Plateau-Descent joint Symmetry On Quasi-Stirling Permutations

Stirling permutations, $ST_n$, are permutations of the multiset $[0,0,1,1,\dots,n,n]$ which avoid the pattern $212$. Elizalde showed, extending a result from Bona, that the bivariate generating function for the plateau and descent statistics, P and D, respectively, is jointly symmetric over $ST_n$. This dissertation considers an extension of the Stirling permutations introduced by Archer et al. called the Quasi-Stirling permutations, $Q_n$. Quasi-Stirling permutations generalize Stirling permutations by requiring only that patterns $1212$ and $2121$ are avoided. While the P and D statistics extend naturally onto $Q_n$ the corresponding bivariate generating function is no longer symmetric. We propose a new statistic, $P_d$, which reduces to $P$ over the Stirling permutations, and we conjecture produces a jointly symmetric generating function with $D$ over $Q_n$. We prove this symmetry for a subset of $Q_n$ in two ways using both a finer partition of the descent statistic as well as a recursion on intransitive trees. This work features heavily the combinatorial analysis of rooted edge labeled plane trees, rooted vertex labeled binary plane trees and intransitive bipartite trees. We generalize the connection to bipartite trees by introducing a new notation which generates tripartite graphs and conjecture the existence of a map on these objects which demonstrates the $P_d-D$ joint symmetry on all of $Q_n$.

Publication Details

Journal
ScholarWorks -A service of University of Vermont Libraries (University of Vermont)
Published
2025-01-01
Primary Topic
Physics and Engineering Research Articles
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Plateau-Descent joint Symmetry On Quasi-Stirling Permutations

ScholarWorks -A service of University of Vermont Libraries (University of Vermont)
Physics and Engineering Research Articles
article

Plateau-Descent joint Symmetry On Quasi-Stirling Permutations

article en

Abstract

Stirling permutations, $ST_n$, are permutations of the multiset $[0,0,1,1,\dots,n,n]$ which avoid the pattern $212$. Elizalde showed, extending a result from Bona, that the bivariate generating function for the plateau and descent statistics, P and D, respectively, is jointly symmetric over $ST_n$. This dissertation considers an extension of the Stirling permutations introduced by Archer et al. called the Quasi-Stirling permutations, $Q_n$. Quasi-Stirling permutations generalize Stirling permutations by requiring only that patterns $1212$ and $2121$ are avoided. While the P and D statistics extend naturally onto $Q_n$ the corresponding bivariate generating function is no longer symmetric. We propose a new statistic, $P_d$, which reduces to $P$ over the Stirling permutations, and we conjecture produces a jointly symmetric generating function with $D$ over $Q_n$. We prove this symmetry for a subset of $Q_n$ in two ways using both a finer partition of the descent statistic as well as a recursion on intransitive trees. This work features heavily the combinatorial analysis of rooted edge labeled plane trees, rooted vertex labeled binary plane trees and intransitive bipartite trees. We generalize the connection to bipartite trees by introducing a new notation which generates tripartite graphs and conjecture the existence of a map on these objects which demonstrates the $P_d-D$ joint symmetry on all of $Q_n$.

ScholarWorks -A service of University of Vermont Libraries (University of Vermont)
Openalex Percentile: Top 46%
Physics and Engineering Research Articles
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.