Modeling and navigation of social information networks in metric spaces

We are living in a world of various kinds of social information networks with small-world and scale-free characteristics. It is still an intriguing problem for researchers to explain how and why so many obviously different networks emerge and share common intrinsic characteristics such as short diameter, higher cluster and power-law degree distribution. Most previous works studied the topology formation and information navigation of complex networks in separated models. In this paper, we propose a metric based range intersection model to explore the topology evolution and information navigation in a synthetic way. We model the network as a set of nodes in a distance metric space where each node has an ID and a range of neighbor information around its ID in the metric space. The range of a node can be seen as the local knowledge or information that the node has around its position in the metric space. The topology is formed by setting up a link between two nodes that have intersected ranges. Information navigation over the network is modeled as a greedy routing process using neighbor links and the distance metric. Different from previous models, we do not assume that nodes join the network one by one and set up link according to the degree distribution of existing nodes or distances between nodes. Range of node is the key factor determining the topology and navigation properties of a network. Moreover, as the ranges of nodes grow, the network evolves from a set of totally isolated nodes to a connected network. Thus, we can easily model the network evolutions in terms of the network size and the individual node information range using the range intersection model. A set of experiments shows that networks constructed using the range intersection model have the scale-free degree distribution, high cluster, short diameter, and high navigability properties that are owned by the real networks.     

Analysis and Implementation of an Open Programmable Router Based on Forwarding and Control Element Separation

A router architecture based upon ForCES (Forwarding and Control Element Separation), which is being standardized by IETF ForCES working group, gains its competitive advantage over traditional router architectures in flexibility, programmability, and cost-effectiveness. In this paper, design and implementation of a ForCES-based router (ForTER) is illustrated. Firstly, the implementation architecture of ForTER is discussed. Then, a layered software model, which well illustrates ForCES features, is proposed. Based on the model, design and implementation of Control Element (CE) and Forwarding Element (FE) in ForTER are introduced in detail. Moreover, security for ForTER is considered and an algorithm to prevent DoS attacks is presented. Lastly, experiments of ForTER are illustrated for routing and running routing protocols, network management, DoS attack prevention, etc. The experimental results show the feasibility of the ForTER design. Consequently, the ForTER implementation basically testifies the feasibility of ForCES architecture and some IETF ForCES specifications.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

可见光与红外图像融合质量评价指标分析

目的 客观评价作为图像融合的重要研究领域,是评价融合算法性能的有力工具。目前,已有几十种不同类型的评价指标,但各应用领域包括可见光与红外图像融合,仍缺少统一的选择依据。为了方便比较不同融合算法性能,提出一种客观评价指标的通用分析方法并应用于可见光与红外图像融合。方法 将可见光与红外图像基准数据集中的客观评价指标分为两类,分别是基于融合图像的评价指标与基于源图像和融合图像的评价指标。采用Kendall相关系数分析融合指标间的相关性,聚类得到指标分组;采用Borda计数排序法统计算法的综合排序,分析单一指标排序和综合排序的相关性,得到一致性较高的指标集合;采用离散系数分析指标均值随不同算法的波动程度,选择充分体现不同算法间差异的指标;综合相关性分析、一致性分析及离散系数分析,总结具有代表性的建议指标集合。结果 在13对彩色可见光与红外和8对灰度可见光与红外两组图像源中,分别统计分析不同图像融合算法的客观评价数据,得到可见光与红外图像融合的建议指标集(标准差、边缘保持度),作为融合算法性能评估的重要参考。相较于现有方法,实验覆盖20种融合算法和13种客观评价指标,并且不依赖主观评价结果。结论...     

校园网网络事件源定位技术研究

为了解决在校园网内部分网络安全事件和故障事件中只能确认攻击和事故的发生,而无法确定攻击和事故的源头这一问题.文章提出一种三步骤的校园网内网网络事件源定位模型.该模型将神经网络和证据理论相结合应用于攻击源定位.提高了校园网内网攻击定位的效率和准确性.     

