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» Constructing Incremental Sequences in Graphs
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CGF
2006
118views more  CGF 2006»
13 years 8 months ago
Texture Adaptation for Progressive Meshes
Level-of-detail modeling is a vital representation for real-time applications. To support texture mapping progressive meshes (PM), we usually allow the whole PM sequence to share ...
Chih-Chun Chen 0002, Jung-Hong Chuang
ENTCS
2008
88views more  ENTCS 2008»
13 years 8 months ago
Behavior-Preserving Simulation-to-Animation Model and Rule Transformations
In the framework of graph transformation, simulation rules define the operational behavior of visual models. Moreover, it has been shown already how to construct animation rules f...
Claudia Ermel, Hartmut Ehrig
EOR
2008
106views more  EOR 2008»
13 years 8 months ago
Convergent Lagrangian heuristics for nonlinear minimum cost network flows
We consider the separable nonlinear and strictly convex single-commodity network flow problem (SSCNFP). We develop a computational scheme for generating a primal feasible solution...
Torbjörn Larsson, Johan Marklund, Caroline Ol...
BMCBI
2010
229views more  BMCBI 2010»
13 years 8 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
STACS
1999
Springer
14 years 27 days ago
Costs of General Purpose Learning
Leo Harrington surprisingly constructed a machine which can learn any computable function f according to the following criterion (called Bc∗ -identification). His machine, on t...
John Case, Keh-Jiann Chen, Sanjay Jain