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An efficient and reliable network alignment serves to find the mapping with maximum similarity between different networks, which is a way to improve our understanding of biology system and biological process. To build such network alignment is a foremost challenging task in computational biology because it involves the bipartite graph matching problem, which is an NP-hard problem. In this work, we...
Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which...
High spatial resolution in x-ray computed tomography (CT) is paramount in many clinical applications. The realization of high-resolution CT requires both high-precision imaging hardware and well-designed image reconstruction algorithms. Compared to the conventional filtered backprojection (FBP), one attractive advantage of model-based iterative reconstruction (MBIR) is the ability to incorporate more...
Iterative reconstruction shows potential for noise reduction and resolution improvements in X-ray CT. The forward projection operator is a key modeling choice in every iterative reconstruction method. The operator may be expressed in terms of the basis functions corresponding to a continuous representation of the image space. In this paper, we study three system models describing the mapping from...
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