The Influencing Factors of Online Consumers’ Return Satisfaction

With the development of the Internet, the transactions of the commodities turned out to be digitized, however, the non face-to-face transactions led to one main problem that commodities possibly do not meet the expectation of consumers’, and will then inevitably result the return problem. How to improve the consumers’ return experience and build their trust has become the focus of business considerations. Based on the research model of the influencing factors of online consumers’ return satisfaction, the author studied 1002 after-sales review samples. Through compiling and labeling the sample data, the author quantifies the consumers’ emotion by emotion analysis and then analysis by multiple linear regressions, the paper provides a base for businesses to improve the quality of return service by validating and explaining the research model

How Does The Business Model Affect The Corporation Performance?

In recent years, the business model has become more and more concerned, and both the academic and business circles are aware of its importance. This paper analyzes and classifies the business model of e-commerce enterprises, as well as refines the e-commerce enterprise performance index. In this paper, the data of 22 e-commerce companies listed in the United States are collected. The classification indicators of e-commerce business model include target market, business pattern and asset structure. Through the combination of three categories of indicators, the selected 22 companies are divided into 10 categories. And the performance of e-commerce enterprises is measured by profitability indicators, operating efficiency indicators and growth potential indicators. Through the one-way ANOVA, it is found that the business model of electronic business company has caused the difference of business performance to some extent, and the influence of different dimensions of performance is also diverse

Mining brain imaging and genetics data via structured sparse learning

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies

Progress in Polygenic Composite Scores in Alzheimer’s and other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies

The Cycle Spinning-based Sharp Frequency Localized Contourlet Transform for Image Denoising

Prof.YAN, as corresponding author, is advisor of Mr.Xiaobo QU.Contourlet transform provides flexible number of directions and captures the intrinsic geometrical structure of images. The efficient directional filter banks with low redundancy of contourlet are very attractive for image processing. However, non-ideal filters are used in the original contourlet transform, especially when combined with laplacian pyramid, which results in pseudo-Gibbs phenomena around singularities for image denoising. Sharp frequency localized contourlet transform (SFLCT) is a new construction contourlet to overcome this drawback by replacing the laplacian pyramid with a new multiscale decomposition which significantly improve the denoising performance than the original form. Unfortunately, the downsampling of SFLCT makes it lack translation invariance. Thus, we employ a cycle spinning (CS) method to improve the denoising performance of SFLCT, named as cycle spinning based SFLCT (CS-SFLCT), by averaging out the translation dependence. Experimental results demonstrate that the proposed CS-SFLCT outperforms SFLCT, contourlet and cycle spinning-based contourlet for denoising in terms of PSNR and in visual effects.This paper is supported by Navigation Science Foundation of China (No.05F07001) and National Natural Science Foundation of China (No.60472081)

Post-Quantum κ\kappa-to-1 Trapdoor Claw-free Functions from Extrapolated Dihedral Cosets

\emph{Noisy trapdoor claw-free function} (NTCF) as a powerful post-quantum cryptographic tool can efficiently constrain actions of untrusted quantum devices. However, the original NTCF is essentially \emph{2-to-1} one-way function (NTCF$^1_2$). In this work, we attempt to further extend the NTCF$^1_2$ to achieve \emph{many-to-one} trapdoor claw-free functions with polynomial bounded preimage size. Specifically, we focus on a significant extrapolation of NTCF$^1_2$ by drawing on extrapolated dihedral cosets, thereby giving a model of NTCF$^1_{\kappa}$ where $\kappa$ is a polynomial integer. Then, we present an efficient construction of NTCF$^1_{\kappa}$ assuming \emph{quantum hardness of the learning with errors (LWE)} problem. We point out that NTCF can be used to bridge the LWE and the dihedral coset problem (DCP). By leveraging NTCF$^1_2$ (resp. NTCF$^1_{\kappa}$), our work reveals a new quantum reduction path from the LWE problem to the DCP (resp. extrapolated DCP). Finally, we demonstrate the NTCF$^1_{\kappa}$ can naturally be reduced to the NTCF$^1_2$, thereby achieving the same application for proving the quantumness.Comment: 34 pages, 7 figure

Learning Trajectories are Generalization Indicators

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective allows for a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information. Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental findings reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities