基于NR-Transformer的集群作业运行时间预测

高性能集群的作业调度通常使用作业调度系统来实现，准确填写作业运行时间能在很大程度上提升作业调度效率。现有的研究通常使用机器学习的预测方式，在预测精度和实用性上还存在一定的提升空间。为了进一步提高集群作业运行时间预测的准确率，考虑先对集群作业日志进行聚类，将作业类别信息添加到作业特征中，再使用基于注意力机制的NR-Transformer网络对作业日志数据建模和预测。在数据处理上，根据与预测目标的相关性、特征的完整性和数据的有效性，从历史日志数据集中筛选出7维特征，并按作业运行时间的长度将其划分为多个作业集，再对各作业集分别进行训练和预测。实验结果表明，相比于传统机器学习和BP神经网络，时序神经网络结构有更好的预测性能，其中NR-Transformer在各作业集上都有较好的性能。     

格点QCD基础求解器及其异构计算实现的性能优化

格点量子色动力学(格点QCD)是研究夸克、胶子等微观粒子间相互作用的重要理论和方法. 通过将时空离散化为四维结构网格, 并将量子色动力学的基本场量定义在网格上, 让研究人员可以使用数值模拟方法, 从第一性原理出发研究强子间相互作用和性质, 但这个过程中的计算量极大, 需要进行大规模并行计算. 格点QCD计算的核心基础为格点QCD求解器, 是程序运行主要的计算热点模块. 本文研究在国产异构计算平台下格点QCD求解器的实现与优化, 提出一套格点QCD求解器的设计实现, 实现了BiCGSTAB求解器, 显著降低了迭代次数; 通过对奇偶预处理技术, 降低了所求问题的计算规模; 针对国产异构加速卡的特点, 优化了Dslash模块的访存操作. 实验测试表明, 相比优化前的求解器获得了约30倍的加速比, 为国产异构超算下格点QCD软件性能优化提供了有益的参考价值.     

基于推理注意力机制的二阶段网络图像修复

现有的图像修复方法在处理大面积缺失或高度纹理化的图像时，通常会产生扭曲的结构或与周围区域不一致的模糊纹理，无法重建合理的图像结构。为此，提出了一种基于推理注意力机制的二阶段网络图像修复方法。首先通过边缘生成网络生成合理的幻觉边缘信息，然后在图像补全网络完成图像的重建工作。为了进一步生成视觉效果更逼真的图像，提高图像修复的精确度，在图像补全网络采用推理注意力机制，有效控制了生成特征的不一致性，从而生成更有效的信息。所提方法在多个数据集上进行了实验验证，结果表明该图像修复方法的结构相似性指数达到了88.9%，峰值信噪比达到了25.56 dB，与现有的图像修复方法相比，该方法具有更高的图像修复精确度，生成的图像更逼真。     

使能未来工厂的5G能力综述

5G系统将移动通信服务从移动电话、移动宽带和大规模机器通信扩展到新的应用领域，即所谓对通信服务有特殊要求的垂直领域。对使能未来工厂的5G能力进行了全面的分析总结，包括弹性网络架构、灵活频谱、超可靠低时延通信、时间敏感网络、安全和定位，而弹性网络架构又包括对网络切片、非公共网络、5G局域网和边缘计算的支持。希望从广度到深度，对相关的理论及技术应用做透彻、全面的梳理，对其挑战做清晰的总结，从而为相关研究和工程技术人员提供借鉴。     

基于云网PoP的弹性边缘网络设计思路探讨

移动边缘云是公司“云+5G”双引擎战略的最佳契合点,边缘网络是发挥移动云“大云”产品和5G网络融合优势,实现云网统筹、构建运营商“连接+计算”核心能力的关键。运营商传统接入网存在云网割裂、分段入云和组网复杂等突出问题,难以适应边缘业务敏捷交付要求。本文通过深入分析边缘云业务特征和技术架构,对标业界主流云商建设实践,研究基于云网PoP网的边缘网络建设思路,创新性提出云网一体化规划设计和建设交付流程变革,基于云网POP统一网络和业务锚点,构建Overlay和Underlay融合双层加速网络架构,探索Spine-leaf化的新型城域接入网实现L3下沉和弹性扩容等方法,实现“云+网+应用”一体化敏捷交付的边缘网络能力。     

