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In this paper we present a mathematical formulation for the omega invariant of a numerical semigroup for each of its minimal generators. The model consists of solving a problem of optimizing a linear function over the efficient set of a…

Optimization and Control · Mathematics 2010-08-06 Víctor Blanco

We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource…

Computation and Language · Computer Science 2020-04-29 Katharina Kann , Samuel R. Bowman , Kyunghyun Cho

Submodular function minimization (SFM) is a fundamental discrete optimization problem which generalizes many well known problems, has applications in various fields, and can be solved in polynomial time. Owing to applications in computer…

Data Structures and Algorithms · Computer Science 2016-11-01 Deeparnab Chakrabarty , Yin Tat Lee , Aaron Sidford , Sam Chiu-wai Wong

We give a deterministic 2^{O(n)} algorithm for computing an M-ellipsoid of a convex body, matching a known lower bound. This has several interesting consequences including improved deterministic algorithms for volume estimation of convex…

Computational Complexity · Computer Science 2014-03-05 Daniel Dadush , Santosh Vempala

Local grammars can be represented in a very convenient way by automata. This paper describes and illustrates an efficient algorithm for the application of local grammars put in this form to lemmatized texts.

cmp-lg · Computer Science 2008-02-03 Mehryar Mohri

The object recognition is a complex problem in the image processing. Mathematical morphology is Shape oriented operations, that simplify image data, preserving their essential shape characteristics and eliminating irrelevancies. This paper…

Computer Vision and Pattern Recognition · Computer Science 2015-07-28 R. P. Prakash , Keerthana S. Prakash , V. P. Binu

Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data. The dictionary learning problem is non-convex, and usually solved via computationally complex iterative…

Machine Learning · Computer Science 2016-11-30 Cristian Rusu , Nuria Gonzalez-Prelcic , Robert Heath

This paper deals with the problem of recognizability of functions l: Sigma* --> M that map words to values in the support set M of a monoid (M,.,1). These functions are called M-languages. M-languages are studied from the aspect of their…

Formal Languages and Automata Theory · Computer Science 2021-02-12 José Ramón González de Mendívil , Federico Fariña

Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly. We collect pairs of naturalistic and synthetic reasoning tasks to…

Large language models (LLMs) have demonstrated potential in reasoning tasks, but their performance on linguistics puzzles remains consistently poor. These puzzles, often derived from Linguistics Olympiad (LO) contests, provide a minimal…

In order to work with mathematical content in computer systems, it is necessary to represent it in formal languages. Ideally, these are supported by tools that verify the correctness of the content, allow computing with it, and produce…

Logic in Computer Science · Computer Science 2020-05-27 Cezary Kaliszyk , Florian Rabe

Interpretations are a fundamental tool in mathematical logic, allowing structures to be encoded within other structures via logical definitions. We study $\MSO$ \emph{multidimensional point interpretations}, where elements of an interpreted…

Logic in Computer Science · Computer Science 2026-05-20 Alexander Rabinovich

Sets with atoms serve as an alternative to ZFC foundations for mathematics, where some infinite, though highly symmetric sets, behave in a finitistic way. Therefore, one can try to carry over analysis of the classical algorithms from finite…

Logic in Computer Science · Computer Science 2021-01-26 Michał R. Przybyłek

Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a…

Computation and Language · Computer Science 2016-01-26 Manaal Faruqui , Ryan McDonald , Radu Soricut

This paper introduces an abstract notion of fragments of monadic second-order logic. This concept is based on purely syntactic closure properties. We show that over finite words, every logical fragment defines a lattice of languages with…

Formal Languages and Automata Theory · Computer Science 2015-03-20 Manfred Kufleitner , Alexander Lauser

This paper describes a computational, declarative approach to prosodic morphology that uses inviolable constraints to denote small finite candidate sets which are filtered by a restrictive incremental optimization mechanism. The new…

Computation and Language · Computer Science 2007-05-23 Markus Walther

Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful…

Computer Vision and Pattern Recognition · Computer Science 2025-04-16 Alexander Köhler , Michael Breuß

We propose to use orthologic as the basis for designing type systems supporting intersection, union, and negation types in the presence of subtyping assumptions. We show how to extend orthologic to support monotonic and antimonotonic…

Programming Languages · Computer Science 2025-07-15 Simon Guilloud , Viktor Kunčak

Monadic decomposibility --- the ability to determine whether a formula in a given logical theory can be decomposed into a boolean combination of monadic formulas --- is a powerful tool for devising a decision procedure for a given logical…

Formal Languages and Automata Theory · Computer Science 2019-05-09 Pablo Barcelo , Chih-Duo Hong , Xuan-Bach Le , Anthony W. Lin , Reino Niskanen

Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient…

Machine Learning · Statistics 2016-10-17 Mathieu Blondel , Akinori Fujino , Naonori Ueda , Masakazu Ishihata