Geo-Social Group Queries with Minimum Acquaintance Constraint

The prosperity of location-based social networking services enables geo-social group queries for group-based activity planning and marketing. This paper proposes a new family of geo-social group queries with minimum acquaintance constraint (GSGQs), which are more appealing than existing geo-social group queries in terms of producing a cohesive group that guarantees the worst-case acquaintance level. GSGQs, also specified with various spatial constraints, are more complex than conventional spatial queries; particularly, those with a strict $k$NN spatial constraint are proved to be NP-hard. For efficient processing of general GSGQ queries on large location-based social networks, we devise two social-aware index structures, namely SaR-tree and SaR*-tree. The latter features a novel clustering technique that considers both spatial and social factors. Based on SaR-tree and SaR*-tree, efficient algorithms are developed to process various GSGQs. Extensive experiments on real-world Gowalla and Dianping datasets show that our proposed methods substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data Bases Journa

Fluctuating Nature of Light-Enhanced dd-Wave Superconductivity: A Time-Dependent Variational Non-Gaussian Exact Diagonalization Study

Engineering quantum phases using light is a novel route to designing functional materials, where light-induced superconductivity is a successful example. Although this phenomenon has been realized experimentally, especially for the high-$T_c$ cuprates, the underlying mechanism remains mysterious. Using the recently developed variational non-Gaussian exact diagonalization method, we investigate a particular type of photoenhanced superconductivity by suppressing a competing charge order in a strongly correlated electron-electron and electron-phonon system. We find that the $d$-wave superconductivity pairing correlation can be enhanced by a pulsed laser, consistent with recent experiments based on gap characterizations. However, we also find that the pairing correlation length is heavily suppressed by the pump pulse, indicating that light-enhanced superconductivity may be of fluctuating nature. Our findings also imply a general behavior of nonequilibrium states with competing orders, beyond the description of a mean-field framework.Comment: 17 pages, 8 figure

Selenium in the Prevention and Treatment of Hepatocellular Carcinoma: From Biomedical Investigation to Clinical Application

Selenium is a micronutrient that had been suggested to reduce the risk of cancer. Hepatocellular carcinoma (HCC), a prevalent disease and one of the most lethal cancers in the world, awaits new alternative treatment strategies to improve patients’ survival. As an essential trace element, selenium has been studied for its anticancer properties in both oxidative stress and inflammatory-related mechanisms that may contribute to HCC growth and metastasis. In recent decades, increasing studies have investigated the potential role of selenium in liver cancer involving several major cancer-associated signaling pathways, metabolic pathways, and antioxidant defense systems both in vitro and in preclinical models. It was also observed that there was an increase in the trend of development of novel selenium nanoparticles and selenium-containing inhibitors aiming to improve the therapeutic efficacy and relative potency of selenium. However, controversies remain with whether a relationship exists between serum selenium level and HCC risk. This chapter aims to summarize the multi-target and multi-pathway in vitro and in vivo pharmacological effects of selenium in HCC, to provide a more comprehensive view and to highlight the recently discovered molecular mechanisms We hope this chapter could outline the correlation of selenium level and the risk of HCC in patients and discuss the clinical application of selenium in HCC prevention and treatment

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh