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A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order ...
Computational models can provide support in addressing challenges of urban planning. However, there exists a conflict between the required model inputs to reflect the decision problem, and the required insights into results for the decision maker to provid ...
Estimation of the trajectory is a fundamental problem in robotics. Introduction of additional measurements in a robotic platform reduces the uncertainty in the trajectory estimate. The limitations on the power and payload in a UAV platform advocates for th ...
Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...
Recent advances in statistical learning and convex optimization have inspired many successful practices. Standard theories assume smoothness---bounded gradient, Hessian, etc.---and strong convexity of the loss function. Unfortunately, such conditions may ...
Schizophrenia is a chronic disorder determined by a complex mix of genetic and environmental factors. To better understand the contributions of human genetic variation to schizophrenia, we performed a genome-wide association study of a validated endophenot ...
A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...
This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions fo ...
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over ma ...
This work tests several classification techniques and acoustic features and further combines them using late fusion to classify paralinguistic information for the ComParE 2018 challenge. We use Multiple Linear Regression (MLR) with Ordinary Least Squares ( ...