Abstract
The realization of tabletop optical analogue experiments of superfluidity relies on the engineering of suitable optical media, with tailored optical properties. This work shows how quantum atomic optical systems can be used to develop highly tunable optical media, with localized control of both linear and nonlinear susceptibility. Introducing the hydrodynamic description of light, the superfluidity of light in these atomic media is investigated through GPU-enhanced numerical simulations, with the numeric observation of the superfluidic signature of suppressed scattering through a defect.

Abstract
This paper addresses the problem of collision avoidance along specified paths in multiple mobile robot systems. These collisions can be represented by points of intersection or coincident segments between paths. The proposal of the work is to model these segments where the collision is possible through fictitious points. In addition, the advantages of the nonlinear versus mixed integer linear formulation, widely used in the literature, are verified. Comparisons were made and it’s proved the superiority of the proposed method with respect to complexity, computational time and inclusion of nonlinear constraints. Moreover, the simulations performed using this technique indicate that the method is promissory for applications in real systems. © Springer International Publishing AG 2018.

Abstract
This paper describes the application of a Brain Emotional Learning (BEL) controller to improve the response of a SDOF structural system under an earthquake excitation using a magnetorheological (MR) damper. The main goal is to study the performance of a BEL based semi-active control system to generate the control signal for a MR damper. The proposed approach consists of a two controllers: a primary controller based on a BEL algorithm that determines the desired damping force from the system response and a secondary controller that modifies the input current to the MR damper to generate a reference damping force. A parametric model of the damper is used to predict the damping force based on the piston motion and also the current input. A Simulink model of the structural system is developed to analyze the effectiveness of the semi-active controller. Finally, the numerical results are presented and discussed. © Springer International Publishing Switzerland 2017.

Abstract
We consider linear sampled data dynamical systems subject to additive and bounded disturbances, and study properties of their forward and backward reach sets as well as robust positively invariant sets. We propose topologically compatible notions for the sampled data forward and backward reachability as well as robust positive invariance. We also propose adequate notions for maximality and minimality of related robust positively invariant sets.

Abstract
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems that enable a fast and easy drop-in replacement for ML libraries. Automated machine learning (autoML) is the field of ML that attempts to answers these needs. We propose autoBagging, an autoML system that automatically ranks 63 bagging workflows by exploiting past performance and metalearning. Results on 140 classification datasets from the OpenML platform show that autoBagging can yield better performance than the Average Rank method and achieve results that are not statistically different from an ideal model that systematically selects the best workflow for each dataset. For the purpose of reproducibility and generalizability, autoBagging is publicly available as an R package on CRAN.

Abstract