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Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational co ...
Multiscale differential equations arise in the modeling of many important problems in the science and engineering. Numerical solvers for such problems have been extensively studied in the deterministic case. Here, we discuss numerical methods for (mean-squ ...
This lecture describes the following topics: • Dynamic Models - Conservation of Mass (Concentration Measurements) - Conservation of Energy (Calorimetry) - Beer’s Law (Spectroscop ...
The modeling of a system composed by a gas phase and organic aerosol particles, and its numerical resolution are studied. The gas-aerosol system is modeled by ordinary differential equations coupled with a mixed-constrained optimization problem. This coupl ...
Meeting temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this paper we aim at achieving an online smooth thermal control action that minimizes the performance l ...
The numerical analysis of a dynamic constrained optimization problem is presented. It consists of a global minimization problem that is coupled with a system of ordinary differential equations. The activation and the deactivation of inequality constraints ...
Meeting temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this paper we aim at achieving an online smooth thermal control action that minimizes the performance l ...
The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational co ...
Stabilized or Chebyshev explicit methods have been widely used in the past to solve stiff ordinary differential equations. Making use of special properties of Chebyshev-like polynomials, these methods have favorable stability properties compared to standar ...