Results 101 to 110 of about 4,634,933 (288)

THE SPACE OF PROBABILISTIC 1-LIPSCHITZ MAPS

, 2018
We introduce and study a natural notion of probabilistic 1-Lipschitz maps. We prove that the space of all probabilistic 1-Lipschitz maps defined on a probabilistic metric space G is also a probabilistic metric space. Moreover, when G is a group, then the
Bachir, Mohammed
core   +1 more source

Probabilistic embedding of discrete sets as continuous metric spaces

, 2009
Blanchard P, Volchenkov D. Probabilistic embedding of discrete sets as continuous metric spaces. Stochastics: An International Journal of Probability and Stochastic Processes.
Blanchard, Philippe, Volchenkov, Dimitry
core   +1 more source

Complementing Engel Classification: Postoperative Scalp EEG as an Objective Indicator in SEEG‐Guided Radiofrequency Thermocoagulation—A Seizure‐Onset Pattern‐Based Stratified Analysis—

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Stereoelectroencephalography‐guided radiofrequency thermocoagulation (SEEG‐RFTC) is a minimally invasive therapy for drug‐resistant focal epilepsy. However, the relationship between specific seizure‐onset patterns (SOPs), particularly low‐voltage fast activity (LVFA), and postoperative scalp EEG normalization remains inadequately ...
Yi‐qing Yang, Rui Zhang, Haiyan Ma, Lu Yang, Xuefeng Qu, Qiqi Chen, Yan Zhang   +6 more
wiley   +1 more source

Application of the Fisher-Rao metric to ellipse detection [PDF]

, 2007
The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is
Stephen J. Maybank, Maybank, Stephen J.
core   +1 more source

Parkinson Disease Subtype Stability and Eventual Convergence

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Previous research identified distinct Parkinson disease (PD) clinical subtypes of “Motor Only,” “Psychiatric & Motor,” and “Cognitive & Motor” based on comprehensive, multidomain classification. Whether these subtypes reflect distinct trajectories or progressive disease stages remains unknown.
Laura M. Rosok, Christina N. Lessov‐Schlaggar, Abdulmunaim M. Eid, Erin R. Foster, Baijayanta Maiti, Hailee Domash, Helen Hwang, Richard J. Perrin, Paul T. Kotzbauer, Joel S. Perlmutter, Meghan C. Campbell   +10 more
wiley   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

A distance geometry-based description and validation of protein main-chain conformation

IUCrJ, 2017
Understanding the protein main-chain conformational space forms the basis for the modelling of protein structures and for the validation of models derived from structural biology techniques. Presented here is a novel idea for a three-dimensional distance
Joana Pereira, Victor S. Lamzin
doaj   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial

Arthritis Care &Research, Accepted Article.
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International 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, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Interference-Aware Probabilistic Routing for Wireless Sensor Networks

Tsinghua Science and Technology, 2012
Wireless communications are prone to the interference, so the data transmission among the nodes in wireless sensor networks deployed in complex environments has the obvious uncertainty.
Junling Lu, Xiaoming Wang
doaj   +1 more source