Results 161 to 170 of about 157,395 (292)
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Publisher Correction: Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics. [PDF]
Genome Biol, 2020 Choi K, Chen Y, Skelly DA, Churchill GA.
europepmc +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source
Advanced Science, EarlyView.Single‐cell and spatial profiling of 110 human thoracic aortic samples reveals a stromal–immune circuit driving aortic dissection. An elastin‐rich fibroblast subset is depleted with age and markedly reduced in disease, weakening aortic wall integrity.
Jing Tao +25 more
wiley +1 more source
Bayesian Model Selection for Harmonic Labelling
, 2007 We present a simple model based on Dirichlet distributions for pitch class proportions within chords, motivated by the task of generating ‘lead sheets ’ (sequences of chord labels) from symbolic musical data.
Daniel Müllensiefen +2 more
core
Advanced Science, EarlyView.STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu +5 more
wiley +1 more source
Bayesian Model Selection Pruning in Predictive Maintenance
Deep Neural Network architecture design significantly impacts the final model performance. The process of searching for optimal architectures, known as Neural Architecture Search (NAS), involves training and evaluating an important number of models. Therefore, mechanisms to reduce the resources required for NAS are highly valuable. This work proposes a
David Solís-Martín +2 more
openaire +2 more sources
A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces
Advanced Science, EarlyView.A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren +5 more
wiley +1 more source
Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change. [PDF]
Neuroinformatics, 2020 Fiford CM +12 more
europepmc +1 more source
Advanced Science, EarlyView.A unique mechanism of catalytic bias regulated by diaphorase‐like subunit in formate dehydrogenase from Rhodobacter aestuarii is revealed. The diaphorase‐like subunit functions act as a biological “voltage rheostat” that controls the slow release of NADH to regulate redox balance, biasing the enzyme's catalytic preference toward CO2 reduction over ...
Kuncheng Zhang +7 more
wiley +1 more source