Results 41 to 50 of about 288,690 (232)

Trajectories of Physical Function in Canadian Children With Juvenile Idiopathic Arthritis

Arthritis Care &Research, EarlyView.
Objective We describe trajectories of physical function in children newly diagnosed with juvenile idiopathic arthritis (JIA) and identify trajectories with persisting functional impairments and associated baseline characteristics. Methods We included patients enrolled in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry ...
Clare Cunningham, Meghan McPherson, Lillian Lim, Roberta A. Berard, Matthew Berkowitz, Jean‐Philippe Proulx‐Gauthier, Brian M. Feldman, Nicole Johnson, Dax G. Rumsey, Heinrike Schmeling, Lori B. Tucker, Thomas Loughin, Kristin M. Houghton, Jaime Guzman, for the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry Investigators, David A. Cabral, Gaëlle Chédeville, Ciarán M. Duffy, Kerstin Gerhold, Jaime Guzman, Linda Hiraki, Adam M. Huber, Heinrike Schmeling, Natalie J. Shiff, Lori B. Tucker, Julie Barsalou, Daniah Basodan, Michelle Batthish, Roberta A. Berard, Nicholas Blanchette, Gilles Boire, Roxana Bolaria, Alessandra Bruns, David A. Cabral, Sarah Campillo, Tania Cellucci, Mercedes Chan, Gaëlle Chédeville, Amieleena Chhabra, Julie Couture, Paul Dancey, Jean‐Jacques De Bruycker, Erkan Demirkaya, Muhammed Dhalla, Ciarán M. Duffy, Talal El Tal, Tommy Gerschman, Jaime Guzman, Liane Heale, Julie Herrington, Kristin M. Houghton, Adan M. Huber, Andrea Human, Nicole Johnson, Roman Jurencak, Bianca Lang, Claire LeBlanc, Lillian Lim, Lily S. H. Lim, Nadia Luca, Jeanine McColl, Tara McGrath, Tamara McMillan, Megan McPherson, Paivi Miettunen, Kimberly Morishita, Honyan Ng, Jonathan Park, Jean‐Philippe Proulx‐Gauthier, Johannes Roth, Evelyn Rozenblyum, Dax G. Rumsey, Heinrike Schmeling, Rosie Scuccimarri, Gordon Soon, Rebeka Stevenson, Elizabeth Stringer, Herman Tam, Lori B. Tucker, Marinka Twilt, Ruud H. J. Verstegen, Karen Watanabe Duffy   +81 more
wiley   +1 more source

Optimal Bayesian Randomization

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1997
Summary Randomization is a puzzle for Bayesians. The intuitive need for randomization is clear, but there is a standard result that Bayesians need not randomize. In this paper we propose a model in which randomization is a strictly optimal procedure.
Berry, Scott M., Kadane, Joseph B.
openaire   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

GPflowOpt: A Bayesian Optimization Library using TensorFlow [PDF]

, 2017
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and
Couckuyt, Ivo, Dhaene, Tom, Knudde, Nicolas, van der Herten, Joachim   +3 more
core   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Bayesian optimization on networks

Journal of Computational Physics
This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
openaire   +2 more sources

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Practical Bayesian optimization in the presence of outliers [PDF]

, 2017
Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference.
Martinez-Cantin, Ruben, McCourt, Michael, Tee, Kevin   +2 more
core   +1 more source

Bayesian Optimization for Min Max Optimization

CoRR, 2020
A solution that is only reliable under favourable conditions is hardly a safe solution. Min Max Optimization is an approach that returns optima that are robust against worst case conditions. We propose algorithms that perform Min Max Optimization in a setting where the function that should be optimized is not known a priori and hence has to be learned ...
Dorina Weichert, Alexander Kister
openaire   +2 more sources