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Operator learning based on neural operators has emerged as a promising paradigm for the data-driven approximation of operators, mapping between infinite-dimensional Banach spaces. Despite significant empirical progress, our theoretical…

Machine Learning · Computer Science 2024-07-04 Samuel Lanthaler

The intuition that a long history is required for the emergence of complexity in natural systems is formalized using the notion of depth. The depth of a system is defined in terms of the number of parallel computational steps needed to…

Statistical Mechanics · Physics 2011-11-09 J. Machta

Although there is a somewhat standard formalization of computability on countable sets given by Turing machines, the same cannot be said about uncountable sets. Among the approaches to define computability in these sets, order-theoretic…

Logic in Computer Science · Computer Science 2022-09-07 Pedro Hack , Daniel A. Braun , Sebastian Gottwald

We introduce a new concept of approximation applicable to decision problems and functions, inspired by Bayesian probability. From the perspective of a Bayesian reasoner with limited computational resources, the answer to a problem that…

Computational Complexity · Computer Science 2025-06-27 Vanessa Kosoy , Alexander Appel

Conjunctive query (CQ) evaluation is NP-complete, but becomes tractable for fragments of bounded hypertreewidth. Approximating a hard CQ by a query from such a fragment can thus allow for an efficient approximate evaluation. While…

Databases · Computer Science 2019-04-02 Pablo Barceló , Miguel Romero , Thomas Zeume

The clone of term operations of an algebraic structure consists of all operations that can be expressed by a term in the language of the structure. We consider bounds for the length and the height of the terms expressing these functions,…

Rings and Algebras · Mathematics 2018-09-20 Erhard Aichinger , Nebojša Mudrinski , Jakub Opršal

In this set of notes we simplify the formulation of the Poincare'-invariant effective string theory in D dimensions by adding an intrinsic metric and embedding its dynamics into the Polyakov formalism. We use this formalism to construct…

High Energy Physics - Theory · Physics 2017-02-21 Simeon Hellerman , Shunsuke Maeda , Jonathan Maltz , Ian Swanson

Using operator algebra, we extend the series for the activity density in a one-dimensional stochastic sandpile with fixed particle density p, the first terms of which were obtained via perturbation theory [R. Dickman and R. Vidigal, J.…

Statistical Mechanics · Physics 2009-11-10 Jurgen F. Stilck , Ronald Dickman , Ronaldo R. Vidigal

We start by an introduction to the basic concepts of computability theory and the introduction of the concept of Turing machine and computation universality. Then se turn to the exploration of trade-offs between different measures of…

Computational Complexity · Computer Science 2011-04-19 Joost J. Joosten , Fernando Soler-Toscano , Hector Zenil

In the paper we present results to develop an irreducible theory of complex systems in terms of self-organization processes of prime integer relations. Based on the integers and controlled by arithmetic only the self-organization processes…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 Victor Korotkikh , Galina Korotkikh

Clarithmetics are number theories based on computability logic (see http://www.csc.villanova.edu/~japaridz/CL/ ). Formulas of these theories represent interactive computational problems, and their "truth" is understood as existence of an…

Logic in Computer Science · Computer Science 2019-03-14 Giorgi Japaridze

Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation.…

Machine Learning · Computer Science 2015-09-21 Alexey Milovanov

Mathematical Selection is a method in which we select a particular choice from a set of such. It have always been an interesting field of study for mathematicians. Combinatorial optimisation is the practice of selecting the best constituent…

Optimization and Control · Mathematics 2024-01-31 Anurag Dutta , K. Lakshmanan , John Harshith , A. Ramamoorthy

The approximate string matching is a fundamental and recurrent problem that arises in most computer science fields. This problem can be defined as follows: Let $D=\{x_1,x_2,\ldots x_d\}$ be a set of $d$ words defined on an alphabet…

Data Structures and Algorithms · Computer Science 2017-01-31 Ibrahim Chegrane

We will find a lower bound on the recognition complexity of the theories that are nontrivial relative to some equivalence relation (this relation may be equality), namely, each of these theories is consistent with the formula, whose sense…

Logic · Mathematics 2023-10-16 Ivan V. Latkin

A novel approach to approximate solutions of Stochastic Differential Equations (SDEs) by Deep Neural Networks is derived and analysed. The architecture is inspired by the notion of Deep Operator Networks (DeepONets), which is based on…

Numerical Analysis · Mathematics 2025-12-23 Martin Eigel , Charles Miranda

The state complexity of the result of a regular operation is often positively correlated with the number of distinct transformations induced by letters in the minimal deterministic finite automaton of the input languages. That is, more…

Formal Languages and Automata Theory · Computer Science 2018-09-07 Sylvie Davies

The Operator axioms have produced new real numbers with new operators. New operators naturally produce new equations and thus extend the traditional mathematical models which are selected to describe various scientific rules. So new…

Numerical Analysis · Computer Science 2021-02-08 Pith Peishu Xie

We provide a Lawvere-style definition for partial theories, extending the classical notion of equational theory by allowing partially defined operations. As in the classical case, our definition is syntactic: we use an appropriate class of…

Logic in Computer Science · Computer Science 2020-11-16 Ivan Di Liberti , Fosco Loregian , Chad Nester , Paweł Sobociński

Diagrammatic reasoning (DR) is pervasive in human problem solving as a powerful adjunct to symbolic reasoning based on language-like representations. The research reported in this paper is a contribution to building a general purpose DR…

Artificial Intelligence · Computer Science 2014-01-17 Bonny Banerjee , B. Chandrasekaran