Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.Comment: To appear in AAAI 201

The well-coordinated linkage between acidogenicity and aciduricity via insoluble glucans on the surface of Streptococcus mutans.

Streptococcus mutans is considered the principal cariogenic bacterium for dental caries. Despite the recognition of their importance for cariogenesis, the possible coordination among S. mutans' main virulence factors, including glucan production, acidogenicity and aciduricity, has been less well studied. In the present study, using S. mutans strains with surface-displayed pH-sensitive pHluorin, we revealed sucrose availability- and Gtf functionality-dependent proton accumulation on S. mutans surface. Consistent with this, using a pH-sensitive dye, we demonstrated that both in vivo cell-produced and in vitro enzymatically synthesized insoluble glucans displayed proton-concentrating ability. Global transcriptomics revealed proton accumulation triggers the up-regulation of genes encoding functions involved in acid tolerance response in a glucan-dependent manner. Our data suggested that this proton enrichment around S. mutans could pre-condition the bacterium for acid-stress. Consistent with this hypothesis, we found S. mutans strains defective in glucan production were more acid sensitive. Our study revealed for the first time that insoluble glucans is likely an essential factor linking acidogenicity with aciduricity. The coordination of these key virulence factors could provide new insights on how S. mutans may have become a major cariogenic pathogen

Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment

Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is , which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment

Photoacoustic speckles: boundary dependence and experimental validation

Photoacoustic tomography (PAT) suppresses speckles by prominent boundary buildups. We theoretically study the dependence of PAT speckles on the boundary roughness, which is quantified by the root-mean-squared (RMS) value and the correlation length of the height. The speckle visibility and the correlation coefficient between the reconstructed and actual boundaries are quantified as a function of the boundary roughness. The statistics of PAT speckles is studied experimentally

Reflection-mode multifocal optical-resolution photoacoustic microscopy

Compared with single-focus optical-resolution photoacoustic microscopy (OR-PAM), multifocal OR-PAM utilizes both multifocal optical illumination and an ultrasonic array transducer, significantly increasing the imaging speed. A reflection-mode multifocal OR-PAM system based on a microlens array that provides multiple foci as well as an ultrasonic array transducer that receives the excited photoacoustic waves from all foci simultaneously is presented. Using a customized microprism to reflect the incident laser beam to the microlens array, the multiple optical foci are aligned confocally with the focal zone of the ultrasonic array transducer. Experiments show the reflection-mode multifocal OR-PAM is capable of imaging microvessels in vivo, and it can image a 6×5×2.5  mm^3 volume at 16 μm lateral resolution in ∼2.5  min, which was limited by the signal multiplexing ratio and laser pulse repetition rate