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Abstract The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied to the lidar system. In this paper, a new observation model used to modify an RM smoother by considering the characteristics of 2D LiDAR data is proposed. Simulation results show that the proposed method achieves a better performance than the original RM tracker in a 2D lidar system.

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

Low-rank representation (LRR) has attracted wide attention of researchers in recent years due to its excellent performance in the exploration of high-dimensional subspace structures. However, in the existing semi-supervised learning problem based on the LRR method, graph construction and semi-supervised learning are two separate steps. Therefore, the existing label information in the data set is not well used to guide the construction of the affinity graph. Therefore, these methods do not guarantee that the final result is a global optimal solution. This paper proposes a graph regularized low-rank representation for semi-supervised learning, called GLR2S2. This method combines the construction of affinity graph with semi supervised learning and unifies them into an optimization framework. By solving the joint optimization problem, the global optimal solution can be obtained. Experimental results on several standard data sets show that the GLR2S2 method proposed in this paper is effective.

Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure through minimizing the nuclear norm. In this paper, considering the problem of fitting a union of subspace to a collection of data points drawn from one more subspaces and corrupted by noise, we pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise. We propose a new algorithm, named Low-Rank and Sparse Subspace Clustering with a Clean dictionary (LRS2C2), by combining SSC and LRR, as the representation is often both sparse and low-rank. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and image clustering.

A new SARS animal model was established by inoculating SARS coronavirus (SARS-CoV) into rhesus macaques (Macaca mulatta) through the nasal cavity. Pathological pulmonary changes were successively detected on days 5-60 after virus inoculation. All eight animals showed a transient fever 2-3 days after inoculation. Immunological, molecular biological, and pathological studies support the establishment of this SARS animal model. Firstly, SARS-CoV-specific IgGs were detected in the sera of macaques from 11 to 60 days after inoculation. Secondly, SARS-CoV RNA could be detected in pharyngeal swab samples using nested RT-PCR in all infected animals from 5 days after virus inoculation. Finally, histopathological changes of interstitial pneumonia were found in the lungs during the 60 days after viral inoculation: these changes were less marked at later time points, indicating that an active healing process together with resolution of an acute inflammatory response was taking place in these animals. This animal model should provide insight into the mechanisms of SARS-CoV-related pulmonary disease and greatly facilitate the development of vaccines and therapeutics against SARS.

Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations., 1. Introduction With the rapid development of science and technology, artificial intelligence has penetrated all aspects of human life more and more deeply. As an important branch of its development, [...]

Antiretroviral therapy is capable of inhibiting HIV replication, but it fails to completely achieve a cure due to HIV persistence. The commonly used HIV cure approach is the "shock and kill" strategy, which employs latency-reversing agents to trigger viral reactivation and boost cellular immunity. Finding the appropriate drug combination for the "shock and kill" strategy would greatly facilitate clinical trials. The toll-like receptor (TLR) 7 agonist GS–9620 and nicotinamide (NAM) are reported as potential latency-reversing agents. Herein, we found the absence of viral reactivation when SHIVSF162P3-aviremic rhesus macaques were treated with GS–9620 monotherapy. However, our findings demonstrate that viral blips emerged in half of the macaques treated with the combination therapy of GS–9620 and NAM. Notably, an increase in the reactivation of the replication-competent latent virus was measured in monkeys treated with the combination therapy. These findings suggest that the GS–9620 and NAM combination could be used as a multipronged HIV latency stimulation approach, with potential for optimizing antiviral therapy design. [ABSTRACT FROM AUTHOR]

This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing. Exploring information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. The aim is to search for clustering patterns that perform a consensus between the patterns from different views. Inspired by variable weighting and co-regularized strategy, this paper studies co-regularized weighting multiview clustering algorithms. Two co-regularized weighting multiview clustering algorithms are proposed from two aspects: pairwise co-regularization and centroid-based co-regularization. Experimental results obtained both on synthetic and real datasets show that the proposed algorithms outperform the main existing multiview clustering algorithms.

In order to get better clustering precision, the traditional clustering algorithms usually need the support of large amount of historical data. The impact it brings about is: the previous clustering algorithm seems not effective if there exists some information losses in the current situation data collection and the division relationship between datasets is not significant. In this study, a novel clustering technique called transfer entropy weighting soft subspace clustering algorithm (T_EWSC) is proposed by employing the historical information. The properties of this algorithm are investigated and performance is evaluated experimentally using real datasets, including UCI benchmarking datasets, high dimensional gene expression datasets. The experimental results demonstrate that the proposed algorithm is able to use historical information to make up for the inadequacy of the current information and perform well.