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Stealthy eavesdropping attacks can slip under the radar of conventional QKD monitoring, but a new machine learning framework detects them with over 88% accuracy.
Early signals from pull requests can predict acceptance with over 95% accuracy, but estimating review effort remains elusive due to complex external factors.
Zero-shot depth estimation in complete darkness is now possible on resource-constrained robots, thanks to a clever combination of event cameras, coded apertures, and synthetic training.
