Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and interatomic forces through examples, thus bypassing the computationally expensive density functional theory calculations. Combining many-body techniques with a deep learning approach, we demonstrate that a fully-connected neural network is able to learn the complex collective behavior of electrons in strongly correlated systems. Specifically, we consider the Anderson-Hubbard (AH) model, which is a canonical system for studying the interplay between electron correlation and strong localization. The ground states of the AH model on a square lattice are obtained using the real-space Gutzwiller method. The obtained solutions are used to train a multi-task multi-layer neural network, which subsequently can accurately predict quantities such as the local probability of double occupation and the quasiparticle weight, given the disorder potential in the neighborhood as the input. arXiv:1810.02323v1 [cond-mat.str-el]
Abstract-The popularity of Android makes it the prime target of the latest surge in mobile malware. Protecting privacy and integrity of information is helpful for Android users. Currently, malicious software often achieve the purpose of privacy theft and malicious chargeback by sending short messages, making phone calls or connecting Internet surreptitiously. We develop a novel solution that supports multiple security policies to provide much of the integrity and privacy that users desire. We present and implement a security framework for Android which consists of both mandatory access control in the kernel layer and role-based access control in the framework layer. It allows users to define their own security policy and provides fine-grained access control to (untrusted) applications. We implemented a prototype system MPdroid for Android 4.0 platform. Experiments show that we can apply this solution to really help users control applications, block malicious software without significant performance overhead.
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