Science

Researchers have developed the first post-quantum sanitizable signature scheme, offering a way to securely alter signed documents without invalidating the signature.

Researchers have developed a refined method for representing and compressing sets of genomic strings, offering improved efficiency and accuracy in bioinformatics applications.

As artificial intelligence increasingly powers space-based systems, a robust security framework is critical to protect against evolving threats to satellite infrastructure.

A new approach minimizes artifacts introduced during lossy compression of scientific datasets, ensuring data quality remains high even with significant size reduction.

Researchers have developed a novel control method that enhances the safety and reliability of complex systems by proactively addressing potential hazards.

As autonomous AI systems become increasingly integrated into software supply chains, a new range of vulnerabilities emerges, demanding a proactive shift in security paradigms.

A new theoretical approach significantly enhances the accuracy of g-tensor calculations, crucial for understanding the magnetic behavior of molecules.

New research challenges the assumptions behind vector quantization, offering solutions to a common problem that hinders generative model performance.

Researchers have developed a new generative model that replaces discrete quantization with a continuous Principal Component Analysis layer, offering improved performance and interpretability.

A new approach to constructing quantum kernels using spectral phase encoding demonstrates increased resilience to noise, offering a pathway to more reliable quantum machine learning.