Abstract: TKG (Temporal Knowledge Graph) reasoning has become a hot research topic in recent years. its purpose is to predict the future by modeling historical information. However, existing research ...
Think about someone you’d call a friend. What’s it like when you’re with them? Do you feel connected? Like the two of you are in sync? In today’s story, we’ll meet two friends who have always been in ...
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Lists and Animated Graphs in webVpython (Glowscript)
Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...
Getting input from users is one of the first skills every Python programmer learns. Whether you’re building a console app, validating numeric data, or collecting values in a GUI, Python’s input() ...
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.
ABSTRACT: Drug repositioning aims to identify new therapeutic applications for existing drugs offering a faster and more cost-effective alternative to traditional drug discovery. Since approved drugs ...
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 ...
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 ...
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