Results 91 to 100 of about 419,205 (270)

Association of Corticospinal Tract Asymmetry With Ambulatory Ability After Intracerebral Hemorrhage

open access: yesAnnals 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

Introducing the Leal Method for the Approximation of Integrals with Asymptotic Behaviour: Special Functions

open access: yesAppliedMath
This work presents the Leal method for the approximation of integrals without known exact solutions, capable of multi-expanding simultaneously at different points.
Hector Vazquez-Leal   +5 more
doaj   +1 more source

Smoothing Approximations for Least Squares Minimization with L1-Norm Regularization Functional

open access: yesInternational Journal of Analysis and Applications, 2021
The paper considers the problem of least squares minimization with L1-norm regularization functional. It investigates various smoothing approximations for the L1-norm functional. It considers Quadratic, Sigmoid and Cubic Hermite functionals. A Tikhonov regularization is then applied to each of the resulting smooth least squares minimization problem ...
Henrietta Nkansah   +2 more
openaire   +1 more source

Glymphatic Dysfunction Reflects Post‐Concussion Symptoms: Changes Within 1 Month and After 3 Months

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mild traumatic brain injury (mTBI) may alter glymphatic function; however, its progression and variability remain obscure. This study examined glymphatic function following mTBI within 1 month and after 3 months post‐injury to determine whether variations in glymphatic function are associated with post‐traumatic symptom severity ...
Eunkyung Kim   +3 more
wiley   +1 more source

Benchmarking least squares support vector machine classifiers. [PDF]

open access: yes
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Baesens, Bart   +7 more
core  

Least squares approximations [PDF]

open access: yes, 1962
Thesis (M.A.)--Boston UniversityThis paper, utilizing the properties of Vector spaces, describes an approach to polynomial approximations of functions defined analytically or by a set of observations over some interval.
Wiener, Marvin
core  

Preconditioning of Radial Basis Function Interpolation Systems via Accelerated Iterated Approximate Moving Least Squares Approximation [PDF]

open access: yes, 2009
The standard approach to the solution of the radial basis function interpo- lation problem has been recognized as an ill-conditioned problem for many years. This is especially true when infinitely smooth basic functions such as multiquadrics or Gaussians are used with extreme values of their associated shape parameters.
Gregory E. Fasshauer, Jack G. Zhang
openaire   +1 more source

Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Freezing of gait (FOG) in people with Parkinson's disease (PwPD) is debilitating and has limited treatments. Modafinil modulates beta/gamma band activity in the pedunculopontine nucleus (PPN), like PPN deep brain stimulation. We therefore tested the hypothesis that Modafinil would improve FOG in PwPD.
Tuhin Virmani   +8 more
wiley   +1 more source

Precautionary Learning and Inflationary Biases [PDF]

open access: yes
Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function ...
Dave, Chetan, Feigenbaum, James
core   +1 more source

Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) [PDF]

open access: yes, 2013
We consider the stochastic approximation problem where a convex function has to be minimized, given only the knowledge of unbiased estimates of its gradients at certain points, a framework which includes machine learning methods based on the minimization
Bach, Francis, Moulines, Eric
core   +3 more sources

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