Results 91 to 100 of about 256,437 (324)
Advanced Intelligent Systems, EarlyView.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Adaptive Macroscopic Ensemble Allocation for Robot Teams Monitoring Spatiotemporal Processes
Advanced Intelligent Systems, EarlyView.We propose an online, environment feedback‐driven macroscopic ensemble approach to adapt robot team task allocation in spatiotemporal environments by controlling robot populations rather than assigning individual robots, all while maintaining robust team performance even for small teams. Our simulation and experimental results show better or comparable
Victoria Edwards +2 more
wiley +1 more source
Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)
Advanced Intelligent Systems, EarlyView.This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley +1 more source
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis [PDF]
, 2021 Yixin Li +9 more
openalex +1 more source
Remote Sensing, 2019 Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels.
Vera Andrejchenko +3 more
doaj +1 more source
Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics. [PDF]
Bioinformatics, 2023 Zhong C, Tian T, Wei Z.
europepmc +1 more source
Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval [PDF]
, 2013 To effectively retrieve objects from large corpus with high accuracy is a challenge task. In this paper, we propose a method that propagates visual feature level similarities on a Markov random field (MRF) to obtain a high level correspondence in image ...
Lu, Peng +3 more
core
On the estimation of Markov random field parameters [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999 We examine the histogram method for estimating the parameters associated with a Markov random field. This method relies on the estimation of the local interaction sums from histogram data. We derive an estimator for these quantities that is optimal in a well-defined sense.
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Some Shannon-McMillan Approximation Theorems for Markov Chain Field on the Generalized Bethe Tree
Journal of Inequalities and Applications, 2011 A class of small-deviation theorems for the relative entropy densities of arbitrary random field on the generalized Bethe tree are discussed by comparing the arbitrary measure with the Markov measure on the generalized Bethe tree.
Zong Decai, Wang Kangkang
doaj