Results 101 to 110 of about 1,472,346 (301)
Exact Integration of Stationary Gaussian Process Kernels
IEEE Open Journal of Signal ProcessingIn applications such as object tracking and financial time series, it is often desirable to model stochastic processes that are the integral of another stationary process, leading potentially to non-stationarity and infinite variance.
Ralph J. McDougall +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Here, we demonstrate that HS1BP3 interacts with Cortactin through a proline‐rich region (PRR3.1) and show that this interaction, and HS1BP3 itself, promote cancer cell proliferation and invasion. Inhibition of this interaction leads to build‐up of TKS5 in multivesicular endosomes and altered secretion of CD63 and CD9, providing an explanation for the ...
Arja Arnesen Løchen +9 more
wiley +1 more source
Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models [PDF]
The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined.
Chris M Strickland +2 more
core
D3SSTrack: Center-Focused State-Space Modeling for Monocular 3D Multi-Object Tracking
MathematicsMonocular 3D multi-object tracking (3D MOT) remains challenging because it is hard to model how objects move over time and to keep correct identities without explicit depth information.
Darius-Ovidiu Firan, Călin-Adrian Popa
doaj +1 more source
Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Fast Filtering and Smoothing for Multivariate State Space Models
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually.
Koopman, S.J.M., Durbin, J.
core
Efficient Bayesian Estimation of Multivariate State Space Models
, 2008 A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of ...
Mengersen, Kerrie L. +3 more
core +1 more source
Applied SciencesSelf-supervised monocular depth estimation (MDE) has emerged as a cost-effective alternative to traditional depth sensing approaches, which often require expensive equipment or complex configurations.
Cătălin-Cristian Botean +1 more
doaj +1 more source
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source
SambaMixer: State of Health Prediction of Li-Ion Batteries Using Mamba State Space Models
IEEE AccessThe state of health (SOH) of a Li-ion battery is determined by complex interactions among its internal components and external factors. Approaches leveraging deep learning architectures have been proposed to predict the SOH using convolutional networks ...
Jose Ignacio Olalde-Verano +3 more
doaj +1 more source