A Comparison of Teaching Models in the West and in China

Models of teaching commonly used in the West and in China are analyzed and compared, using an analytical approach that systematically considers different aspects of the models. The purpose of the exploration is three-fold: (a) to create better understanding of both Chinese and Western models, for mutual insight and to strengthen the development of pedagogical theory building in China; (b) to guide a joint project between the Netherlands and China relative to the development computer-related learning resources for China; and (c) to contribute to better overall understanding of how instructional resources can be adapted for use in both Western and Chinese situations. The analysis provides a contribution for each of these goals

Global unique solvability of inhomogeneous Navier-Stokes equations with bounded density

In this paper, we prove the global existence and uniqueness of solution to d-dimensional (for $d=2,3$) incompressible inhomogeneous Navier-Stokes equations with initial density being bounded from above and below by some positive constants, and with initial velocity $u_0\in H^s(\R^2)$ for $s>0$ in 2-D, or $u_0\in H^1(\R^3)$ satisfying |u_0|_{L^2}|\na u_0|_{L^2} being sufficiently small in 3-D. This in particular improves the most recent well-posedness result in [10], which requires the initial velocity $u_0\in H^2(\R^d)$ for the local well-posedness result, and a smallness condition on the fluctuation of the initial density for the global well-posedness result

Sparse Recovery with Very Sparse Compressed Counting

Compressed sensing (sparse signal recovery) often encounters nonnegative data (e.g., images). Recently we developed the methodology of using (dense) Compressed Counting for recovering nonnegative K-sparse signals. In this paper, we adopt very sparse Compressed Counting for nonnegative signal recovery. Our design matrix is sampled from a maximally-skewed p-stable distribution (0<p<1), and we sparsify the design matrix so that on average (1-g)-fraction of the entries become zero. The idea is related to very sparse stable random projections (Li et al 2006 and Li 2007), the prior work for estimating summary statistics of the data. In our theoretical analysis, we show that, when p->0, it suffices to use M= K/(1-exp(-gK) log N measurements, so that all coordinates can be recovered in one scan of the coordinates. If g = 1 (i.e., dense design), then M = K log N. If g= 1/K or 2/K (i.e., very sparse design), then M = 1.58K log N or M = 1.16K log N. This means the design matrix can be indeed very sparse at only a minor inflation of the sample complexity. Interestingly, as p->1, the required number of measurements is essentially M = 2.7K log N, provided g= 1/K. It turns out that this result is a general worst-case bound