Transient materials capable of disappearing rapidly and completely are critical for transient electronics. End-capped polyoxymethylene (POM) has excellent mechanical properties and thermal stability. However, research concerning the inherent thermal instability of POM without end-capping to obtain transient rather than stable materials, has never been reported. Here, POM without end-capping is proposed as a novel thermally triggered transient solid material that can vanish rapidly by undergoing conversion to a volatile gas, and a chemical vapor deposition method is developed to obtain a smooth POM substrate from the synthesized POM powder. Experimental and theoretical analysis was employed to reveal the mechanism whereby the POM substrate formed and vanished. A Cr/Au/SiO2/Cu memristor device, which was successfully deposited on the POM substrate by physical vapor deposition, exhibits bipolar resistive switching, suggesting that the POM substrate is suitable for use in electrical devices. Thermal triggering causes the POM substrate to vanish as the memristor disintegrates, confirming excellent transient performance. The deposited bulk POM material can completely vanish by thermally triggered depolymerization, and is suitable for physically transient substrates and packaging materials, demonstrating great prospects for application in transient electronics for information security.

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Tremendous efficient optimization methods have been proposed for strongly convex objectives optimization in modern machine learning. For non-strongly convex objectives, a popular approach is to apply a reduction from non-strongly convex to a strongly convex case via regularization techniques. Reduction on objectives with adaptive decrease on regularization tightens the optimal convergence of algorithms to be independent on logarithm factor. However, the initialization of parameter of regularization has a great impact on the performance of the reduction. In this paper, we propose an aggressive reduction to reduce the complexity of optimization for non-strongly convex objectives, and our reduction eliminates the impact of the initialization of parameter on the convergent performances of algorithms. Aggressive reduction not only adaptively decreases the regularization parameter, but also modifies regularization term as the distance between current point and the approximate minimizer. Our aggressive reduction can also shave off the non-optimal logarithm term theoretically, and make the convergent performance of algorithm more compact practically. Experimental results on logistic regression and image deblurring confirm this success in practice.