Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl

REGULARIZED MULTIQUADRIC METHOD FOR SOLVING INVERSE BOUNDARY VALUE PROBLEMS

In this paper, we develop a regularized multiquadric method, which is also a non-iterative numerical method, for solving inverse boundary value problems governed by Laplace equation. The well-known ill-posed Cauchy problem is considered, we assume that the boundary conditions are given only on part of the physical boundary of the solution domain, we have to reconstruct the solution and its normal derivative on the rest un-accessible part of the physical boundary. During the whole solution process, we use the multiquadric and the regularization method to construct a regularized multiquadric method. Numerical experiments are given to demonstrate the effectiveness and efficiency of the proposed method

Association of Nurse-Physician Teamwork and Hospital Surgical Patient Mortality

Interest in the relationship between nurses and physicians has been increasing over the past few decades. Teamwork between the two disciplines was first studied in the 1970s and interest surged again in the 1980s, when evidence suggested that better teamwork saved more lives. This study presents a cross-sectional analysis linking 2006-2007 nurse survey data, hospital administrative data, and patient discharge data. The study sample comprised of 665 hospitals, 1,321,904 patients, and 29,391 nurses. Logistic regression models were used to assess the association between higher levels of nurse-physician teamwork and patient outcomes (30-day mortality and failure-to-rescue). Regression models were also used to examine whether any associations between nurse-physician teamwork and patient outcomes depends upon the level of other modifiable characteristics of hospital nursing (nurse staffing and education levels) in acute hospital settings. Final analysis revealed decreased odds of both 30-day mortality (OR = 0.943, 95% CI 0.930,0.958) and failure-to-rescue (OR = 0.939, 95% CI 0.925, 0.953) for surgical patients cared for in hospitals with better nurse reported nurse-physician teamwork, adjusting for hospital structural characteristics and patient characteristics. In addition, there was a significant interaction between nurse staffing and nurse-physician teamwork on surgical patient 30-day mortality, and failure-to-rescue rates. There was also a significant interaction between nurse education and nurse-physician teamwork on surgical patient 30-day mortality, and failure-to-rescue rates. Our analysis found a trend of decrease in odds of death and failure-to-rescue for hospitals with both higher nurse-physician teamwork scores and lower patient-per-nurse ratios. Similarly, there is a trend of a decrease in odds of death and failure-to-rescue in hospitals with higher nurse-physician teamwork scores and higher proportion of BSN educated nurses. In order for initiatives to improve interprofessional teamwork to have greater impact on patient outcomes, nurse staffing and nurse education need to be at sufficient levels