Nanomaterials (Basel) 2019 Mar 11;9(3). Epub 2019 Mar 11.

Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and The Barcelona Institute of Science and Technology (BIST), Campus UAB, 08193 Bellaterra, Barcelona, Spain.

The understanding of the mean free path (MFP) distribution of the energy carriers in materials (e.g., electrons, phonons, magnons, etc.) provides a key physical insight into their transport properties. In this context, MFP spectroscopy has become an important tool to describe the contribution of carriers with different MFP to the total transport phenomenon. In this work, we revise the MFP reconstruction technique and present a study on the impact of the regularization parameter on the MFP distribution of the energy carriers. By using the L-curve criterion, we calculate the optimal mathematical value of the regularization parameter. The effect of the change from the optimal value in the MFP distribution is analyzed in three case studies of heat transport by phonons. These results demonstrate that the choice of the regularization parameter has a large impact on the physical information obtained from the reconstructed accumulation function, and thus cannot be chosen arbitrarily. The approach can be applied to various transport phenomena at the nanoscale involving carriers of different physical nature and behavior.

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http://dx.doi.org/10.3390/nano9030414 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473983 | PMC |

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IEEE Trans Neural Netw Learn Syst 2021 Jul 16;PP. Epub 2021 Jul 16.

Most modern learning problems are highly overparameterized, i.e., have many more model parameters than the number of training data points. Read More

Appl Opt 2021 Jul;60(19):5669-5677

Image deconvolution is often modeled as an optimization problem for a cost function involving two or more terms that represent the data fidelity and the image domain constraints (or penalties). While a number of choices for modeling the cost function and implementing the optimization algorithms exist, selection of the regularization parameter in the cost function usually involves empirical tuning, which is a tedious process. Any optimization framework provides a family of solutions, depending on the numerical value of the regularization parameter. Read More

Comput Methods Programs Biomed 2021 Jun 23;208:106245. Epub 2021 Jun 23.

Department of Orthopaedic Surgery, RWTH Aachen University Clinic, Aachen, Germany.

*Background And Objective*: Segmentation on carpus provides essential information for clinical applications including pathological evaluations, therapy planning, wrist biomechanical analysis, etc. Along with the acquisition procedure of magnetic resonance (MR) technique, poor quality of wrist images (e.g. Read More

J Chem Phys 2021 May;154(18):184101

Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, USA.

We examine the use of the truncated singular value decomposition and Tikhonov regularization in standard form to address ill-posed least squares problems Ax = b that frequently arise in molecular mechanics force field parameter optimization. We illustrate these approaches by applying them to dihedral parameter optimization of genotoxic polycyclic aromatic hydrocarbon-DNA adducts that are of interest in the study of chemical carcinogenesis. Utilizing the discrete Picard condition and/or a well-defined gap in the singular value spectrum when A has a well-determined numerical rank, we are able to systematically determine truncation and in turn regularization parameters that are correspondingly used to produce truncated and regularized solutions to the ill-posed least squares problem at hand. Read More

J Appl Stat 2021 10;48(8):1513-1526. Epub 2020 Jul 10.

Department of Industrial and Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.

The associations between covariates and the outcomes often vary over time, regardless of whether the covariate is time-varying or time-invariant. For example, we hypothesize that the impact of chronic diseases, such as diabetes and heart disease, on people's physical functions differ with aging. However, the age-varying effect would be missed if one models the covariate simply as a time-invariant covariate (yes/no) with a time-constant coefficient. Read More