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Luis David Garcıa Puente
Luis David Garc´ıa Puente Department of Mathematics and Statistics (936) 294-1581 Sam Houston State University [email protected] Huntsville, TX 77341–2206 http://www.shsu.edu/ldg005/ Professional Preparation Universidad Nacional Autonoma´ de Mexico´ (UNAM) Mexico City, Mexico´ B.S. Mathematics (with Honors) 1999 Virginia Polytechnic Institute and State University Blacksburg, VA Ph.D. Mathematics 2004 – Advisor: Reinhard Laubenbacher – Dissertation: Algebraic Geometry of Bayesian Networks University of California, Berkeley Berkeley, CA Postdoctoral Fellow Summer 2004 – Mentor: Lior Pachter Mathematical Sciences Research Institute (MSRI) Berkeley, CA Postdoctoral Fellow Fall 2004 – Mentor: Bernd Sturmfels Texas A&M University College Station, TX Visiting Assistant Professor 2005 – 2007 – Mentor: Frank Sottile Appointments Colorado College Colorado Springs, CO Professor of Mathematics and Computer Science 2021 – Sam Houston State University Huntsville, TX Professor of Mathematics 2019 – 2021 Sam Houston State University Huntsville, TX Associate Department Chair Fall 2017 – 2021 Sam Houston State University Huntsville, TX Associate Professor of Mathematics 2013 – 2019 Statistical and Applied Mathematical Sciences Institute Research Triangle Park, NC SAMSI New Researcher fellowship Spring 2009 Sam Houston State University Huntsville, TX Assistant Professor of Mathematics 2007 – 2013 Virginia Bioinformatics Institute (Virginia Tech) Blacksburg, VA Graduate Research Assistant Spring 2004 Virginia Polytechnic Institute and State University Blacksburg, -
Arxiv:2011.03572V2 [Math.CO] 17 Dec 2020
ORDER-FORCING IN NEURAL CODES R. AMZI JEFFS, CAITLIN LIENKAEMPER, AND NORA YOUNGS Abstract. Convex neural codes are subsets of the Boolean lattice that record the inter- section patterns of convex sets in Euclidean space. Much work in recent years has focused on finding combinatorial criteria on codes that can be used to classify whether or not a code is convex. In this paper we introduce order-forcing, a combinatorial tool which recognizes when certain regions in a realization of a code must appear along a line segment between other regions. We use order-forcing to construct novel examples of non-convex codes, and to expand existing families of examples. We also construct a family of codes which shows that a dimension bound of Cruz, Giusti, Itskov, and Kronholm (referred to as monotonicity of open convexity) is tight in all dimensions. 1. Introduction A combinatorial neural code or simply neural code is a subset of the Boolean lattice 2[n], where [n] := f1; 2; : : : ; ng. A neural code is called convex if it records the intersection pattern of a collection of convex sets in Rd. More specifically, a code C ⊆ 2[n] is open convex if there d exists a collection of convex open sets U = fU1;:::;Ung in R such that \ [ σ 2 C , Ui n Uj 6= ?: i2σ j2 =σ T S U The region i2σ Ui n j2 =σ Uj is called the atom of σ in U, and is denoted Aσ . The collection U is called an open realization of C, and the smallest dimension d in which one can find an open realization is called the open embedding dimension of C, denoted odim(C). -
Matrix Model Superpotentials and ADE Singularities
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications, Department of Mathematics Mathematics, Department of 2008 Matrix model superpotentials and ADE singularities Carina Curto University of Nebraska - Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/mathfacpub Part of the Mathematics Commons Curto, Carina, "Matrix model superpotentials and ADE singularities" (2008). Faculty Publications, Department of Mathematics. 