The Impact of Big Data on Supply Chain Resilience: the Moderating Effect of Supply Chain Complexity

Big data represents a new era in data exploration. Less is known on how big data impact on supply chain resilience. This paper explores the relationship between big data and supply chain resilience with considering the mediating role of supply chain visibility and the moderating role of supply chain complexity. Based on data obtained from Chinese manufacturing firms, the analysis shows that there is a direct relationship between big data and supply chain resilience. Big data also enhances supply chain resilience by improving visibility. However, contrary to the hypothesis supply chain complexity moderate the relationship in a negative direction

O(\alpha_s) QCD Corrections to Spin Correlations in e−e+→ttˉe^- e^+ \to t \bar t process at the NLC

Using a Generic spin basis, we present a general formalism of one-loop radiative corrections to the spin correlations in the top quark pair production at the Next Linear Collider, and calculate the O(\alpha_s) QCD corrections under the soft gluon approximation. We find that: (a) in Off-diagonal basis, the $O(\alpha_s)$ QCD corrections to $e_L^- e^+$ ($e_R^- e^+$) scattering process increase the differential cross sections of the dominant spin component $t_{\uparrow}\bar{t}_{\downarrow}$ ($t_{\downarrow}\bar{t}_{\uparrow}$) by $\sim 30$ and $\sim (0.1$ depending on the scattering angle for $\sqrt{s}=400 GeV$ and 1 TeV, respectively; (b) in {Off-diagonal basis} (Helicity basis), the dominant spin component makes up 99.8% ($\sim 53$) of the total cross section at both tree and one-loop level for $\sqrt{s}=400 GeV$, and the Off-diagonal basis therefore remains to be the optimal spin basis after the inclusion of $O(\alpha_s)$ QCD corrections.Comment: 12 pages, 4 figures, revised version (a few print mistakes are corrected, some numerical results are modified, and Fig.4 is added

Component SPD Matrices: A lower-dimensional discriminative data descriptor for image set classification

In the domain of pattern recognition, using the SPD (Symmetric Positive Definite) matrices to represent data and taking the metrics of resulting Riemannian manifold into account have been widely used for the task of image set classification. In this paper, we propose a new data representation framework for image sets named CSPD (Component Symmetric Positive Definite). Firstly, we obtain sub-image sets by dividing the image set into square blocks with the same size, and use traditional SPD model to describe them. Then, we use the results of the Riemannian kernel on SPD matrices as similarities of corresponding sub-image sets. Finally, the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets, and CSPDi,j denotes the similarity between i-th sub-image set and j-th sub-image set. Here, the Riemannian kernel is shown to satisfy the Mercer's theorem, so our proposed CSPD matrix is symmetric and positive definite and also lies on a Riemannian manifold. On three benchmark datasets, experimental results show that CSPD is a lower-dimensional and more discriminative data descriptor for the task of image set classification.Comment: 8 pages,5 figures, Computational Visual Media, 201

Factors of Successful E-tailing in China’s Retail Industry: A Case Study

This paper aims to investigate the factors of successful e-tailing in China’s retail industry. A single case study was undertaken in one of the top retailers in China. Both qualitative and quantitative data were collected, including in-depth interviews and focus group interviews with key personnel in the organisation, and questionnaire survey with randomly selected customers of the retailer. A comprehensive combined model of success factors of e-tailing was developed and is presented in detail. Some unique and interesting factors associated Chinese e-tailing have been identified; and some useful practicable suggestions are also provided for Chinese retailers or any businesses who are embarking on e-tailing in China or/and planning to enter the China’s retail market