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We show that all permutations in $S_n$ can be generated by affine unicritical polynomials. We use the $\operatorname{PGL}$ group structure to compute the cycle structure of permutations with low Carlitz rank. The tree structure of the group…

Number Theory · Mathematics 2021-03-22 Anna Chlopecki , Juliano Levier-Gomes , Wayne Peng , Alex Shearer , Adam Towsley

Graph polynomials encode fundamental combinatorial invariants of graphs. Their computation is investigated using tree and path decomposition frameworks, with formal definitions of treewidth, k-trees, and pathwidth establishing the…

Discrete Mathematics · Computer Science 2025-09-29 Mehul Bafna , Shaghik Amirian

Three new graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number…

Quantum Physics · Physics 2019-11-20 P. W. Mills , R. P. Rundle , J. H. Samson , Simon J. Devitt , Todd Tilma , V. M. Dwyer , Mark J. Everitt

The purpose of this note is to point out that the theory of expander graphs leads to an interesting test whether $n$ real numbers $x_1, \dots, x_n$ could be $n$ independent samples of a random variable. To any distinct, real numbers $x_1,…

Statistics Theory · Mathematics 2020-08-05 Stefan Steinerberger

Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have…

Computer Vision and Pattern Recognition · Computer Science 2017-03-02 Renata Khasanova , Pascal Frossard

Weight systems are functions on chord diagrams satisfying so-called Vassiliev's $4$-term relations. They are closely related to finite type knot invariants introduced by Vassiliev. Certain weight systems can be derived from graph…

Combinatorics · Mathematics 2024-01-01 N. Kodaneva , S. Lando

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately, the leading rationalization models often rely on data…

Machine Learning · Computer Science 2022-02-01 Ying-Xin Wu , Xiang Wang , An Zhang , Xiangnan He , Tat-Seng Chua

We develop invariant theory for the quantum group ${\rm U}_q$ of $G_2$ at generic $q$ in the setting of braided symmetric algebras. Let ${\mathcal A}_m$ be the braided symmetric algebra over $m$-copies of the $7$-dimensional simple ${\rm…

Quantum Algebra · Mathematics 2025-09-29 Hongmei Hu , Ruibin Zhang

Graph polynomials are deemed useful if they give rise to algebraic characterizations of various graph properties, and their evaluations encode many other graph invariants. Algebraic: The complete graphs $K_n$ and the complete bipartite…

Combinatorics · Mathematics 2017-03-03 T. Kotek , J. A. Makowsky , E. V. Ravve

Invariant theory is concerned with functions that do not change under the action of a given group. Here we communicate an approach based on tensor networks to represent polynomial local unitary invariants of quantum states. This graphical…

Quantum Physics · Physics 2013-11-13 Jacob Biamonte , Ville Bergholm , Marco Lanzagorta

We develop a calculus based on graph enumeration for $S_n$-equivariant motivic invariants of graphically stratified moduli spaces. We apply our theory to the Deligne--Mumford moduli space $\overline{\mathcal{M}}_{g, n}$ and to the space of…

Algebraic Geometry · Mathematics 2025-10-09 Siddarth Kannan , Terry Dekun Song

We introduce a new graph polynomial that encodes interesting properties of graphs, for example, the number of matchings and the number of perfect matchings. Most importantly, for bipartite graphs the polynomial encodes the number of…

Discrete Mathematics · Computer Science 2010-02-10 Qi Ge , Daniel Stefankovic

In this article, we define and study a geometry and an order on the set of partitions of an even number of objects. One of the definitions involves the partition algebra, a structure of algebra on the set of such partitions depending on an…

Combinatorics · Mathematics 2016-11-01 Franck Gabriel

Group algebras of permutations have proved highly useful in solving a number of problems in large N gauge theories. I review the use of permutations in classifying gauge invariants in one-matrix and multi-matrix models and computing their…

High Energy Physics - Theory · Physics 2016-05-04 Sanjaye Ramgoolam

Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group. Much recent research has…

Machine Learning · Statistics 2020-09-18 Benjamin Bloem-Reddy , Yee Whye Teh

This paper considers a finite group $G$ acting linearly on the variables $V$ of a polynomial algebra, or an exterior algebra, or superpolynomial algebra with both commuting and anticommuting variables. In this setting, the Hilbert series…

Combinatorics · Mathematics 2025-06-12 Trevor Karn , Victor Reiner

Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However,…

Recently in [JHEP 09 (2021) 053], Wang et al. discussed the polynomial ring formed by flavor invariants in the leptonic sector with massive Majorana neutrinos. They have explicitly constructed the finite generating sets of the polynomial…

High Energy Physics - Phenomenology · Physics 2022-02-22 Jianlong Lu

We show that a nontrivial graph isomorphism problem of two undirected graphs, and more generally, the permutation similarity of two given $n\times n$ matrices, is equivalent to equalities of volumes of the induced three convex bounded…

Computational Complexity · Computer Science 2009-11-10 Shmuel Friedland

Conventional machine learning algorithms have traditionally been designed under the assumption that input data follows a vector-based format, with an emphasis on vector-centric paradigms. However, as the demand for tasks involving set-based…

Machine Learning · Computer Science 2024-04-01 Masanari Kimura , Ryotaro Shimizu , Yuki Hirakawa , Ryosuke Goto , Yuki Saito