Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.Comment: Accepted by AAAI 1

Investigations of supernovae and supernova remnants in the era of SKA

Two main physical mechanisms are used to explain supernova explosions: thermonuclear explosion of a white dwarf(Type Ia) and core collapse of a massive star (Type II and Type Ib/Ic). Type Ia supernovae serve as distance indicators that led to the discovery of the accelerating expansion of the Universe. The exact nature of their progenitor systems however remain unclear. Radio emission from the interaction between the explosion shock front and its surrounding CSM or ISM provides an important probe into the progenitor star's last evolutionary stage. No radio emission has yet been detected from Type Ia supernovae by current telescopes. The SKA will hopefully detect radio emission from Type Ia supernovae due to its much better sensitivity and resolution. There is a 'supernovae rate problem' for the core collapse supernovae because the optically dim ones are missed due to being intrinsically faint and/or due to dust obscuration. A number of dust-enshrouded optically hidden supernovae should be discovered via SKA1-MID/survey, especially for those located in the innermost regions of their host galaxies. Meanwhile, the detection of intrinsically dim SNe will also benefit from SKA1. The detection rate will provide unique information about the current star formation rate and the initial mass function. A supernova explosion triggers a shock wave which expels and heats the surrounding CSM and ISM, and forms a supernova remnant (SNR). It is expected that more SNRs will be discovered by the SKA. This may decrease the discrepancy between the expected and observed numbers of SNRs. Several SNRs have been confirmed to accelerate protons, the main component of cosmic rays, to very high energy by their shocks. This brings us hope of solving the Galactic cosmic ray origin's puzzle by combining the low frequency (SKA) and very high frequency (Cherenkov Telescope Array: CTA) bands' observations of SNRs.Comment: To be published in: "Advancing Astrophysics with the Square Kilometre Array", Proceedings of Science, PoS(AASKA14

qPrimerDepot: a primer database for quantitative real time PCR

Gene expression studies employing high throughput real time PCR methods require finding uniform conditions for optimal amplification of multiple targets, often a daunting task. We developed a primer database, qPrimerDepot, which provides optimized primers for all human and mouse RefSeq genes. These primers are designed to amplify desired templates under unified annealing temperature. For most intron-bearing genes, primers flank one of the largest introns thus minimizing background noise due to genomic DNA contamination. The qPrimerDepot database can be accessed at and

Magneto hydrodynamic simulations of the supernova remnant G1.9+0.3

The youngest Galactic supernova remnant G1.9+0.3 shows a discrete feature between its radio and X-ray morphologies. The observed radio morphology features a single maximum in the north, while the X-ray observation shows two opposite 'ears' on the east and west sides. Using 3D magneto hydrodynamical simulations, we investigate the formation of the discrete feature of the remnant. We have tested different parameters for better simulation and reproduced similar discrete features under an environment with density gradient and an environment with clump, which provides a possible explanation of the observation

Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics