Low tensor-train rank with total variation for magnetic resonance imaging reconstruction

The model by imposing the low-rank minimization has been proved to be effective for magnetic resonance imaging(MRI) completion. Recent studies have also shown that imposing tensor train(TT) and total variation(TV) constraint on tensor completion can produce impressive performance, and the lower TT-rank minimization constraint can be represented as the guarantee for global constraint, while the total variation as the guarantee for regional constraint. In our solution, a new approach is utilized to solve TT-TV model. In contrast with imposing the alternating linear scheme, nuclear norm regularization on TT-ranks is introduced in our method as it is an effective surrogate for rank optimization and our solution does not need to initialize and update tensor cores. By applying the alternating direction method of multipliers(ADMM), the optimization model is disassembled into some sub-problems, singular value thresholding can be used as the solution to the first sub-problem and soft thresholding can be used as the solution to the second sub-problem. The new optimization algorithm ensures the effectiveness of data recovery. In addition, a new method is introduced to reshape the MRI data to a higher-dimensional tensor, so as to enhance the performance of data completion. Furthermore, the method is compared with some other methods including tensor reconstruction methods and a matrix reconstruction method. It is concluded that the proposed method has a better recovery accuracy than others in MRI data according to the experiment results.

Deep-learning-based quality filtering of mechanically exfoliated 2D crystals

Two-dimensional(2D)crystals are attracting growing interest in various research fields such as engineering,physics,chemistry,pharmacy,and biology owing to their low dimensionality and dramatic change of properties compared to the bulk counter parts.Among the various techniques used to manufacture 2D crystals,mechanical exfoliation has been essential to practical applications and fundamental research.However,mechanically exfoliated crystals on substrates contain relatively thick flakes that must be found and removed manually,limiting high-throughput manufacturing of atomic 2D crystals and van der Waals heterostructures.Here,we present a deep-learning-based method to segment and identify the thickness of atomic layer flakes from optical microscopy images.Through carefully designing a neural network based on U-Net,we found that our neural network based on Unet trained only with the data based on realistically small number of images successfully distinguish monolayer and bilayer MoS2 and graphene with a success rate of 70–80%,which is a practical value in the first screening process for choosing monolayer and bilayer flakes of all flakes on substrates without human eye.The remarkable results highlight the possibility that a large fraction of manual laboratory work can be replaced by AI-based systems,boosting productivity.

Semi-supervised multi-view clustering with dual hypergraph regularized partially shared non-negative matrix factorization

Real-world data can often be represented in multiple forms and views,and analyzing data from different perspectives allows for more comprehensive learning of the data,resulting in better data clustering results.Non-negative matrix factorization(NMF)is used to solve the clustering problem to extract uniform discriminative low-dimensional features from multi-view data.Many clustering methods based on graph regularization have been proposed and proven to be effective,but ordinary graphs only consider pairwise relationships between samples.In order to learn the higher-order relationships that exist in the sample manifold and feature manifold of multi-view data,we propose a new semi-supervised multi-view clustering method called dual hypergraph regularized partially shared non-negative matrix factorization(DHPS-NMF).The complex manifold structure of samples and features is learned by constructing samples and feature hypergraphs.To improve the discrimination power of the obtained lowdimensional features,semi-supervised regression terms are incorporated into the model to effectively use the label information when capturing the complex manifold structure of the data.Ultimately,we conduct experiments on six real data sets and the results show that our algorithm achieves encouraging results in comparison with some methods.