Results 211 to 220 of about 100,788 (264)
Arthritis Care &Research, EarlyView.Over the past 50 years, the science of pediatric rheumatology has grown exponentially due to an expansion in the understanding of complex rheumatic conditions and a surge in novel targeted therapeutics. Physician‐scientists in the field of pediatric rheumatology have played major roles in these advancements that have improved the care of children ...
Ekemini A. Ogbu +2 more
wiley +1 more source
Arthritis Care &Research, EarlyView.Objective To investigate the association between rheumatoid arthritis (RA) and coronary artery calcium (CAC) prevalence, incidence, and progression over four years in adults without prior cardiovascular disease. Methods A case‐cohort study within the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil) included 585 participants (86 patients with ...
Patrícia Fonseca Estrada +7 more
wiley +1 more source
Arthritis Care &Research, EarlyView.Objective The objective of this article is to identify perceptions of patients with systemic lupus erythematosus (SLE) regarding artificial intelligence (AI)–based online symptom assessment tools, and the potential of these tools to address diagnostic barriers.
Olivia A. Stein +7 more
wiley +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
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Mathematics of Operations Research, 2018
We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and upper bounds ensures that the method is applicable even
Christian Bender +2 more
openaire +3 more sources
We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and upper bounds ensures that the method is applicable even
Christian Bender +2 more
openaire +3 more sources
Explainable dynamic programming
Journal of Functional Programming, 2021Abstract In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one
Martin Erwig, Prashant Kumar
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Aggregation in Dynamic Programming
Operations Research, 1987Reducing the size of a dynamic program through state aggregation can significantly reduce both the data and the computation time required to solve a problem. We develop a new algorithm that combines state aggregation and disaggregation steps within a single-pass procedure. The solution obtained is automatically feasible for the original problem.
James C. Bean +2 more
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Proceedings of the Second IEEE Symposium on Parallel and Distributed Processing 1990, 1994
Recurrence formulations for various problems, such as finding an optimal order of matrix multiplication, finding an optimal binary search tree, and optimal triangulation of polygons, assume a similar form. A. Gibbons and W. Rytter (1988) gave a CREW PRAM algorithm to solve such dynamic programming problems.
Shou-Hsuan Stephen Huang +2 more
openaire +1 more source
Recurrence formulations for various problems, such as finding an optimal order of matrix multiplication, finding an optimal binary search tree, and optimal triangulation of polygons, assume a similar form. A. Gibbons and W. Rytter (1988) gave a CREW PRAM algorithm to solve such dynamic programming problems.
Shou-Hsuan Stephen Huang +2 more
openaire +1 more source