As the scale of quantum bits expands,manual calibration of quantum bits becomes costly and inefficient.Therefore,the automation of quantum measurement and control is imperative.A frame-work for automated quantum measurement and control is constructed in this work,including 3 modules such as measurement and control experiment,data processing and decision judgment,and 4 databases such as measurement and control experiment library,data processing function library,measurement and control experience library and measurement and control database.The automated calibration of single-qubit properties is completed based on this framework.The calibration of readout gates and single-qubit gates for 24 quantum bits is accomplished in a total of 2.7 h,averaging 6.75 min per qubit.An average single-qubit gate fidelity of 99.28%and a readout fidelity of 78.6%is achieved.
In order to protect the data privacy of network users and solve the training difficulties caused by traffic distribution, this paper based on federal semi-supervised learning presents a traffic classification method to solve the problem of a small number of labeled traffic distributed in server, and a large number of non-labeled traffic distributed independently and identically in clients and not shared. On the one hand, this paper adopts the parameter decomposition strategy to avoid interference between different tasks. On the other hand, this paper uses consistency regularization between clients to maximize consensus between similar segment clients to solve the learning problem of variable small sample data. In addition, method in this paper only transfer parameter differences during the federated learning parameter transfer process. The experimental results show that the accuracy gap between our method and the supervised learning training method is minimal, which can effectively protect user privacy and does not require a large amount of labeled data and communication costs.
针对在海量网络数据中识别网络攻击的问题,运用知识图谱推理能力,提出了基于知识图谱推理的网络攻击识别方法.首先利用Turtle文件解析算法将ttl格式文件解析为能够应用于知识嵌入模型的形式;其次,构建系统状态本体,为不同的系统状态实例提供统一的上层描述,提高利用网络安全知识图谱分析系统状态的可行性;最后,利用基于TransH模型得到图模型中的各类实例数据的嵌入表示,并从候选攻击模式中推理潜在的网络攻击行为.通过实验进一步验证了所提方法的可行性和有效性.
网络流量分类对于网络优化、网络安全预警、用户个性化服务等具有重要意义.随着通信和信息技术的发展,传统的基于端口以及深度包检测的分类方法由于私有协议的广泛应用已不能满足需求.基于机器学习的分类算法被应用于流量分类研究,但加密技术为流量分类的特征提取带来一定难度.首先总结了网络流量分类的基本流程;其次分析了分类粒度及其应用场景,并对目前流量分类的主要技术按照监督学习、半监督学习、无监督学习进行了分类研究;最后对网络流量分类技术的发展趋势及面临挑战做了展望,为网络流量分类研究提供一定的参考.
海马在情绪调节过程中发挥关键作用,针对海马的实时功能磁共振成像(real-time func-tional Magnetic Resonance Imaging,rtfMRI)神经反馈训练能够有效增强健康人的情绪、记忆功能.脑网络是空间位置不同的脑区通过功能联系整合形成的网络模式,情绪调节涉及多个脑网络之间信息的协同交互,脑网络间连接特性的变化能够反映大脑情绪功能的改变.针对海马体的神经反馈情绪调节训练,从静息态脑网络的动态特性出发,提取被试与情绪调节相关的默认模式网络(Default Mode Network,DMN)、执行控制网络(Executive Control Network,ECN)和突显网络(Salience Network,SN)等高级认知网络,并分析这些高级认知网络的功能连接特征在神经反馈训练前后的差异.结果表明通过神经反馈训练,健康被试前突显网络与背侧默认模式网络、左侧执行控制网络之间的功能连接显著增强,而左侧执行控制网络与楔前叶网络之间的功能连接显著下降.结果验证了针对海马体的rtfMRI神经反馈情绪调节训练的有效性,并且情绪调节相关的脑网络间功能连接的变化与临床精神病学研究结果一致,或可作为评估神经反馈训练效果的影像学标记.
