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

Additive Property of Drazin Invertibility of Elements

In this article, we investigate additive properties of the Drazin inverse of elements in rings and algebras over an arbitrary field. Under the weakly commutative condition of $ab = \lambda ba$, we show that $a-b$ is Drazin invertible if and only if $aa^{D}(a-b)bb^{D}$ is Drazin invertible. Next, we give explicit representations of $(a+b)^{D}$, as a function of $a, b, a^{D}$ and $b^{D}$, under the conditions $a^{3}b = ba$ and $b^{3}a = ab$.Comment: 17 page

Laser-driven electron and spin-state quantum dynamics in transition metal complexes

Recent advances in attosecond spectroscopy open the door to the understanding correlated motion of valence and core electrons in molecular systems. In this thesis, the density matrix-based TD-RASCI method is used to study the electron and spin-flip dynamics. The dephasing effect of nuclear vibrations is incorporated implicitly making use of an electronic system/vibrational bath partitioning. We theoretically addressed the ultrafast spin-flip dynamics in a transition metal complex which is triggered by isolated sub-fs soft-X-ray pulses as well as X-ray pulse trains

A review of foreign research on the application of virtual reality technology in tourism

As an emerging technology in recent years, virtual reality technology has penetrated people’s fields of material production and life. Nowadays, with the development of technology, the impact of virtual reality technology on the tourism industry is becoming more and more obvious. This paper sorts out and studies the literature of virtual reality and virtual tourism in the past 50 years, discusses the definition of virtual reality and the development of virtual reality technology, sorts out the virtual tourism literature in chronological order, and summarizes the four development stages of virtual tourism. Finally, the existing developments and shortcomings are summarized, and some suggestions for future development are proposed

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