Abstract: 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 approaches ...
Adapting to the stream: An instance-attention GNN method for irregular multivariate time series data
Framework of DynIMTS. The model is a recurrent structure based on a spatial-temporal encoder and consists of three main components: embedding learning, spatial-temporal learning, and graph learning.
Google Gemini for Workspace can be exploited to generate email summaries that appear legitimate but include malicious instructions or warnings that direct users to phishing sites without using ...
This project implements a drug-disease association prediction model using Graph Convolutional Networks (GCN) with advanced data augmentation techniques. The model predicts novel drug-disease ...
ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
A weird phrase is plaguing scientific papers – and we traced it back to a glitch in AI training data
Aaron J. Snoswell receives funding from the Australian Research Council funded Discovery Project "Generative AI and the future of academic writing and publishing" (DP250100074) and has previously ...
SHANGHAI/SINGAPORE Feb 18 (Reuters) - Tiger Brokers said on Tuesday it embedded DeepSeek's model into its AI-powered chatbot, as brokerages and money managers race to capitalise on the Chinese ...
models: Deep learning models developed for surrogating the hydraulic one: contains a base class with common inputs and functions and one for the SWE-GNN and mSWE-GNN models. results: Contains results ...
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