Global Entropy Solutions to the Gas Flow in General Nozzle

We are concerned with the global existence of entropy solutions for the compressible Euler equations describing the gas flow in a nozzle with general cross-sectional area, for both isentropic and isothermal fluids. New viscosities are delicately designed to obtain the uniform bound of approximate solutions. The vanishing viscosity method and compensated compactness framework are used to prove the convergence of approximate solutions. Moreover, the entropy solutions for both cases are uniformly bounded independent of time. No smallness condition is assumed on initial data. The techniques developed here can be applied to compressible Euler equations with general source terms

Establishment of the model system between phytochemicals and gene expression profiles in Macrosclereid cells of Medicago truncatula

Macrosclereid cells, which are a layer in the seed coat of Medicago truncatula, accumulate large amounts of phytochemicals during their development. But little is known about the complex and dynamic changes during macrosclereid cell development. To characterize the phytochemicals and the related gene expression during the development of M. truncatula macrosclereid cells, a high performance liquid chromatography-mass spectrometry (HPLC-MS) assay and microarray study were conducted on transcriptome changes from macrosclereid cell during seed development. A total of 16 flavonoids by HPLC-MS and 4861 genes exhibited significant differences at transcript levels by microarray analysis were identified for macrosclerid cells at six different time points during seed development. 815 abiotic and biotic stress genes, 223 transcriptional factors (TFs), and 155 annotated transporter proteins exhibited differential expression during the development of macrosclereid cells. A total of 102 genes were identified as involved in flavonoid biosynthesis, phenypropanoid biosynthesis, and flavone and flavonol biosynthesis. We performed a weighted gene co-regulatory network (WGCNA) to analyze the gene-flavonoid association and rebuilt the gene regulatory network during macrosclereid cell development. Our studies revealed that macrosclereid cells are, beside as the first barrier of defense against diseases, an excellent model system to investigate the regulatory network that governs flavonoid biosynthesis

A Case of Severe Pleural Effusion and Pulmonary Dysfunction Associated with Occupational Exposure to Asphalt Tar Smoke is Reported

Asphalt and tar transportation personnel are often exposed to the polluted air environment of asphalt fumes, tar and diesel exhaust. This long-term occupational exposure can adversely affect lung function, causing fibrosis, pleural effusion, and inflammation. This paper reports a case of pleural effusion in a 35-year-old male who had been engaged in asphalt paving and transportation for 5 years. There was no occupational exposure protection during the working period. The patient had dyspnea, expectoration, and pleural effusion for more than 1 month. After admission, thoracic drainage and pleural cauterization dissection were performed, and the symptoms were relieved. However, pulmonary fibrosis and visceral pleural thickening are challenging to reverse, and patients still have pulmonary dysfunction and the risk of continuing to develop lung consolidation. Therefore, the personnel engaged in asphalt and tar transportation should be well-protected to reduce occupational exposure

Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization

In this paper, we consider a distributed online convex optimization problem over a time-varying multi-agent network. The goal of this network is to minimize a global loss function through local computation and communication with neighbors. To effectively handle the optimization problem with a high-dimensional and structural constraint set, we develop a distributed online multiple Frank-Wolfe algorithm to avoid the expensive computational cost of projection operation. The dynamic regret bounds are established as $\mathcal{O}(T^{1-\gamma}+H_T)$ with the linear oracle number $\mathcal{O} (T^{1+\gamma})$, which depends on the horizon (total iteration number) $T$, the function variation $H_T$, and the tuning parameter $0<\gamma<1$. In particular, when the prior knowledge of $H_T$ and $T$ is available, the bound can be enhanced to $\mathcal{O} (1+H_T)$. Moreover, we illustrate the significant advantages of the multiple iteration technique and reveal a trade-off between dynamic regret bound, computational cost, and communication cost. Finally, the performance of our algorithm is verified and compared through the distributed online ridge regression problems with two constraint sets

PCN: Point Completion Network

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.Comment: 3DV 2018 oral. Honorable mention for Best Paper awar

BiANE: Bipartite Attributed Network Embedding

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