Dual-energy (DE) breast x-ray imaging involves acquiring images using a lowand high-energy x-ray spectral pair. These images are then subtracted with a weighting factor that eliminates the soft-tissue signal variation present in the breast leaving only contrast that is attributed to an exogenous imaging agent. We have previously demonstrated the potential for silver (Ag) as a contrast material ...

agent technology is an emerging and promising research area in software technology, which increasingly contributes to the development of value-added information systems for large healthcare organizations due to its capability to automatically modify themselves in response to changes in their operating environment. multi agent system (mas) approach is the construction of a complex system as a se...

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the a...

This paper proposes a learning-based finite control set model predictive (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented address one ongoing challenges FCS-MPC converters, i.e., optimal design weighting coefficients appearing objective function for each...

We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to reformulate the user’s questions to elicit the optimal answers. It probes the sy...

This brief presents a new distributed scheme to solve the consensus problem for a group of agents if neither their absolute states nor inter-agent relative states are available. The new scheme considers a random partition of agents into two subgroups at each step and then uses the relative group representative state as feedback information for the consensus purpose. It is then shown that almost...

A general framework for the problem of coordination of multiple competing goals in dynamic environments for physical agents is presented. This approach to goal coordination is a novel tool to incorporate a deep coordination ability to pure reactive agents. The framework is based on the notion of multi-objective optimization. We propose a kind of “aggregating functions” formulation with the part...

Sample weighted multidimensional extensions to existing stochastic dominance, inequality and polarization comparison techniques are introduced and employed to examine whether or not ignoring multidimensional and sample weighting aspects result in misleading inferences. The techniques are employed in the context of a sample of nations, in essence each country in the sample is represented by an a...