Results 71 to 80 of about 47,039 (298)

Relational Neural Markov Random Fields

CoRR, 2021
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these models have been limited to discrete domains due to their limited potential functions.
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi   +2 more
openaire   +3 more sources

A Framework for Satellite Image Classification in the Context of Crisis Mapping Using Markov Random Fields [PDF]

, 2010
In this contribution a framework for classification of high resolution optical satellite images in the context of crisis mapping is proposed and evaluated. Multiscale image information (data model) as well as hierarchical and spatial context information (
Kersten, Jens, Gähler, Monika
core  

PDIA6–SCD1 Axis Rewires Lipid Metabolism to Drive Gastric Cancer Progression

Advanced Science, EarlyView.
Protein disulfide isomerase A6 (PDIA6) is identified as an oncogenic driver in gastric cancer. PDIA6 directly binds and stabilizes SCD1 by limiting its ubiquitin–proteasome‐mediated degradation, thereby sustaining monounsaturated fatty acid (MUFA)‐enriched lipid homeostasis and lipid metabolic reprogramming.
Zhen Tian, Yifan Cheng, Jiajie Zhou, Ruiqi Li, Shuai Zhao, Ben Li, Zijie Xu, Mengli Zi, Yayan Fu, Chenkai Zhang, Qiannan Sun, Shantanu Baral, Sen Wang, Daorong Wang   +13 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Model of Random Field with Piece-Constant Values and Sampling-Restoration Algorithm of Its Realizations

Entropy, 2019
We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels
Yuri Goritskiy, Vladimir Kazakov, Olga Shevchenko, Francisco Mendoza   +3 more
doaj   +1 more source

Learning Moisture‐Induced Damage From Vision: Diffusion Models for Real‐Time Monitoring of Additive Manufacturing Processes

Advanced Science, EarlyView.
We introduce a vision‐based real‐time monitoring system for additive manufacturing that detects subtle moisture‐induced degradation via a diffusion model‐based framework. The approach enables nondestructive assessment of moisture‐induced damage level and mechanical performance and establishes a practical route toward more intelligent, reliable, and ...
Jiyoung Jung, Yuna Yoo, Dharneedar Ravichandran, Dahyun Daniel Lim, Grace X. Gu   +4 more
wiley   +1 more source

Reference fields analysis of a Markov random field model to improve image segmentation [PDF]

, 2010
In Markov random field (MRF) models, parameters such as internal and external reference fields are used. In this paper, the influence of these parameters in the segmentation quality is analyzed, and it is shown that, for image segmentation, a MRF model ...
ERIKA DANAE LOPEZ ESPINOZA, LEOPOLDO ALTAMIRANO ROBLES   +1 more
core  

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

lectin-FITC Markov Random Field segmentation [PDF]

stack of lectin-FITC labelled mouse-brain vasculature acquired with two-photon fluorescence microscope and segmented with Markov Random Field ...
Di Giovanna, Antonino Paolo
core   +3 more sources

Image texture analysis based on Gaussian Markov Random Fields [PDF]

, 2014
Texture analysis is one of the key techniques of image understanding and processing with widespread applications from low level image segmentation to high level object recognition.
Dharmagunawardhana, Chathurika
core