نتایج جستجو برای: dose reference level drl
تعداد نتایج: 1588802 فیلتر نتایج به سال:
Deep Reinforcement Learning (DRL) has had several breakthroughs, from helicopter controlling and Atari games to the Alpha-Go success. Despite their success, DRL still lacks several important features of human intelligence, such as transfer learning, planning and interpretability. We compare two DRL approaches at learning and generalization: Deep Q-Networks and Deep Symbolic Reinforcement Learni...
In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for efficient control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent gifted. As reference, shortcuts to adiabaticity (STA) provide analytical approaches adiabatic speedup by pulse Here, we select single-component control of qubits, resembling ubiquitous two-...
PURPOSE To assess patient radiation doses during cerebral angiography and embolization of intracranial aneurysms across multi-centers and propose a diagnostic reference level (DRL). MATERIALS AND METHODS We studied a sample of 490 diagnostic and 371 therapeutic procedures for intracranial aneurysms, which were performed at 23 hospitals in Korea in 2015. Parameters including dose-area product ...
Introduction: The Diagnostic reference levels (DRLs) play a critical role in the optimization of radiation dose especially, in some conditions like pediatrics. They are useful indicators by which the radiologists can be aware of delivered excess radiation doses to the patients, and take corrective actions if necessary. In order to meet some requirements for establishing the nat...
Isoprenoids are a large family of compounds with essential functions in all domains of life. Most eubacteria synthesize their isoprenoids using the methylerythritol 4-phosphate (MEP) pathway, whereas a minority uses the unrelated mevalonate pathway and only a few have both. Interestingly, Brucella abortus and some other bacteria that only use the MEP pathway lack deoxyxylulose 5-phosphate (DXP)...
This paper investigates the use of deep reinforcement learning (DRL) in the design of a “universal” MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that “best share spectrum with any network(s), in any environmen...
During development, dendrites migrate to their correct locations in response to environmental cues. The mechanisms of dendritic guidance are poorly understood. Recent work has shown that the Drosophila olfactory map is initially formed by the spatial segregation of the projection neuron (PN) dendrites in the developing antennal lobe (AL). We report here that between 16 and 30 h after puparium f...
Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the beh...
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupa...
EasyMiner (easyminer.eu) is a web-based association rule mining software based on the LISp-Miner system. This paper presents a proof-of-concept workflow for learning business rules with EasyMiner from transactional data. The approved rules are exported to the Drools business rules engine in the DRL format. The main focus is the transformation of GUHA association rules to DRL.
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