Results 91 to 100 of about 8,190 (293)

Sparse generalized linear model with L 0 approximation for feature selection and prediction with big omics data

BioData Mining, 2017
Background Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1, SCAD and MC+.
Zhenqiu Liu, Fengzhu Sun, Dermot P. McGovern   +2 more
doaj   +1 more source

Asset allocation by penalized least squares [PDF]

This paper shows how the problem of mean-downside risk portfolio allocation can be cast in terms of penalized least squares (PLS). The penalty is given by a power function of the returns below a certain threshold.
Manganelli, Simone
core  

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Penalized sample average approximation methods for stochastic mathematical programs with complementarity constraints [PDF]

This paper considers a one-stage stochastic mathematical program with a complementarityconstraint (SMPCC) where uncertainties appear in both the objective function and the comple-mentarity constraint, and an optimal decision on both upper and lower level
Xu, Huifu, Ye, Jane J., Liu, Yongchao
core  

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI

Advanced Science, EarlyView.
ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia, Martin J. Graves, Mary McLean, Carolin Pirkl, Carola‐Bibiane Schönlieb, Timo Schirmer, Florian Wiesinger, Matthias J. Ehrhardt   +7 more
wiley   +1 more source

An entropy penalized approach for stochastic optimization with marginal law constraints. Complete version [PDF]

International audienceThis paper focuses on stochastic optimal control problems with constraints in law, which are rewritten as optimization (minimization) of probability measures problem on the canonical space.
Russo, Francesco, Oudjane, Nadia, Bourdais, Thibaut   +2 more
core   +2 more sources

Hierarchically Soft Porous MOF‐Polymer Monolith for Fast and Large‐Scale Moisture Buffering

Advanced Science, EarlyView.
A soft, hierarchical porous monolith that combines metal–organic frameworks (MOFs) with a thermoresponsive polymer matrix enables rapid, large‐scale moisture buffering. The synergistic interface facilitates high‐capacity water capture and low‐energy release for sustainable indoor dehumidification.
Guangxin Ma, Xin Zhou, Weiman Li, Ken‐ichi Otake, Susumu Kitagawa, Xiaoze Wang, Mengjie Cao, Linfeng Nie, Ming‐Shui Yao, Yunfa Chen   +9 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source