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In this paper, an efficient greedy scheduler for zero-forcing dirty-paper coding (ZF-DPC), which can be incorporated in complex Householder QR factorization of the channel matrix, is proposed. The ratio of the complexity of the proposed scheduler to the complexity of the channel matrix factorization required by ZF-DPC is O(M-1), while such ratio for the original greedy scheduler is O(M), where M is the number of transmitters. Therefore, the new scheduler reduces the overhead of scheduling from being the bottleneck of ZF-DPC to being negligible. [ABSTRACT FROM AUTHOR]

We have previously proposed a novel order statistics correlation coefficient (OSCC), which possesses some desirable advantages when measuring linear and monotone nonlinear associations between two signals. However, the understanding of this new coefficient is far from complete. A lot of theoretical questions, such as the expressions of its distribution and moments, remain to be addressed. Motivated by this unsatisfactory situation, in this paper we prove that for samples drawn from bivariate normal populations, the distribution of OSCC is asymptotically equivalent to that of the Pearson's product moment correlation coefficient (PPMCC). We also reveal its close relationships with the other two coefficients, namely, Gini correlation (GC) and Spearman's rho (SR). Monte Carlo simulation results agree with the theoretical findings. [ABSTRACT FROM AUTHOR]

Background: Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. Results: In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. Conclusion: This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel correlation coefficient based on order statistics and rearrangement inequality. The proposed coefficient represents a compromise between the Pearson's linear coefficient and the two rank-based coefficients, namely Spearman's rho and Kendall's tau. Theoretical derivations show that our coefficient possesses the same basic properties as the three classical coefficients. Experimental studies based on four models and six biosignals show that our coefficient performs better than the two rank-based coefficients when measuring linear associations; whereas it is well able to detect monotone nonlinear associations like the two rank-based coefficients. Extensive statistical analyses also suggest that our new coefficient has superior anti-noise robustness, small biasedness, high sensitivity to changes in association, accurate time-delay detection ability, fast computational speed, and robustness under monotone nonlinear transformations. [ABSTRACT FROM AUTHOR]

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing nonlinear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of nonlinear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm. [ABSTRACT FROM AUTHOR]

Motivation: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lees, which is represented by the ratio 23/33, than that between the results of NCA-r and Lees, which is 14/33. Availability: Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm Contact: cqchang@eee.hku.hk [ABSTRACT FROM AUTHOR]

Brain injury often caused irreversible loss of neural tissue and resulted in serious neurological disability. Owing to the extreme complexity of the brain, it is still challenging to regenerate the brain tissue from injury and restore its normal function. Growth factors are critical signaling molecules that promote endogenous neural stem/progenitor cells (NSPCs) proliferation, migration and differentiation, resulting in functional brain recovery from injury. However, the labile nature of growth factor motivated us to develop advanced growth factor delivery strategies to precisely control over its release profile in vivo. In this review, we will discuss growth factor delivery via biomaterials for brain regeneration after injury. This review begins with an overview of some major forms of brain injury. The characteristic properties of growth factors are described to provide a biological basis for their use in the brain regeneration. The specific biomaterials that generally used for delivering growth factor to treat brain injury are also detailed summarized. In particular, we focus on an engineering strategy that promote endogenous repair by creating growth factor concentration gradients in vivo. The last part of the review introduces current challenges and perspectives for growth factor delivery via biomaterials. [ABSTRACT FROM AUTHOR]

This study examined the association between media multitasking and executive function in Chinese adolescents by comparing heavy/high and light/low media multitaskers, i.e., HMMs and LMMs, with self-reports, behavioral measures and functional near-infrared spectroscopy (fNIRS). The participants were 12 HMMs (media multitasking scores above the 75th percentile) and 10 LMMs (media multitasking scores below the 25th percentile) chosen from a sample of 61 adolescents. Each participant completed a self-reported questionnaire on executive function and three executive function cognitive tasks: 2-back, Color Stroop, and Number-letter Determination) while wearing the fNIRS. The results indicated that: (1) the HMMs showed more impairment in executive function than the LMMs based on questionnaire data analysis; (2) there were no significant differences between the HMMs and LMMs in their performance on the cognitive tasks; and (3) the HMMs showed greater prefrontal activation than the LMMs during the 2-back and Color Stroop tasks. These findings implied that media multitasking might be associated with the reduced effectiveness in the brain areas responsible for executive function. These findings provide evidence of the negative relationship between media multitasking and executive function; and indicated the benefits of using multiple assessment methods in studying this topic. [ABSTRACT FROM AUTHOR]

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise. [ABSTRACT FROM AUTHOR]

In this correspondence, we focus on studying the problem of initial condition estimation for chaotic signals within the coupled map lattice (CML) systems. To investigate the effectiveness of a CML initial condition estimation method with different maps and coupling coefficients, the convergence and divergence properties of the inverse CML systems are analyzed. An inverse largest Lyapunov exponent (ILLE) is proposed to investigate the strength of convergence and divergence in the inverse CML systems, and it can determine if the CML initial condition estimation method is effective. Computer simulations are included to verify the relationship between the effectiveness of the CML initial condition estimation method and its corresponding ILLE. [ABSTRACT FROM PUBLISHER]