Deep Multiple Description Coding by Learning Scalar Quantization

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing datasets for "Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning" can be found in the website of https://github.com/mdcnn/Deep-Multiple-Description-Codin

Cascaded Reconstruction Network for Compressive image sensing

The theory of compressed sensing (CS) has been successfully applied to image compression in the past few years, whose traditional iterative reconstruction algorithm is time-consuming. However, it has been reported deep learning-based CS reconstruction algorithms could greatly reduce the computational complexity. In this paper, we propose two efficient structures of cascaded reconstruction networks corresponding to two different sampling methods in CS process. The first reconstruction network is a compatibly sampling reconstruction network (CSRNet), which recovers an image from its compressively sensed measurement sampled by a traditional random matrix. In CSRNet, deep reconstruction network module obtains an initial image with acceptable quality, which can be further improved by residual network module based on convolutional neural network. The second reconstruction network is adaptively sampling reconstruction network (ASRNet), by matching automatically sampling module with corresponding residual reconstruction module. The experimental results have shown that the proposed two reconstruction networks outperform several state-of-the-art compressive sensing reconstruction algorithms. Meanwhile, the proposed ASRNet can achieve more than 1 dB gain, as compared with the CSRNet.Comment: 17 pages,16 figure

Application of Metabolomics for the Diagnosis and Traditional Chinese Medicine Syndrome Differentiation of Chronic Heart Failure

Chronic heart failure (CHF) was characterized by the failure of enough blood supply from the heart to meet the body’s metabolic demands, and the prevalence of CHF continuously increases globally. The personalized diagnosis of Traditional Chinese Medicine (TCM) classifies CHF into several different syndrome types, and integrating Western and TCM to treat CHF has proved a validated therapeutic approach. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers that could assist diagnosis, provide therapy guidance, and evaluate response to therapy for individualized intervention of CHF. Thus, in this review, particular attention will be paid to the past successes in applications of state-of-the-art technology on metabolomics to contribute to biomarker discovery in CHF research

Online Retailing Channel Addition: Risk Alleviation or Risk Maker?

The retailing industry traditionally considers the optimal products selection and pricing problem, a complex and challenging one, from marketing and consumer behavior\u27s perspectives. In this study, we take a risk perspective and offer an alternative solution to tackling the problem, echoing the most recent literature that looks at non-risk aspects, such as expected consumer preference, market size and predicted profitability. Adopting a mean-variance framework, our approach explicitly takes into account the interconnectedness of retail products and their impact on risk at the portfolio (retailer) level. Extending the analysis to multiple-channel decisions, our results suggest that the introduction of a new retailing channel (e.g. online shops) can reduce the portfolio risk, whereas a lack of synergy between the new channel and the existing ones may lead to a negative impact on the overall performance. We also provide managerial implications on several conditions when retailers are more economically inclined to introduce more retail channels. Interestingly, our model indicates that larger retailers are less likely to expand their online platform