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n this paper, we use a thermal camera to distinguish hard and soft swipes performed by a user interacting with a natural surface by detecting differences in the thermal signature of the surface due to heat transferred by the user. Unlike prior work, our approach provides swipe pressure classifiers that are user-agnostic, i.e., that recognize the swipe pressure of a novel user not present in the training set, enabling our work to be ported into natural user interfaces without user-specific calibration. Our approach generates average classification accuracy of 76% using random forest classifiers trained on a test set of 9 subjects interacting with paper and wood, with 8 hard and 8 soft test swipes per user. We compare results of the user-agnostic classification to user-aware classification with classifiers trained by including training samples from the user. We obtain average user-aware classification accuracy of 82% by adding up to 8 hard and 8 soft training swipes for each test user. Our approach enables seamless adaptation of generic pressure classification systems based on thermal data to the specific behavior of users interacting with natural user interfaces. 

Although high-temperature operation (i.e., beyond 150°C) is of great interest for many electronics applications, achieving stable carrier mobilities for organic semiconductors at elevated temperatures is fundamentally challenging. We report a general strategy to make thermally stable high-temperature semiconducting polymer blends, composed of interpenetrating semicrystalline conjugated polymers and high glass-transition temperature insulating matrices. When properly engineered, such polymer blends display a temperature-insensitive charge transport behavior with hole mobility exceeding 2.0 cm²/V·s across a wide temperature range from room temperature up to 220°C in thin-film transistors.