English
Related papers

Related papers: Explicit constructions of Vandermonde sequences us…

200 papers

Distance-increasing maps from binary vectors to permutations, namely DIMs, are useful for the construction of permutation arrays. While a simple mapping algorithm defining DIMs of even length is known, existing DIMs of odd length are either…

Information Theory · Computer Science 2007-07-13 Kwankyu Lee

The development of Monte Carlo algorithms for generating gauge field configurations with dynamical fermions and methods for extracting the most information from ensembles are summarised.

High Energy Physics - Lattice · Physics 2009-11-07 Mike Peardon

Linear codes over finite fields parameterized by functions have proven to be a powerful tool in coding theory, yielding optimal and few-weight codes with significant applications in secret sharing, authentication codes, and association…

Information Theory · Computer Science 2026-02-17 Virginio Fratianni , Sihem Mesnager

Rough sets are efficient for data pre-processing in data mining. Matroids are based on linear algebra and graph theory, and have a variety of applications in many fields. Both rough sets and matroids are closely related to lattices. For a…

Artificial Intelligence · Computer Science 2013-12-17 Qingyin Li , William Zhu

Let $F/F_0$ be a quadratic extension of totally real number fields, and let $E$ be an elliptic curve over $F$ which is isogenous to its Galois conjugate over $F_0$. A quadratic extension $M/F$ is said to be almost totally complex (ATC) if…

Number Theory · Mathematics 2012-04-17 Xavier Guitart , Victor Rotger , Yu Zhao

Abstrct: In this note, by considering fractionally linear functions over a finite field and consequently developing an abstract sequence, we study some of its properties.

Discrete Mathematics · Computer Science 2007-05-23 V. M. Siddlenikov , R. N. Mohan , Moon Ho Lee

Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically, "fully-connected layers with Quaternions" (4D hypercomplex numbers), which replace real-valued matrix multiplications in…

Machine Learning · Computer Science 2021-02-18 Aston Zhang , Yi Tay , Shuai Zhang , Alvin Chan , Anh Tuan Luu , Siu Cheung Hui , Jie Fu

The goal of this article is to study some basic algebraic and combinatorial properties of "generalized $n$-series" over a commutative ring $R$, which are functions $s: \mathbf{Z}_{\geq 0} \to R$ satisfying a mild condition. A special…

Combinatorics · Mathematics 2023-04-11 Sanath K. Devalapurkar , Max L. Misterka

We study a new class of networks, generated by sequences of letters taken from a finite alphabet consisting of $m$ letters (corresponding to $m$ types of nodes) and a fixed set of connectivity rules. Recently, it was shown how a binary…

Disordered Systems and Neural Networks · Physics 2009-02-17 Jie Sun , Takashi Nishikawa , Daniel ben-Avraham

In this short note we provide a few examples of non-isomorphic arithmetically equivalent global function fields. These examples are obtained via well-known technique of adjoining the torsion points of various Drinfeld Modules to realise the…

Number Theory · Mathematics 2021-07-20 Pavel Solomatin

This paper addresses the question of whether it is possible to generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical and transitivity), considering only different…

Statistics Theory · Mathematics 2018-07-04 Marjan Cugmas , Anuška Ferligoj , Aleš Žiberna

Understanding network functionality requires integrating structure and dynamics, and emergent latent geometry induced by network-driven processes captures the low-dimensional spaces governing this interplay. Here, we focus on…

Physics and Society · Physics 2025-12-02 Andrea Filippo Beretta , Davide Zanchetta , Sebastiano Bontorin , Manlio De Domenico

Why do deep neural networks (DNNs) benefit from very high dimensional parameter spaces? Their huge parameter complexities vs stunning performance in practice is all the more intriguing and not explainable using the standard theory of model…

Machine Learning · Computer Science 2025-06-12 Ke Sun , Frank Nielsen

Let $V$ denote an $r$-dimensional $\mathbb{F}_{q^n}$-vector space. For an $m$-dimensional $\mathbb{F}_q$-subspace $U$ of $V$ assume that $\dim_q \left(\langle {\bf v}\rangle_{\mathbb{F}_{q^n}} \cap U\right) \geq 2$ for each non zero vector…

Combinatorics · Mathematics 2025-01-27 Bence Csajbók , Giuseppe Marino , Valentina Pepe

Sum-rank metric codes, as a generalization of Hamming codes and rank metric codes, have important applications in fields such as multi-shot linear network coding, space-time coding and distributed storage systems. The purpose of this study…

Information Theory · Computer Science 2025-07-23 Xuemei Liu , Jiarong Zhang , Gang Wang

Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications, but at considerable computational…

Machine Learning · Computer Science 2022-05-11 Adityanarayanan Radhakrishnan , George Stefanakis , Mikhail Belkin , Caroline Uhler

We develop tools for explicitly constructing categories enriched over generating data and that compose via ordinary scalar and matrix arithmetic arithmetic operations. We characterize meaningful size maps, weightings, and magnitude that…

Category Theory · Mathematics 2023-04-18 Steve Huntsman

The decomposition of arbitrary unitary transformations into sequences of simpler, physically realizable operations is a foundational problem in quantum information science, quantum control, and linear optics. We establish a 1D Quantum Field…

Quantum Physics · Physics 2026-03-20 Javier Álvarez-Vizoso , David Barral

In this paper we use the Vandermonde matrices and their properties to give a new proof of the classical result of Karl Weierstrass about the approximation of continuous functions $f$ on closed intervals, using a sequence of polynomials. The…

Classical Analysis and ODEs · Mathematics 2025-07-02 José M. González Barrios , Alberto Contreras-Cristán , Patricia I. Romero-Mares

Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that…

Machine Learning · Statistics 2025-03-25 Boyao Li , Alexander J. Thomson , Houssam Nassif , Matthew M. Engelhard , David Page
‹ Prev 1 8 9 10 Next ›