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This paper presents a method to determine a set of basis polynomials from the extended Euclidean algorithm that allows Generalized Minimum Distance decoding of Reed-Solomon codes with a complexity of O(nd).

Information Theory · Computer Science 2010-09-08 Sabine Kampf , Martin Bossert

We define generalized Hamming weights for almost affine codes. We show how various aspects and applications of generalized Hamming weights for linear codes, such as Wei duality, generalized Kung's bound, profiles, connection to wire-tap…

Information Theory · Computer Science 2017-03-20 Trygve Johnsen , Hugues Verdure

Recently, a new distance has been introduced for the graphs of two point-to-set operators, one of which is maximally monotone. When both operators are the subdifferential of a proper lower semicontinuous convex function, this distance…

Functional Analysis · Mathematics 2020-09-29 Regina S. Burachik , Minh N. Dao , Scott B. Lindstrom

This paper provides new and improved Singleton-like bounds for Lee metric codes over integer residue rings. We derive the bounds using various novel definitions of generalized Lee weights based on different notions of a support of a linear…

Information Theory · Computer Science 2023-07-13 Jessica Bariffi , Violetta Weger

This paper introduces new constructions of sum-rank metric codes derived from algebraic function fields, as existing results on such codes remain limited. A major challenge lies in the determination of their parameters. We address this…

Information Theory · Computer Science 2025-12-16 Zhu Yunlong , Zhao Chang-An

Service rate is an important, recently introduced, performance metric associated with distributed coded storage systems. Among other interpretations, it measures the number of users that can be simultaneously served by the storage system.…

Information Theory · Computer Science 2020-01-27 Fatemeh Kazemi , Sascha Kurz , Emina Soljanin

In this paper we study global distance estimates and uniform local volume estimates in a large class of sub-Riemannian manifolds. Our main device is the generalized curvature dimension inequality introduced by the first and the third author…

Differential Geometry · Mathematics 2014-07-31 Fabrice Baudoin , Michel Bonnefont , Nicola Garofalo , Isidro H. Munive

We construct six new explicit families of linear maximum sum-rank distance (MSRD) codes, each of which has the smallest field sizes among all known MSRD codes for some parameter regime. Using them and a previous result of the author, we…

Information Theory · Computer Science 2022-04-21 Umberto Martínez-Peñas

Reduced rank regression (RRR) is a widely employed model for investigating the linear association between multiple response variables and a set of predictors. While RRR has been extensively explored in various works, the focus has…

Statistics Theory · Mathematics 2024-04-30 The Tien Mai

It was recently shown that RGHW (relative generalized Hamming weight) exactly expresses the security of linear ramp secret sharing scheme. In this paper we determine the true value of the asymptotic metric for RGHW previously proposed by…

Information Theory · Computer Science 2015-02-04 Ryutaroh Matsumoto

Latent variable models represent a useful tool for the analysis of complex data when the constructs of interest are not observable. A problem related to these models is that the integrals involved in the likelihood function cannot be solved…

Methodology · Statistics 2015-03-05 Silvia Bianconcini , Silvia Cagnone , Dimitris Rizopoulos

The usual Euclidean distance may be generalized to extended objects such as polymers or membranes. Here, this distance is used for the first time as a cost function to align structures. We examined the alignment of extended strands to…

Soft Condensed Matter · Physics 2009-08-05 Ali R. Mohazab , Steven S. Plotkin

The main purpose of this paper is to further study the structure, parameters and constructions of the recently introduced minimal codes in the sum-rank metric. These objects form a bridge between the classical minimal codes in the Hamming…

Combinatorics · Mathematics 2023-03-14 Martino Borello , Ferdinando Zullo

Functionals in geometric probability are often expressed as sums of bounded functions exhibiting exponential stabilization. Methods based on cumulant techniques and exponential modifications of measures show that such functionals satisfy…

Probability · Mathematics 2009-09-29 Yu Baryshnikov , P. Eichelsbacher , T. Schreiber , J. E. Yukich

This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive…

Optimization and Control · Mathematics 2009-10-21 Silvere Bonnabel , Rodolphe Sepulchre

We consider the geometric problem of determining the maximum number $n_q(r,h,f;s)$ of $(h-1)$-spaces in the projective space $\operatorname{PG}(r-1,q)$ such that each subspace of codimension $f$ does contain at most $s$ elements. In coding…

Combinatorics · Mathematics 2026-05-01 Jozefien D'haeseleer , Sascha Kurz

In this paper we present several values for the next-to-minimal weights of projective Reed-Muller codes. We work over $\mathbb{F}_q$ with $q \geq 3$ since in IEEE-IT 62(11) p. 6300-6303 (2016) we have determined the complete values for the…

Information Theory · Computer Science 2017-03-20 Cícero Carvalho , Victor G. L. Neumann

The generalised Gegenbauer functions of fractional degree (GGF-Fs), denoted by ${}^{r\!}G^{(\lambda)}_\nu(x)$ (right GGF-Fs) and ${}^{l}G^{(\lambda)}_\nu(x)$ (left GGF-Fs) with $x\in (-1,1),$ $\lambda>-1/2$ and real $\nu\ge 0,$ are special…

Numerical Analysis · Mathematics 2020-06-02 Wenjie Liu , Li-Lian Wang

In this paper, we study relative metric regularity of set-valued mappings with emphasis on directional metric regularity. We establish characterizations of relative metric regularity without assuming the completeness of the image spaces, by…

Optimization and Control · Mathematics 2013-04-30 Huynh Van Ngai , Michel A. Théra

Randomized Hadamard Transforms (RHTs) have emerged as a computationally efficient alternative to the use of dense unstructured random matrices across a range of domains in computer science and machine learning. For several applications such…

Machine Learning · Computer Science 2022-03-04 Yeshwanth Cherapanamjeri , Jelani Nelson
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