Modular Distributed Fault Diagnosis for Adaptive Structures using Local Models
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.862
Abstract: Abstract Adaptivity has become a promising concept in civil engineering to improve the load-bearing behavior of buildings and to reduce material consumption. However, the integrated actuators and sensors increase the complexity and adversely affect the… read more here.
Keywords: diagnosis; diagnosis adaptive; local models; distributed fault ... See more keywords
Determination of the macroscopic residual stress: Modeling and experimental evaluation of texture and local grain-interaction
Sign Up to like & getrecommendations! Published in 2020 at "Materials Characterization"
DOI: 10.1016/j.matchar.2020.110609
Abstract: Abstract When the texture is present in the sheet, it is extremely difficult to test the residual stress based on the X-ray diffraction method. Based on the two extreme traditional Voigt and Reuss models, this… read more here.
Keywords: texture; residual stress; local models; method ... See more keywords
EC-DNN: A new method for parallel training of deep neural networks
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.01.072
Abstract: Abstract Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. In the typical parallelization framework, called MA-DNN, the parameters of local models are periodically averaged to get… read more here.
Keywords: training deep; deep neural; neural networks; global model ... See more keywords
Kriging Riemannian Data via Random Domain Decompositions
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2020.1853548
Abstract: Abstract Data taking value on a Riemannian manifold and observed over a complex spatial domain are becoming more frequent in applications, for example, in environmental sciences and in geoscience. The analysis of these data needs… read more here.
Keywords: kriging riemannian; riemannian data; local models; random domain ... See more keywords
Local models of astrophysical discs
Sign Up to like & getrecommendations! Published in 2017 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/stx2038
Abstract: Local models of gaseous accretion discs have been successfully employed for decades to describe an assortment of small-scale phenomena, from instabilities and turbulence, to dust dynamics and planet formation. For the most part, they have… read more here.
Keywords: models astrophysical; astrophysical discs; local models;
Proofs of Network Quantum Nonlocality in Continuous Families of Distributions.
Sign Up to like & getrecommendations! Published in 2022 at "Physical review letters"
DOI: 10.1103/physrevlett.130.090201
Abstract: The study of nonlocality in scenarios that depart from the bipartite Einstein-Podolsky-Rosen setup is allowing one to uncover many fundamental features of quantum mechanics. Recently, an approach to building network-local models based on machine learning… read more here.
Keywords: network; proofs network; quantum nonlocality; local models ... See more keywords
Seasonal Local Models for Glucose Prediction in Type 1 Diabetes
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2019.2956704
Abstract: Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to… read more here.
Keywords: local models; type diabetes; prediction; glucose prediction ... See more keywords
Practical Private Aggregation in Federated Learning Against Inference Attack
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3201231
Abstract: Federated learning (FL) enables multiple worker devices share local models trained on their private data to collaboratively train a machine learning model. However, local models are proved to imply the information about the private data… read more here.
Keywords: worker; federated learning; privacy; local models ... See more keywords
Can We Trust the Similarity Measurement in Federated Learning?
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2024.3516567
Abstract: Is it secure to measure the reliability of local models by similarity in federated learning (FL)? This paper delves into an unexplored security threat concerning applying similarity metrics, such as the $L_{2}$ norm, Euclidean distance,… read more here.
Keywords: local models; faker; similarity metrics; model ... See more keywords
RSAM: Byzantine-Robust and Secure Model Aggregation in Federated Learning for Internet of Vehicles Using Private Approximate Median
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2023.3341637
Abstract: In Internet-of-Vehicles (IoVs), Federated Learning (FL) is increasingly used by smart vehicles to process various sensing data. FL is a collaborative learning approach that enables vehicles to train a shared machine learning (ML) model by… read more here.
Keywords: rsam; local models; secure; model ... See more keywords
The test function conjecture for local models of Weil-restricted groups
Sign Up to like & getrecommendations! Published in 2020 at "Compositio Mathematica"
DOI: 10.1112/s0010437x20007162
Abstract: We prove the test function conjecture of Kottwitz and the first named author for local models of Shimura varieties with parahoric level structure attached to Weil-restricted groups, as defined by B. Levin. Our result covers… read more here.
Keywords: restricted groups; function conjecture; local models; test function ... See more keywords