Results 191 to 200 of about 3,211,049 (333)

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Optimization of ISAC Trade-Off via Covariance Matrix Allocation in Multi-User Systems. [PDF]

Entropy (Basel)
Prado-Alvarez D, Calabuig D, Inca S, Monserrat JF.   +3 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Coded Aperture Optimization in X-Ray Computed Tomography via Sparse Covariance Matrix Estimation. [PDF]

Sensors (Basel)
Jiang Y, Mao T, Zhou J, Zhao Q, Yin J, Yi X, Wu H.   +6 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Adaptive Covariance Matrix for UAV-Based Visual-Inertial Navigation Systems Using Gaussian Formulas. [PDF]

Sensors (Basel)
Cong Y, Su W, Jiang N, Zong W, Li L, Xu Y, Xu T, Wu P.   +7 more
europepmc   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

Robust adaptive beamforming based on covariance matrix reconstruction with annular uncertainty set constraints. [PDF]

PLoS One
Xing G, Yao Z, Wei H, Hu Y.
europepmc   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

, 2017
Olivier Ledoit, Michael Wolf
semanticscholar   +1 more source