Results 291 to 300 of about 171,350 (313)
Advanced Science, EarlyView.This study leverages the complementary advantages of label‐free nanoplasmonic biosensing and multichannel fluorescence microscopy to develop a multimodal imaging system for real‐time, spatiotemporal monitoring of cellular activities at the single‐cell level.
Saeid Ansaryan +5 more
wiley +1 more source
Improving Adult Vision Through Pathway‐Specific Training in Augmented Reality
Advanced Science, EarlyView.Traditional perceptual training approaches are limited by stimulus specificity, treatment efficacy, and patient compliance. A novel altered reality (AR) method is developed to enhance pathway‐specific functions in human adults while performing everyday activities.
Yige Gao +4 more
wiley +1 more source
The Hypoxia‐Associated High‐Risk Cell Subpopulation Distinctly Enhances the Progression of Glioma
Advanced Science, EarlyView.This study suggests the potential roles of a novel hypoxia‐associated CDC20+KIF20A+PTTG1+ cell subpopulation in glioma progression. This high‐risk glioma cell subpopulation is therapeutically vulnerable to glioma progression and may play important roles in glioma standard‐of‐care therapeutic resistance.
Quan Wan +13 more
wiley +1 more source
Advanced Science, EarlyView.Pulmonary hypertension caused by fibrosing mediastinitis has a five‐year survival rate of just 56%. Current diagnostic approaches are complex for patients with mobility limitations and can lead to misdiagnoses due to their nonspecific clinical features.
Yating Zhao +17 more
wiley +1 more source
DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics
Advanced Science, EarlyView.A molecular computing approach is presented with weighted signal amplification. Polymerase‐mediated strand displacement is employed to assign weights to target miRNAs, reflecting the miRNAs’ diagnostic values, followed by amplification of the weighted signals using localized DNA catalytic hairpin assembly.
Hongyang Zhao +6 more
wiley +1 more source
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Neural Computing & Applications, 2003
In this paper we introduce a novel neural reinforcement learning method. Unlike existing methods, our approach does not need a model of the system and can be trained directly using the measurements of the system. We achieve this by only using one function approximator and approximate the improved policy from this.
Stephan ten Hagen, Ben Kröse
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In this paper we introduce a novel neural reinforcement learning method. Unlike existing methods, our approach does not need a model of the system and can be trained directly using the measurements of the system. We achieve this by only using one function approximator and approximate the improved policy from this.
Stephan ten Hagen, Ben Kröse
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IEEE Transactions on Neural Networks, 2000
This paper develops the theory of quad-Q-learning which is a new learning algorithm that evolved from Q-learning. Quad-Q-learning is applicable to problems that can be solved by "divide and conquer" techniques. Quad-Q-learning concerns an autonomous agent that learns without supervision to act optimally to achieve specified goals.
Harry Wechsler, Clifford Clausen
openaire +3 more sources
This paper develops the theory of quad-Q-learning which is a new learning algorithm that evolved from Q-learning. Quad-Q-learning is applicable to problems that can be solved by "divide and conquer" techniques. Quad-Q-learning concerns an autonomous agent that learns without supervision to act optimally to achieve specified goals.
Harry Wechsler, Clifford Clausen
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2021 American Control Conference (ACC), 2021
It is well known that the extension of Watkins' algorithm to general function approximation settings is challenging: does the “projected Bellman equation” have a solution? If so, is the solution useful in the sense of generating a good policy? And, if the preceding questions are answered in the affirmative, is the algorithm consistent?
Gergely Neu +3 more
openaire +2 more sources
It is well known that the extension of Watkins' algorithm to general function approximation settings is challenging: does the “projected Bellman equation” have a solution? If so, is the solution useful in the sense of generating a good policy? And, if the preceding questions are answered in the affirmative, is the algorithm consistent?
Gergely Neu +3 more
openaire +2 more sources
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
Engineering Applications of Artificial Intelligence, 2013Reinforcement learning (RL) has been applied to many fields and applications, but there are still some dilemmas between exploration and exploitation strategy for action selection policy. The well-known areas of reinforcement learning are the Q-learning and the Sarsa algorithms, but they possess different characteristics.
Yin-Hao Wang +2 more
openaire +2 more sources
2020 3rd International Conference on Control and Robots (ICCR), 2020
Mutual learning is an emerging technique for improving performance in machine learning models by allowing functions to learn from each other. In this paper, we present an updated version of Q-learning, improved with the application of mutual learning techniques. We apply this algorithm to a traditional reinforcement learning control problem and compare
Cameron Reid, Snehasis Mukhopadhyay
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Mutual learning is an emerging technique for improving performance in machine learning models by allowing functions to learn from each other. In this paper, we present an updated version of Q-learning, improved with the application of mutual learning techniques. We apply this algorithm to a traditional reinforcement learning control problem and compare
Cameron Reid, Snehasis Mukhopadhyay
openaire +2 more sources