Code-switching and teaching Chinese as a second language

This book aims to explore the nature of code-switching. The purpose is to find out how this works and thereby inform language-teaching strategies. It focuses on Chinese / English bilinguals with special emphasis on younger students living in two linguistic worlds (Chinese and English). The book examines code-switching in relation to several aspects: grammatical structures, tonal facilitation, contextual factors, speakers' social background aspects and their participation in school language programs.

A new urban planning code's impact on walking: The residential environments project

Objectives. We examined whether people moving into a housing development designed according to a state government livable neighborhoods subdivision code engage in more walking than do people who move to other types of developments.

Methods. In a natural experiment of 1813 people building homes in 73 new housing developments in Perth, Western Australia, we surveyed participants before and then 12 and 36 months after moving. We measured self-reported walking using the Neighborhood Physical Activity Questionnaire and collected perceptions of the environment and self-selection factors. We calculated objective measures of the built environment using a Geographic Information System.

Results. After relocation, participants in livable versus conventional developments had greater street connectivity, residential density, land use mix, and access to destinations and more positive perceptions of their neighborhood (all P < .05). However, there were no significant differences in walking over time by type of development (P > .05).

Conclusions. Implementation of the Livable Neighborhoods Guidelines produced more supportive environments; however, the level of intervention was insufficient to encourage more walking. Evaluations of new urban planning policies need to incorporate longer term follow-up to allow time for new neighborhoods to develop.

Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint

Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed and an efficient multiplicative update algorithm is developed based on the natural gradient optimization. The proposed method can be applied to the blind source separation (BSS) problem, a hot topic with many potential applications, especially if the sources are mutually dependent. Simulation results of BSS for images show the superiority of the proposed method.

Blind source separation by fully nonnegative constrained iterative volume maximization

Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method.

Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.