161 research outputs found
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, , 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 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 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
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