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» A Framework for Implicitly Tracking Data
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CORR
2010
Springer
183views Education» more  CORR 2010»
13 years 6 months ago
Discovering shared and individual latent structure in multiple time series
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared fe...
Suchi Saria, Daphne Koller, Anna Penn
APVIS
2009
13 years 8 months ago
Visualizing time-varying features with TAC-based distance fields
To analyze time-varying data sets, tracking features over time is often necessary to better understand the dynamic nature of the underlying physical process. Tracking 3D time-vary...
Teng-Yok Lee, Han-Wei Shen
BMCBI
2010
259views more  BMCBI 2010»
13 years 2 months ago
Rule-based spatial modeling with diffusing, geometrically constrained molecules
Background: We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our appro...
Gerd Gruenert, Bashar Ibrahim, Thorsten Lenser, Ma...
TSP
2010
13 years 2 months ago
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimat...
Daniel Clark, Branko Ristic, Ba-Ngu Vo, Ba-Tuong V...
IJHPCA
2008
131views more  IJHPCA 2008»
13 years 7 months ago
De Novo Ultrascale Atomistic Simulations On High-End Parallel Supercomputers
We present a de novo hierarchical simulation framework for first-principles based predictive simulations of materials and their validation on high-end parallel supercomputers and ...
Aiichiro Nakano, Rajiv K. Kalia, Ken-ichi Nomura, ...