VR-based AO+MI paradigm is found to enhance sensorimotor connectivity and promote large-scale network integration, indicating more coordinated neural dynamics during MI tasks, and a hierarchical graph learning framework tailored for VR-based MI decoding is proposed.
Kai-Yue Du, Wen-Wen Chang, Wei-Xuan Kong et al.· Journal of Neural Engineerin...· 0 citations
This record contains the datasets, trained graph neural network models, and source code supporting the manuscript “Enhancing protein binding site residue prediction with graph neural networks: impacts of cutoff distance and feature selection.”
Serena H. Chen· Zenodo (CERN European Organi...· 0 citations
Antibody–drug conjugates (ADCs) represent a rapidly expanding class of targeted cancer therapeutics that combine the high selectivity of monoclonal antibodies with the potent cytotoxic activity of small-molecule drugs. Their clinical success relies on the simultaneous optimization of multiple interdependent parameters,...
Alberto Ocaña, J. R. Espinosa, C. Alonso-Moreno et al.· Signal Transduction and Targ...· 0 citations
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Reproducibility software and frozen analysis artifacts for the study "Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI". The release includes the deterministic evaluation harness, recovered per-seed outer-fold generation partitions, the reconstructed mean-fo...
Keliang Wan, Herman Z. Q. Chen, Guixian Liu et al.· Zenodo (CERN European Organi...· 0 citations
Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...
Sagor Das, Md. Tamzid Islam, Sanzida Afrin et al.· Zenodo (CERN European Organi...· 0 citations
Journal Entry Testing (JET) is a fundamental audit procedure to identify any potential misstatements, fraud, and management override of controls. Traditional rule-based JET methods suffer from high false positive rates and limited ability to detect complex anomaly patterns. Recent work has shown that large language mod...
Jiaming Chen, Yuanjie Jin, Manqing Wang et al.· Preprints.org· 0 citations
Stress evolution during carbon fiber reinforced polymer (CFRP) drilling governs drilling-induced damage and strongly affects hole quality and the structural reliability of aerospace components. Rapid and accurate prediction of stress-field evolution is essential for online process optimization and damage control. Exper...
Lin Peng, Zhanwei Zhou, Weilin He et al.· Engineering Applications of...· 0 citations
Reproducibility software and frozen analysis artifacts for the study "Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI". The release includes the deterministic evaluation harness, recovered per-seed outer-fold generation partitions, the reconstructed mean-fo...
Keliang Wan, Herman Z. Q. Chen, Guixian Liu et al.· Zenodo (CERN European Organi...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.