34. https://digitalcommons.unl.edu/mathfacpub/34 This Article is brought to you for free and open access by the Mathematics, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications, Department of Mathematics by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. c 2008 International Press Adv. Theor. Math. Phys. 12 (2008) 355–406 Matrix model superpotentials and ADE singularities Carina Curto Department of Mathematics, Duke University, Durham, NC, USA CMBN, Rutgers University, Newark, NJ, USA [email protected] Abstract We use F. Ferrari’s methods relating matrix models to Calabi–Yau spaces in order to explain much of Intriligator and Wecht’s ADE classifi- cation of N = 1 superconformal theories which arise as RG fixed points of N = 1 SQCD theories with adjoints. We find that ADE superpoten- tials in the Intriligator–Wecht classification exactly match matrix model superpotentials obtained from Calabi–Yau with corresponding ADE sin- gularities. Moreover, in the additional O, A, D and E cases we find new singular geometries. These “hat” geometries are closely related to their ADE counterparts, but feature non-isolated singularities. As a byproduct, we give simple descriptions for small resolutions of Gorenstein threefold singularities in terms of transition functions between just two co-ordinate charts. -
Duke University News May 10, 2005
MathDuke University News May 10, 2005 Mathematics since 1978, she has been a major Events participant in Calculus reform, and has been very active in the Women in Science and Engineering Math Department Party (WISE) program.This program o ers academic, nancial and social support to female undergrad- A large group of undergraduate and gradu- uate students in those elds. ate students mixed with the faculty at the an- nual mathematics department party on April 28. Jordan Ellenberg On the day after classes ended, members of the Duke mathematical community packed the de- In a March 1 talk scheduled by DUMU, Jordan partment lounge to enjoy sandwiches and con- Ellenberg of Princeton University and the Uni- versation in an informal setting. This party is the versity of Wisconsin gave an enjoyable talk relat- traditional occasion for the faculty to honor the ing the card game Set to questions in combinato- graduating students, contest participants and re- rial geometry. A dozen DUMU students enjoyed search students for their hard work and accom- chatting with him at dinner after his talk. plishments. Many math majors received the Set is a simple but addictive card game played 2005 Duke Math shirt, some received certi cates with a special 81-card deck. A standard "folk- and a few took home generous cash prizes. A lore question" among players of this game is: good time was had by all. what is the largest number of cards that can be on the table which do not allow a legal play?He Graduation Luncheon explained how this question, which seems to be At noon, immediately after Graduation Exer- about cards, is actually about geometry over a - cises on Sunday May 15, senior math majors nite eld.He presented several results and ended and their families will meet in the LSRC dining with a number of open mathematical problems room. -
Dissipation in the Abelian Sandpile Model
Technische Universiteit Delft Faculteit Elektrotechniek, Wiskunde en Informatica Faculteit Technische Natuurwetenschappen Delft Institute of Applied Mathematics Dissipation in the Abelian Sandpile Model Verslag ten behoeve van het Delft Institute of Applied Mathematics als onderdeel ter verkrijging van de graad van BACHELOR OF SCIENCE in Technische Wiskunde en Technische Natuurkunde door HENK JONGBLOED Delft, Nederland Augustus 2016 Copyright c 2016 door Henk Jongbloed. Alle rechten voorbehouden. BSc verslag TECHNISCHE WISKUNDE en TECHNISCHE NATUURKUNDE \Dissipation in the Abelian Sandpile Model" HENK JONGBLOED Technische Universiteit Delft Begeleiders Prof. dr.ir. F.H.J. Redig Dr.ir. J.M. Thijssen Overige commissieleden Dr. T. Idema Dr. J.L.A. Dubbeldam Dr. J.A.M. de Groot Augustus, 2016 Delft Abstract The Abelian Sandpile model was originally introduced by Bak, Tang and Wiesenfeld in 1987 as a paradigm for self-organized criticality. In this thesis, we study a variant of this model, both from the point of view of mathematics, as well as from the point of view of physics. The effect of dissipation and creation of mass is investigated. By linking the avalanche dynamics of the infinite-volume sandpile model to random walks, we derive some criteria on the amount of dissipation and creation of mass in order for the model to be critical or non-critical. As an example we prove that a finite amount of conservative sites on a totally dissipative lattice is not critical, and more generally, if the distance to a dissipative site is uniformly bounded from above, then the model is not critical. We apply also applied a renormalisation method to the model in order to deduce its critical exponents and to determine whether a constant bulk dissipation destroys critical behaviour. -
