We study sensor minimization problems in the context of fault diagnosis. Fault diagnosis consists in synthesizing a diagnoser that observes a given plant and identifies faults in...
Abstract—In this paper, we develop passive network tomography techniques for inferring link-level anomalies like excessive loss rates and delay from path-level measurements. Our ...
Energy minimization algorithms for bio-molecular systems are critical to applications such as the prediction of protein folding. Conventional energy minimization methods such as th...
Xiaochun Weng, Lutz Hamel, Lenore M. Martin, Joan ...
This paper presents a new compaction algorithm to improve the yield of IC layout. The yield is improved by reducing the area where the faults are more likely to happen known as cr...
There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learne...