数值堆材料辐照损伤并行动力学蒙特卡罗软件MISA-AKMC的设计与实现

材料辐照损伤模拟是数值堆软件的重要内容。原子动力学蒙特卡罗（AKMC）方法是研究核材料辐照行为的重要手段，可在保持原子级别精度下，有效地扩展模拟的时间尺度到秒甚至年量级。但在面向实际应用需求时，其仍面临内存限制和复杂的计算量等挑战。通过并行计算技术提升计算效率是解决这一挑战的有效手段。本文论述一款并行AKMC程序MISA-AKMC的设计原理与实现技术。MISA-AKMC实现了一种并行KMC模拟框架，重点包括sub-lattice并行算法的实现、加速优化方法、转发通信方法、KMC模型接口等。基于该框架，开发了空位-间隙的演化模型，可实现热老化模拟和析出模拟。通过并行性能测试获得了良好的并行性能，结合算例结果验证说明了MISA-AKMC的正确性和可靠性。     

Clinical significance of contact lens related changes of ocular surface tissue observed on optical coherence images

PurposeTo investigate the relationship between the real contact lens imprint into the conjunctival tissue, observed by optical coherence tomography (OCT) and conjunctival staining and contact lens wearing comfort.Methods17 participants (mean age = 26.6 SD ± 3.6 years; 7 females) were fitted with three different contact lenses base curves of the same silicone hydrogel custom lens type (Visell 50; Hecht Contactlinsen, Au, Germany) in a randomised order. One lens was optimally fitted according to the manufacturer's recommendation, one fitted 0.4 mm flatter and one fitted 0.4 mm steeper. After 4 h of lens wear the contact lens edge in the area of the conjunctiva was imaged nasally and temporally using OCT (Optovue iVue SD-OCT). To correct the artefact due to optical distortion with OCT, the imprint of all worn lenses was measured on a glass plate afterwards. Conjunctival staining in the limbal region after 4 h of lens wear was classified using the CCLRU Grading Scale. Comfort scoring was based on visual analog scales from 0 (very poor) to 100 (excellent).ResultsThe mean conjunctival imprint of all contact lens edges was 32.0 ± 8.1 μm before and 7.3 ± 6.5 μm after distortion correction of the OCT images. The distortion corrected conjunctival imprint with the 0.4 mm steeper lens (11.5 ± 6.2 μm) was statistically significantly greater compared to the optimally fitted lens (6.5 ± 5.9 μm) (One-way ANOVA followed Tukey-test; p = 0.017) and greater compared to the 0.4 mm flatter lens (3.9 ± 5.3 μm) (p < 0.001). There was no statistically significant difference between the optimally fitted lens and the 0.4 mm flatter lens (p = 0.209). The nasally measured imprint (11.4 ± 9.0 μm) was significantly greater than the temporally measured (3.3 ± 7.6 μm) (p < 0.001). There was no statistically significant correlation between the amount of conjunctival imprint and the graded conjunctival staining (p = 0.346) or the wearer’s comfort (p = 0.735).ConclusionsContact lens edges imaged by OCT exhibited displacement artefacts. The observed conjunctival imprints are a combination of real conjunctival compression and artefacts. A deeper imprint of the contact lens into the conjunctiva caused by a steeper base curve was not related to clinically significant staining or changes in comfort after 4 h of lens wear. The observed differences between nasal and temporal imprint are likely to be caused by variations of conjunctival thickness and the shape of the underlying sclera.     

一种基于GN算法的动态图划分方法

随着图规模的急剧增长，对动态图进行实时处理的需求日益增加。大多现有的算法针对静态图划分是有效的，直接用其处理动态图会带来较大的通信开销。针对该问题，提出一种基于GN算法的动态图划分方法。首先收集一段时间内加入动态图中的顶点；然后，利用GN算法对这些新加入的顶点进行预划分，产生若干个内部联系紧密的社区；最后，将预划分产生的社区结果插入到已经划分好的当前图中。实验从交叉边数和负载均衡度两方面将该方法与传统流式划分方法进行比较，结果表明， 在公开数据集上，该方法的交叉边数降低了13%，负载均衡度减少了42.3%。由此可见，该方法的划分质量明显优于传统的流式划分方法。     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.