Probabilistic graphical models - Open Access .click

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Model Predictive Control for Magnetically Actuated Cellbots

Advanced Robotics Research, EarlyView.
Magnetically actuated cellbots, formed by embedding microrobots in living cells, are steered along precise paths using a model predictive controller enhanced with a Gaussian process disturbance estimator. The data‐efficient method calibrates each cellbot individually, cutting modeling error by roughly 60% and enabling accurate navigation through cell ...
Mehdi Kermanshah +8 more
wiley +1 more source

A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models. [PDF]

Comput Cardiol (2010), 2018
Coll-Font J, Wang L, Brooks DH.
europepmc +1 more source

Approximate Implication for Probabilistic Graphical Models

Journal of Artificial Intelligence Research
The graphical structure of Probabilistic Graphical Models (PGMs) represents the conditional independence (CI) relations that hold in the modeled distribution. Every separator in the graph represents a conditional independence relation in the distribution, making them the vehicle through which new conditional independence relations are inferred and ...
openaire +3 more sources

An Anti‐Amyloidogenic Approach to Specifically Block Memory Consolidation in Mice for Therapeutic Intervention

Advanced Science, EarlyView.
Functional amyloid conversion of the translation regulator CPEB3 is essential for long‐term memory persistence. This study demonstrates that targeting this molecular switch with the anti‐amyloidogenic peptide QBP1 selectively blocks CPEB3 fibrillation without disrupting phase separation.
Paula López‐García +13 more
wiley +1 more source

Estimation in causal graphical models [PDF]

Pearl (2000), Spirtes et al (1993) and Lauritzen (2001) set up a new framework to encode the causal relationships between the random variables by a causal Bayesian network.
Daneshkhah, Alireza
core

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu +7 more
wiley +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen +7 more
wiley +1 more source

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction

IEEE Signal Processing Magazine, 2018
Anurag Arnab +9 more
semanticscholar +1 more source

From Pores to Pavement: Advanced Modeling of Aluminosilicates for Scalable Carbon Capture in Concrete

Advanced Science, EarlyView.
This study presents a transferable modeling framework for carbon capture using aluminosilicates, integrating Universal Isotherm Modeling with experimental data. It reveals how ultramicropores, alumina content, and amine functionalization influence CO2 adsorption energetics.
Pooja Anil Kumar Nair +4 more
wiley +1 more source

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. [PDF]

Adv Neural Inf Process Syst
Li B +4 more
europepmc +1 more source

bayesian inference
bayesian network
bayesian probability

inference
probabilistic relevance model
data modeling

markov chain
graph
markov process