16 papers · ranked by Valyu relevance
Heng Zheng, You, Haochen, Zijun Liu + 6 more
Text-attributed graphs require models to effectively integrate both structural topology and semantic content. Recent approaches apply large language models to graphs by linearizing structures into token sequences through random walks. These methods create concise graph vocabularies to replace verbose natural language…
Addison Crump, Alexi Turcotte, José Antonio Zamudio Amaya, Andreas Zeller
Language-based testing combines context-free grammar definitions with semantic constraints over grammar elements to generate test inputs. By pairing context-free grammars with constraints, users have the expressiveness of unrestricted grammars while retaining simple structure. However, producing inputs in the presence…
Lucas Y. Tian, Daniel J. Hanuska, Kedar Garzón Gupta, Yue Liu + 3 more
Humans and other animals can solve new problems, even on the first attempt. This capacity to generate novel problem-solving behavior has been hypothesized to depend on brain mechanisms for recombining units of knowledge using systems of procedural rules, or grammars. Yet, whether and how the brain represents and…
Matteo Ciccaglione, Pierciro Caliandro, Alessandro Pellegrini
In this article, we present Tahr, a framework that allows taking attribute grammar specifications and generating a set of software artefacts that can be used programmatically to operate on text compliant with the grammars. Tahr can be used as an algorithmic workbench to test different manipulations of attribute…
Jesus Antonio Motta, Carolina Fernandez, Maria del Mar Motta
In this work, we present a machine learning model for identifying pathogenic DNA variants. The model was learned from the analysis of normal and pathogenic sequences extracted from the ClinVar database (supported by NCBI). This analysis was based on a conceptual semantic model of DNA sequences converted to peptide…
Javad Sarvestan, Andrea Pozzi, Timothy Cook, Christophe Gaudet-Blavignac + 6 more
Background Interoperability has been a challenge for half a century. Led by an informatics view of the world, the quest for interoperability has evolved from typing and categorizing data to building increasingly complex models. In parallel with the development of these models, the field of terminologies and ontologies…
Wajahath Mohammed
We present the first application of pregroup grammar-based quantum compositional natural language processing (QNLP) to Arabic; a morphologically rich, free-word-order language whose structural complexity provides a uniquely demanding testbed for theories of meaning composition in quantum circuits. Our system converts…
Authors not listed
RNA molecules fold into complex three-dimensional structures that determine their function. A wide range of mathematical frameworks, such as chord diagrams, fatgraphs, and context-free grammars, have been used to represent these structures; however, these models have largely been developed from mathematical motivations…
Feifei Li, Xiao Chen, Xiaoyu Sun, Xi Xiao + 4 more
Grammar inference for complex programming languages remains a significant challenge, as existing approaches fail to scale to realworld datasets within practical time constraints. In our experiments, none of the state-of-the-art tools, including Arvada, Treevada and Kedavra were able to infer grammars for complex…
Weixing Zhang, Bowen Jiang, Rahul Sharma, Regina Hebig + 1 more
In model-driven engineering, metamodel evolution leads to the need to adapt corresponding grammars to maintain consistency, which typically requires tedious manual work. Existing rule-based methods can achieve partial automation but have limitations when handling complex grammar scenarios. This paper proposes a Large…
Authors not listed
Perovskite solar cell performance depends on the joint configuration of materials, interfaces, and layer-specific physical parameters, forming a structured design space that is naturally sequential but rarely modeled as such. This work introduces PervoTransformer, a transformer-based framework that represents complete…
Giuliana Nardacchione, Pierluigi Zoccolotti, Chiara Valeria Marinelli, Nicola Molinaro
Artificial grammar learning (AGL) has frequently been employed to investigate the procedural hypothesis of dyslexia. However, most studies did not distinguish whether performance depended upon the acquisition of grammatical rules (procedural memory), distributional knowledge (statistical learning) or reference to…
Yanjie Huang, Guangye Lv, Anyue Cheng, Wei Xie + 12 more
Programmable design of RNA sequences with defined functions remains a central challenge in biology. Despite recent advances, existing RNA generative models lack robust controllable design capabilities and are constrained by short context windows, limiting their capacity to model the complex evolutionary manifold of…
Laura Baitenova, Saule Tussupova, Saken Mambetov, Gauhar Munaitbas + 1 more
The Kazakh language, as an agglutinative and morphologically rich language, presents significant challenges for the development of natural language processing (NLP) tools. Traditional rule-based analyzers provide full coverage but lack flexibility; statistical and neural models handle disambiguation more effectively…
Eun-Jin Kim, Yun-Kyung Lee, Sang-Min Lee, Jeong-Nyeo Kim + 4 more
Ordinary users encounter various documents on the network every day, such as news articles, emails, and messages, and most are vulnerable to malicious attacks. Malicious attack methods continue to evolve, making neural network-based malware detection increasingly appealing to both academia and industry. Recent studies…
Matthieu Vilain, Stéphane Aris-Brosou
The ever-growing amount of available biological data leads modern analysis to be performed on large datasets. Unfortunately, bioinformatics tools for preprocessing and analyzing data are not always designed to treat such large amounts of data efficiently. Notably, this is the case when encoding DNA and RNA sequences…