Reproducibility and stability of N–Al codoped p-type ZnO thin films

Reproducible and stable p-type ZnO thin films have been prepared by the N–Al codoping method. Secondary ion mass spectroscopy measurements demonstrate that N and Al are incorporated into ZnO. The resistivity, carrier concentration, and Hall mobility are typically of 50–100 Ωcm, 1×10¹⁷–8×10¹⁷ cm⁻³, and 0.1–0.6 cm²/Vs, respectively, for the N–Al codoped p-type ZnO films. Hall measurement, X-ray diffraction, and optical transmission were carried out to investigate the changes of the properties with the storage period. Results show that the p-type characteristics of the N–Al codoped ZnO films are of acceptable reproducibility and stability. In addition, the N–Al codoped p-type ZnO films have good crystallinity and optical quality. The properties are time independent.     

Robust Video Text Detection with Morphological Filtering Enhanced MSER

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to vi...     

东方Cheese发酵过程中异黄酮、苷元及组分的研究

东方Cheese经毛霉发酵能去除某些过敏源,这种无盐制品深受日本民众欢迎。文中通过正交试验优化了东方Cheese中异黄酮的提取条件。采用了三波长二标样法和HPLC测定异黄酮含量和组成。检测了东方Cheese发酵过程中异黄酮含量及组成的变化:异黄酮的总量降低了37.90%;其中糖苷由34.02μg/mL降至3.11μg/mL(即已有90.86%糖苷转化为苷元),苷元含量则相应的由0.97μg/mL升至18.62μg/mL。认为异黄酮总量降低以及组成变化的主要因素是糖苷到苷元分子质量的降低和样品含水量提高;发酵后期限制组成变化的主要因素是底物浓度的降低。     

石墨炉原子吸收光谱测定葡萄酒中的砷

砷是一种毒性元素,砷中毒是一种以皮肤损害为主的全身性疾病,过量的砷进入人体内会破坏细胞的氧化还原能力,影响细胞正常代谢,进而引起人体组织损害和机体障碍,可直接引起人的中毒死亡.葡萄酒中砷的污染主要来源于含砷农药以及经含砷废水浇灌过的土壤.因此,在国标GB2762-2005<食品中污染物限量>中对果酒的砷含量有限量,所以砷含量是葡萄酒卫生指标的检测项目之一.     

Serum concentration and mRNA expression in milk somatic cells of toll-like receptor 2, toll-like receptor 4, and cytokines in dairy cows following intramammary inoculation with Escherichia coli

The objective of the current study was to investigate the toll-like receptors (TLR), including the soluble forms sTLR2 and sTLR4, involved in innate immune responses of dairy cows to experimentally induced Escherichia coli mastitis. Six clinically healthy Holstein dairy cows received an intramammary inoculation of E. coli O111:K58 between 63 and 83 d postpartum. Concentrations of sTLR2 and sTLR4, the proinflammatory cytokines IL-6 and tumor necrosis factor-α (TNF-α), and acute phase proteins serum amyloid A (SAA) and haptoglobin (Hp) in blood were measured by ELISA. Furthermore, 10 mL of milk was collected from challenged quarters immediately before inoculation and at 6, 12, 24, 48, and 72 h after inoculation, and mRNA expression of selected genes, including TLR2, TLR4, IL-1β, IL-6, TNF-α, and IL-8, was quantified by real-time PCR. Escherichia coli intramammary infection elicited a decrease in the circulating levels of leukocytes. Rectal temperature was elevated at 6 h postinoculation (PI). Similarly, the serum concentrations of TNF-α, IL-6, and SAA increased at 6 h PI. However, serum concentrations of sTLR2, sTLR4, and Hp did not differ after challenge. The mRNA expression of TLR2, IL-1β, and IL-8 in milk somatic cells increased at 12 h PI, whereas a decreased IL-6 mRNA expression was detected from 6 to 48 h PI. In conclusion, we found that TLR2 mRNA expression increased in milk somatic cells collected from infected quarters of cows challenged with E. coli, whereas the concentrations of sTLR2 and sTLR4 remained unchanged after challenge. Thus, sTLR2 and sTLR4 may protect the host by sequestrating pathogen-associated molecular patterns during E. coli mastitis.