane Seuret issn : 0224-8999 À propos de la couverture. Cette image réalisée par Alejandro Rivera (Institut Fourier) est le graphe de la partie positive du champ libre gaussien sur le tore plat 2=(2á2) projeté sur la somme des espaces propres du laplacien de valeur propre plus petite que 500. Les textures et couleurs ajoutées sur le graphe sont pure- ment décoratives. La fonction a été calculée avec C++ comme une somme de polynômes trigonométriques avec des poids aléatoires bien choisis, le graphe a été réalisé sur Python et les textures et couleurs ont été réalisées avec gimp. -
Lecture19.Pptx
Outline Foundations of Computer Graphics (Fall 2012) §. Basic ideas of sampling, reconstruction, aliasing CS 184, Lectures 19: Sampling and Reconstruction §. Signal processing and Fourier analysis §. http://inst.eecs.berkeley.edu /~cs184 Implementation of digital filters §. Section 14.10 of FvDFH (you really should read) §. Post-raytracing lectures more advanced topics §. No programming assignment §. But can be tested (at high level) in final Acknowledgements: Thomas Funkhouser and Pat Hanrahan Some slides courtesy Tom Funkhouser Sampling and Reconstruction Sampling and Reconstruction §. An image is a 2D array of samples §. Discrete samples from real-world continuous signal (Spatial) Aliasing (Spatial) Aliasing §. Jaggies probably biggest aliasing problem 1 Sampling and Aliasing Image Processing pipeline §. Artifacts due to undersampling or poor reconstruction §. Formally, high frequencies masquerading as low §. E.g. high frequency line as low freq jaggies Outline Motivation §. Basic ideas of sampling, reconstruction, aliasing §. Formal analysis of sampling and reconstruction §. Signal processing and Fourier analysis §. Important theory (signal-processing) for graphics §. Implementation of digital filters §. Also relevant in rendering, modeling, animation §. Section 14.10 of FvDFH Ideas Sampling Theory Analysis in the frequency (not spatial) domain §. Signal (function of time generally, here of space) §. Sum of sine waves, with possibly different offsets (phase) §. Each wave different frequency, amplitude §. Continuous: defined at all points; discrete: on a grid §. High frequency: rapid variation; Low Freq: slow variation §. Images are converting continuous to discrete. Do this sampling as best as possible. §. Signal processing theory tells us how best to do this §. Based on concept of frequency domain Fourier analysis 2 Fourier Transform Fourier Transform §. Tool for converting from spatial to frequency domain §. -
Martin J. Strauss April 22, 2015
Martin J. Strauss April 22, 2015 Work Address: Dept. of Mathematics University of Michigan Ann Arbor, MI 48109-1043 [email protected] http://www.eecs.umich.edu/~martinjs/ +1-734-717-9874 (cell) Research Interests • Sustainable Energy. • Fundamental algorithms, especially randomized and approximation algorithms. • Algorithms for massive data sets. • Signal processing and and computational harmonic analysis. • Computer security and cryptography. • Complexity theory. Pedagogical Interests • Manipulative Mathematics (teaching via pipe cleaners, paper folding and other mutilation, etc.) • Kinesthetic Mathematics (teaching through movement, including acting out algorithms, per- mutations, and other transformations). Education • Ph.D., Mathematics, Rutgers University, October 1995. Thesis title: Measure in Feasible Complexity Classes. Advisor: Prof. E. Allender, Dept. of Computer Science. • A.B. summa cum laude, Mathematics, Columbia University, May 1989. Minor in Computer Science. Employment History • Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2011{present. • Associate Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2008{2011. • Assistant Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2004{2008. • Visiting Associate Research Scholar, Program in Applied and Computational Mathematics, Princeton University, Sept. 2006{Feb. 2007. Page 1 of 17 • Principal investigator, AT&T Laboratories|Research, 1997{2004. Most recent position: Principal Technical Staff Member, Internet and Network Systems Research Center. • Consultant, Network Services Research Center, AT&T Laboratories, 1996. • Post-Doctoral Research Associate, Department of Computer Science, Iowa State University, September 1995{May 1996. • Intern. Speech recognition group, IBM Watson Research Center, summers, 1989{1990. -
And Bandlimiting IMAHA, November 14, 2009, 11:00–11:40
Time- and bandlimiting IMAHA, November 14, 2009, 11:00{11:40 Joe Lakey (w Scott Izu and Jeff Hogan)1 November 16, 2009 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Outline Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Outline sampling theory and history Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting connecting sampling and time- and bandlimiting The multiband case Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting The multiband case Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting R −2πitξ Fourier transform: bf (ξ) = f (t) e dt R _ Bandlimiting: -
Local Covering Optimality of Lattices: Leech Lattice Versus Root Lattice E8
Local Covering Optimality of Lattices: Leech Lattice versus Root Lattice E8 Achill Sch¨urmann, Frank Vallentin ∗ 10th November 2004 Abstract We show that the Leech lattice gives a sphere covering which is locally least dense among lattice coverings. We show that a similar result is false for the root lattice E8. For this we construct a less dense covering lattice whose Delone subdivision has a common refinement with the Delone subdivision of E8. The new lattice yields a sphere covering which is more than 12% less dense than the formerly ∗ best known given by the lattice A8. Currently, the Leech lattice is the first and only known example of a locally optimal lattice covering having a non-simplicial Delone subdivision. We hereby in particular answer a question of Dickson posed in 1968. By showing that the Leech lattice is rigid our answer is even strongest possible in a sense. 1 Introduction The Leech lattice is the exceptional lattice in dimension 24. Soon after its discovery by Leech [Lee67] it was conjectured that it is extremal for several geometric problems in R24: the kissing number problem, the sphere packing problem and the sphere covering problem. In 1979, Odlyzko and Sloane and independently Levenshtein solved the kissing number problem in dimension 24 by showing that the Leech lattice gives an optimal solution. Two years later, Bannai and Sloane showed that it gives the unique solution up to isometries (see [CS88], Ch. 13, 14). Unlike the kissing number problem, the other two problems are still open. Recently, Cohn and Kumar [CK04] showed that the Leech lattice gives the unique densest lattice sphere packing in R24. -
