Representation Learning for Attributed Multiplex Heterogeneous Network

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn

Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed Individual and Structural Graph Information Bottlenecks (IS-GIB). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings. To leverage the structural intra- and inter-domain correlations, we propose Structural Graph Information Bottleneck (S-GIB). Specifically for a batch of graphs with multiple domains, S-GIB first computes the pair-wise input-input, embedding-embedding, and label-label correlations. Then it minimizes the mutual information between input graph and embedding pairs while maximizing the mutual information between embedding and label pairs. The critical insight of S-GIB is to simultaneously discard spurious features and learn invariant features from a high-order perspective by maintaining class relationships under multiple distributional shifts. Notably, we unify the proposed I-GIB and S-GIB to form our complementary framework IS-GIB. Extensive experiments conducted on both node- and graph-level tasks consistently demonstrate the superior generalization ability of IS-GIB. The code is available at https://github.com/YangLing0818/GraphOOD.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training

Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images. Most existing methods address this challenge by using scene layouts, which are image-like representations of scene graphs designed to capture the coarse structures of scene images. Because scene layouts are manually crafted, the alignment with images may not be fully optimized, causing suboptimal compliance between the generated images and the original scene graphs. To tackle this issue, we propose to learn scene graph embeddings by directly optimizing their alignment with images. Specifically, we pre-train an encoder to extract both global and local information from scene graphs that are predictive of the corresponding images, relying on two loss functions: masked autoencoding loss and contrastive loss. The former trains embeddings by reconstructing randomly masked image regions, while the latter trains embeddings to discriminate between compliant and non-compliant images according to the scene graph. Given these embeddings, we build a latent diffusion model to generate images from scene graphs. The resulting method, called SGDiff, allows for the semantic manipulation of generated images by modifying scene graph nodes and connections. On the Visual Genome and COCO-Stuff datasets, we demonstrate that SGDiff outperforms state-of-the-art methods, as measured by both the Inception Score and Fr\'echet Inception Distance (FID) metrics. We will release our source code and trained models at https://github.com/YangLing0818/SGDiff.Comment: Code and models shall be released at https://github.com/YangLing0818/SGDif

Enhancement of Efficiency and Lifetime of Blue Organic Light-Emitting Diodes Using Two Dopants in Single Emitting Layer

We have demonstrated efficient blue organic light-emitting diode with the structure of indium tin oxide/4,4′,4″-tris(N-(2-naphthyl)-N-phenyl-amino)triphenylamine/1,4-bis[N-(1-naphthyl)-N′-phenylamino]-4,4′-diamine/9,10-di(2-naphthyl)anthracene (ADN): 1-4-di-[4-(N,N-di-phenyl)amino]styryl-benzene (DSA-ph) 3 wt%/tris-(8-hydroxyquinoline)aluminum/LiF/Al. Improved efficiencies and longer operational lifetime were obtained by codoping a styrylamine-based dopant BD-3 (0.1 wt%) into the emitting layer of ADN doped with DSA-ph compared to the case of non-codoping. This was due to the improved charge balance and expansion of exciton recombination zone. The better charge balance was obtained by reducing the electron mobility of ADN which was higher than the hole mobility in the case of non-codoping

Ranking-Incentivized Quality Preserving Content Modification

The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings. We present an automatic method for quality-preserving modification of document content -- i.e., maintaining content quality -- so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed. The method replaces a passage in the document with some other passage. To select the two passages, we use a learning-to-rank approach with a bi-objective optimization criterion: rank promotion and content-quality maintenance. We used the approach as a bot in content-based ranking competitions. Analysis of the competitions demonstrates the merits of our approach with respect to human content modifications in terms of rank promotion, content-quality maintenance and relevance.Comment: 10 pages. 8 figures. 3 table

