Results 61 to 70 of about 1,566,699 (318)

CSF Levels of NPTX2 Are Associated With Less Brain Atrophy Over Time in Cognitively Unimpaired Individuals

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Neuronal pentraxin 2 (NPTX2) is a synaptic protein involved in synaptic plasticity and regulation of neuronal excitability. Lower baseline cerebrospinal fluid (CSF) NPTX2 levels have been shown to be associated with an earlier onset of mild cognitive impairment (MCI), a pre‐dementia syndrome, even after CSF Alzheimer's Disease (AD)
Juan P. Vazquez   +12 more
wiley   +1 more source

The dipole form of the gluon part of the BFKL kernel

open access: yes, 2007
The dipole form of the gluon part of the colour singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before.
A. Papa   +47 more
core   +1 more source

Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong   +7 more
wiley   +1 more source

Kernel Mean Shrinkage Estimators [PDF]

open access: yes, 2016
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji   +4 more
core   +2 more sources

Quantum tomography, phase space observables, and generalized Markov kernels

open access: yes, 2009
We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase space observable with a regular kernel state.
Abramowitz M   +15 more
core   +1 more source

Network Localization of Fatigue in Multiple Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks ...
Olli Likitalo   +12 more
wiley   +1 more source

On the Complexity of Learning with Kernels [PDF]

open access: yes, 2014
A well-recognized limitation of kernel learning is the requirement to handle a kernel matrix, whose size is quadratic in the number of training examples.
Cesa-Bianchi, Nicolò   +2 more
core   +1 more source

A tutorial on kernel density estimation and recent advances [PDF]

open access: yes, 2017
This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features.
Yen-Chi Chen
semanticscholar   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah   +7 more
wiley   +1 more source

On the Existence of (k,l)-Kernels in Infinite Digraphs: A Survey

open access: yesDiscussiones Mathematicae Graph Theory, 2014
Let D be a digraph, V (D) and A(D) will denote the sets of vertices and arcs of D, respectively.
Galeana-Sánchez H., Hernández-Cruz C.
doaj   +1 more source

Home - About - Disclaimer - Privacy