Results 71 to 80 of about 496,139 (318)

Home‐Based Tele‐tDCS in Amyotrophic Lateral Sclerosis: Feasibility, Safety, and Preliminary Efficacy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with limited treatment options. Transcranial direct current stimulation (tDCS) shows promise as a neuromodulatory intervention in various neurological disorders, but its application in ALS, particularly in a remote, home‐based format, remains underexplored.
Sangeetha Madhavan, Shravni Deshmukh, Mark Cummings, Aditi Doshi, Kourosh Rezania, Sally Freels, Gina Sawa   +6 more
wiley   +1 more source

Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing [PDF]

arXiv, 2022
Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model.
arxiv  

Scaling Up Synthetic Cell Production Using Robotics and Machine Learning Toward Therapeutic Applications

Advanced Biology, EarlyView.
Synthetic cells (SCs) hold great promise for biomedical applications, but manual production limits scalability. This study presents an automated method for large‐scale SC synthesis, integrating robotic liquid handling and machine learning‐driven high‐throughput characterization.
Noga Sharf‐Pauker, Ido Galil, Omer Kfir, Gal Chen, Rotem Menachem, Jeny Shklover, Avi Schroeder, Shanny Ackerman   +7 more
wiley   +1 more source

PLMM: Personal Large Language Models on Mobile Devices [PDF]

arXiv, 2023
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled from traditional large language models but more adaptive to local users' personal information such as education background and hobbies. We classify the large language models into three levels: the personal level, expert level and traditional level.
arxiv  

Sparse Personalized Federated Learning

IEEE Transactions on Neural Networks and Learning Systems
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical diversity among clients, limited computing capability among clients' equipments, and the excessive communication overhead between the server and clients.
Xiaofeng Liu, Yinchuan Li, Qing Wang, Xu Zhang, Yunfeng Shao, Yanhui Geng   +5 more
openaire   +3 more sources

Advances in Hybrid Icing and Frosting Protection Strategies for Optics, Lens, and Photonics in Cold Environments Using Thin‐Film Acoustic Waves

Advanced Engineering Materials, EarlyView.
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong, Zhangbin Ji, Luke Haworth, Yihao Guo, Jaime del Moral, Stefan Jacob, Ana Borras, Agustin R. Gonzalez‐Elipe, Jikai Zhang, Jian Zhou, Glen McHale, Yong‐Qing Fu   +11 more
wiley   +1 more source

Benchmark for Personalized Federated Learning

IEEE Open Journal of the Computer Society
Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets.
Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka   +3 more
doaj   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Personalized Learning Based on Artificial Intelligence: How Ready Are Modern Students for New Educational Opportunities

Высшее образование в России
One of the key advantages of integrating artificial intelligence (AI) technologies into education is the creation of conditions for the implementation of a personalized learning model – a system developing individual potential, in which the learner is ...
P. V. Sysoyev
doaj   +1 more source

Personal Learning Environment

International Journal for e-Learning Security, 2013
Virtual Learning Environments (VLE) have become popular in higher education in recent years due to their ability to provide additional and flexible solutions for students and researchers. However, the limitations of VLEs have led to the development of a new generation of VLE – the Personal Learning Environment (PLE).
Ali H. Al-Bayatti, Mafawez T. Alharbi, Amelia Platt   +2 more
openaire   +2 more sources