Low-Frequency Repetitive Transcranial Magnetic Stimulation Ameliorates Cognitive Function and Synaptic Plasticity in APP23/PS45 Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is a chronic neurodegenerative disease leading to dementia, which is characterized by progressive memory loss and other cognitive dysfunctions. Recent studies have attested that noninvasive repetitive transcranial magnetic stimulation (rTMS) may help improve cognitive function in patients with AD. However, the majority of these studies have focused on the effects of high-frequency rTMS on cognitive function, and little is known about low-frequency rTMS in AD treatment. Furthermore, the potential mechanisms of rTMS on the improvement of learning and memory also remain poorly understood. In the present study, we reported that severe deficits in spatial learning and memory were observed in APP23/PS45 double transgenic mice, a well known mouse model of AD. Furthermore, these behavioral changes were accompanied by the impairment of long-term potentiation (LTP) in the CA1 region of hippocampus, a brain region vital to spatial learning and memory. More importantly, 2-week low-frequency rTMS treatment markedly reversed the impairment of spatial learning and memory as well as hippocampal CA1 LTP. In addition, low-frequency rTMS dramatically reduced amyloid-β precursor protein (APP) and its C-terminal fragments (CTFs) including C99 and C89, as well as β-site APP-cleaving enzyme 1 (BACE1) in the hippocampus. These results indicate that low-frequency rTMS noninvasively and effectively ameliorates cognitive and synaptic functions in a mouse model of AD, and the potential mechanisms may be attributed to rTMS-induced reduction in Aβ neuropathology

Experimental observation of topological Fermi arcs in type-II Weyl semimetal MoTe2

Weyl semimetal is a new quantum state of matter [1-12] hosting the condensed matter physics counterpart of relativisticWeyl fermion [13] originally introduced in high energy physics. The Weyl semimetal realized in the TaAs class features multiple Fermi arcs arising from topological surface states [10, 11, 14-16] and exhibits novel quantum phenomena, e.g., chiral anomaly induced negative mag-netoresistance [17-19] and possibly emergent supersymmetry [20]. Recently it was proposed theoretically that a new type (type-II) of Weyl fermion [21], which does not have counterpart in high energy physics due to the breaking of Lorentz invariance, can emerge as topologically-protected touching between electron and hole pockets. Here, we report direct spectroscopic evidence of topological Fermi arcs in the predicted type-II Weyl semimetal MoTe2 [22-24]. The topological surface states are confirmed by directly observing the surface states using bulk-and surface-sensitive angle-resolved photoemission spectroscopy (ARPES), and the quasi-particle interference (QPI) pattern between the two putative Fermi arcs in scanning tunneling microscopy (STM). Our work establishes MoTe2 as the first experimental realization of type-II Weyl semimetal, and opens up new opportunities for probing novel phenomena such as exotic magneto-transport [21] in type-II Weyl semimetals.Comment: submitted on 01/29/2016. Nature Physics, in press. Spectroscopic evidence of the Fermi arcs from two complementary surface sensitive probes - ARPES and STS. A comparison of the calculated band structure for T_d and 1T' phase to identify the topological Fermi arcs in the T_d phase is also included in the supplementary informatio

Flow angle from intermediate mass fragment measurements

Directed sideward flow of light charged particles and intermediate mass fragments was measured in different symmetric reactions at bombarding energies from 90 to 800 AMeV. The flow parameter is found to increase with the charge of the detected fragment up to Z = 3-4 and then turns into saturation for heavier fragments. Guided by simple simulations of an anisotropic expanding thermal source, we show that the value at saturation can provide a good estimate of the flow angle, $\Theta_{flow}$, in the participant region. It is found that $\Theta_{flow}$ depends strongly on the impact parameter. The excitation function of $\Theta_{flow}$ reveals striking deviations from the ideal hydrodynamical scaling. The data exhibit a steep rise of \Theta_{\flow} to a maximum at around 250-400 AMeV, followed by a moderate decrease as the bombarding energy increases further.Comment: 28 pages Revtex, 6 figures (ps files), to appear in Nucl.Phys.