Results 171 to 180 of about 1,080,302 (365)
Objectives This study aims to develop hip morphology‐based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations. Methods We combined data from nine prospective cohort studies participating in the World COACH consortium ...
Myrthe A. van den Berg+26 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.
Majid Khalilizad+4 more
wiley +1 more source
Objective We conducted formative research aimed at identifying solutions that address inequitable health outcomes in lupus due to adverse social determinants of health. Methods We conducted a search for keywords which provided insights into potential solutions and initiatives underway. An advisory panel of lupus experts iteratively reviewed the list of
Joy Buie+11 more
wiley +1 more source
Learning Low Rank Matrices from O(n) Entries
Raghunandan H. Keshavan+2 more
openalex +2 more sources
Rank Constraints on Joint Dictionary Learning for Image Recognition [PDF]
Haohao Meng, Yufeng Chen
openalex +1 more source
Objective The objective was to identify factors determining acute arthritis resolution and safety with colchicine and prednisone in acute calcium pyrophosphate (CPP) crystal arthritis. Methods We conducted a post hoc analysis of the COLCHICORT trial, which compared colchicine and prednisone for the treatment of acute CPP crystal arthritis, using a ...
Tristan Pascart+14 more
wiley +1 more source
Constrained Low-Rank Learning Using Least Squares-Based Regularization [PDF]
Ping Li+5 more
openalex +1 more source
MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach
The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus.
Hua Yang, Teresa Gonçalves
doaj +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
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
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva+4 more
wiley +1 more source