Claims Browser

Existing modifications that enforce boundary conditions explicitly suffer from impractical restrictions including boundary smoothness, uniform grids, and separable box-like domains

"existing modifications and approaches that do enforce boundary conditions explicitly suffer from impractical restrictions, including boundary smoothness, uniform grids, and separable, box-like domains"

Most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly

"most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly"

Current graph anomaly detection methods fail to adequately consider the importance of each attribute in node feature vectors, leading to loss of fine-grained information

"they fail to adequately consider the importance information of each attribute in the node feature vector, leading to the loss of fine-grained information"

Current LLM-based ontology learning systems fail to extract non-taxonomic relations due to limitations of closed, taxonomy-oriented relation vocabularies

"However, no non-taxonomic relations are extracted, highlighting limitations of closed, taxonomy-oriented relation vocabularies"

Existing continual learning methods for LLMs that constrain parameter updates or introduce task-specific modules are limited by their reliance on explicit task boundaries during training, making them inapplicable to realistic task-free scenarios

"existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during training, limiting their applicability to realistic task-free scenarios."

Existing graph anomaly detection approaches insufficiently utilize node labels

"they insufficiently utilize node labels for GAD"

Existing graph anomaly detection approaches use static basic functions for graph filters which cannot effectively adapt to frequency-domain distribution of graph data

"they use static basic function to constructed graph filter which cannot effectively adapt to the frequency-domain distribution of graph data"