Nuclear deterrence and cyber warfare: coexistence or competition?

Nuclear deterrence and cyber war seem almost antithetical in their respective intellectual pedigrees. Nuclear weapons are unique in their ability to create mass destruction in a short time. Information or “cyber” weapons, at least for the most part, aim at sowing confusion or mass disruption instead of widespread physical destruction. Nevertheless, there are some intersections between cyber and nuclear matters, and these have the potential to become troublesome for the future of nuclear deterrence. For example, cyber attacks might complicate the management of a nuclear crisis. As well, information attacks on command-control and communications systems might lead to a mistaken nuclear launch based on false warnings, to erroneous interpretations of data or to panic on account of feared information blackout. It is not inconceivable that future nuclear strike planning will include a preliminary wave of cyber strikes or at least a more protracted “preparation of the battlefield” by roaming through enemy networks to plant malware or map vulnerabilities.     

From Nationalism to Factionalism: Faultlines in the Naga Insurgency

Do ceasefires or peace talks create fragmentation in the insurgent groups? Rather than proposing claims that can offer predictions about armed groups behaviour under ceasefires or peace processes, the analysts tend to focus largely on the dynamics between state and non-state actor. The experts pay little attention to overtime changes in social and local political context which might contribute to propelling a rebel group towards fragmentation and factionalism. The present study intends to fill this gap by exploring the shifting role of public opinion and ethnic support for the peace talks to ascertain whether it can increase the likelihood of factionalism in rebel groups or not. This article applies this approach to the case of Naga National Movement (1947–2015) in India, and finds that the proposed variable appears to have increased the frequency of factionalism in the movement.     

Explaining Cohesion in an Insurgent Organization: The Case of the Mizo National Front

Despite the prevalence of violent uprisings against the states, existing literature has produced a generalized explanation of the causes and onset of such uprisings ignoring crucial aspects of (non-state) armed group cohesion, such as their capabilities to manage intra-group tensions and rivalries. In this paper, I examine recent research in the field of civil conflict to understand the importance of investigating underlying constraints which can cause serious impediments to the capabilities of a state during the course of a war. The paper then focuses on the importance of internal strategic resources, such as pre-war and wartime sociopolitical networks in which insurgents mobilize, recruit new cadres and maintain intra-group cooperation, to suggest how it can contain the consequences of counterinsurgency operations and other state strategies on the internal cohesion of an insurgent group. A detailed case study of the Mizo National Front (MNF) in India demonstrates the argument’s plausibility.     

稀疏卷积计算高效数据加载与输出缓存策略

针对现有神经网络加速器在处理稀疏神经网络时存在的数据加载效率低、乘加资源利用率低、输出缓存寻址逻辑复杂等问题,提出了稀疏卷积计算高效数据加载与输出缓存策略。将属于同一输入通道的非零输入特征图像数据和非零权重进行全对全乘累加运算,降低了非零数据配对难度,提高了乘加资源利用率;通过采用输入驻留计算,以及密集型循环加载特征图像数据,大幅减少了数据片外调取次数;优化了输出缓存设计,解决了现有方案中存在的输出缓存地址访问争用、存储拥塞等问题。实验表明,与采用类似架构的细粒度脉动加速器相比,在处理单元面积上减少了21.45%;在数据加载速度方面平均提高了117.71%在平均乘法器利用率方面提高了11.25%,达到89%。     

融合空间偏好和语义的个体活动识别方法

个体活动识别对用户画像、个性化推荐、异常行为检测、群体行为分析和基于活动的资源配置优化具有重要价值。提出了一种基于稀疏的社交媒体签到数据的个体活动语义识别方法,从签到数据中提取活动行为的时间周期性和趋势性特征,并采用空间偏好量化算法,从个体与群体活动的空间关联中提取群体和个体的空间访问偏好,使用自然语言嵌入工具BERT模型提取访问兴趣点的语义。时间特征、空间偏好特征和访问兴趣点名称语义特征共同构成表征群体、个体偏好的时空联合特征,通过极限梯度提升分类器对其进行分类,得到活动语义识别结果。在Foursquare数据集上的对比实验和消融实验中验证了所提活动语义识别模型可以有效提升活动语义识别的准确性。     

