Impact of the family planning policy to sex ratio at birth and urban-rural income gap——restudy about the economic and social consequences of the family planning policy

计划生育政策作为一项基本国策已经在我国贯彻执行了30余年，这30余年同时是中国经济高速增长的30余年。经济的高速增长，使得人们只关注与计划生育政策所带来的诸如人口红利等促进经济增长的因素，而忽视和掩盖了计划生育政策所带来的种种弊端。随着近几年生育率下降、人口老龄化、出生性别比偏高以及人口红利的消失等一系列跟计划生育政策有着密切关系问题的加剧以及舆论环境的放松，这项自制定之初就饱受争议的政策又重新引起了学术界广泛的争论和普遍的关注。本文正是在这样的时代背景下，在现代计量经济学的研究框架下，结合理论和实证研究，运用现代统计分析工具和方法，对计划生育政策与偏高的出生性别比之间的关系以及计划生育政策与...As a state policy,China’s family planning rules have implemented more than three decades,over the past three decades,China’s also experienced rocketing economic growth.China’s three-decade-long economic boom draw people’s attention only on the benefits of China’s family planning rules,like demographic dividend,but neglect and cover up the problems that the rules bring.As the relaxation of the publ...学位：经济学硕士院系专业：经济学院_政治经济学学号：1532011115205

MATLAB遗传算法工具箱及其军事应用

用MATLAB语言及MATLAB语言编制的优化工具箱进行优化设计具有语言简单、函数丰富、用法比较灵活、编程效率高等特点。本文简要阐述了遗传算法的基本原理,并对英国Sheffield大学的MATLAB遗传算法工具箱作了简要的介绍,探讨了其在军事目标分配中的应用

An Improved Algorithm for Interactive Dynamic Influence Diagrams

交互式动态影响图(I-dIdS)是基于概率图形理论的多智能体动态交互决策的图模型.为缓解该模型状态空间随时间片增加呈指数级增长的趋势,文中基于行为等价的基本思想压缩状态空间,提出构建EPSIlOn行为等价类的方法:利用有向无环图表示其它AgEnT可能的信度和行为,把信度在空间上接近的模型聚为一类,实现自顶向下合并行为等价模型.该过程避免求解状态空间中的所有候选模型,节省了存储空间和计算时间.模型实例上的仿真结果显示了该算法的有效性.Interactive Dynamic Influence Diagrams(I-DIDs), as graphic models based on probabilistic graphical theory, are proposed to represent, the sequential decision-making problem over multiple time steps in the presence of other interacting agents.The algorithms for solving I-DIDs are haunted by the challenge of an exponentially growing space of candidate models ascribed to other agents over time.In this paper, in order to reduce the candidate model space according the behaviorally equivalent theory, a more efficient way to construct Epsilon behavior equivalence classes is discussed that using belief-behavior graph (BBG).A method of solving I-DIDs approximately is presented, which avoids solving all candidate models by clustering models with beliefs that are spatially close and selecting a representative one from each cluster.The simulation results show the validity of the improved algorithm.国家自然科学基金资助项目(60975052

Modeling and optimization of RGV system based on improved QPSO

为提高自动小车存取系统中轨道导引小车系统的出入库作业效率,提出了一种基于改进量子微粒群的优化方法。分析了轨道导引小车系统出入库作业任务队列特征,建立了数学模型。在此基础上利用量子微粒群算法进行优化调度,并在该算法中引入高斯变异算子,克服了其容易陷入局部最优的缺点。通过仿真实验表明了方法的可行性和有效性。To improve the performance of Rail-Guided Vehicles System(RGVS) in Automatic Vehicle Storage and Retrieval Systems(AVS/RS),an optimization method based on improved Quantum Particle Swarm Optimization(QPSO) was proposed.Firstly,sequencing characteristics of tasks in RGVS were analyzed,and a mathematical model was established.Then,a QPSO algorithm was proposed to solve the scheduling problem.Meanwhile,the Gaussian mutation operator was introduced into this algorithm to overcome its shortcoming of falling into local convergence.Finally,feasibility and effectiveness of the presented method was shown by experimental results.国家自然科学基金资助项目(60975052);厦门大学国家“211三期工程建设”资助项目(0630-E62000)---

Holistic CNN Compression via Low-rank Decomposition with Knowledge Transfer

近日，国际顶级学术刊物《IEEE Transactions on Pattern Analysis and Machine Intelligence》（PAMI）接收了厦门大学信息科学与技术学院纪荣嵘团队的最新研究成果“Holistic CNN Compression via Low-rank Decomposition with Knowledge Transfer”。PAMI是计算机科学领域最顶级的国际期刊，其影响因子为 9.45。 该论文提出了一种统一的全局卷积神经网络压缩框架，简称为LRDKT，其目标在于统一加速与压缩卷积神经网络。该工作是厦门大学博士生林绍辉和导师纪荣嵘教授团队的阶段性研究成果，目前论文相关代码已开源。团队该方向的前期成果已经发表在AAAI/IJCAI等CCF-A类国际会议上。该论文由我校博士生林绍辉与其导师纪荣嵘教授（通讯作者）、硕士研究生陈超、悉尼大学陶大成教授、美国罗彻斯特大学罗杰波教授等合作完成，这也是我校研究生第二次在计算机领域的最顶级刊物上以第一作者身份发表论文，标志着我校信息学科研究生培养质量的突破。【Abstract】Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant memory and computation consumption, which prohibits their usage in resource-limited environments such as mobile or embedded devices. To address the above issues, the existing approaches typically focus on either accelerating the convolutional layers or compressing the fully-connected layers separatedly, without pursuing a joint optimum. In this paper, we overcome such a limitation by introducing a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers. First, a low-rank decomposition (LRD) scheme is proposed to remove redundancies across both convolutional kernels and fully-connected matrices, which has a novel closed-form solver to significantly improve the efficiency of the existing iterative optimization solvers. Second, a novel knowledge transfer (KT) based training scheme is introduced. To recover the accumulated accuracy loss and overcome the vanishing gradient, KT explicitly aligns outputs and intermediate responses from a teacher (original) network to its student (compressed) network. We have comprehensively analyzed and evaluated the compression and speedup ratios of the proposed model on MNIST and ILSVRC 2012 benchmarks. In both benchmarks, the proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. We also demonstrate the proposed compression scheme for the task of transfer learning,including domain adaptation and object detection, which show exciting performance gains over the state-of-the-arts. Our source code and compressed models are available at https://github.com/ShaohuiLin/LRDKT.This work is supported by the National Key R&D Program (No.2017YFC0113000, No.2016YFB1001503), Natural Science Foundation of China (No.U1705262, No.61705262,No.61772443, No.61572410). 该项研究得到了国家重点研发专项（No.2017YFC0113000, and No.2016YFB1001503）、国家自然科学基金联合重点项目（No.U1705262）的资助

Heat Transfer of Channel Type Wheel Heat Exchanger under Frosting Condition

Formation of frost on the surface of exhaust outlets causes the performance degradation of channel type wheel fresh air ventilators when operating under low ambient temperature in winter. In this study, a detailed model for the channel type wheel heat exchangers under frosting condition was established, including a frosting sub-model and a channel type wheel heat exchanger sub- model. Analysis on the heat transfer characteristics of a frosted channel type wheel heat exchanger was performed under different ambient conditions. In addition, the intervals of defrosting were obtained under different operating conditions. The computing results are in agreement with the experimental data