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This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
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These are likely to be older patients who have suffered falls and those with broken hips.
苹果对此回应称，并无证据表明高管在推介 AI 进展时预知关键的 Siri AI 功能会大幅跳票，更无证据显示延迟导致了 iPhone 16 系列的销售下滑。
Dunstan said the venue has managed to do more than just shake off its troubled opening, and was getting plaudits from those who perform there.