325 research outputs found
Bridging Consumersâ Self-Brand Distance through Virtual-Reality: Perspective from Presence Experiences
Virtual-reality (VR) technology seems to be an efficient tool for consumer-brand relationship management since it could affect individualsâ psychological distance by enhancing their presence experiences. However, the effects of VR on individualsâ psychological distance are inconsistent. Based on the customer experience framework and construal level theory, these inconsistent effects could be attributed to the two aspects, namely, internal components of presence experience (i.e., immersive presence and realistic presence) and different impacts of vividness modes (i.e., modeling mode and panoramic mode). To address the above research gap, this study plans to investigate the relationships among consumersâ self-brand distance, presence experiences, vividness modes, and interactivity. An experiment will be conducted to collect empirical data in the VR-simulated shopping environment. The analysis of covariance could be used to examine the hypotheses. This research could offer implications to the literature and practice related to VR shopping and consumer-brand relationship management
Incorporating Artificial Intelligence into Student Academic Writing in Higher Education: The Use of Wordtune by Chinese international students
A Research on Tourism E-Business of Shanxi
E-business has become a new growth point of current economic development in the world. The natural adaptability between tourism and E-business gives birth to tourism E-business. The tourism resources in Shanxi are unique, yet its tourism development level and the abundant resources are not harmonious. To transform its resource advantage into economic advantage, developing tourism E-business is very urgent and necessary. In this article, through quantitative analysis and qualitative analysis methods, we find out the problems existing in the development of tourism E-business in Shanxi, and put forward corresponding development measures, especially the mobile E-tourism based on 3G technology, which is a new pattern and will become the development direction of Shanxiâs tourism E-business in the future. The research aims at providing practical references for the tourism management departments and enterprises in Shanxi to enhance its competitiveness and promote its sustainable development
How Online Extended Reality (XR) Promotes Consumer Offline Engagement
Using extended-reality (XR) simulation to replicate physical surroundings has become increasingly prevalent in engaging online consumers with offline businesses. However, the efficacy of this XR technology remains ambiguous. To justify the huge investments in XR-related technologies, we investigate the impacts of extended surroundings on consumersâ offline engagement with associated businesses. Specifically, we utilize a natural experimental design on a leading housing platform that applies XR simulation to present the surrounding environment of housing estates. By combining propensity score matching and difference-in-differences, our findings indicate that extended surroundings increase consumer offline engagement outcomes, particularly word-of-mouth volume, and valence. Furthermore, we examine the heterogeneous effects moderated by three business characteristics. To our knowledge, this is the first to examine the impacts of XR simulation of extended surroundings. Therefore, this research offers significant implications for the literature and practice related to XR and omnichannel marketing
S2SNet: A Pretrained Neural Network for Superconductivity Discovery
Superconductivity allows electrical current to flow without any energy loss,
and thus making solids superconducting is a grand goal of physics, material
science, and electrical engineering. More than 16 Nobel Laureates have been
awarded for their contribution to superconductivity research. Superconductors
are valuable for sustainable development goals (SDGs), such as climate change
mitigation, affordable and clean energy, industry, innovation and
infrastructure, and so on. However, a unified physics theory explaining all
superconductivity mechanism is still unknown. It is believed that
superconductivity is microscopically due to not only molecular compositions but
also the geometric crystal structure. Hence a new dataset, S2S, containing both
crystal structures and superconducting critical temperature, is built upon
SuperCon and Material Project. Based on this new dataset, we propose a novel
model, S2SNet, which utilizes the attention mechanism for superconductivity
prediction. To overcome the shortage of data, S2SNet is pre-trained on the
whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet
makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under
Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to
predict superconductivity with only information of crystal structures. This
work is beneficial to superconductivity discovery and further SDGs. Code and
datasets are available in https://github.com/zjuKeLiu/S2SNetComment: Accepted to IJCAI 202
Linguistic experience acquisition for novel stimuli selectively activates the neural network of the visual word form area
The human ventral visual cortex is functionally organized into different domains that sensitively respond to different categories, such as words and objects. There is heated debate over what principle constrains the locations of those domains. Taking the visual word form area (VWFA) as an example, we tested whether the word preference in this area originates from the bottom-up processes related to word shape (the shape hypothesis) or top-down connectivity of higher-order language regions (the connectivity hypothesis). We trained subjects to associate identical, meaningless, non-word-like figures with high-level features of either words or objects. We found that the word-feature learning for the figures elicited the neural activation change in the VWFA, and learning performance effectively predicted the activation strength of this area after learning. Word-learning effects were also observed in other language areas (i.e., the left posterior superior temporal gyrus, postcentral gyrus, and supplementary motor area), with increased functional connectivity between the VWFA and the language regions. In contrast, object-feature learning was not associated with obvious activation changes in the language regions. These results indicate that high-level language features of stimuli can modulate the activation of the VWFA, providing supportive evidence for the connectivity hypothesis of words processing in the ventral occipitotemporal cortex
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Although current deep learning-based methods have gained promising
performance in the blind single image super-resolution (SISR) task, most of
them mainly focus on heuristically constructing diverse network architectures
and put less emphasis on the explicit embedding of the physical generation
mechanism between blur kernels and high-resolution (HR) images. To alleviate
this issue, we propose a model-driven deep neural network, called KXNet, for
blind SISR. Specifically, to solve the classical SISR model, we propose a
simple-yet-effective iterative algorithm. Then by unfolding the involved
iterative steps into the corresponding network module, we naturally construct
the KXNet. The main specificity of the proposed KXNet is that the entire
learning process is fully and explicitly integrated with the inherent physical
mechanism underlying this SISR task. Thus, the learned blur kernel has clear
physical patterns and the mutually iterative process between blur kernel and HR
image can soundly guide the KXNet to be evolved in the right direction.
Extensive experiments on synthetic and real data finely demonstrate the
superior accuracy and generality of our method beyond the current
representative state-of-the-art blind SISR methods. Code is available at:
https://github.com/jiahong-fu/KXNet.Comment: Accepted by ECCV202
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