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In this paper, we present an efficient algorithm to predict the probability distribution of the circuit delay while accounting for spatial correlations. We exploit the structure of the covariance matrix to decouple the correlated variables to independent ones in linear-time, as opposed to conventional techniques which have a cubic-time complexity.(More)
In biological systems formed by living cells, the small populations of some reactant species can result in inherent randomness which cannot be captured by traditional deterministic approaches. In that case, a more accurate simulation can be obtained by using the Stochastic Simulation Algorithm (SSA). Many stochastic realizations are required to capture(More)
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as " seeds " , in order to automatically learn pattern rules from parsed data, which then can extract new instances of the relation and its projections. We propose a novel rule(More)
We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision(More)
Event-related potentials (ERPs) were recorded to explore the electrophysiological correlates of reward processing in the social comparison context when subjects performed a simple number estimation task that entailed monetary rewards for correct answers. Three social comparison stimulus categories (three relative reward levels/self reward related to the(More)
Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge. This type of automated knowledge building requires a decent level of precision, which is hard to achieve with automatically acquired rule sets learned from unlabeled data by means of distant or minimal supervision. This paper shows how precision(More)
Elastic-net regularization is a successful approach in statistical modeling. It can avoid large variations which occur in estimating complex models. In this paper, elastic-net regularization is extended to a more general setting, the matrix recovery (matrix completion) setting. Based on a combination of the nuclear-norm minimization and the Frobenius-norm(More)