Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ) over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI) index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches

No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-$k$ results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.Comment: 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for possible publicatio

Niclosamide enhances abiraterone treatment via inhibition of androgen receptor variants in castration resistant prostate cancer.

Considerable evidence from both clinical and experimental studies suggests that androgen receptor variants, particularly androgen receptor variant 7 (AR-V7), are critical in the induction of resistance to enzalutamide and abiraterone. In this study, we investigated the role of AR-V7 in the cross-resistance of enzalutamide and abiraterone and examined if inhibition of AR-V7 can improve abiraterone treatment response. We found that enzalutamide-resistant cells are cross-resistant to abiraterone, and that AR-V7 confers resistance to abiraterone. Knock down of AR-V7 by siRNA in abiraterone resistant CWR22Rv1 and C4-2B MDVR cells restored their sensitivity to abiraterone, indicating that AR-V7 is involved in abiraterone resistance. Abiraterone resistant prostate cancer cells generated by chronic treatment with abiraterone showed enhanced AR-V7 protein expression. Niclosamide, an FDA-approved antihelminthic drug that has been previously identified as a potent inhibitor of AR-V7, re-sensitizes resistant cells to abiraterone treatment in vitro and in vivo. In summary, this preclinical study suggests that overexpression of AR-V7 contributes to resistance to abiraterone, and supports the development of combination of abiraterone with niclosamide as a potential treatment for advanced castration resistant prostate cancer

Efficient Truss Maintenance in Evolving Networks

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work

Multi-stage feature decorrelation constraints for improving CNN classification performance

For the convolutional neural network (CNN) used for pattern classification, the training loss function is usually applied to the final output of the network, except for some regularization constraints on the network parameters. However, with the increasing of the number of network layers, the influence of the loss function on the network front layers gradually decreases, and the network parameters tend to fall into local optimization. At the same time, it is found that the trained network has significant information redundancy at all stages of features, which reduces the effectiveness of feature mapping at all stages and is not conducive to the change of the subsequent parameters of the network in the direction of optimality. Therefore, it is possible to obtain a more optimized solution of the network and further improve the classification accuracy of the network by designing a loss function for restraining the front stage features and eliminating the information redundancy of the front stage features .For CNN, this article proposes a multi-stage feature decorrelation loss (MFD Loss), which refines effective features and eliminates information redundancy by constraining the correlation of features at all stages. Considering that there are many layers in CNN, through experimental comparison and analysis, MFD Loss acts on multiple front layers of CNN, constrains the output features of each layer and each channel, and performs supervision training jointly with classification loss function during network training. Compared with the single Softmax Loss supervised learning, the experiments on several commonly used datasets on several typical CNNs prove that the classification performance of Softmax Loss+MFD Loss is significantly better. Meanwhile, the comparison experiments before and after the combination of MFD Loss and some other typical loss functions verify its good universality

UniFlexView : a unified framework for consistent construction of BPMN and BPEL process views

Process view technologies allow organizations to create different granularity levels of abstraction of their business processes, therefore enabling a more effective business process management, analysis, interoperation, and privacy controls. Existing research proposed view construction and abstraction techniques for block-based (ie, BPEL) and graph-based (ie, BPMN) process models. However, the existing techniques treat each type of the two types of models separately. Especially, this brings in challenges for achieving a consistent process view for a BPEL model that derives from a BPMN model. In this paper, we propose a unified framework, namely UniFlexView, for supporting automatic and consistent process view construction. With our framework, process modelers can use our proposed View Definition Language to specify their view construction requirements disregarding the types of process models. Our UniFlexView's system prototype has been developed as a proof of concept and demonstration of the usability and feasibility of our framework. © 2019 John Wiley & Sons, Ltd