Results 91 to 100 of about 1,409,223 (260)
Objective We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk +16 more
wiley +1 more source
Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing
Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images.
Si Wang +4 more
doaj +1 more source
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Positive definite estimation of large covariance matrix using generalized nonconvex penalties
This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed.
Fei Wen +3 more
doaj +1 more source
Dynamic Precipitation during High‐Pressure Torsion of a Magnesium–Manganese Alloy
An ultrafine‐grained alloy is produced by high‐pressure torsion of solutionized Mg–1.35 wt% Mn. Precipitation of nanometer‐scale Mn particles during deformation provides pinning sites. This prevents the formation of a bimodal grain structure and results in a finer grain size than for pure Mg.
Julian M. Rosalie, Anton Hohenwarter
wiley +1 more source
Robust and Sparse Regression via γ-Divergence
In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation, and the corresponding divergences ...
Takayuki Kawashima, Hironori Fujisawa
doaj +1 more source
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models. In the centralized setting, sparse training addresses resource constraints, while in the distributed setting, local training addresses communication costs.
Meinhardt, Georg +3 more
openaire +2 more sources
AbstractWe define sparse saturated fusion systems and show that, for odd primes, sparse systems are constrained. This simplifies the proof of the Glauberman–Thompson p-Nilpotency Theorem for fusion systems and a related theorem of Stellmacher. We then define a more restrictive class of saturated fusion systems, called extremely sparse systems, that are
openaire +3 more sources
Bimetallic (NiFe) and trimetallic (NiFeCr) nanoalloys (NAs) are synthesized using corresponding oxide mixtures using microwave hydrogen plasma within a few milliseconds. The process simultaneously 1) reduces metal oxides to metals; 2) downsizes the particles from micrometers to nanometers; and 3) blends the metals to form NAs.
Sachin Kumar +5 more
wiley +1 more source
Sparse Bayesian Registration [PDF]
We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of ...
Le Folgoc, Loic +3 more
openaire +3 more sources

