Results 51 to 60 of about 82,167 (267)

Lattice valued relations and automata

Discrete Applied Mathematics, 1984
This paper discusses homogeneity by proving the central classification theorem in terms of a group quotient for lattice valued relations. Furthermore, it is shown how any group can be employed to construct a ''very homogeneous'' object. The specification of automata as lattice valued relation is devoted.
A. Muir, M. W. Warner
openaire   +2 more sources

Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs

Advanced Engineering Materials, EarlyView.
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, Osamu Waseda, Bernd Bayerlein, Simon Stier, Steffen Brinckmann, Abril Azócar Guzmán, Sarath Menon, Matthias A. Popp, Jesper R. Pedersen, Ivano E. Castelli, Jörg Waitelonis, Wolfgang Wenzel, Jörg Neugebauer, Jörg Schaarschmidt, Tilmann Hickel   +14 more
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Learning Valued Relations from Data [PDF]

, 2011
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community.
Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets   +4 more
openaire   +3 more sources

Experience‐Dependent Reorganization of Hippocampal CA3 Neuronal Ensembles Associates With Memory Generalization

Advanced Science, EarlyView.
Mice can transfer the learned rule of spatial working memory to guide similar but novel tasks. Hippocampal CA3 populational activity dynamically reorganize during memory generalization, shifting from task‐specific to generalized coding over testing days. Sparse yet redundant neural representations of CA3 enable rule transfer and cognitive map formation,
Da Song, Zilu Zhu, Yujun Deng, Yujie Song, Hui Li, Junjun Ni, Zhenzhen Quan, Yan Yan, Hong Qing   +8 more
wiley   +1 more source

Disaggregation of Bipolar-Valued Outranking Relations [PDF]

, 2008
In this article, we tackle the problem of exploring the structure of the data which is underlying a bipolar-valued outranking relation. More precisely, we show how the performances of alternatives and weights related to criteria can be determined from three different formulations of the bipolar-valued outranking relations, which are given beforehand.
Meyer, Patrick, Bisdorff, Raymond, Marichal, Jean-Luc   +2 more
openaire   +4 more sources

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

Advanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

Valuing birds: a quantitative approach to explore relational values

Ecosystems and People
This study examines the concept of environmental relational values (RVs) by exploring the case of the human-nature relationships of birdwatchers. Because RVs are context-sensitive, they are difficult to capture quantitatively.
Zélie Stauffer, Norman Backhaus, Anna Deplazes Zemp   +2 more
doaj   +1 more source

DyProL: Dynamic Ensemble Representation Learning for Protein–Nucleic Acid Binding Site Prediction

Advanced Science, EarlyView.
Protein function is represented as a dynamic conformational ensemble rather than a single static structure. A multi‐conformation geometric attention framework aligns, clusters, and learns representative states to capture residue‐ and ensemble‐level signals. Integrating structural dynamics improves interpretable protein‐NA binding prediction and reveals
Pengpai Li, Yiman Liu, Liya Liang, Rongming Liu   +3 more
wiley   +1 more source