Leonardo da Vinci is usually credited with conducting the first systematic study of friction in the late 15th century, a subfield now known as tribology that deals with the dynamics of interacting surfaces in relative motion. Da Vinci's notebooks depict how he pulled rows of blocks using weights and pulleys, an approach that is still used in frictional studies today, as well as examining the friction produced in screw threads, wheels, and axles. The authors of this latest paper used an experimental setup similar to da Vinci's.
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The selfie camera is the same as on the S26 and S26+. The S26 Ultra has 50MP ultrawide and 200MP wide lenses, along with dual 10MP 3x and 50MP 5x telephoto sensors. The resolutions of those cameras are the same as on the S25 Ultra, but the main 200M and 5x telephoto sensors now have wider apertures to let in more light. The S26 Ultra of course has the camera software features (and other AI features) found in the S26 and S26+.
GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.