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In this paper, we overview the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the semantic gap in terms of a classical architecture for multimedia processing and discuss a ...
Multiplayer environments are thought to increase and prolongate active participation in robot-aided rehabilitation. We expect that environments linking patients with their spouses will particularly foster active participation. Thus, we developed two multip ...
Here we present the new project “Indoor Chemical Human Emissions and Reactivity” (ICHEAR), which is focused on examining the role of human emissions on indoor air chemistry. The specific goal of this project is to investigate the impact of exhaled and derm ...
Successful adaptation to complex environments depends on the balance of at least two systems: a flexible but slow goal-directed system encoding action-outcome associations and an efficient but rigid habitual system linking responses to preceding stimuli. R ...
Orifices are largely used in pipeline systems. The most common application is to measure the discharge of the water flowing through. However, orifices may be used to adapt hydroelectric power plants submitted to moderate increase of installed generation ca ...
The main goal of this project is by using MPM which is in continuum formulation to reproduce the wear mechanism in larger scale simulation which is performed in atomistic scale by Professor Jean-François Molinari. Then, having experience and getting inform ...
What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a se ...
What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a se ...
This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K player ...
To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In ...