Robust Influence Maximization

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding $k$ seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.Comment: 12 pages, 4 figures, Technical Report, contains proofs for the paper appeared in KDD'201

Religiosity and cross‐country differences in trade credit use

Using the firm‐level data over 1989–2012 from 53 countries, we find religiosity in a country is positively associated with trade credit use by local firms. Specifically, after controlling for firm‐ and country‐level factors as well as industry and year effects, we show that trade credit use is higher in more religious countries. Moreover, both creditor rights and social trust in a country enhance the positive association between religiosity and trade credit use, while the quality of national‐level disclosure mitigates the aforementioned positive association. These results are robust to alternative measures of religiosity, alternative sampling requirements and potential endogeneity concerns

Precise Color Control of Red-Green-Blue Light-Emitting Diode Systems

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Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning

Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained Convolutional Neural Network (CNN). Then, we utilize a Branched Fully Convolutional Neural Network (BFCN) for learning structural and textural representations, respectively. In addition, we design a Sorted Matching Mean Square Error (SM-MSE) metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across dataset without additional training.Comment: Published in TIP 201

Polytypism and Unexpected Strong Interlayer Coupling of two-Dimensional Layered ReS2

The anisotropic two-dimensional (2D) van der Waals (vdW) layered materials, with both scientific interest and potential application, have one more dimension to tune the properties than the isotropic 2D materials. The interlayer vdW coupling determines the properties of 2D multi-layer materials by varying stacking orders. As an important representative anisotropic 2D materials, multilayer rhenium disulfide (ReS2) was expected to be random stacking and lack of interlayer coupling. Here, we demonstrate two stable stacking orders (aa and a-b) of N layer (NL, N>1) ReS2 from ultralow-frequency and high-frequency Raman spectroscopy, photoluminescence spectroscopy and first-principles density functional theory calculation. Two interlayer shear modes are observed in aa-stacked NL-ReS2 while only one interlayer shear mode appears in a-b-stacked NL-ReS2, suggesting anisotropic-like and isotropic-like stacking orders in aa- and a-b-stacked NL-ReS2, respectively. The frequency of the interlayer shear and breathing modes reveals unexpected strong interlayer coupling in aa- and a-b-NL-ReS2, the force constants of which are 55-90% to those of multilayer MoS2. The observation of strong interlayer coupling and polytypism in multi-layer ReS2 stimulate future studies on the structure, electronic and optical properties of other 2D anisotropic materials

Performance of Photosensors in the PandaX-I Experiment

We report the long term performance of the photosensors, 143 one-inch R8520-406 and 37 three-inch R11410-MOD photomultipliers from Hamamatsu, in the first phase of the PandaX dual-phase xenon dark matter experiment. This is the first time that a significant number of R11410 photomultiplier tubes were operated in liquid xenon for an extended period, providing important guidance to the future large xenon-based dark matter experiments.Comment: v3 as accepted by JINST with modifications based on reviewers' comment

Sialylation of vasorin by ST3Gal1 facilitates TGF-β1-mediated tumor angiogenesis and progression.

ST3Gal1 is a key sialyltransferase which adds α2,3-linked sialic acid to substrates and generates core 1 O-glycan structure. Upregulation of ST3Gal1 has been associated with worse prognosis of breast cancer patients. However, the protein substrates of ST3Gal1 implicated in tumor progression remain elusive. In our study, we demonstrated that ST3GAL1-silencing significantly reduced tumor growth along with a notable decrease in vascularity of MCF7 xenograft tumors. We identified vasorin (VASN) which was shown to bind TGF-β1, as a potential candidate that links ST3Gal1 to angiogenesis. LC-MS/MS analysis of VASN secreted from MCF7, revealed that more than 80% of its O-glycans are sialyl-3T and disialyl-T. ST3GAL1-silencing or desialylation of VASN by neuraminidase enhanced its binding to TGF-β1 by 2- to 3-fold and thereby dampening TGF-β1 signaling and angiogenesis, as indicated by impaired tube formation of HUVECs, suppressed angiogenesis gene expression and reduced activation of Smad2 and Smad3 in HUVEC cells. Examination of 114 fresh primary breast cancer and their adjacent normal tissues showed that the expression levels of ST3Gal1 and TGFB1 were high in tumor part and the expression of two genes was positively correlated. Kaplan Meier survival analysis showed a significantly shorter relapse-free survival for those with lower expression VASN, notably, the combination of low VASN with high ST3GAL1 yielded even higher risk of recurrence (p = 0.025, HR = 2.967, 95% CI = 1.14-7.67). Since TGF-β1 is known to transcriptionally activate ST3Gal1, our findings illustrated a feedback regulatory loop in which TGF-β1 upregulates ST3Gal1 to circumvent the negative impact of VASN