The core idea of LCQHNN is to center on quantum feature amplification (Quantum Feature Amplification) while combining a classical stability optimization strategy, establishing an efficient information ...

An AI detector starts with structure. It looks at sentence length. It tracks how often similar sentence shapes appear. Repeated rhythm matters more than topic. Human writing shows variation. Some ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...

A new study presents a zero-shot learning (ZSL) framework for maize cob phenotyping, enabling the extraction of geometric ...

Background Up to half of patients with infective endocarditis (IE) require cardiac surgery. Although anaemia is common, its ...

There are several methods for detecting whether a piece of text was written by AI. They all have limitations – and probably ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

This important study combines optogenetic manipulations and wide-field imaging to show that the retrosplenial cortex controls behavioral responses to whisker deflection in a context-dependent manner.

AI text detectors promise certainty in a moment when synthetic writing is everywhere, but the technology simply cannot ...