Micro-credit companies mushroomed in China in recent years. Those companies are requiring a much more efficient and accurate way to assess credit risk. Therefore, there is a growing trend in applying machine learning ...
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The spatial clues in the new medium of depth image sequences offer the potential opportunities to solve difficult problems in human detection, such as varying illumination, approximate appearance and occlusions. There...
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Crowd simulation has been very significant and widely applied in many fields of virtual reality. How to greatly increase the efficiency and realism becomes the focus of the research about crowd simulation. In this pap...
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Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted conside...
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Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics ***,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell ***,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named ***,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 *** experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.
The real-time character is highly required in generating the scenes in virtual reality. It will be sharply reduced when with large amount of scene models. So we require the appropriate algorithm to improve this proble...
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Multi-pose face recognition always subjects to the limitation of training set, because we can't get adequate face samples to construct the face pose space or to extract effective pose-robust face features. In this...
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Visual human tracking is often disturbed by object occlusion, similar appearance and the clutter of background. These problems may be partially resolved using multiple cameras, but it is difficult to estimate the accu...
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Geometry image is a kind of mesh representation method which presents a three-dimensional geometric model with a regular structure. Because of such a uniform representation, geometry image has become an important tool...
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An adaptively weighted sub-pattern sparsity preserving projections (Aw-SpSPP) is proposed for face recognition in this paper. The conventional SPP algorithm is a linear dimensionality reduction method based on compres...
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The Local Binary Pattern (LBP) algorithm is popular and widely used in 2D pattern recognition. While in this paper, we successfully apply the LBP on 3D face recognition for the first time, and propose a novel framewor...
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