Results 51 to 60 of about 2,194,014 (300)

On the lowest eigenvalue of a pseudo-differential operator [PDF]

open access: yesProceedings of the National Academy of Sciences, 1979
Positive lower bounds for pseudo-differential operators with nonnegative symbols are derived; the bounds in particular yield subelliptic estimates for operators arising as sums of squares of vector fields.
Fefferman, C., Phong, D. H.
openaire   +2 more sources

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif   +17 more
wiley   +1 more source

On the Pseudo-Differential Operator (−x−1D)ν

open access: yes, 1995
For a certain Frechet space F consisting of complex-valued C∞ even functions defined on R and rapidly decreasing as |x| → ∞, we show that if ν is any complex number, •The pseudo-differential operator (−) is an automorphism on .•Re α > 0, is an ...
Singh, O.P.
core   +1 more source

Operator Ordering and Solution of Pseudo-Evolutionary Equations

open access: yesAxioms, 2019
The solution of pseudo initial value differential equations, either ordinary or partial (including those of fractional nature), requires the development of adequate analytical methods, complementing those well established in the ordinary differential ...
Nicolas Behr   +2 more
doaj   +1 more source

WKB analysis of the linear problem for modified affine Toda field equations

open access: yesJournal of High Energy Physics, 2023
We study the WKB analysis of the solutions to the linear problem for a modified affine Toda field equation, which is equivalent to the higher-order ordinary differential equation (ODE) studied in the ODE/IM correspondence.
Katsushi Ito, Mingshuo Zhu
doaj   +1 more source

Adding a low frequency limit to fractional wave propagation models

open access: yesFrontiers in Physics, 2023
Power-law attenuation in elastic wave propagation of both compressional and shear waves can be described with multiple relaxation processes. It may be less physical to describe it with fractional calculus medium models, but this approach is useful for ...
Sverre Holm   +2 more
doaj   +1 more source

Arterial Spin‐Labeling MRI at the Cortical‐CSF Interface: A Novel Biomarker in Alzheimer Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background/Objective Arterial spin‐labeling (ASL) MRI can measure perfusion signal adjacent to CSF spaces and may provide information regarding CSF‐adjacent water transport physiology. We developed an automated pipeline to extract cortical‐CSF interface (IF) perfusion for comparison between Alzheimer disease (AD) and cognitively normal ...
Mona Asghariahmadabad   +22 more
wiley   +1 more source

Stage‐Specific Patterns of Imaging and Non‐Motor Biomarkers in Spinocerebellar Ataxia Type 3

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Advancements in disease‐modifying therapies for spinocerebellar ataxia Type 3 (SCA3) underscore the need for robust biomarkers to monitor disease progression and evaluate treatment efficacy. Although motor phenotypes have been well characterized, less is known about non‐motor and imaging‐based features across different disease stages.
Chun Li   +17 more
wiley   +1 more source

TableE12-Pseudo-bulk-analysis-of-scRNA-seq-EMT-differential-gene-expression-list.xlsx

open access: yes, 2022
TableE12-Pseudo-bulk-analysis-of-scRNA-seq-EMT-differential-gene-expression-list ...
Adam Williams (12021065)   +1 more
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy