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https://www.selleckchem.com/products/ferrostatin-1.html The results of experiments conducted exceed the state of the art on both Biwi and Ponting'04 datasets as well as approaching those of the best performing methods on the challenging AFLW2000 database. In addition, the applications to GOTCHA Video Dataset demonstrate that FASHE successfully operates in-the-wild.Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT2).As atomic clocks and frequency standards are increasingly operated in situations where they are exposed to environmental disturbances, it becomes more necessary to understand how variations of each clock component impact the clock output, in particular the local oscillator (LO). Most microwave atomic clocks in operation today use quartz crystal LOs with excellent short-term noise variation but large unwanted long-term drift. Fortunately, this slow drift is mitigated by repeatedly comparing the atomic reference frequency to the LO and applying corrections each iteration through a control algorithm. This article focuses on the shot-to-shot corrections themselves. To optimize clock performance, it is important to determine wheth

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