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Fragile topology (FT) opens a new direction in topological photonics, but a new type of photonic crystal (PC) with FT remains to be proposed. In this Letter, the double-site honeycomb lattice (DSHL) PC is proposed by rotating the double dielectric rods (DDR) six times, forming unit cell, and then arraying the unit cells in a triangular lattice. Quantum spin Hall effect occurs by manipulating the DDR in the tangential and radial directions of the unit cell. First, the band structures of DSHL PCs with different structural parameters are calculated, and the laws of topological phase transition are analyzed statistically. Then, to prove the FT properties of two groups of topological nontrivial DSHL PCs, the Wannier-center positions of the bulk bands are calculated by the Wilson–Loop method. Finally, the topological edge states and two groups of topological corner states, which are in the same bulk-state bandgap, are realized successfully. The DSHL PC provides good platforms for both the research of topological photonics and the device design and application, which has a broad prospect.

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The Internet of Things (IoT) and mobile systems nowadays are required to perform more intensive computation, such as facial detection, image recognition and even remote gaming, etc. Due to the limited computation performance and power budget, it is sometimes impossible to perform these workloads locally. As high-performance GPUs become more common in the cloud, offloading the computation to the cloud becomes a possible choice. However, due to the fact that offloaded workloads from different devices (belonging to different users) are being computed in the same cloud, security concerns arise. Side channel attacks on GPU systems have been widely studied, where the threat model is the attacker and the victim are running on the same operating system. Recently, major GPU vendors have provided hardware and library support to virtualize GPUs for better isolation among users. This work studies the side channel attacks from one virtual machine to another where both share the same physical GPU. We show that it is possible to infer other user's activities in this setup and can further steal others deep learning model.

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Keywords: four rotor aircraft adaptive control algorithm (4R2AC) algorithm, back-stepping (BS). Abstract.A novel four rotor aircraft adaptive control algorithm, taking into account fluffy neural system, is described in this paper. Concentrating on the unverifiable streamlined coefficients which describe the push and torques, an adaptive controller for a four-rotor aircraft is composed in view of Immersion and Invariance approach. A moderately basic model parameterized concerning a couple of obscure parameters is created in order to manage the complex multivariable parameter estimation. For the estimation of those parameters, the four rotor aircraft adaptive control algorithm (4R2AC) algorithm is utilized. The control algorithm is based on the optimized fuzzy neural network method. Numerical recreation delineates the heartiness of the controller against the parametric vulnerabilities and outside aggravations.

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