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https://www.selleckchem.com/products/fht-1015.html Millions of videos are ubiquitously generated and shared everyday. Releasing videos would be greatly beneficial to social interactions and the community but may result in severe privacy concerns. To the best of our knowledge, most of the existing privacy preserving techniques for video data focus on detecting and blurring the sensitive regions in the video. Such simple privacy models have two major limitations (1) they cannot quantify and bound the privacy risks, and (2) they cannot address the inferences drawn from the background knowledge on the involved objects in the videos. In this paper, we first define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans and vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. We have conducted extensive experiments on three real videos captured for pedestrians on the streets. The experimental results demonstrate that the generated synthetic videos lie close to the original video for retaining good utility while ensuring rigorous privacy guarantee.Many processes in chemistry and biology involve interactions of a ligand with its molecular target. Interest in the mechanism governing such interactions has dominated theoretical and experimental analysis for over a century. The interpretation of molecular recognition has evolved from a simple rigid body association of the ligand with its target to appreciation of the key role played by conformational transitions. Two conceptually distinct descriptions have had a profound impact on our understanding of mechanisms of ligand binding. The first description, referred to as induced fit, assumes that conformational chang

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