Multi-output Gaussian process (MGP) is commonly used as a transfer learning method to leverage information among multiple outputs. A key advantage of MGP is providing uncertainty quantification for prediction, which i...
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A statistical object detection and tracking mutual feedback scheme,combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectationmaximization (EM) Kalman filter algorithm,is propos...
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A statistical object detection and tracking mutual feedback scheme,combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectationmaximization (EM) Kalman filter algorithm,is proposed in this *** space object detection stage,PCA provides compact and decorrelated feature space,the tracked object feature is statistically represented as GMM in RGB color space,objects are detected by maximum a posteriori (MAP) *** temporal tracking stage,the tracked object is determined by the Bhattacharyya similarity measurement,the object position of consecutive frame is predicted by EM Kalman filter *** integration of object detection and tracking spatio-temporal mutual feedback scheme can decrease the accumulation *** have applied the proposed method to object detection and tracking under the partial occlusion and the changes of moving speed with encouraging results.
Influence maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness of finding an optimal seed s...
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In this work, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their st...
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Prophet inequalities are a central object of study in optimal stopping theory. A gambler is sent values in an online fashion, sampled from an instance of independent distributions, in an adversarial, random or selecte...
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The advancement of single-cell RNA-sequencing (scRNA-seq) technologies allow us to study the individual level cell-type-specific gene expression networks by direct inference of genes’ conditional independence structu...
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In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle...
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The EM (expectation-maximization) algorithm is regarded as an MM (Majorization-Minimization) algorithm for maximum likelihood estimation of statistical models. Expanding this view, this paper demonstrates that by choo...
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Influence maximization—the problem of identifying a subset of k influential seeds (vertices) in a network—is a classical problem in network science with numerous applications. The problem is NP-hard, but there exist...
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expectation propagation (EP) is a family of algorithms for performing approximate inference in probabilistic models. The updates of EP involve the evaluation of moments-expectations of certain functions-which can be e...
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