Results 61 to 70 of about 14,505 (295)
Advanced Science, EarlyView.We propose the Full‐Body AI Agent, a multi‐scale collaborative framework with 7 biological‐layer agents. It unifies multi‐omics/clinical data via standardized protocols, enabling phenotype‐guided closed‐loop reasoning, quantitative evaluation, and LLM safeguards, with promising applications in tumor metastasis modeling and precision drug development ...
Aoqi Wang +11 more
wiley +1 more source
Quantifying uncertainty of machine learning methods for loss given default
Frontiers in Applied Mathematics and Statistics, 2022 Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is
Matthias Nagl +2 more
doaj +1 more source
On the Everettian epistemic problem [PDF]
, 2007 Recent work in the Everett interpretation has suggested that the problem of probability can be solved by understanding probability in terms of rationality.
Greaves, Hilary
core
Ethical Precision in Nanoscale Brain Interfacing
Advanced Science, EarlyView.As brain interfaces approach the nanoscale, precision no longer only measures—it knows, predicts, and potentially reshapes the mind. This work argues that traditional ethics fails under such conditions and proposes a shift toward continuous, operation‐based governance using the recovery–discovery framework to track, constrain, and responsibly steer ...
Guilherme Wood
wiley +1 more source
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
, 2023 In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we propose an ensemble of Normalizing Flows (NF), which are state-of-the-art
Meger, David, Berry, Lucas
core +1 more source
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
Epistemic Uncertainty Propagation in Power Models
Electronic Notes in Theoretical Computer Science, 2018 Abstract Data-centers have recently experienced a fast growth in energy demand, mainly due to cloud computing, a paradigm that lets the users access shared computing resources (e.g., servers, storage, etc.). Several techniques have been proposed in order to alleviate this problem, and numerous power models have been adopted to predict the servers ...
Gribaudo, Marco +2 more
openaire +1 more source
Advanced Science, EarlyView.ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan +8 more
wiley +1 more source
Uncertainty Modelling in Metamodels for Fire Risk Analysis
Safety, 2021 In risk-related research of fire safety engineering, metamodels are often applied to approximate the results of complex fire and evacuation simulations.
Florian Berchtold +3 more
doaj +1 more source
Advanced Science, EarlyView.Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang +2 more
wiley +1 more source