Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories
Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in ...
Yongbin Tan +5 more
doaj +1 more source
No fruits without color: Cross-modal priming and EEG reveal different roles for different features across semantic categories. [PDF]
Argiris G, Rumiati RI, Crepaldi D.
europepmc +1 more source
Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen +14 more
wiley +1 more source
Redefining linguistic categories through network theory
Categorization is a dynamic cognitive process that organizes human knowledge. As such, categorization processes are integral of the linguistic system and are primarily manifested in the conceptual categories represented in semantic memory.
Natividad Hernández Muñoz +2 more
doaj +1 more source
Syntactico-semantic learning of categorial grammars [PDF]
Natural language learning seems, from a formal point of view, an enigma. As a matter of fact, every human being, given nearly exclusively positive examples (as psycholinguists have noticed) is able at the age of about five to master his/her mother tongue. Though, no linguistically interesting class of formal languages is learnable with positive data in
openaire +2 more sources
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang +3 more
wiley +1 more source
Cross-linguistic generic categories like evidentiality, tense, aspect, number, and person are entrenched in linguistic theory. However, it is not clear whether there is much empirical substance to them.
Kasper Boye
doaj +1 more source

