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Comprehensive Summary: Stimulus‐sensitive surfaces with tunable morphologies exhibit a wide range of applications in the fields of surface science and engineering. Herein, a cost‐effective yet practical strategy is proposed to fabricate photo‐sensitive patterning surface on film/substrate wrinkle system based on an azo‐containing polyblend. By manipulating the stress field of the bilayer system globally and/or locally upon the stress relaxation triggered by the reversible cis‐trans isomerization of the azobenzene, heating/cooling triggered surface wrinkles on the polyblend films could be tailor‐made with visible‐light‐irradiation. Notably, upon selective photo‐irradiation, bespoke surface patterns may be cyclically generated or eliminated, allowing these reconfigurable patterned polyblend surfaces to be used as rewritable information storage media for non‐ink printing. The as‐prepared photo‐printed information patterns with high‐resolution are shown to be rewritable for multiple cycles and legible for over 90 d in dark ambient conditions. This study not only provides a versatile strategy for flourishing the stimulus‐sensitive systems, but also sheds light on the stress relaxation‐triggered morphological evolution of the wrinkling polyblend films. [ABSTRACT FROM AUTHOR]

Password guessing is an important issue in user security and privacy protection. Using generative adversarial network (GAN) to guess passwords is a new strategy emerging in recent years, which exploits the discriminator's evaluation of passwords to guide the update of the generator so that password guessing sets can be produced. However, the sampling process of discrete data from a categorical distribution is not differentiable so that backpropagation does not work well. In this paper, we propose a novel password guessing model named G-Pass, which consists of two main components. The first is a new network structure, which modifies the generator from the convolutional neural network (CNN) to long short-term memory- (LSTM-) based network and employs multiple convolutional layers in the discriminator to provide more informative signals for generator updating. The second is Gumbel-Softmax with temperature control for training GAN on passwords. Experimental results show the proposed G-Pass outperforms PassGAN in password quality and cracking rate. Moreover, by dynamically adjusting one parameter during the training process, a trade-off between sample diversity and quality can be achieved with our proposed model. [ABSTRACT FROM AUTHOR]