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混沌作为一种非周期、类随机以及初值敏感的非线性动力学现象，是保密通信重要的传输载体。针对传统的混沌同步方式结构复杂和实现难度大的问题，文章提出通过现场可编程门阵列（FPGA）实现神经网络的方法来对混沌进行同步。在发射端使用FPGA，根据洛伦兹状态方程生成混沌信号，并对传输信号进行实时加密，利用神经网络对混沌发射系统进行精确的数学建模，在接收端通过训练好的神经网络模型对信号进行解密，并将神经网络模型在FPGA上验证通过，最终成功实现了对10 Mbit/s信号的实时加密和解密。该方法极大简化了混沌保密通信系统的同步结构，为点对多点的实时混沌保密通信提供了可能。

针对传统混沌激光雷达系统结构复杂和成本高的问题，文章提出了采用现场可编程门阵列（FPGA）和任意波形发生器（AWG）两种发射方案，基于改进的Logistic映射公式实时动态生成数字混沌信号。将在电上生成的数字信号调制到直调激光器上生成光域的混沌信号，混沌光信号作为激光雷达发射信号，进而配合光学收发系统搭建成套的激光雷达探测系统，对一个反射率为50%的待测物进行距离测量，无需复杂的光学结构即可实现厘米级别的准确测距。通过实验比较连续和二值化处理后的混沌信号测距效果，发现二值化混沌信号的测距准确度比连续混沌高一倍以上，对于不同频率下的方波、正弦波和混沌干扰信号具有更强的抗干扰能力，展现出更大的应用优势。

The current research on hybrid memory systems based on DRAM and NVM is promising in the field of system architecture, especially the research on data placement in hybrid memory systems has become a research hotspot in domestic and foreign studies. This paper studied the data placement strategy in hybrid memory architecture. On the basis of introducing the common hybrid memory architectures, it comprehensively analyzed the design ideas of existing data placement strategies, mainly involving hardware/software mechanisms, memory access characteristics, static/dynamic analysis, machine intelligence, trigger modes, and granularity selection, and summarized the data placement optimization in hybrid memory in terms of performance, power consumption, and endurance. The comprehensive analysis reveals that existing data placement strategies in hybrid memory still have limitations in terms of memory architecture, data migration, computational cost, and global optimization. In the future, there is still a lot of research space and development prospects in improving architectural design and memory management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current research on the application of artificial intelligence techniques to the field of system architecture is promising, especially the research on applying deep learning to data prefetching in multicore architectures has become a research hotspot at home and abroad. This work studied the cache prefetching task based on deep learning and defined the deep learning cache prefetch model formally. Based on the introduction of current common multi-core cache architectures and prefetching techniques, this paper comprehensively analyzed the design ideas of existing typical cache prefetchers based on deep learning. The application of deep learning neural network in the field of multicore cache prefetching mainly adopts machine learning methods such as deep neural network, recurrent neural network, long and short-term memory network and attention mechanism. A comprehensive comparative analysis of existing deep learning-based data prefetching hierarchical neural models reveals that deep learning-based multicore cache prefetching techniques still have certain computational cost, model optimization, and practicality. In the future, there is still much room for research exploration and development prospect in adaptive prefetching models and the practicality of neural network prefetching models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tobacco Science & Technology is the property of Tobacco Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of insufficient feature extraction ability of deep clustering algorithm on multivariate time series data( MTS), This paper proposes a new deep clustering structure model (MDTC. In order to extract the key features of MTS and realize dimensionality reduction, one-dimensional convolution learns the attribute and temporal dimension feature representation of MTS and the autoencoder structure composed of network layers such as recurrent neural network; To improve the model’s ability to represent temporal features, this paper proposes MCBAM temporal attention module, which is used to enhance the representation features of different time periods in the MTS sequence. This paper conducts experiments on nine publicly available UEA multivariate time series datasets, compared with other autoencoders, the autoencoder structure of the model improves by 2%-9% on 7 data sets; Compared with other attention modules, the MCBAM module of the model improves by 0.3%-2% on the six datasets. Experiments show the effectiveness of the MDTC model structure and MCBAM module, and the model has excellent performance compared with other clustering algorithms. [ABSTRACT FROM AUTHOR]

Objective: To examine the effects of guilt and shame in the process of ego depletion reducing prosocial behavior in college students. Methods: Totally 92 college students（test 1） and 92 college students（test 2）were selected and divided into guilt group, shame group and neutral group according to different test conditions. Each group first completed the Stroop task, and then used the story-situation elicitation method in test 1 and the autobiographical recall emotion elicitation method in test 2 to induce corresponding emotions respectively, and finally used the dictator game to measure the level of prosocial behavior. Results: The results of Test 1 and Test 2 found that the scores of the dictator game were higher in the guilt group and the shame group than in the neutral group（Ps ＜0.05）.Conclusion: The guilt and shame emotions may cause college students to make more prosocial behavior after ego depletion, that is, both could reduce the negative impact of ego depletion on prosocial behavior. [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Mental Health Journal / Zhongguo Xinli Weisheng Zazhi is the property of Chinese Mental Health Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Pre-execution helper threads were needed to perform dynamic prefetching in the prefetch process. However, the traditional static enumeration parameter value was keep constant during in the execution of prefetching, so that the method can not effectively provide prefetching quality. To solve this problem, this paper proposed a method of prefetching quality control based on prefetching rate. Firstly, it analyzed the model of helper thread prefetching QoS optimization. Secondly, based on the previous work, this paper put forward the algorithm of parameter value adjustment based on prefetching rate. In the end, it evaluated and analyzed the algorithm on the real commercial multi-core platform. Experimental results were as follows. With the proposed prefetching adjusting algorithm, the geometric average performance speed up of the benchmarks was 1. 114. It was similar to the traditional static enumeration method’s geometric average performance speed up which was 1. 135. The experimental results show that the proposed algorithm can solve the helper thread prefetching quality adjust problem, as well can quickly obtain the similar prefetching performance compared with the static enumeration method. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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