Results 81 to 90 of about 11,006,319 (257)

Self supervised learning based emotion recognition using physiological signals

Frontiers in Human Neuroscience
IntroductionThe significant role of emotional recognition in the field of human-machine interaction has garnered the attention of many researchers. Emotion recognition based on physiological signals can objectively reflect the most authentic emotional ...
Min Zhang, YanLi Cui
doaj   +1 more source

APTES‐Mediated Ti‐Squarate MOF/n‐GaN Nanowire Photoanodes for Efficient Photoelectrochemical Water Splitting

Advanced Functional Materials, EarlyView.
The APTES linker establishes a permanent interfacial dipole, reducing the work function of n‐GaN from 3.55 to 3.29 eV. This strengthens the built‐in electric field, significantly accelerating hole extraction to drive the oxygen evolution reaction (OER). Ultimately, this tailored charge‐separation architecture achieves a remarkable 4.78‐fold enhancement
Jeong‐Kyun Oh, Sergio Carrasco, Sooyeon Ra, Seok‐Ho Seo, Chandran Bagavath, Sung‐Un Kim, Min‐Seok Lee, Jong‐Su Kim, Miriam Vos‐Gracia, Dan Na, Kyung‐Hwan Jin, Sangmin An, Dong Ick Son, Cheul‐Ro Lee, Patricia Horcajada, Yong‐Ho Ra   +15 more
wiley   +1 more source

Programming Neuromorphic Plasticity Through Ionic and Electronic Coupling in Organic Electrochemical Transistors

Advanced Functional Materials, EarlyView.
Neuromorphic plasticity in OECTs is governed by the post‐stimulus evolution of coupled ionic and electronic states. Semiconductor chemistry and morphology, electrolyte composition and interfaces, and device architecture regulate ion injection, transport, confinement, and release, shifting responses from rapidly relaxing STP to metastable memory and ...
Inho Lee, Hyoik Jang, Ji Hwan Kim, Jonghyun Won, Namhee Kim, Wonseok Shin, Giwon Lee, Eunho Lee, Daegun Kim, Sungjun Park   +9 more
wiley   +1 more source

Evolutionary Machine Learning Meets Self-Supervised Learning: A Comprehensive Survey

IEEE Access
Research that combines Evolutionary Machine Learning and Self-Supervised Learning has been steadily increasing in recent years, suggesting that the combination of these two areas can help both in shaping evolutionary processes and in automating the ...
Adriano Vinhas, Joao Correia, Penousal Machado   +2 more
doaj   +1 more source

Self‐Adaptive Gradient Interphase Enables Durable Solid‐State Lithium Metal Batteries

Advanced Functional Materials, EarlyView.
This work presents a machine learning‐guided interfacial design that forms a self‐adaptive graded interphase on lithium metal anode. A B105M40 composite interlayer evolves into lithiophilic, porous, and fast‐ion‐conducting phases, enabling intimate contact, dendrite suppression, and uniform lithium deposition.
Hongmin Liu, Rui Li, Yitao Lou, Xinran Gao, Min Xu, Shijian Wang, Nana Wang, Huakun Liu, Guoxiu Wang, Zhongchao Bai   +9 more
wiley   +1 more source

SELF-SUPERVISED LEARNING BY COMPRESSING REPRESENTATIONS FOR LIGHTWEIGHT MODELS

, 2022
Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models.
Abbasi Koohpayegani, Soroush
core   +1 more source

Material Coupling in Hybrid Neuromorphic Devices: From Fundamentals to System‐Level Applications

Advanced Functional Materials, EarlyView.
Hybrid neuromorphic devices utilize heterointerfaces between emerging semiconductors, where charge transfer and trapping, ion or defect modulation, and photogating are translated into synaptic functions. This paper discusses coupling strategies that tailor interfacial electronic and ionic properties in organic semiconductors, metal oxides, two ...
Minsu So, Sunwoo Jeong, Youngmin Han, Fabrizio Torricelli, Hocheon Yoo, Junhwan Choi   +5 more
wiley   +1 more source

Unsupervised end-to-end training with a self-defined target

Neuromorphic Computing and Engineering
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy, Damien Querlioz, Julie Grollier   +4 more
doaj   +1 more source

NeuroVisio SyncPatch: A Skin‐Conformal Multimodal EMG–Motion Sensing Platform for Quantitative Neuromuscular Rehabilitation and Performance Monitoring

Advanced Healthcare Materials, EarlyView.
NeuroVisio SyncPatch integrates skin‐conformal electromyography (EMG) electrodes with camera‐tracked markers to jointly assess muscle activity and three‐dimensional knee kinematics during rehabilitation. Parallel feedback pathways provide real‐time kinematic guidance during movement and post‐trial neuromuscular feedback.
Bohyung Choi, Yoo‐Jin Jun, Heewon Choee, Kun‐Do Lee, Heesoo Kim, Yu Seok Hwang, Choong Yeon Kim, Seongok Chae, Juhyun Lee, Minkyu Je, Jae‐Woong Jeong, Hyung‐Soon Park   +11 more
wiley   +1 more source

Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis

Big Data and Cognitive Computing
Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge.
Elie Neghawi, Yan Liu
doaj   +1 more source