Stable Feature Selection from Brain sMRI

Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are applied to select the optimal features and achieve high diagnosis accuracy. The lasso, however, usually results in independent unstable features. Stability, a manifest of reproducibility of statistical results subject to reasonable perturbations to data and the model, is an important focus in statistics, especially in the analysis of high dimensional data. In this paper, we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of Alzheimer's disease. In addition to sparsity, our model incorporates two important pathological priors: the spatial cohesion of lesion voxels and the positive correlation between the features and the disease labels. To optimize the model, we propose an efficient algorithm by proving a novel link between total variation and fast network flow algorithms via conic duality. Experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state-of-the-arts

Analytical Studies on a Modified Nagel-Schreckenberg Model with the Fukui-Ishibashi Acceleration Rule

We propose and study a one-dimensional traffic flow cellular automaton model of high-speed vehicles with the Fukui-Ishibashi-type (FI) acceleration rule for all cars, and the Nagel-Schreckenberg-type (NS) stochastic delay mechanism. By using the car-oriented mean field theory, we obtain analytically the fundamental diagrams of the average speed and vehicle flux depending on the vehicle density and stochastic delay probability. Our theoretical results, which may contribute to the exact analytical theory of the NS model, are in excellent agreement with numerical simulations.Comment: 3 pages previous; now 4 pages 2 eps figure

Input-output-based genuine value added and genuine productivity in China\u27s industrial sectors (1995-2010)

The rapid growth of China\u27s economy has brought about huge losses of natural capital in the form of natural resource depletion and damages from carbon emissions. This paper recalculates value added, capital formation, capital stock, and related multifactor productivity in China\u27s industrial sectors by further developing the genuine savings method of the World Bank. The sector-level natural capital loss was calculated using China\u27s official input–output table and their extensions for tracing final consumers. The capital output elasticity in the productivity estimation was adjusted based on these tables. The results show that although the loss of natural capital in China\u27s industrial sectors in terms of value added has slowed, the impacts on their productivity during the past decades is still quite clear