Global versus Localized Generative Adversarial Nets

In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. Compared with the classic GAN that {\em globally} parameterizes a manifold, the Localized GAN (LGAN) uses local coordinate charts to parameterize distinct local geometry of how data points can transform at different locations on the manifold. Specifically, around each point there exists a {\em local} generator that can produce data following diverse patterns of transformations on the manifold. The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. Furthermore, it can prevent the manifold from being locally collapsed to a dimensionally deficient tangent subspace by imposing an orthonormality prior between tangents. This provides a geometric approach to alleviating mode collapse at least locally on the manifold by imposing independence between data transformations in different tangent directions. We will also demonstrate the LGAN can be applied to train a robust classifier that prefers locally consistent classification decisions on the manifold, and the resultant regularizer is closely related with the Laplace-Beltrami operator. Our experiments show that the proposed LGANs can not only produce diverse image transformations, but also deliver superior classification performances

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

Autotoxin affects the rhizosphere microbial community structure by influencing the secretory characteristics of grapevine roots

Autotoxins secreted by roots into the soil can trigger rhizosphere microecological imbalances and affect root secretory properties resulting in conditions such as replanting disease. However, information on the effect of autotoxins on root secretion characteristics and regulation of the composition of rhizosphere microorganisms by altered root exudates is limited. In this study, autotoxin ρ-hydroxybenzoic acid (4-HBA) was added to the soil of potted grapevine seedlings, CO2 pulse-labeling, and DNA stable isotope probing were used to track the rhizosphere microbiome that assimilates root exudates. Bacterial and fungal microbiomes that assimilated plant-derived carbon were identified by high-throughput sequencing. Results showed that 4-HBA treatment altered bacterial and fungal communities in 13C-labeled organisms, with a lower abundance of beneficial bacteria (e.g., Gemmatimonas, Streptomyces, and Bacillus) and a higher abundance of potential pathogen fungi (e.g., Fusarium, Neocosmospora, Gibberella, and Fusicolla) by changing the composition of root exudates. The exogenous addition of upregulated compound mixtures of root exudates reduced the abundance of beneficial bacterial Bacillus and increased the abundance of potential pathogen fungi Gibberella. These results suggest that 4-HBA can alter root secretion properties and altered root exudates may enrich certain potential pathogens and reduce certain beneficial bacteria, thereby unbalancing the structure of the rhizosphere microbial community