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Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. This phenomenon is known as \emph{simplicity bias}. By viewing…

Dynamical Systems · Mathematics 2024-05-14 Kamaludin Dingle , Mohammad Alaskandarani , Boumediene Hamzi , Ard A. Louis

Binomial Cayley graphs are obtained by considering the binomial coefficient of the weight function of a given Cayley graph and a natural number. We introduce these objects and study two families: one associated with symmetric groups and the…

Combinatorics · Mathematics 2024-09-06 Bernat Bassols-Cornudella , Francesco Viganò

We consider nine geometric systems: Miquel dynamics, P-nets, integrable cross-ratio maps, discrete holomorphic functions, orthogonal circle patterns, polygon recutting, circle intersection dynamics, (corrugated) pentagram maps and the short…

Dynamical Systems · Mathematics 2024-06-10 Niklas Christoph Affolter , Béatrice de Tilière , Paul Melotti

Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph with non-trivial topological features. Data-driven approximations of…

Statistical Mechanics · Physics 2024-04-26 Vaiva Vasiliauskaite , Nino Antulov-Fantulin

We extend our two-scale neural-network method for scalar singularly perturbed problems with one small parameter to dynamical systems with multiple small parameters. To accommodate multiple small parameters, we use a single effective scale…

Numerical Analysis · Mathematics 2026-05-05 Qiao Zhuang , Taorui Wang , Rita Wanjiku , Majid Bani-Yaghoub , Zhongqiang Zhang

Matrix functions extend scalar function concepts to linear operators, offering a unified framework with broad applications in mathematics, science, and engineering. Classical definitions--via power series, spectral calculus, or Jordan…

Functional Analysis · Mathematics 2025-10-21 Shih-Yu Chang

Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through…

Computer Vision and Pattern Recognition · Computer Science 2015-12-18 Anoop Cherian , Suvrit Sra

In this paper an algorithm is given to determine all possible structurally different linearly conjugate realizations of a given kinetic polynomial system. The solution is based on the iterative search for constrained dense realizations…

Dynamical Systems · Mathematics 2016-03-08 Bernadett Acs , Gabor Szederkenyi , Zsolt Tuza , Zoltan Andras Tuza

In this work we will present algorithms for decoding rank metric codes. First we will look at a new decoding algorithm for Gabidulin codes using the property of Dickson matrices corresponding to linearized polynomials. We will be using a…

Information Theory · Computer Science 2018-02-01 Tovohery Hajatiana Randrianarisoa

We propose a batchwise monotone algorithm for dictionary learning. Unlike the state-of-the-art dictionary learning algorithms which impose sparsity constraints on a sample-by-sample basis, we instead treat the samples as a batch, and impose…

Machine Learning · Computer Science 2015-02-03 Huan Wang , John Wright , Daniel Spielman

A complex system comprises multiple interacting entities whose interdependencies form a unified whole, exhibiting emergent behaviours not present in individual components. Examples include the human brain, living cells, soft matter, Earth's…

The multivariate resultant is a fundamental tool of computational algebraic geometry. It can in particular be used to decide whether a system of n homogeneous equations in n variables is satisfiable (the resultant is a polynomial in the…

Computational Complexity · Computer Science 2013-02-12 Bruno Grenet , Pascal Koiran , Natacha Portier

Recovering the digital input of a time-discrete linear system from its (noisy) output is a significant challenge in the fields of data transmission, deconvolution, channel equalization, and inverse modeling. A variety of algorithms have…

Optimization and Control · Mathematics 2020-12-03 Sophie M. Fosson

Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks. This paper views uncertain system models as unknown or uncertain smooth functions…

Machine Learning · Computer Science 2021-06-25 Diego Agudelo-España , Yassine Nemmour , Bernhard Schölkopf , Jia-Jie Zhu

In many problems of data-driven modeling for dynamical systems, the governing equations are not known a priori and must be selected phenomenologically from a large set of candidate interactions and basis functions. In such situations, point…

Applications · Statistics 2026-04-14 Shuhei Kashiwamura , Yusuke Kato , Hiroshi Kori , Masato Okada

Delayed processes are ubiquitous throughout biology. These delays may arise through maturation processes or as the result of complex multi-step networks, and mathematical models with distributed delays are increasingly used to capture the…

Populations and Evolution · Quantitative Biology 2024-10-15 Tyler Cassidy

A polynomial transform is the multiplication of an input vector $x\in\C^n$ by a matrix $\PT_{b,\alpha}\in\C^{n\times n},$ whose $(k,\ell)$-th element is defined as $p_\ell(\alpha_k)$ for polynomials $p_\ell(x)\in\C[x]$ from a list…

Information Theory · Computer Science 2011-07-14 Aliaksei Sandryhaila , Jelena Kovacevic , Markus Pueschel

Many materials, processes, and structures in science and engineering have important features at multiple scales of time and/or space; examples include biological tissues, active matter, oceans, networks, and images. Explicitly extracting,…

Fluid Dynamics · Physics 2021-01-12 Daniel Floryan , Michael D. Graham

We give an algorithm for finding network encoding and decoding equations for error-free multicasting networks with multiple sources and sinks. The algorithm given is efficient (polynomial complexity) and works on any kind of network…

Information Theory · Computer Science 2007-07-13 Angela I. Barbero Diez , Oyvind Ytrehus

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconception in the…

Applications · Statistics 2020-05-19 Omid Sedehi , Costas Papadimitriou , Lambros S. Katafygiotis
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