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The “Just Accepted” manuscripts have gone through the peer-review processes and been accepted for publication. The “Just Accepted” manuscripts are uploaded to the JUSTC website after being polished in a timely fashion, prior to technical editing and formatting as well as author proofing. “Just Accepted” is a free service that allows authors to make their results immediately available to the research community upon the acceptance of their manuscripts. Once the manuscripts have been technically edited and formatted, they will be transferred to the “ASAP Articles” website from the “Just Accepted” website. Please be advised that technical editing and formatting may introduce minor changes to the manuscripts which may affect their contents, and all legal disclaimers that apply to JUSTC pertain. In no event shall JUSTC be held responsible for errors or consequences arising from the use of any information contained in the “Just Accepted” manuscripts. To cite the “Just Accepted” manuscripts, please use their Digital Object Identifiers (e.g., doi: 10.52396/JUSTC-202x-0xxx), which remain identical for all formats of their publication.
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Alternative modified Cholesky decomposition of the precision matrix of longitudinal data
Fei Lu, Yuting Zeng
, Available online  , doi: 10.52396/JUSTC-2023-0127
Article Views:64 HTML (64) Full article PDF
Abstract:
The correlation matrix might be of scientific interest for longitudinal data. However, few studies have focused on both robust estimation of the correlation matrix against model misspecification and robustness to outliers in the data, when the precision matrix possesses a typical structure. In this paper, we propose an alternative modified Cholesky decomposition (AMCD) for the precision matrix of longitudinal data, which results in robust estimation of the correlation matrix against model misspecification of the innovation variances. A joint mean-covariance model with multivariate normal distribution and AMCD is established, the quasi-Fisher scoring algorithm is developed, and the maximum likelihood estimators are proven to be consistent and asymptotically normally distributed. Furthermore, a double-robust joint modeling approach with multivariate Laplace distribution and AMCD is established, and the quasi-Newton algorithm for maximum likelihood estimation is developed. The simulation studies and real data analysis demonstrate the effectiveness of the proposed AMCD method.
The impact of external search, tie strength, and absorptive capacity on new product development performance
Huijun Yang, Wei Wang
, Available online  , doi: 10.52396/JUSTC-2022-0170
Article Views:138 HTML (138) Full article PDF
Abstract:
This study examines the influences of external search breadth and depth on new product development performance from a knowledge-based view. In particular, we introduce tie strength and absorptive capacity as two contextual variables in this study. The findings from data on 281 Chinese firms indicate that search breadth facilitates new product creativity, whereas search depth facilitates development speed. Tie strength weakens the relationships between search breadth and new product creativity but strengthens the relationship between search depth and development speed. Furthermore, the synergistic effect of tie strength and absorptive capacity negatively moderates the relationship between search breadth and new product creativity but positively moderates the relationship between search depth and development speed.
Estimation of peer pressure in dynamic homogeneous social networks
Jie Liu, Pengyi Wang, Jiayang Zhao, Yu Dong
, Available online  , doi: 10.52396/JUSTC-2023-0035
Article Views:288 HTML (288) Full article PDF
Abstract:
Social interaction with peer pressure is widely studied in social network analysis. Game theory can be utilized to model dynamic social interaction and one class of game network models assumes that peopleos decision payoff functions hinge on individual covariates and the choices of their friends. However, peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model. For this reason, we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks. The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios. To estimate peer pressure in the model, we first present two algorithms based on the initialize expand merge method and the polynomial-time two-stage method to estimate homogeneity parameters. Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure. Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error. We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.
Physically plausible and conservative solutions to Navier-Stokes equations using Physics-Informed CNNs
Jianfeng Li, Liangying Zhou, Jingwei Sun, Guangzhong Sun
, Available online  , doi: 10.52396/JUSTC-2022-0174
Article Views:151 HTML (151) Full article PDF
Abstract:
Physics-informed Neural Network (PINN) is an emerging approach for efficiently solving partial differential equations (PDEs) using neural networks. Physics-informed Convolutional Neural Network (PICNN), a variant of PINN enhanced by convolutional neural networks (CNNs), has achieved better results on a series of PDEs since the parameter-sharing property of CNNs is effective to learn spatial dependencies. However, applying existing PICNN-based methods to solve Navier-Stokes equations can generate oscillating predictions, which are inconsistent with the laws of physics and the conservation properties. To address this issue, we propose a novel method that combines PICNN with the finite volume method to obtain physically plausible and conservative solutions to Navier-Stokes equations. We derive the second-order upwind difference scheme of Navier-Stokes equations using the finite volume method. Then we use the derived scheme to calculate the partial derivatives and construct the physics-informed loss function. The proposed method is assessed by experiments on steady-state Navier-Stokes equations under different scenarios, including convective heat transfer, lid-driven cavity flow, etc. The experimental results demonstrate that our method can effectively improve the plausibility and the accuracy of the predicted solutions from PICNN.