Abelian Sandpile Model on Symmetric Graphs
Abelian Sandpile Model on Symmetric Graphs Natalie J. Durgin Francis Edward Su, Advisor David Perkinson, Reader May, 2009 Department of Mathematics Copyright c 2009 Natalie J. Durgin. The author grants Harvey Mudd College the nonexclusive right to make this work available for noncommercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copy- ing is by permission of the author. To disseminate otherwise or to republish re- quires written permission from the author. Abstract The abelian sandpile model, or chip-firing game, is a cellular automaton on finite directed graphs often used to describe the phenomenon of self- organized criticality. Here we present a thorough introduction to the theory of sandpiles. Additionally, we define a symmetric sandpile configuration, and show that such configurations form a subgroup of the sandpile group. Given a graph, we explore the existence of a quotient graph whose sand- pile group is isomorphic to the symmetric subgroup of the original graph. These explorations are motivated by possible applications to counting the domino tilings of a 2n × 2n grid. Contents Abstract iii Acknowledgments ix 1 Introduction to the Theory of Sandpiles 1 1.1 Origins of the Model . .1 1.2 Model Structure and Definitions . .3 1.3 Standard Sandpile Theorems . .7 1.4 Sandpile Literature . 13 1.5 Alternate Approaches to Standard Theorems . 14 2 Symmetric Sandpiles 19 2.1 Applications to Tiling Theorems . 19 2.2 Graphs with Symmetry . 21 2.3 Summary of Notation . 25 2.4 The Symmetric Sandpile Subgroup . 26 3 Developing a Quotient Graph 29 3.1 The Row Quotient . -
The Sandpile Load-Balancing Problem for Latency Minimization
THE SANDPILE LOAD BALANCER AND LATENCY MINIMIZATION PROBLEM BERJ CHILINGIRIAN MASSACHUSETTS INSTITUTE OF TECHNOLOGY Abstract. I present the sandpile load balancer, an application of the abelian sandpile model to dynamic load balancing. The sandpile load balancer exploits the self-organized criticality of the abelian sandpile model to distribute web traffic across a network of servers. I also propose the sandpile load balancer latency minimization problem in which the goal is to minimize the latency of packets across the network of servers. I show the number of possible strategies for solving this problem and explore heuristic-based approaches via simulation. 1. Introduction Bak, Tang, and Wiesenfeld introduced the abelian sandpile model in 1987. Their seminal paper was concerned with modeling the avalanches that occur in growing piles of sand and how the frequency of such avalanches relates to pink noise [1]. Dhar extended their work in 1990 by proving several mathematical properties of the abelian sandpile model [2]. One such property is self-organized criticality: the tendency of the system to converge on a critical state in which it is simultaneously stable and on the edge of chaos. Coincidentally, self-organized criticality has also been observed in a variety of dynamic systems in nature [1]. The notion of self-organized criticality conveniently applies to dynamic load bal- ancing in which a network of servers distribute incoming traffic in order to \balance" the processing burden. This paper is concerned with the application of the abelian sand pile model to dynamic load balancing in order to exploit self-organized criti- cality for a real-world problem. -
Harmonic Dynamics of the Abelian Sandpile
