Results 121 to 130 of about 24,864 (292)
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Truthful Linear Regression [PDF]
We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy ...
Cummings, Rachel +2 more
core +3 more sources
Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses +3 more
wiley +1 more source
This study presents a compact, three IMU wearable system that enables accurate motion capture and robust gait‐feature extraction, thereby supporting reliable machine learning‐based balance evaluation. Accurate assessment of balance is critical for fall prevention and targeted rehabilitation, particularly in older adults and individuals with ...
Seok‐Hoon Choi +8 more
wiley +1 more source
A Risk Comparison of Ordinary Least Squares vs Ridge Regression [PDF]
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized)
Dean P. Foster +4 more
core +3 more sources
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi +3 more
wiley +1 more source
A universal catalyst design framework integrating weighted atom‐centered symmetry function (wACSF) descriptors with machine learning accurately predicts adsorption energies for 2e− water oxidation reaction. Microkinetic modeling and experimental validation confirm the framework's universality, establishing a powerful paradigm for rational ...
Zhijian Liu +17 more
wiley +2 more sources
Bayesian Analyses of Ridge Regression Prooblems
A Bayesian formulation of the ridge regression problem is considerd, which derives from a direct specification of prior informations about parameters of general linear regression model when data suffer from a high degree of multicollinearity.A new ...
H. M. Gorgees
doaj
We address multilayer PCB anomaly detection for smart manufacturing by physical reservoir computing with HfO2‐based memristors. Crystallinity‐tuned HfO2 suppresses ferroelectricity while preserving the high‐k insulating state and confers strong short‐term memory.
Yongho Lee +7 more
wiley +1 more source