融合图嵌入的光滑主成分分析网络图像识别算法

主成分分析网络(principal component analysis network, PCANet)是一种简单的深度学习算法,在图像识别领域具有优秀的性能。将图嵌入思想融入PCANet,提出一种新的图像识别算法光滑主成分分析网络(Smooth-PCANet)。为了验证Smooth-PCANet算法的有效性,在人脸、手写体字符以及图片等不同数据集上构建实验,并将Smooth-PCANet与多种基于深度学习的图像识别算法作了对比。实验结果证明,Smooth-PCANet算法比PCANet获得了更高的识别性能,并且更有效地避免了过拟合,在小样本训练时具有显著优势。     

联合生成对抗网络和检测网络的SAR图像目标检测

针对合成孔径雷达图像目标检测中存在的样本获取困难且数量有限问题,提出了联合生成对抗网络和检测网络的学习模型。利用原始训练集对特别设计的超快区域卷积神经网络进行预训练;再通过基于注意力机制的深度学习生成对抗网络生成高质量合成样本,并输入检测网络进行预测;依据预测信息和概率等价类属标签分配策略为新生样本提供注释信息,并以一定占比对原始训练集进行扩充;利用扩充数据集对检测网络进行再训练。多组仿真实验证明,所提框架能够有效提升网络检测效率和性能。     

分布式异构集群中节点优先级调优算法

节点优先级常用于评价异构集群中节点的性能,因此节点优先级评价指标权重的选择非常重要。采用层次分析法(analytic hierarchy process, AHP)建立了节点优先级评价指标体系,计算得到各指标的初始权重,并使用BP神经网络对初始权重进行优化。训练时,BP网络输入为集群运行中采集的节点实时资源数据,输出为节点的优先级。分析网络训练完成后得到的权重矩阵可以获得各优先级评价指标的优化权重。实验表明,基于AHP和BP的节点优先级评价模型可以更加准确地分析节点性能。相比于Spark默认算法和权重未优化的对照算法,使用调优后的节点优先级可以有效提高集群性能。运行不同工作量的相同负载时,集群平均性能分别提高了16.64%和9.76%;处理相同工作量的不同负载时,集群的平均性能分别提高了12.49%和6.54%。     

军用发动机控制系统技术分析及改进研究

控制系统是航空发动机的重要组成部分。文章对国外航空技术强国,特别是美国和俄罗斯的航空发动机控制系统技术的发展历程进行了分析,着重介绍了某发动机控制系统的技术特点。针对该发动机控制系统提出了数字化改进方案,可以有效减轻控制系统重量,挖掘发动机潜力,并提升发动机性能。从航空发动机控制系统的发展历程可以看出,全权限数字电子控制技术(FADEC)是航空发动机控制系统发展的必然趋势,会对航空技术的发展产生巨大的推动作用。     

科学管理、精益管理与精确作战工程保障研究

设计是作战行动筹划的重要步骤,精确工程保障需要对工程保障行动进行精密的设计和精确管理与控制。科学管理采用科学实验的方法,通过劳动方法标准化和差别计件工资制度,有效提高了工作效率,其基本原理经过百余年的实际检验,被证明是一个成熟有效的管理方法。借鉴科学管理理论,需要对工程保障行动进行动作研究,进而研究工程保障行动的作业标准,以期提高其保障效率。精益管理是在科学管理在新的历史时期的发展与改进,它使用了准时制、零库存、均衡生产、持续改进等先进的管理理念和六西格玛管理、成组技术等先进技术,为基于信息系统体系作战的工程保障能力生成提供了有益的借鉴,可加深我们对精确工程保障的理解,促进精确工程保障研究的可操作性。