Results 81 to 90 of about 16,597 (241)
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Explaining Imitation Learning Through Frames
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited.
Boyuan Zheng +4 more
openaire +2 more sources
Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses
Nitrogen incorporation into the conjugated backbone of donor–acceptor polymers enables efficient charge transfer and deep ion embedding in organic electrochemical synaptic transistors (OESTs). This molecular‐level design enhances non‐volatile synaptic properties, providing a new strategy for developing high‐performance and reliable neuromorphic devices.
Donghwa Lee +5 more
wiley +1 more source
LEARNING OF IMITATION AND LEARNING THROUGH IMITATION IN THE WHITE RAT
The purposes of this experiment were as follows; 1) to test whether learning of imitation and learning through imitation are possible or not, and 2) to compare the effect of learning through imitation with that of trial and error learning. Thirty three albino rats were trained to follow a leader rat to obtain reward on the elevated T maze.
YUTAKA HARUKI, TADAYOSHI TSUZUKI
openaire +2 more sources
Intermixing‐Driven Growth of Highly Oriented Indium Phosphide on Black Phosphorus
This study demonstrates controlled intermixing and compound formation at the In/black phosphorus (BP) interface, leading to highly oriented InP formation. Comprehensive structural and electrical analyses reveal tunable bandgap behavior governed by competing BP thinning and charge‐transfer effects, underscoring the critical role of interfacial compound ...
Tae Keun Yun +6 more
wiley +1 more source
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak +14 more
wiley +1 more source
Transfer Learning for Prosthetics Using Imitation Learning
Workshop paper, Black in AI, NeurIPS ...
Mohammedalamen, Montaser +2 more
openaire +2 more sources
Mimetic learning, learning by imitation, constitutes one of the most important forms of learning. Mimetic learning does not, however, just denote mere imitation or copying: Rather, it is a process by which the act of relating to other persons and worlds ...
Christoph Wulf
doaj +1 more source
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source
Modular diffractive deep neural network metasurfaces encode and reconstruct holograms across layer combinations and wavelengths, enabling secure, multifunctional operation. Each layer acts independently yet composes jointly, yielding up to m(2N −1) channels for m wavelengths and N layers.
Cherry Park +4 more
wiley +1 more source