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth
Guangkai Xu, Feng Zhao
, Available online  , doi: 10.52396/JUSTC-2023-0061
Article Views:228 HTML (228) Full article PDF
Abstract:
Monocular depth estimation methods have achieved excellent robustness on diverse scenes, usually by predicting affine-invariant depth, up to an unknown scale and shift, rather than metric depth in that it is much easier to collect large-scale affine-invariant depth training data. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction, the unknown scale and shift residing in per-frame prediction may cause the predicted depth to be inconsistent. To tackle this problem, we propose a locally weighted linear regression method to recover the scale and shift map with very sparse anchor points, which ensures the consistency along consecutive frames. Extensive experiments show that our method can drop the Rel error of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks. Besides, we merge 6.3 million RGBD images to train robust depth models. By locally recovering scale and shift, our produced ResNet50-backbone model even outperforms the state-of-the-art DPT ViT-Large model. Combined with geometry-based reconstruction methods, we formulate a new dense 3D scene reconstruction pipeline, which benefits from both the scale consistency of sparse points and the robustness of monocular methods. By performing simple per-frame prediction over a video, the accurate 3D scene geometry can be recovered.
Asymmetric connectedness between China’s carbon and energy markets based on TVP-VAR model
Yu Dong, Xue Yuan, Yuting Wei
, Available online  , doi: 10.52396/JUSTC-2022-0144
Article Views:175 HTML (175) Full article PDF
Abstract:
An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management. In this paper, we examine the asymmetric propagation of return spillovers between carbon and energy markets at the sector level. To achieve that, we improve the Diebold-Yilmaz index by a time-varying vector autoregressive (TVP-VAR) model. In a unified network, our daily dataset includes the closing prices of the Hubei carbon market, Shenzhen carbon market, coal futures, and energy stock index. The findings reveal that both the Hubei and Shenzhen pilots typically generate net information spillovers on energy futures. In connection with energy stocks, the Hubei carbon market acts as a net receiver, while the Shenzhen carbon market is a net transmitter. Compared with the Hubei pilot, the Shenzhen pilot is more tightly connected to the energy markets. Furthermore, the spillovers of the carbon markets exhibit significant asymmetry. In most cases, they have more substantial impacts on the energy markets when the prices of emission allowances rise. The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.
Live-streaming Selling Strategies for Competitive Firms
Quan Du, Zhixin Chen, Jie Wu, Xiang Ji
, Available online  , doi: 10.52396/JUSTC-2022-0171
Article Views:388 HTML (388) Full article PDF
Abstract:
The booming live-streaming commerce has significantly changed the traditional e-commerce model, thus attracting much attention from both industry and academia. In recent years, an increasing number of scholars have applied analytical models to explore live-streaming strategies for firms in different scenarios. However, the previous literature mainly considers monopolists, while in the real world, competition is not rare. To fill this gap between the literature and practical observations, this paper applies a game theoretical model to study live-streaming adoption and pricing strategy for firms under competitive environments. The results show that, for competitive firms, the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect. Additionally, compared to the case in which no firm adopts live-streaming, competitive firms do not always benefit from the adoption of live-streaming selling. The paper also shows that competition plays a negative role in inducing a firm to adopt live-streaming.
LightAD: Accelerating AutoDebias with Adaptive Sampling
Yang Qiu, Hande Dong, Jiawei Chen, Xiangnan He
, Available online  , doi: 10.52396/JUSTC-2022-0100
Article Views:288 HTML (288) Full article PDF
Abstract:
In recommendation systems, the bias issue is ubiquitous as the data is collected from user behaviors rather than reasonable experiments. AutoDebias, which resorts to meta learning to find appropriate debiasing configurations, i.e., pseudo-labels and confidence weights for all user-item pairs, has been demonstrated as a generic and effective solution in tackling various biases. Nevertheless, setting pseudo-labels and weights for every user-item pair can be a time-consuming process. Therefore, AutoDebias suffers from a huge computational cost, making it less applicable to real cases. Although stochastic gradient descent with a uniform sampler can be applied to accelerate training, it would significantly deteriorate model convergence and stability. To overcome this problem, we propose LightAutoDebias (short as LightAD), which equips AutoDebias with a specialized importance sampling strategy. The sampler can adaptively and dynamically draw informative training instances, which brings provably better convergence and stability than the standard uniform sampler. Extensive experiments on three benchmark datasets validate that our LightAD accelerates AutoDebias by several magnitudes while maintaining almost equal accuracy.
