Output feedback stabilizer design of Boolean networks based on network structure

In genetic regulatory networks,a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in genes.We present analytical investigations on output feedback stabilizer design of Boolean networks(BNs)to achieve global stabilization via the semi-tensor product method.Based on network structure information describing coupling connections among nodes,an output feedback stabilizer is designed to achieve global stabilization.Compared with the traditional pinning control design,the output feedback stabilizer design is not based on the state transition matrix of BNs,which can efficiently determine pinning control nodes and reduce computational complexity.Our proposed method is efficient in that the calculation of the state transition matrix with dimension 2^n×2^n is avoided;here n is the number of nodes in a BN.Finally,a signal transduction network and a D.melanogaster segmentation polarity gene network are presented to show the efficiency of the proposed method.Results are shown to be simple and concise,compared with traditional pinning control for BNs.

Interactive image segmentation with a regression based ensemble learning paradigm

To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.

Stability of Boolean networks with state-dependent random impulses

We investigate the stability of Boolean networks(BNs)with impulses triggered by both states and random factors.A hybrid index model is used to describe impulsive BNs.First,several necessary and sufficient conditions for forward completeness are obtained.Second,based on the stability criterion of probabilistic BNs and the forward completeness criterion,the necessary and sufficient conditions for the finite-time stability with probability one and the asymptotical stability in distribution are presented.The relationship between these two kinds of stability is discussed.Last,examples and time-domain simulations are provided to illustrate the obtained results.

A Novel Minkowski-distance-based Consensus Clustering Algorithm

Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. Finally, this consensus clustering algorithm is applied to a froth flotation process.