Delay and Sum (DAS) is one of the most common beamforming algorithms for photoacoustic imaging (PAI) reconstruction. Based on calculating beamformed signal with simple delaying and summing, DAS can function in a quick response and is quite suitable for real-time PAI. However, high sidelobes and intense artifacts may appear when using DAS due to summing with unnecessary data. In this paper, a beamforming algorithm called Multiple Delay and Sum with Enveloping (multi-DASE) is introduced to solve this problem. Compared to DAS, the multi-DASE algorithm calculates not only the initial value of the beamformed signal but also the complete N-shaped photoacoustic signal for each pixel. Through computer simulation, a phantom experiment and experiment on human finger joint, the multi-DASE algorithm is compared with other beamforming methods in removing artifacts by evaluating the quality of the reconstructed images. Furthermore, by rearranging the calculation sequences, the multi-DASE algorithm can be computing in parallel using GPU acceleration to meet the needs of real-time clinical application.

The details of the host workloads in cloud computing environment and the application demands of the real world computing system are becoming so complex that it throws a big challenge to the major cloud infrastructure vendors. To achieve service level agreements between users and cloud service vendors, it is essential to apply accurate prediction of future host load, which is also significant to improve the resource allocation and utilization in cloud computing. Although that there were several various methods and models developed, few of them can acquire the long-term temporal dependencies appropriately to make accurate predictions. In this paper, we apply a GRU based Encoder-Decoder network(GRUED) which contains two gated recurrent neural networks(GRUs) to address these issues. Thorough empirical studies based upon the Google resources usage traces and the traditional Unix system load traces demonstrate that our proposed method outperforms other state- of-the-art approaches for the prediction of multi-step-ahead host workload in cloud computing.

The robustness of complex networks against attack has been an important issue for decades. Most of the previous studies focused on targeted attack (TA) and random attack (RA), while recently localized attack (LA) has drawn the attention of researchers. However, the existing studies related to LA mainly aim to reveal the properties on various network topologies so that the strategy to enhance network robustness against LA is still not well studied. In this paper, we propose a global disassortative rewiring strategy to enhance the robustness of scale-free networks against LA without changing the degree distribution. The validations are conducted on simulated scale-free networks and two real-life networks. As global disassortative rewiring strategy outperforms the other strategies, it can be proved effective in enhancing network robustness against LA and may contribute to future network risk reduction. In addition, by avoiding calculating and comparing the localized-robustness measurement within each rewire operation, our strategy offers a significant advantage in computational efficiency.