Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Polychrony as Chinampas
Algorithms, v 16(4), 193
01 Apr 2023
Abstract
In this paper, we study the flow of signals through linear paths with the nonlinear condition that a node emits a signal when it receives external stimuli or when two incoming signals from other nodes arrive coincidentally with a combined amplitude above a fixed threshold. Sets of such nodes form a polychrony group and can sometimes lead to cascades. In the context of this work, cascades are polychrony groups in which the number of nodes activated as a consequence of other nodes is greater than the number of externally activated nodes. The difference between these two numbers is the so-called profit. Given the initial conditions, we predict the conditions for a vertex to activate at a prescribed time and provide an algorithm to efficiently reconstruct a cascade. We develop a dictionary between polychrony groups and graph theory. We call the graph corresponding to a cascade a chinampa. This link leads to a topological classification of chinampas. We enumerate the chinampas of profits zero and one and the description of a family of chinampas isomorphic to a family of partially ordered sets, which implies that the enumeration problem of this family is equivalent to computing the Stanley-order polynomials of those partially ordered sets.
Metrics
3 Record Views
Details
- Title
- Polychrony as Chinampas
- Creators
- Eric Dolores-Cuenca - Yonsei UniversityJose Antonio Arciniega-Nevarez - Universidad de GuanajuatoAnh Nguyen - Drexel UniversityAmanda Yitong Zou - University of Michigan–Ann ArborLuke Van Popering - Emelex, Tallahassee, FL 32308 USANathan Crock - Florida State UniversityGordon Erlebacher - Florida State UniversityJose L. L. Mendoza-Cortes - Michigan State University
- Publication Details
- Algorithms, v 16(4), 193
- Publisher
- Mdpi
- Number of pages
- 24
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Arts and Sciences
- Web of Science ID
- WOS:000977760800001
- Scopus ID
- 2-s2.0-85153947835
- Other Identifier
- 991021860723004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods