Results 91 to 100 of about 544,487 (316)
Advanced Functional Materials, EarlyView.A novel stratum corneum‐inspired zwitterionic hydrogel is developed for intelligent, flexible sensors, featuring intrinsic water retention and anti‐freezing properties. The quasi‐gel, composed of hygroscopic polymers and bound water, maintains its softness across a wide range of humidity.
Meng Wu +8 more
wiley +1 more source
Deep Reinforcement Learning for Conversational AI [PDF]
arXiv, 2017 Deep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems which offer a better knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels ...
arxiv
Heat Conduction Modulation in Incommensurate Twisted Stacking of Transition‐Metal Dichalcogenide
Advanced Functional Materials, EarlyView.The interlayer thermal conductance in twisted bilayer TMDs is initially investigated experimentally by the thermoreflectance method. The overlap of lattice vibrations within individual layers and the interlayer interactions, as elucidated through both Raman spectroscopy and molecular dynamics simulations, are demonstrated to be critical factors in ...
Bin Xu +6 more
wiley +1 more source
Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning [PDF]
, 2008 According to the existing problems in RL area, such as low learning speed and tradeoff between exploration and exploitation, SIRL and QRL methods are introduced based on the theory of RL and quantum computation in this chapter, which follows the developing roadmap from the superposition-inspired methods to the RL methods in quantum systems.
Dao-Yi Dong, Chun-Lin Chen
openaire +3 more sources
Role of Reinforcement of Learning Across the Continuum of Education: A Scoping Review
Journal of the Dow University of Health Sciences, 2019 Introduction:Behaviorism is a paradigm of learning which covers various learning theories proposed by behavioral psychologists over the past century. Although, most of these theories have now become obsolete, due to a better understanding of the learning
Ayesha Younas, Faryal Azhar, Uzma Urooj
doaj +1 more source
Self‐organized Criticality in Neuromorphic Nanowire Networks With Tunable and Local Dynamics
Advanced Functional Materials, EarlyView.Memristive nanowire networks (NWNs) are shown to be electrically tunable to a critical state where specific local dynamics evaluated by multiterminal characterization are exploited as feature selection in nonlinear transformation (NLT) tasks.
Fabio Michieletti +3 more
wiley +1 more source
Mayo Clinic Proceedings: Digital HealthTo assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors.
Amy Bucher, PhD +2 more
doaj
Lineage Evolution Reinforcement Learning [PDF]
arXiv, 2020 We propose a general agent population learning system, and on this basis, we propose lineage evolution reinforcement learning algorithm. Lineage evolution reinforcement learning is a kind of derivative algorithm which accords with the general agent population learning system. We take the agents in DQN and its related variants as the basic agents in the
arxiv
Autonomous Control of Extrusion Bioprinting Using Convolutional Neural Networks
Advanced Functional Materials, EarlyView.This work presents a novel computer vision system for high‐fidelity monitoring of extrusion‐based bioprinting and a correction system utilizing convolutional neural networks for error mitigation. This system has demonstrated high detection accuracy and extrusion correction abilities that advance the state of the art toward accelerated printing ...
Daniel Kelly +4 more
wiley +1 more source
Active Learning‐Driven Discovery of Sub‐2 Nm High‐Entropy Nanocatalysts for Alkaline Water Splitting
Advanced Functional Materials, EarlyView.High‐entropy nanoparticles (HENPs) hold great promise for electrocatalysis, yet optimizing their compositions remains challenging. This study employs active learning and Bayesian Optimization to accelerate the discovery of octonary HENPs for hydrogen and oxygen evolution reactions.
Sakthivel Perumal +5 more
wiley +1 more source