Jie Tan

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Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals(More)
In this paper, we give an up-to-date survey on physically-based fluid animation research. As one of the most popular approaches to simulate realistic fluid effects, physically-based fluid animation has spurred a large number of new results in recent years. We classify and discuss the existing methods within three categories: Lagrangian method, Eulerian(More)
In computer animation, a common technique for tracking the motion of characters is the proportional-derivative (PD) controller. In this paper, we introduce a new formulation of the PD controller that allows arbitrarily high gains, even at large time steps. The key to our approach is to determine the joint forces and torques while taking into account the(More)
In this paper, we study global exponential stability problem for inertial BAM neural networks with time-varying delay via periodically intermittent control. By utilizing suitable variable substitution, the second-order system can be transformed into first-order differential equations. It is shown that the states of the inertial BAM neural networks with(More)
We present a physically-based system to simulate and control the locomotion of soft body characters without skeletons. We use the finite element method to simulate the deformation of the soft body, and we instrument a character with muscle fibers to allow it to actively control its shape. To perform locomotion, we use a variety of intuitive controls such as(More)
Michigan-style learning classifier systems have availed themselves as a promising modeling and data mining strategy for bioinformaticists seeking to connect predictive variables with disease phenotypes. The resulting 'model' learned by these algorithms is comprised of an entire population of rules, some of which will inevitably be redundant or poor(More)