A Survey on Intelligent Iterative Methods for Solving Sparse Linear Algebraic Equations

Efficiently solving sparse linear algebraic equations is an important research topic of numerical simulation. Commonly used approaches include direct methods and iterative methods. Compared with the direct methods, the iterative methods have lower computational complexity and memory consumption, and are thus often used to solve large-scale sparse linear equations. However, there are numerous iterative methods, parameters and components needed to be carefully chosen, and an inappropriate combination may eventually lead to an inefficient solution process in practice. With the development of deep learning, intelligent iterative methods become popular in these years, which can intelligently make a sufficiently good combination, optimize the parameters and components in accordance with the properties of the input matrix. This survey then reviews these intelligent iterative methods. To be clearer, we shall divide our discussion into three aspects: a method aspect, a component aspect and a parameter aspect. Moreover, we summarize the existing work and propose potential research directions that may deserve a deep investigation

Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is one novel fast quantitative imaging framework for simultaneous quantification of multiple parameters with pseudo-randomized acquisition patterns. The accuracy of the resulting multi-parameters is very important for clinical applications. In this paper, we derived signal evolutions from the anomalous relaxation using a fractional calculus. More specifically, we utilized time-fractional order extension of the Bloch equations to generate dictionary to provide more complex system descriptions for MRF applications. The representative results of phantom experiments demonstrated the good accuracy performance when applying the time-fractional order Bloch equations to generate dictionary entries in the MRF framework. The utility of the proposed method is also validated by in-vivo study.Comment: Accepted at 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019

AutoAMG(θ\theta): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations $Ax=b$. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter $\theta$. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to $\theta$; the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better $\theta$ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG($\theta$) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal $\theta$, which can adaptively output $\theta$ values for different matrices. Numerical experiments show that AutoAMG($\theta$) can achieve significant speedup compared to the default $\theta$ value

A Study of the Effect of Heat-Treatment on the Morphology of Nafion Ionomer Dispersion for Use in the Passive Direct Methanol Fuel Cell (DMFC)

Aggregation in heat-treated Nafion ionomer dispersion and 117 membrane are investigated by 1H and 19F Nuclear Magnetic Resonance (NMR) spectra, spin-lattice relaxation time, and self-diffusion coefficient measurements. Results demonstrate that heat-treatment affects the average Nafion particle size in aqueous dispersions. Measurements on heat-treated Nafion 117 membrane show changes in the 1H isotropic chemical shift and no significant changes in ionic conductivity. Scanning electron microscopy (SEM) analysis of prepared cathode catalyst layer containing the heat-treated dispersions reveals that the surface of the electrode with the catalyst ink that has been pretreated at ca. 80 °C exhibits a compact and uniform morphology. The decrease of Nafion ionomer’s size results in better contact between catalyst particles and electrolyte, higher electrochemically active surface area, as well as significant improvement in the DMFC’s performance, as verified by electrochemical analysis and single cell evaluation

Objectness Supervised Merging Algorithm for Color Image Segmentation

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms

Decoding microbial genomes to understand their functional roles in human complex diseases

Complex diseases such as cardiovascular disease (CVD), obesity, inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D), and cancer have become a major burden to public health and affect more than 20% of the population worldwide. The etiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g., dietary habits), and by their interactions. Besides this, increasing pieces of evidence now highlight that the intestinal microbiota may contribute substantially to the health and disease of the human host via their metabolic molecules. Therefore, decoding the microbial genomes has been an important strategy to shed light on their functional potential. In this review, we summarize the roles of the gut microbiome in complex diseases from its functional perspective. We further introduce artificial tools in decoding microbial genomes to profile their functionalities. Finally, state-of-the-art techniques have been highlighted which may contribute to a mechanistic understanding of the gut microbiome in human complex diseases and promote the development of the gut microbiome-based personalized medicine.</p

Bioactive Peptides from Angelica sinensis

Since excessive reactive oxygen species (ROS) is known to be associated with aging and age-related diseases, strategies modulating ROS level and antioxidant defense systems may contribute to the delay of senescence. Here we show that the protein hydrolyzate from Angelica sinensis was capable of increasing oxidative survival of the model animal Caenorhabditis elegans intoxicated by paraquat. The hydrolyzate was then fractionated by ultrafiltration, and the antioxidant fraction (<3 kDa) was purified by gel filtration to obtain the antioxidant A. sinensis peptides (AsiPeps), which were mostly composed of peptides with <20 amino acid residues. Further studies demonstrate that AsiPeps were able to reduce the endogenous ROS level, increase the activities of the antioxidant enzymes superoxide dismutase and catalase, and decrease the content of the lipid peroxidation product malondialdehyde in nematodes treated with paraquat or undergoing senescence. AsiPeps were also shown to reduce age pigments accumulation and extend lifespan but did not affect the food-intake behavior of the nematodes. Taken together, our results demonstrate that A. sinensis peptides (AsiPeps) are able to delay aging process in C. elegans through antioxidant activities independent of dietary restriction