The development of self-adaptive software requires the engineering of an adaptation engine that controls and adapts the underlying adaptable software by means of feedback loops. The adaptation engine often describes the adaptation by using runtime models representing relevant aspects of the adaptable software and particular activities such as analysis and planning that operate on these runtime ...

Recognition models often assume that subjects use specific evidence values (decision criteria) to adaptively parse continuous memory evidence into response categories (e.g., "old," "new"). Although explicit pretest instructions influence criterion placement, these criteria appear extremely resistant to change once testing begins. We tested criterion sensitivity to local feedback using a novel b...

We present a novel relevance feedback (RF) method that uses not only the surface information in texts, but also the latent information contained therein. In the proposed method, we infer the latent topic distribution in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribu...

We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedback model motivated by second-price auctions, we obtain algorithms for Lipsch...

Synopsis Most of the convergence results appearing so far for delayed Lotka-Volterra-type systems require that undelayed negative feedback dominate both delayed feedback and interspecific interactions. Such a requirement is rarely met in real systems. In this paper we present convergence criteria for systems without instantaneous feedback. Roughly, our results suggest that in a Lotka-Volterra-t...

This work focuses on output feedback control of a class of multi-input multi-output nonlinear systems with timevarying uncertain variables and input constraints. A robust dynamic output feedback controller is constructed through combination of a stabilizing bounded robust multivariable state feedback controller with high-gain observers and saturation filters. The state feedback component is des...