Harmonic dynamics of the abelian sandpile Moritz Langa,1 and Mikhail Shkolnikova aInstitute of Science and Technology Austria, 3400 Klosterneuburg, Austria Edited by Yuval Peres, Microsoft Research, Redmond, WA, and approved January 3, 2019 (received for review July 12, 2018) The abelian sandpile is a cellular automaton which serves as “avalanche.” Due to the loss of particles at the boundaries of the the archetypical model to study self-organized criticality, a phe- domain, this process eventually terminates (ref. 4, theorem 1), nomenon occurring in various biological, physical, and social pro- and the “relaxed” sandpile reaches a stable configuration which cesses. Its recurrent configurations form an abelian group, whose is independent of the order of topplings (ref. 3, p. 13). The distri- identity is a fractal composed of self-similar patches. Here, we ana- bution of avalanche sizes—the total number of topplings after a lyze the evolution of the sandpile identity under harmonic fields random particle drop—follows a power law (1) and is thus scale of different orders. We show that this evolution corresponds to invariant. However, the critical exponent for this power law is yet periodic cycles through the abelian group characterized by the unknown (5). smooth transformation and apparent conservation of the patches Soon after the introduction of the sandpile model (1), it was constituting the identity. The dynamics induced by second- and observed that the set of stable configurations can be divided third-order harmonics resemble smooth stretchings and transla- into two classes, recurrent and transient ones (6). Thereby, a tions, respectively,while the ones induced by fourth-order harmon- stable configuration is recurrent if it appears infinitely often ics resemble magnifications and rotations. -
Combinatorial Geometry of Threshold-Linear Networks
COMBINATORIAL GEOMETRY OF THRESHOLD-LINEAR NETWORKS CARINA CURTO, CHRISTOPHER LANGDON, AND KATHERINE MORRISON Abstract. The architecture of a neural network constrains the potential dynamics that can emerge. Some architectures may only allow for a single dynamic regime, while others display a great deal of flexibility with qualitatively different dynamics that can be reached by modulating connection strengths. In this work, we develop novel mathematical techniques to study the dynamic con- straints imposed by different network architectures in the context of competitive threshold-linear n networks (TLNs). Any given TLN is naturally characterized by a hyperplane arrangement in R , and the combinatorial properties of this arrangement determine the pattern of fixed points of the dynamics. This observation enables us to recast the question of network flexibility in the language of oriented matroids, allowing us to employ tools and results from this theory in order to charac- terize the different dynamic regimes a given architecture can support. In particular, fixed points of a TLN correspond to cocircuits of an associated oriented matroid; and mutations of the matroid correspond to bifurcations in the collection of fixed points. As an application, we provide a com- plete characterization of all possible sets of fixed points that can arise in networks through size n = 3, together with descriptions of how to modulate synaptic strengths of the network in order to access the different dynamic regimes. These results provide a framework for studying the possible computational roles of various motifs observed in real neural networks. 1. Introduction An n-dimensional threshold-linear network (TLN) is a continuous dynamical system given by the nonlinear equations: n X (1)x _ i = −xi + Wijxj + bi i = 1; : : : ; n j=i + wherex _ i = dxi=dt denotes the time derivative, bi and Wij are all real numbers, Wii = 0 for each i, and [x]+ = maxfx; 0g. -
Abelian Sandpile Model on Symmetric Graphs Natalie Durgin Harvey Mudd College
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Scholarship@Claremont Claremont Colleges Scholarship @ Claremont HMC Senior Theses HMC Student Scholarship 2009 Abelian Sandpile Model on Symmetric Graphs Natalie Durgin Harvey Mudd College Recommended Citation Durgin, Natalie, "Abelian Sandpile Model on Symmetric Graphs" (2009). HMC Senior Theses. 