A5068217099- Sparse and Compressive Sensing Techniques
- Advanced Numerical Methods in Computational Mathematics
- Numerical methods in inverse problems
- Optimization and Variational Analysis
- Advanced Optimization Algorithms Research
- Image and Signal Denoising Methods
- Stability and Controllability of Differential Equations
- Computational Fluid Dynamics and Aerodynamics
- Advanced Mathematical Modeling in Engineering
- Fluid Dynamics and Turbulent Flows
- Contact Mechanics and Variational Inequalities
- COVID-19 epidemiological studies
- Medical Image Segmentation Techniques
- Advanced Numerical Analysis Techniques
- Fluid Dynamics and Vibration Analysis
- Rheology and Fluid Dynamics Studies
- Advanced MRI Techniques and Applications
- Numerical methods for differential equations
- Photoacoustic and Ultrasonic Imaging
- Reservoir Engineering and Simulation Methods
- Matrix Theory and Algorithms
- Medical Imaging Techniques and Applications
- Iterative Methods for Nonlinear Equations
- Model Reduction and Neural Networks
- Elasticity and Material Modeling
European Bioinformatics Institute
2024-2025
Wellcome Trust
2024
National Polytechnic School
2014-2023
Autonomous University of Tlaxcala
2020-2023
Technological University of Mexico
2022-2023
Instituto Tecnológico de Apizaco
2020
Universidad Internacional De La Rioja
2020
Universidad de La Rioja
2020
International University
2020
University of Puerto Rico, Medical Sciences Campus
2020
We consider a bilevel optimisation approach for parameter learning in higher-order total variation image reconstruction models. Apart from the least squares cost functional, naturally used learning, we propose and analyse an alternative based on Huber-regularised TV seminorm. Differentiability properties of solution operator are verified first-order optimality system is derived. Based adjoint information, combined quasi-Newton/semismooth Newton algorithm proposed numerical problems....
We propose a nonsmooth PDE-constrained optimization approach for the determination of correctnoise model in total variation (TV) image denoising. Anoptimization problem weightscorresponding to different types noise distributions is stated and existence an optimal solution isproved. A tailored regularization approximation optimalparameter values proposed thereafter its consistencystudied. Additionally, differentiability operatoris proved optimality system characterizing optimalsolutions each...
Second-order sufficient optimality conditions are established for the optimal control of semilinear elliptic and parabolic equations with pointwise constraints on state. In contrast to former publications this subject, cone critical directions is smallest possible in sense that second-order closest associated necessary ones. The theory developed distributed controls domains up dimension three. Moreover, problems boundary discussed spatial two one, respectively.
The discovery of the theory compressed sensing brought realisation that many inverse problems can be solved even when measurements are “incomplete”. This is particularly interesting in magnetic resonance imaging (MRI), where long acquisition times limit its use. In this work, we consider problem learning a sparse sampling pattern used to optimally balance time versus quality reconstructed image. We use supervised approach, making assumption our training data representative enough new...
We study the qualitative properties of optimal regularisation parameters in variational models for image restoration. The are solutions bilevel optimisation problems with restoration problem as constraint. A general type regulariser is considered, which encompasses total variation (TV), generalized (TGV) and infimal-convolution (ICTV). prove that under certain conditions on given data derived by exist. crucial point existence proof turns out to be boundedness away from $0$ we this paper....
We consider the problem of image denoising in presence noise whose statistical properties are a combination two different distributions. focus on distributions frequently considered applications, such as salt & pepper and Gaussian, Gaussian Poisson mixtures. derive variational model that features total variation regularization term data discrepancy encoding mixed an infimal convolution terms single-noise give derivation this by joint maximum posteriori (MAP) estimation. Classical models...
Abstract. The assimilation of satellite spectral sounder data requires fast and accurate radiative transfer models for retrieving surface atmospheric variables. This study proposes a novel methodology to automatically parameterize optical depths within the RTTOV scheme using statistical thresholds across pressure levels LASSO regression induce sparsity. Numerical experiments with VIIRS infrared channels demonstrate that this approach significantly reduces computational costs while...
ObjectiveThe purpose of this investigation was to compare the repeatability an intraoral scanner (3Shape TRIOS) with traditional visual method for dental shade matching in patients and assess influence ambient lighting observer's sex experience on matching. An additional aim determine color dimension which is greater both scanner.MethodsThirty observers (15 men 15 women), grouped by professional experience, selected right maxillary central incisor 10 three different occasions under...
Abstract This article is devoted to the numerical simulation of time‐dependent convective Bingham flow in cavities. Motivated by a primal‐dual regularization stationary model, family regularized problems introduced. Well posedness proved, and convergence solutions solution original multiplier system verified. For each system, fully discrete approach studied. A stable finite element approximation space together with second‐order backward differentiation formula for time discretization are...
Optimal control problems governed by a class of elliptic variational inequalities the second kind are investigated. Applications include optimal viscoplastic fluid flow and simplified friction. Based on Tikhonov regularization dual problem, family primal-dual regularized is introduced, convergence solutions towards solution original problem verified. For each an optimality condition derived, system for obtained as limit ones. Thanks to structure proposed regularization, complementarity...
We consider a bilevel optimization approach in function space for the choice of spatially dependent regularization parameters TV image denoising models. First- and second-order optimality conditions problem are studied when spatially-dependent parameter belongs to Sobolev . A combined Schwarz domain decomposition-semismooth Newton method is proposed solution full system local superlinear convergence semismooth verified. Exhaustive numerical computations finally carried out show suitability approach.
In this paper we consider the distributed optimal control of Navier–Stokes equations in presence pointwise mixed control‐state constraints. After deriving a first order necessary condition, regularity constraint multiplier is investigated. Second sufficient optimality conditions are studied as well. last part paper, semismooth Newton method applied for numerical solution problem. The convergence proved and experiments carried out.
Abstract The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is collaborative effort with central mission aggregating detailed experimental evidence in machine‐readable format, supported by controlled vocabularies standard ontologies. IntAct database ( www.ebi.ac.uk/intact ), as an IMEx...
Abstract The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated reference database for molecular complexes. It unifying web resource linking aggregated data on composition, topology and the function of macromolecular complexes from 28 species. In addition to significantly extending number complexes, we have massively extended coverage human complexome through incorporation high confidence assemblies predicted by machine-learning algorithms trained large-scale experimental...