RORS: Enhanced Rule-based OWL Reasoning on Spark

The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. The performance of the rule-based OWL reasoning is often sensitive to the rule execution order. In this paper, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. Firstly, we divide all rules (27 in total) into four main classes, namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and schema rules (8 rules) since, as we investigated, those triples corresponding to the first three classes of rules are overwhelming (e.g., over 99% in the LUBM dataset) in our practical world. Secondly, based on the interdependence among those entailment rules in each class, we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules. Finally, we implement the new rule execution order on Spark in a prototype called RORS. The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).Comment: 12 page

Smart worm-like micelles responsive to CO₂/N₂ and light dual stimuli

CO₂/N₂ and light dual stimuli-reponsive worm-like micelles (WLMs) were obtained by addition of a relatively small amount of a switchable surfactant, 4-butyl-4´-(4-N,N-dimethylhexyloxy-amine) azobenzene bicarbonate (AZO-B6-CO₂), sensitive to the same triggers into a binary aqueous solution of cetyltrimethyl ammonium bromide (CTAB) and sodium salycilate (NaSal)

Review on structural fatigue of NiTi shape memory alloys: Pure mechanical and thermo-mechanical ones

AbstractStructural fatigue of NiTi shape memory alloys is a key issue that should be solved in order to promote their engineering applications and utilize their unique shape memory effect and super-elasticity more sufficiently. In this paper, the latest progresses made in experimental and theoretical analyses for the structural fatigue features of NiTi shape memory alloys are reviewed. First, macroscopic experimental observations to the pure mechanical and thermo-mechanical fatigue features of the alloys are summarized; then the state-of-arts in the mechanism analysis of fatigue rupture are addressed; further, advances in the construction of fatigue failure models are provided; finally, summary and future topics are outlined

Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201