18 papers · ranked by Valyu relevance
Felix Weitkämper
We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases…
Dan R. Johnson, James C. Kaufman, Brendan S. Baker, John D. Patterson + 8 more
'John D. Patterson' 'Baptiste Barbot' 'Adam E. Green' 'Janet van Hell' 'Evan Kennedy' 'Grace F. Sullivan' 'Christa L. Taylor' 'Thomas Ward' 'Roger E. Beaty'] We developed a novel conceptualization of one component of creativity in narratives by integrating creativity theory and distributional semantics theory. We…
Germán Vidal
Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. In particular, we consider systems that combine traditional rewriting rules with probabilities. Then…
Pedro H. Azevedo de Amorim, Christopher Wai‐Kei Lam
With the wide spread of deep learning and gradient descent inspired optimization algorithms, differentiable programming has gained traction. Nowadays it has found applications in many different areas as well, such as scientific computing, robotics, computer graphics and others. One of its notoriously difficult problems…
Melissa Franch, Elizabeth A. Mickiewicz, James L. Belanger, Brad Joiner + 12 more
As we listen to speech, our brains track the meanings of the words we hear. Recent successes of large language models suggest that distributed population geometry can capture rich semantic relationships between words. Motivated by this idea, we hypothesized that semantic information in the brain may likewise be…
Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig
Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called…
Leonardo Fernandino, Lisa L. Conant
The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that lexical semantic representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this…
Leo Henry, Thomas Neele, Mohammad Reza Mousavi, Matteo Sammartino
Active automata learning infers automaton models of systems from behavioral observations, a technique successfully applied to a wide range of domains. Compositional approaches for concurrent systems have recently emerged. We take a significant step beyond available results, including those by the authors, and develop a…
Jingjing Tang, Li Wang, Jing Huang, Aiye Shi + 1 more
Semantic feature recognition in colour images is required for identifying uneven patterns in object detection and classification. The semantic features are identified by segmenting the colorimetric sensor array features through machine learning paradigms. Semantic segmentation is a method for identifying distinct…
Chrysafis Hartonas
This article initiates the semantic study of distribution-free normal modal logic systems, laying the semantic foundations and anticipating further research in the area. The article explores roughly the same area, though taking a different approach, with a recent article by Bezhanishvili, de Groot, Dmitrieva and…
Nina Haslinger, Alain Noindonmon Hien, Emil Eva Rosina, Viola Schmitt + 1 more
Universal quantifiers differ in whether they are restricted to distributive interpretations, like English every, or permit non-distributive interpretations, like English all. This interpretational difference is traditionally captured by positing two unrelated lexical entries for distributive and non-distributive…
Melissa Franch, Elizabeth A. Mickiewicz, James L. Belanger, Assia Chericoni + 10 more
As we listen to speech, our brains actively compute the meaning of individual words. Inspired by the success of large language models (LLMs), we hypothesized that the brain employs vectorial coding principles, such that meaning is reflected in distributed activity of single neurons. We recorded responses of hundreds of…
Edgar Beck, Carsten Bockelmann, Armin Dekorsy, Changchuan Yin
Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon’s classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and…
Jiatong Wu, Sen Wang, Kai Niu, Yifei She + 2 more
Classical Algorithmic Information Theory (AIT) provides a rigorous foundation for information-based similarity measurement, but classical formulations and their compression-based approximations largely operate at the syntactic level, making them sensitive to surface-level variation and insufficient for semantic…
Daphne Wang, Mehrnoosh Sadrzadeh
Sheaves are mathematical objects that describe the globally compatible data associated with open sets of a topological space. Original examples of sheaves were continuous functions; later they also became powerful tools in algebraic geometry, as well as logic and set theory. More recently, sheaves have been applied to…
Valentina Elce, Giorgia Bontempi, Serena Scarpelli, Bianca Pedreschi + 5 more
Dreams are universal yet deeply personal experiences. While memory and personal concerns influence dream content, the impact of other individual, generalizable traits remains poorly understood. To address this gap, we built a multimodal dataset including dream and wakefulness reports, alongside demographic…
Authors not listed
This work presents the LabIMotion extension for the Chemotion Electronic Lab Notebook (ELN), expanding its capabilities from organic chemistry to support interdisciplinary research and enabling the description of workflows. LabIMotion enhances documentation by introducing customizable components structured across three…
Junsup Song, Dimitris Karagiannis, Moonkun Lee, Maurizio Mongelli + 1 more
'Jerome Guzzi'] Process algebra is one of the most suitable formal methods to model smart IoT systems for smart cities. Each IoT in the systems can be modeled as a process in algebra. In addition, the nondeterministic behavior of the systems can be predicted by defining probabilities on the choice operations in some…