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Existing approach to model sensor movement data as pairwise connections in networks implicitly assumes the Markov property and loses higher-order movement patterns. While the higher-order network (HON) captures higher-order movement patterns, there has not yet been a visualization tool tailored for HON. Based on our prior work, in this demo we present HoNVis, a comprehensive visualization and interactive...
In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative...
In this paper, we formulate a probabilistic point set matching problem under variational Bayesian framework and propose an iterative algorithm in which the posteriors of parameters are updated in sequence until a local optimum is reached. This variational Bayesian registration approach explicitly accounts for the matching uncertainty in terms of the parameters and is thus less prone to local optima...
There are some difficulties in using Linearity Regression method to predict the cost of MLRS development under the small sample situation. On the basis of the capacity of dealing with the nonlinear of ANN and the learning capacity of Rough Sets (RS), a new cost estimating method combined with RS and neural network is brought forward, which can use the Relative Reduce theory in Rough Sets to learn...
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