… Silent data corruptions (SDCs) or silent errors are one of the … vector machine regression,
to detect SDCs that occur in HPC applications that can be characterized by an impact error …

… Because we have no information about how silent errors will exhibit themselves, we use five
… SDC detectors based on online support vector regression. Our detectors are built on spatial, …

… about how silent errors will exhibit themselves, we use four different error distributions (shown
in … For MACORD, a single run of support vector regression takes about 5×10 −4 , boosting …

… We note here that, due to the positive definiteness of tion of all n-vectors becomes a Hilbert
… The literature seems to be silent on numerical solutions of (3.30) except to remark on the …

… networks classification [17] and support vector machine [18]. Among those models, support
vector machine (SVM) is an outstanding model in soft error field. The SVM is selected in this …

… a neighborhood of the point where the error occurred can identify it even after … silent errors
in different HPC applications. We show that for certain types of applications, large silent errors …

… , silent errors threaten the validity of computed results. We propose a new paradigm for detecting
silent errors … schemes, a vector of difference measures, and an error detection criterion. …

… efficiently, classification and regression are all needed. The CART is selected in this paper
as it is an outstanding model in performing classification and regression in soft error field. …

… One goal of a gene knockdown is to maximally silence the target gene. Unfortunately, if not
… We report correlation coefficients (R) and MSE error rates between predicted and target …

… If we only adapt mean vectors and covariance matrices of the acoustic model and denote
A … In adaptation, the silence model was not adapted. Furthermore, we found that a better …