Online Shopping Behavior in Cross-cultural Context: An Empirical Research in China

As a newly evolved emergence from e-business, social commerce has attracted increasingly attention from both researchers and practitioners. Distinguished from the majority of extant research paradigm, the current empirical study extends social commerce research into cross-cultural context and unveils the underlying mechanism through which two dimensions of social media usage (informational and socializing) impact user’s intention to purchase on social commerce websites, thereby facilitating online shopping behaviors. In addition, the research demonstrates the role of cultural distance as a boundary condition attenuating the positive effects of social media usage in cross-cultural social commerce application. Research implications and limitations for future venues are also discussed

Earth Mover's Distance as a metric to evaluate the extent of charge transfer in excitations using discretized real-space densities

This paper presents a novel theoretical measure, $\mu^{\text{EMD}}$, based on the Earth Mover's Distance, for quantifying the density shift caused by electronic excitations in molecules. As input, the EMD metric uses only the discretized ground and excited state electron densities in real space, rendering it compatible with almost all electronic structure methods used to calculate excited states. The EMD metric is compared against other popular theoretical metrics for describing the extent of electron-hole separation in a wide range of excited states (valence, Rydberg, charge-transfer, etc). The results showcase the EMD metric's effectiveness across all excitation types and suggest that it is useful as an additional tool to characterize electronic excitations. The study also reveals that $\mu^{\text{EMD}}$ can function as a promising diagnostic tool for predicting the failure of pure exchange-correlation functionals. Specifically, we show statistical relationships between the functional-driven errors, the exact exchange content within the functional, and the magnitude of $\mu^{\text{EMD}}$ values.Comment: 31 pages, 7 figure

Probabilistic hesitant fuzzy multiple attribute decisionmaking based on regret theory for the evaluation of venture capital projects

The selection of venture capital investment projects is one of the most important decision-making activities for venture capitalists. Due to the complexity of investment market and the limited cognition of people, most of the venture capital investment decision problems are highly uncertain and the venture capitalists are often bounded rational under uncertainty. To address such problems, this article presents an approach based on regret theory to probabilistic hesitant fuzzy multiple attribute decision-making. Firstly, when the information on the occurrence probabilities of all the elements in the probabilistic hesitant fuzzy element (P.H.F.E.) is unknown or partially known, two different mathematical programming models based on water-filling theory and the maximum entropy principle are provided to handle these complex situations. Secondly, to capture the psychological behaviours of venture capitalists, the regret theory is utilised to solve the problem of selection of venture capital investment projects. Finally, comparative analysis with the existing approaches is conducted to demonstrate the feasibility and applicability of the proposed method

Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

Seismic damage analysis due to near-fault multipulse ground motion

Near-fault pulse-like ground motion is a significant class of seismic records since it tends to cause more severe damage to structures than ordinary ground motions. However, previous researches mainly focus on single-pulse ground motions. The multipulse ground motions that exist in records receive rare attention. In this study, an analysis procedure is proposed to investigate the effect of multipulse ground motions on structures by integrating finite element analysis and an identification method that features each pulse in the multipulse ground motion satisfying the same evaluation criteria. First, the Arias intensity, wavelet-based cumulative energy distribution, and response spectra of identified non-, single-, and multipulse ground motions are compared. Then, the seismic damage on frame structures, a soil slope, and a concrete dam under non-, single-, and multipulse ground motions are analyzed. Results show that the spectral velocity of multipulse ground motions is significantly greater than those of non- and single-pulse ground motions and potentially contains multiple peaks in the long-period range. Seismic damage evaluation indicates that the maximum interstory drift of frame structures with high fundamental periods under multipulse ground motions is about twice that of nonpulse ground motions. Similar characteristics also exist in the soil slope and the concrete dam. Therefore, multipulse ground motions potentially cause more severe damage to structures compared to non- and single-pulse ground motions. The findings of this study facilitate the recognition of the increased seismic demand imposed by the multipulse ground motion in engineering practices, provide new possibilities for ground motion selection in seismic design validation, and shed new light on seismic hazard and risk analysis in near-fault regions

Evidence of Indium impurity band in superconducting (Sn,In)Te thin films

Sn1-xInxTe has been synthesized and studied recently as a candidate topological superconductor. Its superconducting critical temperature increases with Indium concentration. However, the role of Indium in altering the normal state band structure and generating superconductivity is not well-understood. Here, we explore this question in Sn1-xInxTe (0<x<0.3) thin films, characterized by magneto-transport, infrared transmission and photoemission spectroscopy measurement. We show that Indium is forming an impurity band below the valence band edge which pins the Fermi energy and effectively generates electron doping. An enhanced density-of-states due to this impurity band leads to the enhancement of superconducting transition temperature measured in multiple previous studies. The existence of the In impurity band and the role of In as a resonant impurity should be more carefully considered when discussing the topological nature of Sn1-xInxTe

MBE growth of axion insulator candidate EuIn2As2

The synthesis of thin films of magnetic topological materials is necessary to achieve novel quantized Hall effects and electrodynamic responses. EuIn2As2 is a recently predicted topological axion insulator that has an antiferromagnetic ground state and an inverted band structure but that has only been synthesized and studied as a single crystal. We report on the synthesis of c-axis oriented EuIn2As2 films by molecular beam epitaxy on sapphire substrates. By careful tuning of the substrate temperature during growth, we stabilize the Zintl phase of EuIn2As2 expected to be topologically non-trivial. The magnetic properties of these films reproduce those seen in single crystals but their resistivity is enhanced when grown at lower temperatures. We additionally find that the magnetoresistance of EuIn2As2 is negative even up to fields as high as 31T but while it is highly anisotropic at low fields, it becomes nearly isotropic at high magnetic fields above 5T. Overall, the transport characteristics of EuIn2As2 appear similar to those of chalcogenide topological insulators, motivating the development of devices to gate tune the Fermi energy to reveal topological features in quantum transport