Modeling Relation Paths for Representation Learning of Knowledge Bases

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.Comment: 10 page

Alternating Direction Method of Multipliers Based on ℓ2,0\ell_{2,0}-norm for Multiple Measurement Vector Problem

In this paper, we propose an alternating direction method of multipliers (ADMM)-based optimization algorithm to achieve better undersampling rate for multiple measurement vector (MMV) problem. The core is to introduce the $\ell_{2,0}$-norm sparsity constraint to describe the joint-sparsity of the MMV problem, which is different from the widely used $\ell_{2,1}$-norm constraint in the existing research. In order to illustrate the better performance of $\ell_{2,0}$-norm, first this paper proves the equivalence of the sparsity of the row support set of a matrix and its $\ell_{2,0}$-norm. Afterward, the MMV problem based on $\ell_{2,0}$-norm is proposed. Moreover, building on the Kurdyka-Lojasiewicz property, this paper establishes that the sequence generated by ADMM globally converges to the optimal point of the MMV problem. Finally, the performance of our algorithm and comparison with other algorithms under different conditions is studied by simulated examples.Comment: 24 pages, 5 figures, 4 table

Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Social Regularisation in a BPR-based Venue Recommendation Systems

No abstract available

K\"ahler Finsler manifolds with curvatures bounded from below

We obtain a partial parallelism of the complex structure on K\"ahler Finsler manifolds. As applications, we prove Synge-Tsukamoto theorem and Bonnet-Myers theorem for positively curved K\"ahler Finsler manifolds. Moreover, we generalize a comparison theorem due to Ni-Zheng by introducing the notion of orthogonal Ricci curvature to K\"ahler Finsler geometry

Expression, characterization, and localization of acetylcholinesterase-1 from the African malaria mosquito, Anopheles gambiae

Findings and Conclusions:Acetylcholinesterases (AChEs) play an essential role in neurotransmission at cholinergic synapses in vertebrates and invertebrates. Anopheles gambiae has two AChE genes, ace1 and ace2. The properties of AgAChEs remain unknown, and the complex resistance mechanisms can cause the problem to directly correlate a mutation with the resistance phenotype. The purposes of this study are to express and purify AChE1, characterize it in vitro, and localize its expression in vivo. In this study, a cDNA fragment of AChE1 from an A. gambiae EST was subcloned and expressed. The optimized three-step purification scheme took approximately eight hours and yielded 51% of the protein with a specific activity of 523U/mg. A pH of 7.0-8.0 is the best range for AgAChE1 reaction with acetylcholine. The enzyme size is 65 kDa and 130 kDa on SDS-polyacrylamide gels under reducing and nonreducing conditions, respectively. AgAChE1 hydrolyzes ATC 14-fold faster than BTC. The IC50, ki, and Kd demonstrated that AgAChE1 is highly sensitive to inhibition by BW284C51 instead of ethopropazine, and the affinity of BW284C51 is greater than that of ethopropazine. These findings indicate that Ag AChE1 is a true AChE, which exerts the physiological function of ACh hydrolysis at cholinergic synapses. In situ hybridization and immunohistochemistry showed that ace 1 is expressed mainly in the central nervous system. The procedures of Ag AChE1 purification and asymmetric PCR for making ISH probes could be used for similar studies in other insect species. The data are useful for understanding Ag AChE1 and for developing selective insecticides to control the African malaria mosquito