217. https://scholarship.claremont.edu/hmc_theses/217 This Open Access Senior Thesis is brought to you for free and open access by the HMC Student Scholarship at Scholarship @ Claremont. It has been accepted for inclusion in HMC Senior Theses by an authorized administrator of Scholarship @ Claremont. For more information, please contact [email protected]. Abelian Sandpile Model on Symmetric Graphs Natalie J. Durgin Francis Edward Su, Advisor David Perkinson, Reader May, 2009 Department of Mathematics Copyright c 2009 Natalie J. Durgin. The author grants Harvey Mudd College the nonexclusive right to make this work available for noncommercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copy- ing is by permission of the author. To disseminate otherwise or to republish re- quires written permission from the author. Abstract The abelian sandpile model, or chip-firing game, is a cellular automaton on finite directed graphs often used to describe the phenomenon of self- organized criticality. Here we present a thorough introduction to the theory of sandpiles. Additionally, we define a symmetric sandpile configuration, and show that such configurations form a subgroup of the sandpile group. Given a graph, we explore the existence of a quotient graph whose sand- pile group is isomorphic to the symmetric subgroup of the original graph. -
Homotopy Theory and Neural Information Networks
Homotopy Theory and Neural Information Networks Matilde Marcolli (Caltech, University of Toronto & Perimeter Institute) Focus Program on New Geometric Methods in Neuroscience Fields Institute, Toronto, 2020 Matilde Marcolli (Caltech, University of Toronto & Perimeter Institute)Homotopy Theory and Neural Information Networks • based on ongoing joint work with Yuri I. Manin (Max Planck Institute for Mathematics) • related work: M. Marcolli, Gamma Spaces and Information, Journal of Geometry and Physics, 140 (2019), 26{55. Yu.I. Manin and M. Marcolli, Nori diagrams and persistent homology, arXiv:1901.10301, to appear in Mathematics of Computer Science. • building mathematical background for future joint work with Doris Tsao (Caltech neuroscience) • this work partially supported by FQXi FFF Grant number: FQXi-RFP-1804, SVCF grant number 2018-190467 Matilde Marcolli (Caltech, University of Toronto & Perimeter Institute)Homotopy Theory and Neural Information Networks Motivation N.1: Nontrivial Homology Kathryn Hess' applied topology group at EPFL: topological analysis of neocortical microcircuitry (Blue Brain Project) formation of large number of high dimensional cliques of neurons (complete graphs on N vertices with a directed structure) accompanying response to stimuli formation of these structures is responsible for an increasing amount of nontrivial Betti numbers and Euler characteristics, which reaches a peak of topological complexity and then fades proposed functional interpretation: this peak of non-trivial homology is necessary for the -
Carina Curto · Curriculum Vitae
Carina Curto · Curriculum Vitae Department of Mathematics tel: (814) 863 9119 The Pennsylvania State University email: [email protected] 331 McAllister Building website: http://www.personal.psu.edu/cpc16/ University Park, State College, PA 16802 CV last updated: September 24, 2020 Research · Theoretical and mathematical neuroscience Interests · Applied algebra, geometry, and topology · Neural networks and neural codes: theory, modeling, and data analysis Education & Employment Academic The Pennsylvania State University (PSU) State College, PA Positions Professor of Mathematics (2019–) Aug 2014–present co-Associate Head for Graduate Studies (2018–) Associate Professor of Mathematics (2014–2019) Member of the Center for Neural Engineering & the Institute for Neuroscience University of Nebraska-Lincoln (UNL) Lincoln, NE Assistant Professor of Mathematics Aug 2009–Aug 2014 Courant Institute, New York University (NYU) New York, NY Courant Instructor (Mathematics) Sep 2008–Aug 2009 Rutgers, The State University of New Jersey Newark, NJ Center for Molecular and Behavioral Neuroscience May 2005–Aug 2008 Postdoctoral Associate in the lab of Kenneth D. Harris (Neuroscience) Education Duke University Durham, NC Ph.D. in Mathematics (Algebraic Geometry & String Theory) Aug 2000–May 2005 Advisor: David R. Morrison Harvard University Cambridge, MA A.B. in Physics (Cum Laude) Sep 1996–Jun 2000 Iowa City West High School Iowa City, IA Valedictorian (4.0 GPA) Aug 1992–Jun 1996 Additional MBL Neuroinformatics Course, Woods Hole, MA Aug 2006 Education IAS