Results 91 to 100 of about 401,511 (278)
Artificial Intelligence in the Life SciencesThe estimation of uncertainties associated with predictions from quantitative structure–activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources ...
Hannah Rosa Friesacher +5 more
doaj +1 more source
Present and Future of Model Uncertainty Quantification in Process Systems Engineering
Chemical Engineering Transactions, 2019 This contribution investigates the impact of model uncertainty quantification techniques in different areas of process systems engineering (PSE), namely dynamic optimization, predictive maintenance, soft-sensor systems and risk assessment, using three ...
Francesco Rossi +3 more
doaj +1 more source
Association of Corticospinal Tract Asymmetry With Ambulatory Ability After Intracerebral Hemorrhage
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Ambulatory ability after intracerebral hemorrhage (ICH) is important to patients. We tested whether asymmetry between ipsi‐ and contra‐lesional corticospinal tracts (CSTs) assessed by diffusion tensor imaging (DTI) is associated with post‐ICH ambulation.
Yasmin N. Aziz +25 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The Gold Coast criteria permit diagnosis of amyotrophic lateral sclerosis (ALS) even without upper motor neuron (UMN) signs. However, whether ALS patients with UMN signs (ALSwUMN) and those without (ALSwoUMN) share similar characteristics and prognoses remains unclear.
Hee‐Jae Jung +7 more
wiley +1 more source
Uncertainty quantification and optimal decisions [PDF]
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017 A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and ...
openaire +4 more sources
Real‐World Performance of CSF Kappa Free Light Chains in the 2024 McDonald Criteria
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Kappa free light chains (KFLCs) in the cerebrospinal fluid (CSF) have a similar performance to CSF‐restricted oligoclonal bands (OCB) for multiple sclerosis (MS) diagnosis. To help with implementation, we set out to resolve several remaining uncertainties: (1) performance in a real‐world cohort and the 2024 McDonald criteria; (2 ...
Maya M. Leibowitz +11 more
wiley +1 more source
Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
IEEE Access, 2018 Uncertainty quantification plays a critical role in the process of decision making and optimization in many fields of science and engineering. The field has gained an overwhelming attention among researchers in recent years resulting in an arsenal of ...
H. M. Dipu Kabir +3 more
doaj +1 more source
Whole‐Body Pattern of Muscle Degeneration and Progression in Sarcoglycanopathies
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To characterize whole‐body intramuscular fat distribution pattern in patients with sarcoglycanopathies and explore correlations with disease severity, duration and age at onset. Methods Retrospective, cross‐sectional, multicentric study enrolling patients with variants in one of the four sarcoglycan genes who underwent whole‐body ...
Laura Costa‐Comellas +39 more
wiley +1 more source
Uncertainty quantification for deep learning
Environmental Data ScienceWe present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies.
Peter Jan van Leeuwen +2 more
doaj +1 more source
Optimal Uncertainty Quantification
, 2011 We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call Optimal Uncertainty Quantification (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal ...
Owhadi, H. +4 more
openaire +2 more sources