An Efficient Failure Recovery Scheme for Service Composition in Pervasive Computing

During the execution of service composition, if one component service fails, a failure recovery mechanism is needed to ensure that the running process is not interrupted and the failed service can be replaced quickly and efficiently. In this paper, we propose an efficient failure recovery scheme for rapid reconstruction of services compositions. Sufficient conditions about substitution and keeping state-consistent between services are proposed. Further, the algorithm for keeping state-consistent between services is proposed. The innovation of this paper is that the failure service will be substituted and the failure service’ state will be transformed into the substituting service’ state to improve the performance of the failure recovery scheme. And the prototype system is implemented. Simulation experiments demonstrate the good performance of the proposed failure recovery scheme

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts.Comment: Accepted by AAAI'2

Van der Waals Engineering of Ferromagnetic Semiconductor Heterostructures for Spin and Valleytronics

The integration of magnetic material with semiconductors has been fertile ground for fundamental science as well as of great practical interest toward the seamless integration of information processing and storage. Here we create van der Waals heterostructures formed by an ultrathin ferromagnetic semiconductor CrI3 and a monolayer of WSe2. We observe unprecedented control of the spin and valley pseudospin in WSe2, where we detect a large magnetic exchange field of nearly 13 T and rapid switching of the WSe2 valley splitting and polarization via flipping of the CrI3 magnetization. The WSe2 photoluminescence intensity strongly depends on the relative alignment between photo-excited spins in WSe2 and the CrI3 magnetization, due to ultrafast spin-dependent charge hopping across the heterostructure interface. The photoluminescence detection of valley pseudospin provides a simple and sensitive method to probe the intriguing domain dynamics in the ultrathin magnet, as well as the rich spin interactions within the heterostructure.Comment: Supplementary Materials included. To appear in Science Advance

The design and protocol of acupuncture for migraine prophylaxis: A multicenter randomized controlled trial

Background: Many studies have already reported encouraging results in the prophylactic therapy of migraine by acupuncture, but there seems to be a lack of high quality randomized controlled trials from China. We design and perform a randomized controlled clinical trial to evaluate the efficacy of acupuncture compared with flunarizine in the prophylactic therapy of patients with migraine without aura in China. Methods: This trial is a multicenter, prospective, randomized controlled clinical trial. The 140 migraine patients are randomly allocated to two different groups. The acupuncture groups (n = 70) is treated with acupuncture and placebo medicine; while the control group (n = 70) is treated with sham acupuncture and medicine (Flunarizine). Both Flunarizine and placebo are taken 10 mg once per night for the first 2 weeks and then 5 mg once per night for the next 2 weeks. Patients in both groups receive 12 sessions of verum/sham acupuncture in 4 weeks. Discussion: The study design and the long term clinical practice of acupuncturists guarantee a high external validity for the results. The results of our trial will be helpful to supply the evidence on the efficacy of acupuncture for migraine prophylaxis in China. Trial Registration: The trial is registered at Controlled Clinical Trials: ISRCTN49839714.Medicine, Research & ExperimentalSCI(E)0ARTICLEnull1

Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content

Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process. In particular, our method exploits unsupervised semantic information in the learning process to capture the intrinsic similarity through a graph regularization. Moreover, we use Hilbert Schmidt Independence Criterion (HSIC) to explore the unsupervised semantic diversity information among multi-view contents of one web item. The overall objective is to minimize the loss function of multi-view non-negative matrix factorization that combines with an intra-semantic similarity graph regularizer and an inter-semantic diversity term. Compared with some state-of-the-art methods, we demonstrate the effectiveness of our proposed method on a large real-world dataset Doucom and the other three smaller datasets