We introduce a new generative system called Edit Everything, which can take image and
text inputs and produce image outputs. Edit Everything allows users to edit images using …

Inferring missing information from current facts in a knowledge graph (KG) is the target of the
link prediction task. Currently, existing methods embed the entities and relations of KG as a …

Abstract Knowledge graphs are multi-relation heterogeneous graphs. Thus, the existence of
numerous multi-relation entities imposes a tough challenge to the modelling of the …

Abstract Knowledge graphs are sizeable graph-structured knowledge with both abstract
and concrete concepts in the form of entities and relations. Recently, convolutional neural …

Prompt engineering is an essential technique for enhancing the abilities of large language
models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in …

Existing benchmarks for Large Language Models (LLMs) mostly focus on general or specific
domain capabilities, overlooking structured output capabilities. We introduce SoEval, a …

Knowledge graph embedding is efficient method for reasoning over known facts and
inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based …

Graph neural network has become the mainstream model of Knowledge-aware Recommendation
because of its ability to capture higher-order information. Contrastive learning, due to …

Background Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal
obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension…

Rational design and construction of step-scheme (S-scheme) photocatalyst has received
much attention in the field of CO 2 reduction because of its great potential to solve the current …