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» Distinguishing between cause and effect
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AI
2002
Springer
13 years 9 months ago
The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Steven B. Gillispie, Michael D. Perlman
PRESENCE
2002
185views more  PRESENCE 2002»
13 years 9 months ago
EyeTap Devices for Augmented, Deliberately Diminished, or Otherwise Altered Visual Perception of Rigid Planar Patches of Real-Wo
Diminished reality is as important as augmented reality, and both are possible with a device called the Reality Mediator. Over the past two decades, we have designed, built, worn,...
Steve Mann, James Fung
JOCN
2010
99views more  JOCN 2010»
13 years 8 months ago
"Virus and Epidemic": Causal Knowledge Activates Prediction Error Circuitry
■ Knowledge about cause and effect relationships (e.g., virus– epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events...
Daniela B. Fenker, Mircea Ariel Schoenfeld, Michae...
JSAC
2010
129views more  JSAC 2010»
13 years 8 months ago
An adaptive link layer for heterogeneous multi-radio mobile sensor networks
—An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node...
Jeremy Gummeson, Deepak Ganesan, Mark D. Corner, P...
TMC
2010
137views more  TMC 2010»
13 years 8 months ago
Asymptotic Distortion Performance of Source-Channel Diversity over Multihop and Relay Channels
—A key challenge in the design of real-time wireless multimedia systems is the presence of fading coupled with strict delay constraints. A very effective answer to this problem i...
Karim G. Seddik, Andres Kwasinski, K. J. Ray Liu