The influence of different types of satisfaction on loyalty on C2C online shopping platform: From the perspective of sellers and the platform
Yanan Lu, Qian Huang, Yuting Wang
, Available online  , doi: 10.52396/JUSTC-2022-0128
Article Views:282 HTML (282) Full article PDF
Abstract:
With the rise and development of major types of platforms, the competition for resources has become extremely fierce, and the market share of C2C platforms has been seriously threatened by the loss of resources. Therefore, building and maintaining buyers’ satisfaction and loyalty to C2C platforms is critical to the survival and sustainability of C2C platforms in China. However, the current knowledge on how platform satisfaction and loyalty are constructed in the C2C e-commerce environment is incomplete. In this study, seller-based satisfaction and platform-based satisfaction are constructed separately. We further distinguish seller-based transaction satisfaction into economic and social satisfaction and explore their antecedents and consequences. To test our research hypotheses, we conduct a survey and collect data from a real online market (Taobao website). The results show that seller-based transaction satisfaction positively affects platform-based overall satisfaction and loyalty; perceived product quality, perceived assurance, and perceived price fairness all have a significant effect on economic satisfaction, whereas perceived relationship quality and perceived empathy significantly influence social satisfaction. These findings help us understand the literature related to customer satisfaction in the context of C2C in China and provide inspiration for online sellers and platforms.
A hybrid trade-old-for-new and trade-old-for-remanufactured supply chain with carbon tax
Yu Dong, Wuqing Liao
, Available online  
Article Views:151 HTML (151) Full article PDF
Abstract:
Facing serious environmental problems, governments and manufacturers are taking action to reduce carbon emissions. Among these endeavors, carbon tax policy are widely adopted by governments, trade-old-for-new (TON) and trade-old-for- remanufactured (TOR) are offered by manufacturers and subsidized by governments. To explore the effects of remanufacturer competition and carbon tax on the manufacturer’s TON and TOR decisions and the environment, we formulate three profit maximization models and present some theoretical and numerical analyses. The results show that, under the remanufacturer competition and carbon tax, the manufacturer’s optimal price and production decisions mainly depend on consumer willingness and carbon tax rate. A higher consumer willingness to manufacturer’s remanufactured products will decrease the demand for the manufacturer’s TON, but it always increases the demand foe the manufacturer’s TOR. A higher consumer willingness to remanufacturer’s products will not affect the demand for the manufacturer’s TON; however, it will reduce the demand for manufacturer’s TOR. In addition, we find that a higher carbon tax rate always reduces total carbon emission reduction, and it may increase the manufacturer’s profit due to the increase in TOR demand.
Investigating the mechanisms driving the seasonal variations in surface PM2.5 concentrations over East Africa with the WRF-Chem model
Nkurunziza Fabien Idrissa, Chun Zhao, Qiuyan Du, Shengfu Lin, Kagabo Safari Abdou, Weichen Liu, Xiaodong Wang
, Available online  , doi: 10.52396/JUSTC-2022-0142
Article Views:323 HTML (323) Full article PDF
Abstract:
Most previous studies on surface PM2.5 concentrations over East Africa focused on short-term in situ observations. In this study, the WRF-Chem model combined with in situ observations is used to investigate the seasonal variation in surface PM2.5 concentrations over East Africa. WRF-Chem simulations are conducted from April to September 2017. Generally, the simulated AOD is consistent with satellite retrieval throughout the period, and the simulations depicted the seasonal variation in PM2.5 concentrations from April to September but underestimated the concentrations throughout the period due to the uncertainties in local and regional emissions over the region. The composition analysis of surface PM2.5 concentrations revealed that the dominant components were OIN and OC, accounting for 80% and 15% of the total concentrations, respectively, and drove the seasonal variation. The analysis of contributions from multiple physical and chemical processes indicated that the seasonal variation in surface PM2.5 concentrations was controlled by the variation in transport processes, PBL mixing, and dry and wet deposition. The variation in PM2.5 concentrations from May to July is due to wind direction changes that control the transported biomass burning aerosols from southern Africa, enhanced turbulent mixing of transported aerosols at the upper level to the surface and decreased wet deposition from decreased rainfall from May to July.

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