Optimal Path Problems with Second-Order Stochastic Dominance Constraints

This paper studies optimal path problems integrated with the concept of second order stochastic dominance. These problems arise from applications where travelers are concerned with the trade off between the risks associated with random travel time and other travel costs. Risk-averse behavior is embedded by requiring the random travel times on the optimal paths to stochastically dominate that on a benchmark path in the second order. A general linear operating cost is introduced to combine link- and path-based costs. The latter, which is the focus of the paper, is employed to address schedule costs pertinent to late and early arrival. An equivalent integer program to the problem is constructed by transforming the stochastic dominance constraint into a finite number of linear constraints. The problem is solved using both off-the-shelf solvers and specialized algorithms based on dynamic programming (DP). Although neither approach ensures satisfactory performance for general large-scale problems, the numerical experiments indicate that the DP-based approach provides a computationally feasible option to solve medium-size instances (networks with several thousand links) when correlations among random link travel times can be ignored.     

VideoGraph: a non-linear video representation for efficient exploration

In this paper we introduce VideoGraph, a novel non-linear representation for scene structure of a video. Unlike classical linear sequential organization, VideoGraph concentrates the video content across the time line by structuring scenes and materializes with two-dimensional graph, which enables non-linear exploration on the scenes and their transitions. To construct VideoGraph, we adopt a sub-shot induced method to evaluate the spatio-temporal similarity between shot segments of video. Then, scene structure is derived by grouping similar shots and identifying the valid transitions between scenes. The final stage is to represent the scene structure using a graph with respect to scene transition topology. Our VideoGraph can provide a condensed representation in the scene level and facilitate a non-linear manner to browse videos. Experimental results are presented to demonstrate the effectiveness and efficiency by using VideoGraph to explore and access the video content.     

Learning high-dimensional correspondence via manifold learning and local approximation

The recent years have witnessed a surge of interests of learning high-dimensional correspondence, which is important for both machine learning and neural computation community. Manifold learning–based researches have been considered as one of the most promising directions. In this paper, by analyzing traditional methods, we summarized a new framework for high-dimensional correspondence learning. Within this framework, we also presented a new approach, Local Approximation Maximum Variance Unfolding. Compared with other machine learning–based methods, it could achieve higher accuracy. Besides, we also introduce how to use the proposed framework and methods in a concrete application, cross-system personalization (CSP). Promising experimental results on image alignment and CSP applications are proposed for demonstration.     

采用主监视窗扩展技术的增强现实三维注册方法

在增强现实三维注册中,采用重新检测策略时顿卡现象严重。提出一种采用主监视窗扩展技术的增强现实三维注册方法。在检测阶段,引入特征点可信度和主监视窗,并设计主监视窗的分割、扩展和调控策略,大幅提升了计算速度;在跟踪阶段,采用基于图像金字塔的光流算法进行特征点跟踪。实验证明,该方法不仅有效缓解了顿卡现象,也提升了系统整体的实时性。     

乙苯/乙烯烷基化反应中EA-8402分子筛催化剂的失活动力学研究

用内循环无梯度反应器,在593～633K范围内考察了在乙苯/乙烯进行定向烷基化反应合成对二乙苯时,EA-8402分子筛催化剂的失活行为,建立了相应的失活动力学模型: -da/dt=0.3271exp(-12.94/T)P_(EB)~(0.02)P_(E(?))~(0.383)·α这一模型可作为该反应的宏观动力学模型的补充。由失活级数d=1,按Levenspiel判据推断:其失活机理应该是不受内扩散阻力控制的平行-连串失活。     

基于Nutch与元搜索引擎技术的高校网络舆情监测系统研究

针对高校网络舆情监测工作的不足,结合高校舆情信息的传播特点,提出了在校园网范围内基于Nutch搜索引擎技术进行信息检索,在互联网上采用元搜索引擎技术获取相关信息的舆情监测系统构建方案。采用关键词特征库匹配方式自动进行网络舆情监测,及时准确地发现网络舆情信息;探讨了高校对于网络舆情的监测、分析、引导和反馈等环节的处理方法,从而形成完善的应对机制。     

Investigation and improvement of reversible microfluidic devices based on glass–PDMS–glass sandwich configuration

Reversibly assembled microfluidic devices are dismountable and reusable, which is useful for a number of applications such as micro- and nano-device fabrication, surface functionalization, complex cell patterning, and other biological analysis by means of spatial–temporal pattern. However, reversible microfluidic devices fabricated with current standard procedures can only be used for low-pressure applications. Assembling technology based on glass–PDMS–glass sandwich configuration provides an alternative sealing method for reversible microfluidic devices, which can drastically increase the sealing strength of reversibly adhered devices. The improvement mechanism of sealing properties of microfluidic devices based on the sandwich technique has not been fully characterized, hindering further improvement and broad use of this technique. Here, we characterize, for the first time, the effect of various parameters on the sealing strength of reversible PDMS/glass hybrid microfluidic devices, including contact area, PDMS thickness, assembling mode, and external force. To further improve the reversible sealing of glass–PDMS–glass microfluidic devices, we propose a new scheme which exploits mechanical clamping elements to reinforce the sealing strength of glass–PDMS–glass sandwich structures. Using our scheme, the glass–PDMS–glass microchips can survive a pressure up to 400 kPa, which is comparable to the irreversibly bonded PDMS microdevices. We believe that this bonding method may find use in lab-on-a-chip devices, particularly in active high-pressure-driven microfluidic devices.     

宽带直流放大器的增益控制设计与研究

首先进行了方案论证与比较,分析各种方案的优点缺点,最后选择亚德诺半导体公司生产的低噪声增益可控集成运算放大器AD603和电流反馈型宽带运算放大器AD811等器件设计宽带直流放大器。输入级采用两级AD603级联,输出级设计通频带0~10MHz的带宽,通过单片机可以对放大器增益进行控制。该放大器具有频带宽、功率高、增益可调、带宽可选择等特点。此外,对提高直流放大器的各种性能指标提出了多种具体措施,在要求较高的系统中具有较强的实用性。     

Supervised discrete hashing through similarity learning

Multimedia Tools and Applications - Supervised hashing has achieved better accuracy than unsupervised hashing in many practical applications owing to its use of semantic label information. However,...     

Person re-identification based on multi-appearance model

Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.