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Tiny AI model could strengthen real-time fault diagnosis for high-speed train bogies

Researchers developed a lightweight fault-diagnosis framework for high-speed train bogies using selective knowledge distillation-based domain adaptation. The approach improves cross-domain diagnostic accuracy by at least 2.1% while keeping the final model size to 28.5 kB.

SourceBeijing Institute of Technology Press Co., Ltd·JournalGreen Energy and Intelligent Transportation·TypeExperimental study·DateApr 13, 2026

PolyU scholar honored with the Hong Kong Engineering Science and Technology Award for contributions to Web3 and digital economy

Prof. AU Man Ho Allen has been recognized with the prestigious Hong Kong Engineering Science and Technology (HKEST) Award 2024-25 for his outstanding contributions to the Web3 ecosystem and the digital economy. His research focuses on developing practical, secure, and privacy-preserving cryptographic solutions.

ECNU Review of Education study calls for reconstructing human education in an uncertain world

The study highlights the need to reconstruct human education due to increasing uncertainties in politics, economy, science, and technology. A proposed strategy includes rebuilding an educational contract based on understanding and cooperation, developing a dynamic lifelong education system, and exploring AI-empowered education models.

SourceECNU Review of Education·JournalECNU Review of Education·DateMar 27, 2025

Graz language database improves automatic speech recognition of Austrian German

Researchers at Graz University of Technology developed a new database to improve speech recognition of Austrian German using speech data from 38 speakers. They found that traditional HMM-based systems are more robust for short sentences and dialectal language, while transformer-based models excel with longer sentences and context.

SourceGraz University of Technology·JournalComputer Speech & Language·DateDec 12, 2024

New guidance for ensuring AI safety in clinical care published in JAMA by UTHealth Houston, Baylor College of Medicine researchers

Experts from UTHealth Houston and Baylor College of Medicine developed a pragmatic approach to monitor and manage AI systems in healthcare organizations. The guidance emphasizes the need for robust governance systems, testing processes, and transparency with patients to ensure safe AI adoption.

SourceUniversity of Texas Health Science Center at Houston·JournalJAMA·TypeCommentary/editorial·DateNov 27, 2024

Can the bias in algorithms help us see our own?

A new study by Carey Morewedge and colleagues found that people are more likely to recognize bias in algorithmic decisions than their own. This is because algorithms can codify and amplify human bias, but also reveal structural biases in society. The research suggests ways to increase awareness of biases and correct them.

SourceBoston University·JournalProceedings of the National Academy of Sciences·TypeObservational study·DateApr 9, 2024