Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware Computing. [PDF]
Adv Sci (Weinh)Zhuge Y +9 more
europepmc +1 more source
Neural network approximations to posterior densities: an analytical approach [PDF]
In Hoogerheide, Kaashoek and Van Dijk (2002) the class of neural networksampling methods is introduced to sample from a target (posterior)distribution that may be multi-modal or skew, or exhibit strong correlationamong the parameters.
Hoogerheide, L.F. +2 more
core
Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source
Toward ambulatory monitoring of vocal behavior at the physiological level using deep ensembles and Bayesian neural networks. [PDF]
JASA Express LettZhang Z.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
Advanced Intelligent Discovery, EarlyView.This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin. [PDF]
PLoS OneEzéckiel Houénafa S +3 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in Scenario Analysis & Optimization. [PDF]
Comput Chem EngIseri F +4 more
europepmc +1 more source