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A structure made of tapered three-core fiber and CO2 laser notch long-period fiber grating is proposed in this paper. By adjusting the taper waist diameter of a three-core fiber, this structure is made suitable for simultaneous strain and temperature measurements. The effectiveness of the proposed structure in realizing simultaneous measurement sensitivity is verified by measuring the sensitivities of the two peaks at the variation of the wavelength and the depth difference. Experimental results indicate that the wavelength sensitivities of the two peaks are -2.96 nm mε-1, 42.9 pm °C-1 and -1.52 nm mε-1, 47.4 pm °C-1 for strain and temperature, respectively, and the depth sensitivities of two peaks can reach 2.5 dB mε-1, 0.007 dB °C-1 and -2.8 dB mε-1, -0.013 dB°C-1. It can therefore be concluded that the proposed structure can be realized by adjusting the taper waist diameter of a three-core fiber for simultaneous strain and temperature measurements.

Recently, graph convolutional networks (GCNs) and their variants have achieved remarkable successes for the graph-based semisupervised node classification problem. With a GCN, node features are locally smoothed based on the information aggregated from their neighborhoods defined by the graph topology. In most of the existing methods, the graph typologies only contain positive links which are deemed as descriptions for the feature similarity of connected nodes. In this article, we develop a novel GCN-based learning framework that improves the node representation inference capability by including negative links in a graph. Negative links in our method define the inverse correlations for the nodes connected by them and are adaptively generated through a neural-network-based generation model. To make the generated negative links beneficial for the classification performance, this negative link generation model is jointly optimized with the GCN used for class inference through our designed training algorithm. Experiment results show that the proposed learning framework achieves better or matched performance compared to the current state-of-the-art methods on several standard benchmark datasets. [ABSTRACT FROM AUTHOR]

Compiler bugs may be triggered when programs are optimized with optimization sequences. However, diagnosing compiler optimization sequence bugs is difficult due to limited debugging information. Although some techniques (e.g., DiWi and RecBi) have been proposed to automatically localize compiler bugs, no systematic work has been conducted to automatically localize compiler optimization sequence bugs. In this article, we propose LocSeq, a novel technique to automatically localize compiler optimization sequence bugs of LLVM. The core insight of LocSeq is based on the fact that the behaviors of optimizations may be influenced by each other, and thus, the innocent files may be excluded by constructing bug-free optimization sequences. First, given a buggy optimization sequence that triggers a compiler bug, in LocSeq, we transform the problem of the localization for a compiler optimization sequence bug to the problem of the construction for bug-free optimization sequences, which are helpful to localize buggy compiler files. Then, a constrained genetic algorithm is presented in LocSeq to generate a set of bug-free optimization sequences that share similar compiler execution traces with the buggy optimization sequence. Finally, LocSeq leverages a spectrum-based bug localization technique to localize the compiler optimization sequence bug by comparing the execution traces between bug-free optimization sequences and the buggy optimization sequence. To evaluate the effectiveness of LocSeq, we build a benchmark, including 60 optimization sequence bugs of LLVM, and compare LocSeq with the state-of-the-art techniques DiWi and RecBi. The experimental results show that LocSeq significantly outperforms DiWi and RecBi by up to 366.66%/72.27% and 250.00%/56.00% for localizing optimization sequence bugs within Top-1/5 files, respectively. [ABSTRACT FROM AUTHOR]

Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. Evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality; and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model. [ABSTRACT FROM AUTHOR]