We describe a hierarchical volumetric representation of input meshes yielding isocontour output progressively and with decreasing geometric error. The maximum tolerable error bound can be prespeci ed, or incrementally reduced receiving the extra detail in the isosurface with minimal redundancy. We use a hierarchical tetrahedral representation generated via recursive edge-bisections and approxim...

We study a dynamic principal-agent relationship with adverse selection and limited commitment. We show that when the relationship is subject to productivity shocks, the principal may be able to improve her value over time by progressively learning the agent’s private information. She may even achieve her first best payoff in the long-run. The relationship may also exhibit path dependence, with ...

Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper, we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates ...

In this study, the progressive collapse potential of seismically designed steel plate shear wall (SPSW) systems is investigated using the alternate path method, and their performances are compared with those of the conventional special moment frame (SMF) systems. Nonlinear static and dynamic analyses are conducted to follow the progressive collapse of the structures, and their ability of absorb...