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AI-enabled Cyber-Physical Systems (CPS) are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions. Existing approaches, such as rule-based filtering, training-time regularization, or diffusion-based reconstruction, either operate outside...
A three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition and demonstrates the value of AI-driven multi-omics integration for precision oncology.
LogiCP is a novel FL framework that integrates formal logic reasoning with uncertainty quantification (UQ) to support scalable and personalized learning with theoretical guarantees, achieving up to a 95% improvement in client-level MSE while maintaining strong scalability.
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