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Functional programmers have an established tradition of using traversals as a design pattern to work with recursive data structures. The technique is so prolific that a whole host of libraries have been designed to help in the task of…

Programming Languages · Computer Science 2018-05-18 Csongor Kiss , Matthew Pickering , Nicolas Wu

Neither the classical nor intuitionistic logic traditions are perfectly-aligned with the purpose of reasoning about computation, in that neither tradition can permit unconstrained recursive definitions without inconsistency: recursive…

Programming Languages · Computer Science 2026-01-27 Elliot Bobrow , Bryan Ford , Stefan Milenkovic

Monads are a useful tool for structuring effectful features of computation such as state, non-determinism, and continuations. In the last decade, several generalisations of monads have been suggested which provide a more fine-grained model…

Programming Languages · Computer Science 2020-05-04 Dominic Orchard , Philip Wadler , Harley Eades

Artificial Intelligence (AI) applications in automation systems are usually distributed systems whose development and integration involve several experts. Each expert uses its own domain-specific modeling language and tools to model the…

Artificial Intelligence · Computer Science 2023-06-22 Marvin Schieseck , Philip Topalis , Alexander Fay

Differentially-algebraic (D-algebraic) functions are solutions of polynomial equations in the function, its derivatives, and the independent variables. We revisit closure properties of these functions by providing constructive proofs. We…

Algebraic Geometry · Mathematics 2024-08-27 Rida Ait El Manssour , Anna-Laura Sattelberger , Bertrand Teguia Tabuguia

We study the problem of modeling a binary operation that satisfies some algebraic requirements. We first construct a neural network architecture for Abelian group operations and derive a universal approximation property. Then, we extend it…

Machine Learning · Computer Science 2021-02-25 Kenshin Abe , Takanori Maehara , Issei Sato

Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs…

Neural and Evolutionary Computing · Computer Science 2018-02-27 Simone Scardapane , Steven Van Vaerenbergh , Danilo Comminiello , Aurelio Uncini

I will present a way to implement graph algorithms which is different from traditional methods. This work was motivated by the belief that some ideas from software engineering should be applied to graph algorithms. Re-usability of software…

Data Structures and Algorithms · Computer Science 2010-03-24 Marco Nissen

Logic rules are powerful for expressing complex reasoning and analysis problems. At the same time, they are inconvenient or impossible to use for many other aspects of applications. Integrating rules in a language with sets and functions,…

Programming Languages · Computer Science 2022-05-31 Yanhong A. Liu , Scott D. Stoller , Yi Tong , Bo Lin , K. Tuncay Tekle

This paper presents the main features of a system that aims to transform regular expressions into shorter equivalent expressions. The system is also capable of computing other operations useful for simplification, such as checking the…

Symbolic Computation · Computer Science 2023-07-14 Baudouin Le Charlier

Cross-user variability poses a significant challenge in sensor-based Human Activity Recognition (HAR) systems, as traditional models struggle to generalize across users due to differences in behavior, sensor placement, and data…

Machine Learning · Computer Science 2025-06-04 Xiaozhou Ye , Kevin I-Kai Wang

The problem of implementing a class of functions with particular conditions by using monotonic multilayer functions is considered. A genetic algorithm is used to create monotonic functions of a certain class, and these are implemented with…

Neural and Evolutionary Computing · Computer Science 2012-11-06 Yukihiro Kamada , Kiyonori Miyasaki

The choice of how to represent an abstract type can have a major impact on the performance of a program, yet mainstream compilers cannot perform optimizations at such a high level. When dealing with optimizations of data type…

Programming Languages · Computer Science 2024-09-13 Viktor Palmkvist , Anders Ågren Thuné , Elias Castegren , David Broman

Powerful formalisms for abstract argumentation have been proposed, among them abstract dialectical frameworks (ADFs) that allow for a succinct and flexible specification of the relationship between arguments, and the GRAPPA framework which…

Artificial Intelligence · Computer Science 2020-04-22 Gerhard Brewka , Martin Diller , Georg Heissenberger , Thomas Linsbichler , Stefan Woltran

Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been…

Machine Learning · Computer Science 2023-10-12 Yiqiao Li , Jianlong Zhou , Yifei Dong , Niusha Shafiabady , Fang Chen

We provide a computational definition of the notions of vector space and bilinear functions. We use this result to introduce a minimal language combining higher-order computation and linear algebra. This language extends the Lambda-calculus…

Quantum Physics · Physics 2019-03-14 Pablo Arrighi , Gilles Dowek

Pattern matching is a powerful tool which is part of many functional programming languages as well as computer algebra systems such as Mathematica. Among the existing systems, Mathematica offers the most expressive pattern matching.…

Symbolic Computation · Computer Science 2017-05-03 Manuel Krebber

Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we define function spaces and corresponding symmetric products…

Differential Geometry · Mathematics 2026-03-27 Jo Andersson Stokke , Ronny Bergmann , Martin Hanik , Christoph von Tycowicz

The objective of automata learning is to reconstruct the implementation of a hidden automaton, to which only a teacher has access. The learner can ask certain kinds of queries to the teacher to gain more knowledge about the hidden…

Formal Languages and Automata Theory · Computer Science 2026-02-19 Thorsten Wißmann

Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty? We introduce a new task tailored to assess generalization over different complexities and present results that…