Combining traditional stochastic volatility models with the power of long short-term memory networks offers a significant leap forward in predicting fluctuations in financial markets.
This research explores the potential of dynamic Bayesian networks to improve the accuracy of expected shortfall calculations, a key metric for modern risk management.
A new analysis reveals the systemic environmental risks posed by artificial intelligence, extending far beyond direct energy consumption.
A new deep learning system leverages the power of Transformer networks and optical flow to automatically identify traffic accidents as they happen.
Researchers have developed a novel foundation model, Brain-Semantoks, capable of learning abstract representations of brain activity and offering unprecedented insights into the complex patterns of neural processing.
New research demonstrates that intelligently applied AI support can significantly improve outcomes for at-risk undergraduate students.
New infrastructure unlocks comprehensive analysis of the Aave protocol across multiple blockchains, providing a clearer view into the world of decentralized lending.
New research explores how individuals are leveraging AI chatbots like ChatGPT to tackle long-term life planning, and where these tools fall short.
A new framework leverages knowledge graphs and large language models to critically assess sustainability claims and expose misleading environmental reporting.
A novel deep learning architecture leverages spectral entropy and feature fusion to dramatically improve the accuracy and robustness of magnetic core loss prediction.