CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.     

基于动态依赖的类间测试顺序研究

类间集成测试顺序决定着测试成本的大小,为了得到合适的测试顺序,提出了一种基于动态依赖的类间测试顺序的方法。首先分析对象关系图中类间依赖关系,然后运用边删除规则去除环路,最后运用有向无环图的拓扑序列给出类的测试顺序。仿真结果表明,本文的方法较Briand的方法减少了42%的测试桩。此方法满足最小化测试桩的需要,提高了测试效率,减少了测试成本。     

安全关键系统的栈空间分析研究

由于存储空间异常引起的系统故障,系统开发或维护人员很难从失效特征直接判断到根原因。另外,存储空间异常通常会引起整个系统崩溃,对于安全关键系统,如航空电子系统,系统的失效往往会引起坠机或人员伤亡,因此,对安全关键系统的空间分析显得尤为重要。由于系统栈空间的分析涉及系统动态运行状态,在程序的任意点都有可能被中断服务程序或实时操作系统的其它任务中断。此外系统的栈分析需要基于目标码等特性,再次增加了对于系统栈空间分析的难度。本文从栈空间分析的必要性出发,提出一种综合中断抢占图的栈分析理论和方法,并就安全关键系统如何保证栈空间使用的确定性进行论述。最后,就安全关键软件系统FCOS的栈空间分析过程进行详细的阐述。     

图表示学习方法研究综述

针对图表示方法的相关解析任务进行了研究,从形式化定义出发,首先以不同核心技术作为分类标准将图表示学习方法划分为五大类,其包括基于降维解析、矩阵分解、随机游走、深度学习和其他表示学习方法。其次通过归纳与对比分析梳理各类技术发展脉络,进而深层次展现各类图表示方法的优劣。随后结合图表示学习的常用数据集、评估方法和应用领域的归纳分析,展开动态性、可扩展性、可解释性和可解析性的四维剖析。最后总结并展望了图表示学习的未来研究趋势与发展方向。     

融合全局和近邻协同信息的会话推荐算法

现有基于会话的推荐算法主要通过挖掘单个目标会话的项目转换关系进行推荐,对来自其他不同会话中项目之间的复杂转换信息考虑较少。为此,提出一种融合全局和近邻协同信息的会话推荐算法SFGN-GNN,同时考虑来自全局与近邻会话的协同信息,以充分挖掘用户偏好。通过学习会话表示来表达用户偏好,先按目标会话与近邻会话的成对项目转移关系构建近邻图,依据所有会话中的成对项目转移关系构建全局图,再利用图神经网络获取目标会话节点近邻级和全局级的项目表示,采用融合门融合得到会话级项目表示,并在其中嵌入项目在目标会话中的位置信息和时间信息,然后通过软注意力机制得到最终的会话表示,最后经过softmax函数预测下一个可能交互的项目。在两个数据集上的实验验证了SFGN-GNN算法有效性。     

锌釉SiO2-Al2O3性状图的基础研究

研究了锌釉的SiO2-Al2O3性状图。该实验是在确定锌釉的塞格尔式和SiO2、Al2O3变化图的情况下进行，即固定塞格尔式中碱性成分的摩尔量，而按照SiO2、Al2O3变化图有规律的改变SiO2、Al2O3的含量。分别在1100℃、1150℃、1200℃三个温度下，采用氧化气氛烧成制得样品。用肉眼法对样品进行表观效果分析，从而得到相应条件下的锌釉SiO2-Al2O3性状图。     

Coupling-and-decoupling: A hierarchical model for occlusion-free object detection

Handling occlusion is a very challenging problem in object detection. This paper presents a method of learning a hierarchical model for X-to-X occlusion-free object detection (e.g., car-to-car and person-to-person occlusions in our experiments). The proposed method is motivated by an intuitive coupling-and-decoupling strategy. In the learning stage, the pair of occluding X?s (e.g., car pairs or person pairs) is represented directly and jointly by a hierarchical And–Or directed acyclic graph (AOG) which accounts for the statistically significant co-occurrence (i.e., coupling). The structure and the parameters of the AOG are learned using the latent structural SVM (LSSVM) framework. In detection, a dynamic programming (DP) algorithm is utilized to find the best parse trees for all sliding windows with detection scores being greater than the learned threshold. Then, the two single X?s are decoupled from the declared detections of X-to-X occluding pairs together with some non-maximum suppression (NMS) post-processing. In experiments, our method is tested on both a roadside-car dataset collected by ourselves (which will be released with this paper) and two public person datasets, the MPII-2Person dataset and the TUD-Crossing dataset. Our method is compared with state-of-the-art deformable part-based methods, and obtains comparable or better detection performance.     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

Group‐in‐a‐Box Meta‐Layouts for Topological Clusters and Attribute‐Based Groups: Space‐Efficient Visualizations of Network Communities and Their Ties

An important part of network analysis is understanding community structures like topological clusters and attribute‐based groups. Standard approaches for showing communities using colour, shape, rectangular bounding boxes, convex hulls or force‐directed layout algorithms remain valuable, however our Group‐in‐a‐Box meta‐layouts add a fresh strategy for presenting community membership, internal structure and inter‐cluster relationships. This paper extends the basic Group‐in‐a‐Box meta‐layout, which uses a Treemap substrate of rectangular regions whose size is proportional to community size. When there are numerous inter‐community relationships, the proposed extensions help users view them more clearly: (1) the Croissant–Doughnut meta‐layout applies empirically determined rules for box arrangement to improve space utilization while still showing inter‐community relationships, and (2) the Force‐Directed layout arranges community boxes based on their aggregate ties at the cost of additional space. Our free and open source reference implementation in NodeXL includes heuristics to choose what we have found to be the preferable Group‐in‐a‐Box meta‐layout to show networks with varying numbers or sizes of communities. Case study examples, a pilot comparative user preference study (nine participants), and a readability measure‐based evaluation of 309 Twitter networks demonstrate the utility of the proposed meta‐layouts.     

图解法和卡丘金法库岸塌岸分析对比研究

分析库水冲蚀、浪蚀作用下库岸的塌岸类型及其主要因素,对其主要破坏方式进行预测,利用图解法和卡丘金法分别对该库岸的20个剖面塌岸宽度进行计算情况.计算结果表明,通过卡丘金法计算得到的库岸塌岸宽度与实际情况相差很大,而图解法的计算结果符合实际情况.对于实际工程,可以灵活分析和运用.