Results 91 to 100 of about 110,828 (307)

Efficient Electromagnetic Near-Field Scanning Using Physics-Informed Gaussian Process Regression

IEEE Access
This paper proposes a novel approach combining prior physics-based Gaussian Process Regression (GPR) with Bayesian Optimization for efficient and accurate electromagnetic near-field scanning.
Tomas Monopoli, Xinglong Wu, Sergio Amedeo Pignari, Johannes Wolf, Flavia Grassi   +4 more
doaj   +1 more source

Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

Advanced Functional Materials, EarlyView.
A reconfigurable RRAM platform utilizing thermally pre‐formed filaments (TPFs) is developed to realize robust hardware security. By exploiting the thermodynamic stochasticity of TPFs, exceptionally reliable physically unclonable functions (PUFs) are achieved.
Seongbin Kwon, Taewon Lee, Dohyeong Kim, Sungmin Hwang, Sungjoon Kim   +4 more
wiley   +1 more source

JGPR: a computationally efficient multi-target Gaussian process regression algorithm [PDF]

Multi-target regression algorithms are designed to predict multiple outputs at the same time, and allow us to take all output variables into account during the training phase. Despite the recent advances, this context of machine learning is still an open
Nabati, M., Ghorashi, Seyed, Shahbazian, R.   +2 more
core   +1 more source

Chemical Maturation Controls Bioavailability of Fetuin‐A‐Mineral Complexes in Biomineralization

Advanced Functional Materials, EarlyView.
The role of Fetuin‐A‐mineral complexes in matrix mineralization is unclear. Here, Fetuin‐A‐mineral complexes were found to exist in functionally distinct states that determine mineral bioavailability: chemically labile calciprotein monomers directly mineralize collagen fibrils, while chemically matured primary calciprotein particles require cellular ...
Judith M. Schaart, Luco Rutten, Shaina V. To, Alisha A. Shah, Martijn Martens, Elena Macías‐Sánchez, Willi Jahnen‐Dechent, Nico Sommerdijk, Anat Akiva   +8 more
wiley   +1 more source

Nonparametric identification of linearizations and uncertainty using Gaussian process models – application to robust wheel slip control [PDF]

, 2005
Gaussian process prior models offer a nonparametric approach to modelling unknown nonlinear systems from experimental data. These are flexible models which automatically adapt their model complexity to the available data, and which give not only mean ...
Hansen, J., Murray-Smith, R., Johansen, T.A.   +2 more
core  

Classification of protein interaction sentences via gaussian processes [PDF]

, 2009
The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions.
Polajnar, T., Rogers, S., Tamara Polajnar, Mark Girolami, Girolami, M., Simon Rogers   +5 more
core   +1 more source

Optimal querying for communication-efficient ADMM using Gaussian process regression

Franklin Open
In distributed optimization schemes consisting of a group of agents connected to a central coordinator, the optimization algorithm often involves the agents solving private local sub-problems and exchanging data frequently with the coordinator to solve ...
Aldo Duarte, Truong X. Nghiem, Shuangqing Wei   +2 more
doaj   +1 more source

Privacy-Aware Gaussian Process Regression

Technometrics
We propose a novel theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unwilling to share a high-fidelity supervised learning model built from their data with the public due to privacy concerns.
Tuo, Rui, Chen, Haoyuan, Bhattacharya, Raktim   +2 more
openaire   +2 more sources

Scaling Gaussian Process Regression with Derivatives

CoRR, 2018
Appears at Advances in Neural Information Processing Systems 32 (NIPS ...
David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson   +4 more
openaire   +3 more sources

High‐Throughput Digital Decoding of Vascular Heterogeneity in Patient‐Specific Tumor Microenvironments

Advanced Healthcare Materials, EarlyView.
ABSTRACT Quantitative characterization of vascular heterogeneity in complex microphysiological systems (MPS), particularly within patient‐derived tumor microenvironments, remains a major challenge for scalable disease modeling and therapeutic evaluation.
Jungseub Lee, Wooju Park, Sujin Hyung, Sangmin Jung, Kyungho Lee, Junseok Jeon, Noo Li Jeon, Jeeyun Lee, Hye Ryoun Jang, Jihoon Ko   +9 more
wiley   +1 more source