Research of E-Business Innovative Training based on CDIO Educational Philosophy

CDIO is on behalf of Conceive, Design, Implement and Operate. The carrier of CDIO engineering education philosophy is a project which allows students to take the initiative, practical, organic link between the curriculums for learning. E-Business specialty has a property combination of management and engineering. Therefore, with CDIO educational philosophy, to promote E-Business specialty and industry associated, to improve innovation and practical ability for Management talent for Applied, is the current employment situation and development trend of higher education, and a useful attempt to improve teaching quality construction. This paper analyzes of the current status of E-Business specialty and the implementation feasibility of E-Business specialty reform based on CDIO education，gives a innovative training model based on CDIO in Chengdu University of Information Technology

Learning Accurate Performance Predictors for Ultrafast Automated Model Compression

In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.Comment: Accepted to IJC

Low Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse. In particular, the recent formulation of structure learning as a continuous optimization problem proved to have considerable advantages over the traditional combinatorial formulation, but the performance of the resulting algorithms is still wanting when the target graph is relatively large and dense. In this paper we propose a novel approach to mitigate this problem, by exploiting a low rank assumption regarding the (weighted) adjacency matrix of a DAG causal model. We establish several useful results relating interpretable graphical conditions to the low rank assumption, and show how to adapt existing methods for causal structure learning to take advantage of this assumption. We also provide empirical evidence for the utility of our low rank algorithms, especially on graphs that are not sparse. Not only do they outperform state-of-the-art algorithms when the low rank condition is satisfied, the performance on randomly generated scale-free graphs is also very competitive even though the true ranks may not be as low as is assumed

Identifying risk factors affecting exercise behavior among overweight or obese individuals in China

BackgroundThe disease burden caused by obesity has increased significantly in China. Less than 30% of those who are obese meet the weekly physical activity standards recommended by the WHO. Risk factors that influence exercise behavior in people with obesity remain unclear.MethodsBased on the survey from the Chinese General Social Survey program (CGSS) in 2017, 3,331 subjects were identified and enrolled in the univariate and multiple probit regression models. We aimed to identify the association between SRH and the exercise behavior of obese people and further explore the influencing factors of active physical activity in this group of people.ResultsThe proportion of active physical activity in obese people was 25%. Groups with better SRH, higher education and income were more likely to participate in sports. Obese people who lived in rural areas, were unmarried or divorced, or fell within the age range of 35–40 had a significantly lower percentage of engagement in active physical activity.ConclusionsThe proportion of people with obesity who meet the WHO recommendation for physical activity in China is not ideal. Health promotion programs for those who are obese need to be further strengthened and targeted, especially for rural areas, low-income families, and middle-aged obese people

Water-dispersible and quasi-superparamagnetic magnetite nanoparticles prepared in a weakly basic solution at the low synthetic temperature

Magnetite nanoparticles were prepared in a weakly basic solution at the low reaction temperature by the co-precipitation method. As a comparison, the oxidative precipitation method was also applied in this study. The structure, morphology, and other properties of the obtained samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), transmission electron microscope (TEM), and thermogravimetric analysis (TGA). The above characterization data indicate that small size and narrow size distribution are found for magnetite nanoparticles prepared by the co-precipitation method. Further magnetic property and Zeta potential results illuminate that magnetite nanoparticles prepared by this method display a quasi-superparamagnetic property and a good dispersion in the aqueous solution. Based on the investigation results, the magnetite nanoparticles with a quasi-superparamagnetic property and a fine dispersion can be facilely prepared in a weakly basic solution at the low reaction temperature by the co-precipitation method