Context sensitivity is essential for achieving the precision in inter-procedural static analysis. To be (fully) context sensitive, top-down analysis needs to fully inline all statements of the callees at each callsite, leading to statement explosion. Compositional analysis, which inlines summaries of the callees, scales up but often loses precision, as it is not strictly context sensitive. We propose a compositional and strictly context sensitive framework for static analysis. This framework is based on one key observation: a compositional static analysis often loses precision only on some critical statements that need to be analyzed context sensitively. Our approach hybridly inlines the critical statements and the summaries of non-critical statements of each callee, thus avoiding the re-analysis of non-critical ones. In addition, our analysis lazily summarizes the critical statements, by stopping propagating the critical statements once the calling context accumulated is adequate. Hybrid Inlining can be as precise as context sensitive top-down analysis. We have designed and implemented a pointer analysis based on this framework. It can analyze large Java programs from the Dacapo benchmark suite and industry in minutes. In our evaluation, compared to context insensitive analysis, Hybrid Inlining just brings 65% and 1% additional time overhead on Dacapo and industrial applications respectively.
By utilizing the improved split Hopkinson pressure bar (SHPB) test device, uniaxial, constant-speed cyclic, and variable-speed cyclic impact compression tests were conducted on weakly weathered granite samples. By combining nuclear magnetic resonance (NMR) and triaxial seepage tests, this study investigated the change laws in the mechanical properties, porosity evolution, and permeability coefficients of the samples under cyclic impacts. The results showed that in constant-speed cyclic impacts with increasing impact times, deformation modulus decreased, whilst porosity firstly decreased and then increased. Furthermore, dynamic peak strength firstly increased and then decreased whereas peak strain constantly increased before failure of the samples. In the variable-speed cyclic impacts, as impact times increased, deformation modulus firstly increased and then declined with damage occurring after four impact times. The compaction process weakened and even disappeared with increasing initial porosity. Three types of pores were found in the samples that changed in multiscale under cyclic loading. In general, small pores extended to medium- and large-sized pores. After three variable-speed cyclic impacts, the porosity of the samples was larger than the initial porosity and the permeability coefficient was greater than its initial value. The results demonstrate that the purpose of enhancing permeability and keeping the ore body stable can be achieved by conducting three variable-speed cyclic impacts on the samples.
Nonvolatile charge trap memory is an important part of the continuous development of information technology. As a 2-dimensional (2D) material with fantastic physical characteristics, molybdenum disulfide (MoS2) has been receiving extensive attention for its potential applications in electronic devices. However, while various attempts have been made to devise its charge-trap gate stack, it's still impossible to avoid a certain performance degradation. Here, a MoS2-based nonvolatile charge trapping memory device with a charge-trap gate stack formed by implanting N ions into SiO2 is reported. The fabricated N-implanted memory devices with the energy of 6.5 keV and the dose of 1 × 1015 ions cm−2 exhibit a high on/off current ratio up to 107, a large memory window of 9.1 V, and a high program/erase speed of 10/100 µs. Moreover, the memory device shows an excellent cycling endurance of more than 104 cycles. By combining the MoS2 channel with the N-implanted charge-trap gate stack, this research opens up a fascinating field of nonvolatile charge trap memory devices.
Local robustness verification can verify that a neural network is robust wrt. any perturbation to a specific input within a certain distance. We call this distance Robustness Radius. We observe that the robustness radii of correctly classified inputs are much larger than that of misclassified inputs which include adversarial examples, especially those from strong adversarial attacks. Another observation is that the robustness radii of correctly classified inputs often follow a normal distribution. Based on these two observations, we propose to validate inputs for neural networks via runtime local robustness verification. Experiments show that our approach can protect neural networks from adversarial examples and improve their accuracies.
Abstract An N‐doped TiO 2 model reveals a conceptually different mechanism for activating the N dopant based on delocalized orbital hybridization through O vacancy incorporation. Synchrotron‐based X‐ray absorption spectroscopy, time‐resolved fluorescence, and DFT studies revealed that O vacancy incorporation can effectively stimulate the delocalization of N impurity states through p‐band orbital modulation, which leads to a significant enhancement in photocarrier lifetime. Consequently, this effect also results in a remarkable increase in the incident photon‐to‐electron conversion efficiency in the range of 400–550 nm compared to that of conventional N‐incorporated TiO 2 (15 % versus 1 % at 450 nm). This work reveals the fundamental necessity of orbital modulation in the band engineering of metal oxides for driving solar water splitting and beyond.
In article number 2000779, Shikuan Yang, Xiangheng Xiao, and co-workers develop springtail-inspired superamphiphobic ordered nanohoodoo arrays with quasi-doubly reentrant structures. The arrays have outstanding performance and wide applications. The simple and massive production of the superamphiphobic nanohoodoo structures will push their practical application processes in diverse fields where wettability and liquid repellency need to be carefully engineered.
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