Results 71 to 80 of about 8,113 (297)
Neural NetworksTemporal knowledge graph reasoning (TKGR) aims to predict future events by inferring missing entities with dynamic knowledge structures. Existing LLM-based reasoning methods prioritize contextual over structural relations, struggling to extract relevant subgraphs from dynamic graphs.
Shiqi Fan +5 more
openaire +3 more sources
Advanced Science, EarlyView.This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen +7 more
wiley +1 more source
Document-Level Future Event Prediction Integrating Event Knowledge Graph and LLM Temporal Reasoning [PDF]
Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present ...
Huanran Wang +3 more
core +1 more source
Advanced Science, EarlyView.The cellular actors of oxytocin signaling are under intense scrutiny. A brain‐wide anatomical and functional analysis in mice and rats reveals widespread expression of oxytocin receptors in astrocytes. These receptors are functionally active and, in the nucleus accumbens, selectively regulate male social affiliation.
Clémence Denis +32 more
wiley +1 more source
A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion [PDF]
A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities.
Feng Chen +4 more
core +1 more source
Subgraph Reasoning on Temporal Knowledge Graphs for Forecasting Based on Relaxed Temporal Relations
MathematicsReasoning over Temporal Knowledge Graphs (TKGs) aims to forecast future events based on historical ones. Existing approaches typically enforce strict temporal order constraints among past events; however, such rigidity limits the effective exploitation ...
Meini Yang +3 more
doaj +1 more source
Towards Foundation Model on Temporal Knowledge Graph Reasoning
CoRRTemporal Knowledge Graphs (TKGs) store temporal facts with quadruple formats (s, p, o, t). Existing Temporal Knowledge Graph Embedding (TKGE) models perform link prediction tasks in transductive or semi-inductive settings, which means the entities, relations, and temporal information in the test graph are fully or partially observed during training ...
Jiaxin Pan 0003 +6 more
openaire +2 more sources
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Friend, Not Foe: Lowered Tissue Reactivity to Long‐Term Polyimide Implants
Advanced Science, EarlyView.The choice of optimal neural probe designs remains a major challenge in the field of neurotechnology. This study investigated the biocompatibility of several probe variations, including material, thickness, width, and implantation strategy. It highlights the clear advantage of soft polyimide probes over stiff silicon probes for better device ...
Corinne Orlemann +11 more
wiley +1 more source
PITL2MONA: Implementing a Decision Procedure for Propositional Interval Temporal Logic [PDF]
, 2004 Interval Temporal Logic (ITL) is a finite-time linear temporal logic with applications in hardware verification, temporal logic programming and specification of multimedia documents.
Howard Bowman +3 more
core +1 more source