Condition Length and Complexity for the Solution of Polynomial Systems
Condition length and complexity for the solution of polynomial systems Diego Armentano∗ Carlos Beltr´an† Universidad de La Rep´ublica Universidad de Cantabria URUGUAY SPAIN [email protected] [email protected] Peter B¨urgisser‡ Felipe Cucker§ Technische Universit¨at Berlin City University of Hong Kong GERMANY HONG KONG [email protected] [email protected] Michael Shub City University of New York U.S.A. [email protected] October 2, 2018 Abstract Smale’s 17th problem asks for an algorithm which finds an approximate arXiv:1507.03896v1 [math.NA] 14 Jul 2015 zero of polynomial systems in average polynomial time (see Smale [17]). The main progress on Smale’s problem is Beltr´an-Pardo [6] and B¨urgisser- Cucker [9]. In this paper we will improve on both approaches and we prove an important intermediate result. Our main results are Theorem 1 on the complexity of a randomized algorithm which improves the result of [6], Theorem 2 on the average of the condition number of polynomial systems ∗Partially supported by Agencia Nacional de Investigaci´on e Innovaci´on (ANII), Uruguay, and by CSIC group 618 †partially suported by the research projects MTM2010-16051 and MTM2014-57590 from Spanish Ministry of Science MICINN ‡Partially funded by DFG research grant BU 1371/2-2 §Partially funded by a GRF grant from the Research Grants Council of the Hong Kong SAR (project number CityU 100813). 1 which improves the estimate found in [9], and Theorem 3 on the complexity of finding a single zero of polynomial systems. This last Theorem is the main result of [9]. -
Volume Estimates for Equiangular Hyperbolic Coxeter Polyhedra
Algebraic & Geometric Topology 9 (2009) 1225–1254 1225 Volume estimates for equiangular hyperbolic Coxeter polyhedra CHRISTOPHER KATKINSON An equiangular hyperbolic Coxeter polyhedron is a hyperbolic polyhedron where all dihedral angles are equal to =n for some fixed n Z, n 2. It is a consequence of 2 Andreev’s theorem that either n 3 and the polyhedron has all ideal vertices or that D n 2. Volume estimates are given for all equiangular hyperbolic Coxeter polyhedra. D 57M50; 30F40 1 Introduction An orientable 3–orbifold, Q, is determined by an underlying 3–manifold, XQ and a trivalent graph, †Q , labeled by integers. If Q carries a hyperbolic structure then it is unique by Mostow rigidity, so the hyperbolic volume of Q is an invariant of Q. Therefore, for hyperbolic orbifolds with a fixed underlying manifold, the volume is a function of the labeled graph †. In this paper, methods for estimating the volume of orbifolds of a restricted type in terms of this labeled graph will be described. The orbifolds studied in this paper are quotients of H3 by reflection groups generated by reflections in hyperbolic Coxeter polyhedra. A Coxeter polyhedron is one where each dihedral angle is of the form =n for some n Z, n 2. Given a hyperbolic 2 Coxeter polyhedron P , consider the group generated by reflections through the geodesic planes determined by its faces, .P/. Then .P/ is a Kleinian group which acts on H3 with fundamental domain P . The quotient, O H3= .P/, is a nonorientable D orbifold with singular locus P.2/ , the 2–skeleton of P . -
Dagrep-V005-I006-Complete.Pdf
Volume 5, Issue 6, June 2015 Computational Social Choice: Theory and Applications (Dagstuhl Seminar 15241) Britta Dorn, Nicolas Maudet, and Vincent Merlin ................................ 1 Complexity of Symbolic and Numerical Problems (Dagstuhl Seminar 15242) Peter Bürgisser, Felipe Cucker, Marek Karpinski, and Nicolai Vorobjov . 28 Sparse Modelling and Multi-exponential Analysis (Dagstuhl Seminar 15251) Annie Cuyt, George Labahn, Avraham Sidi, and Wen-shin Lee ................... 48 Logics for Dependence and Independence (Dagstuhl Seminar 15261) Erich Grädel, Juha Kontinen, Jouko Väänänen, and Heribert Vollmer . 70 DagstuhlReports,Vol.5,Issue6 ISSN2192-5283 ISSN 2192-5283 Published online and open access by Aims and Scope Schloss Dagstuhl – Leibniz-Zentrum für Informatik The periodical Dagstuhl Reports documents the GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, program and the results of Dagstuhl Seminars and Germany. Online available at Dagstuhl Perspectives Workshops. http://www.dagstuhl.de/dagpub/2192-5283 In principal, for each Dagstuhl Seminar or Dagstuhl Perspectives Workshop a report is published that Publication date contains the following: February, 2016 an executive summary of the seminar program and the fundamental results, Bibliographic information published by the Deutsche an overview of the talks given during the seminar Nationalbibliothek (summarized as talk abstracts), and The Deutsche Nationalbibliothek lists this publica- summaries from working groups (if applicable). tion in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at This basic framework can be extended by suitable http://dnb.d-nb.de. contributions that are related to the program of the seminar, e. g. summaries from panel discussions or License open problem sessions. This work is licensed under a Creative Commons Attribution 3.0 DE license (CC BY 3.0 DE).