Results 151 to 160 of about 3,448,461 (253)
Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market [PDF]
This paper examines effects of realized covariance matrix estimators based on high-frequency data on large-scale minimum-variance equity portfolio optimization.
Masato Ubukata
core +2 more sources
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
The K-Step Spatial Sign Covariance Matrix [PDF]
The Sign Covariance Matrix is an orthogonal equivariant estimator of mul- tivariate scale. It is often used as an easy-to-compute and highly robust estimator.
Yadine, A., Croux, C., Dehon, C.
core
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Bootstrapping heteroskedasticity consistent covariance matrix estimator [PDF]
Recent results of Cribari-Neto and Zarkos (1999) show that bootstrap methods can be successfully used to estimate a heteroskedasticity robust covariance matrix estimator. In this paper, we show that the wild bootstrap estimator can be calculated directly,
Emmanuel Flachaire
core
A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design
Advanced Intelligent Discovery, EarlyView.Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung +10 more
wiley +1 more source
Particle Swarm Optimization as a Noise‐Tolerant Strategy for High‐Entropy Alloy Research
Advanced Intelligent Discovery, EarlyView.A customized particle swarm optimization algorithm is benchmarked against batch Bayesian optimization across four high‐entropy alloy oxygen‐reduction landscapes. While both methods locate the global optimum comparably under noise‐free conditions, Bayesian optimization’s surrogate model tends to overfit noisy data.
Ahmad Tirmidzi +2 more
wiley +1 more source
Random Covariance Heterogeneity in Discrete Choice Models [PDF]
The area of discrete choice modelling has developed rapidly in recent years. In particular, continuing refinements of the Generalised Extreme Value (GEV) model family have permitted the representation of increasingly complex patterns of substitution and ...
Stephane Hess, John Polak, Denis Bolduc
core
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley +1 more source