International audienceWe present a novel framework based on a continuous fluid simulator for general simulation of realistic bubbles, with which we can handle as many significant dynamic bubble effects as possible. To capture a very thin liquid film of bubbles, we have developed a regional level set method allowing multi-manifold interface tracking. Based on the definitions of regional distance and its five operators, the implementation of the regional level set method is very easy. An implicit surface of liquid film with arbitrary thickness can be reconstructed from the regional level set function. To overcome the numerical instability problem, we exploit a new semi-implicit surface tension model which is unconditionally stable and makes the simulation of surface tension dominated phenomena much more efficient. An approximated film thickness evolution model is proposed to control the bubble's lifecycle. All these new techniques combine into a general framework that can produce various realistic dynamic effects of bubbles

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Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. For the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the query complexity remains high and such attacks can be defended easily by deploying the defense mechanism. In this study, we aim to learn a generalized substitute model called MetaSimulator that can mimic the functionality of the unknown target models. To this end, we build the training data with the form of multi-tasks by collecting query sequences generated in the attack of various existing networks. The learning consists of a double-network framework, including the taskspecific network and MetaSimulator network, to learn the general simulation capability. Specifically, the task-specific network computes each task's meta-gradient, which is further accumulated from multiple tasks to update MetaSimulator to improve generalization. When attacking a target model that is unseen in training, the trained MetaSimulator can simulate its functionality accurately using its limited feedback. As a result, a large fraction of queries can be transferred to MetaSimulator in the attack, thereby reducing the high query complexity. Comprehensive experiments conducted on CIFAR-10, CIFAR-100, and TinyIm-ageNet datasets demonstrate the proposed approach saves twice the number of queries on average compared with the baseline method. The source code is released on https: //github.com/machanic/MetaSimulator.