Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at: https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Metabolomics Analysis of Soybean Hypocotyls in Response to Phytophthora sojae Infection

Soybean is one of the most important economic and oil crops across the world. Phytophthora root rot (PRR), caused by Phytophthora sojae (P. sojae), is a major disease in most soybean-growing regions worldwide. Here, we investigated metabolic changes in hypocotyls of two soybean lines, Nannong 10-1 (resistant line, R) and 06-070583 (susceptible line, S), at two time points (12 and 36 hpi) after P. sojae infection and metabolic differences between the R line and the S line. In total, 90 differentially accumulated metabolites (DAMs) were identified after P. sojae infection; the levels of 50 metabolites differed between the R line and the S line. There are 28 DAMs that not only differentially accumulated between the R line and the S line but also differentially accumulated after P. sojae infection. Based on the changes of these DAMs in response to P. sojae infection in different lines and at different timepoints, and the differences in the contents of these DAMs between the R line and the S line, we speculated that DAMs, including sugars (monosaccharides and oligosaccharides), organic acids (oxalic acid, cumic acid), amino acid derivatives, and other secondary metabolites (mannitol, octanal, hypoxanthine, and daidzein etc.) may participate in the metabolic-level defense response of soybean to P. sojae. In this study, an integrated pathway-level analysis of transcriptomics (obtained by RNA-Seq) and metabolomics data illustrated the poor connections and interdependencies between the metabolic and transcriptional responses of soybean to P. sojae infection. This work emphasizes the value of metabolomic studies of plant–pathogen interactions and paves the way for future research of critical metabolic determinants of the soybean-P. sojae interaction

Spin-phonon scattering-induced low thermal conductivity in a van der Waals layered ferromagnet Cr2_2Si2_2Te6_6

Layered van der Waals (vdW) magnets are prominent playgrounds for developing magnetoelectric, magneto-optic and spintronic devices. In spintronics, particularly in spincaloritronic applications, low thermal conductivity ($\kappa$) is highly desired. Here, by combining thermal transport measurements with density functional theory calculations, we demonstrate low $\kappa$ down to 1 W m$^{-1}$ K$^{-1}$ in a typical vdW ferromagnet Cr$_2$Si$_2$Te$_6$. In the paramagnetic state, development of magnetic fluctuations way above $T_\mathrm{c}=$ 33 K strongly reduces $\kappa$ via spin-phonon scattering, leading to low $\kappa \sim$ 1 W m$^{-1}$ K$^{-1}$ over a wide temperature range, in comparable to that of amorphous silica. In the magnetically ordered state, emergence of resonant magnon-phonon scattering limits $\kappa$ below $\sim$ 2 W m$^{-1}$ K$^{-1}$, which would be three times larger if magnetic scatterings were absent. Application of magnetic fields strongly suppresses the spin-phonon scattering, giving rise to large enhancements of $\kappa$. Our calculations well capture these complex behaviours of $\kappa$ by taking the temperature- and magnetic-field-dependent spin-phonon scattering into account. Realization of low $\kappa$ which is easily tunable by magnetic fields in Cr$_2$Si$_2$Te$_6$, may further promote spincaloritronic applications of vdW magnets. Our theoretical approach may also provide a generic understanding of spin-phonon scattering, which appears to play important roles in various systems.Comment: 14 pages, 6 figures, accepted for publication in Advanced Functional Material