Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm

Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computationally difficult to fit, particularly when the dimension of the data is large. To efficiently estimate the hub graphical models, we introduce a two-phase algorithm. The proposed algorithm first generates a good initial point via a dual alternating direction method of multipliers (ADMM), and then warm starts a semismooth Newton (SSN) based augmented Lagrangian method (ALM) to compute a solution that is accurate enough for practical tasks. The sparsity structure of the generalized Jacobian ensures that the algorithm can obtain a nice solution very efficiently. Comprehensive experiments on both synthetic data and real data show that it obviously outperforms the existing state-of-the-art algorithms. In particular, in some high dimensional tasks, it can save more than 70\% of the execution time, meanwhile still achieves a high-quality estimation.Comment: 28 pages,3 figure

Residential Spatial Differentiation Based on Urban Housing Types-An Empirical Study of Xiamen Island, China

Residential spatial differentiation, also called residential segregation, is a representation of the differentiation of social stratum in economic income, social status, education degree, lifestyle, and other aspects, based on an urban geographical space. In this paper, Xiamen Island is taken as example to calculate the dissimilarity index and the multi-group dissimilarity index at three scales (districts, sub-districts, and communities) by using the land area, population size, and green space area of different housing types. The characteristics of residential differentiation are analyzed. It is found that both spatial differentiation and multi-group spatial differentiation have significant scale effects. The smaller the scale of the spatial statistics unit, the larger the spatial differentiation and multi-group spatial differentiation. Significant differences are found in residential differentiation among different housing types. The residential differentiation is, not only demonstrated in land area and population size, but also in the resources of green space. More importantly, a balanced allocation of green space will help to reduce the degree of residential differentiation. With urban expansion and social-economic development, residential spatial differentiation will likely change. An understanding of residential differentiation is a guide for urban master planning and detailed regulatory planning. It will help to promote social harmonious development and urban sustainable development by the reasonable configuration of land and resources

Preparation of Biochar-Based Composites and Its Application in Remediation of Organic Polluted Environment

Biochar has the advantages of high carbon content, large specific surface area, well-developed pore structure and good adsorption performance. It is widely used in the fields of soil quality improvement, environmental pollution remediation, carbon sequestration and emission reduction. In view of the practical problems such as low volume density of biochar itself, difficulty in solid-liquid separation, and urgent need to further improve its long-term effectiveness and stability, the biochar composites with higher remediation efficiency for polluted environment were prepared by combining biochar with metal materials, photocatalysts and clay minerals, which attracted much attention. Through a systematic review of the preparation methods and environmental pollution control applications of biochar-based composite materials, and the removal mechanism of organic pollutants in the environment was analyzed, and the future research and development trend of biochar materials was put forward, so as to provide new ideas for the preparation and practical application of high-efficiency biochar composite materials

Effects of biochar immobilized microorganisms on the enzyme activity and remediation of petroleum hydrocarbon in contaminated soil

The high-temperature pyrolysis oxidation method was adopted to prepare reed biochar, and reed biochar was used as the immobilized material to load the dominant petroleum hydrocarbon degrading bacteria, and the effects of biochar immobilized microorganisms on the remediation effect and enzyme activity of petroleum hydrocarbon contaminated soil were studied. The results showed that after 40 days of remediation of petroleum hydrocarbon contaminated soil by immobilized microorganisms with reed biochar, the removal rate of petroleum hydrocarbon in soil reached 55.01%, which was significantly higher than that of single biochar (45.82%) and blank treatment group (24.83%). It was also found that biochar immobilized microorganisms could significantly improve the activities of dehydrogenase, catalase, urease and polyphenol oxidase in soil. The use of biochar immobilized microorganism technology can not only improve the remediation efficiency of petroleum hydrocarbon contaminated soil, but can also significantly increase the soil biological enzyme activity