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Poisson inverse problems [PDF]
The Annals of Statistics, 2006 In this paper we focus on nonparametric estimators in inverse problems for Poisson processes involving the use of wavelet decompositions. Adopting an adaptive wavelet Galerkin discretization, we find that our method combines the well-known theoretical ...
Antoniadis, Anestis, Bigot, Jéremie
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Deep learning methods for inverse problems [PDF]
PeerJ Computer Science, 2022 In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer ...
Shima Kamyab +3 more
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Semantic regularization of electromagnetic inverse problems [PDF]
Nature CommunicationsSolving ill-posed inverse problems typically requires regularization based on prior knowledge. To date, only prior knowledge that is formulated mathematically (e.g., sparsity of the unknown) or implicitly learned from quantitative data can be used for ...
Hongrui Zhang +5 more
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Solving inverse problems via weak contractive maps
Journal of Numerical Analysis and Approximation Theory, 2008 We prove a "collage'' theorem for weak contractive maps and we use it for inverse problems.
Ştefan M. Şoltuz
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Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer
BMC Bioinformatics, 2020 Background Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes.
Ana Cernea +7 more
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Bayesian Random Tomography of Particle Systems
Frontiers in Molecular Biosciences, 2021 Random tomography is a common problem in imaging science and refers to the task of reconstructing a three-dimensional volume from two-dimensional projection images acquired in unknown random directions. We present a Bayesian approach to random tomography.
Nima Vakili +2 more
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Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
Molecules, 2020 We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value ...
Óscar Álvarez-Machancoses +2 more
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Economic Cycles of Carnot Type
Entropy, 2021 Originally, the Carnot cycle was a theoretical thermodynamic cycle that provided an upper limit on the efficiency that any classical thermodynamic engine can achieve during the conversion of heat into work, or conversely, the efficiency of a ...
Constantin Udriste +2 more
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Partial Inverse Sturm-Liouville Problems
Mathematics, 2023 This paper presents a review of both classical and modern results pertaining to partial inverse spectral problems for differential operators. Such problems consist in the recovery of differential expression coefficients in some part of the domain (a ...
Natalia P. Bondarenko
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Limit cycles in models of circular gene networks regulated by negative feedback loops
BMC Bioinformatics, 2020 Background The regulatory feedback loops that present in structural and functional organization of molecular-genetic systems and the phenomenon of the regulatory signal delay, a time period between the moment of signal reception and its implementation ...
Vitaly A. Likhoshvai +2 more
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