Unlike conventional lasers, diffusive random lasers (DRLs) have no resonator to trap light and no high-Q resonances to support lasing. Because of this lack of sharp resonances, the DRL has presented a challenge to conventional laser theory. We present a theory able to treat the DRL rigorously and provide results on the lasing spectra, internal fields, and output intensities of DRLs. Typically D...

background with the increase of x-ray use for medical diagnostic purposes, knowing the given doses is necessary in patients for comparison with reference levels. the concept of reference doses or diagnostic reference levels (drls) has been developed as a practical aid in the optimization of patient protection in diagnostic radiology. objectives to assess the radiation doses to neonates from dia...

Over the last two decades, our ever-increasing ability to manipulate the mouse genome has resulted in a variety of genetically defined mouse models of depression and other psychiatric and neurological disorders. However, it is still the case that some relevant rodent models for depression and antidepressant action have been validated experimentally in rats only and not in mice. An important exa...

Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of black-box nature deep neural networks. In this paper, we propose an empirical approach explain strategies DRL agents for First, use linear model hindsight as reference model, which finds best weights by assuming knowing actu...

The Geoscience Laser Altimeter System (GLAS), a spaceborne light detection and ranging (lidar) sensor, has acquired over 250 million lidar observations over forests globally, an unprecedented dataset of vegetation height information. To be useful, GLAS must be calibrated to measurements of height used in forestry inventory and ecology. Airborne discrete return lidar (DRL) can characterize veget...

Deep Reinforcement Learning (DRL) proved to be successful for solving complex control problems and has become a hot topic in the field of energy systems control, but particular case thermal storage (TES) systems, only few studies have been reported, all them with complexity degree TES system far below one this study. In paper, we step forward through DRL architecture able deal an innovative hyb...

This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and dialog acts with logical formulae. In order to make learning inquiry dialog policies more effective, we introduce a logical formula embedding framework based on a recursive neural network. The results of experiments to ...

Current research on Deep ReinforcementLearning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The control PHEV energy management are co-optimized such that DRL policy directly outputs power split between engine electric motor. testing results show ca...

Background. Medical applications of ionizing radiation are by far the largest man-made source of radiation exposure for the population in most developed countries. A good practice in diagnostic radiology should produce an image containing all necessary information needed for accurate diagnosis and should result in the minimum dose to the patient. After introduction of diagnostic reference level...