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Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.

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The motion of liquid metal has potential applications ranging from micro-pumps and self-fueled motors to rapid cooling and drug delivery. In this study, we systematically investigate the effects of the radius of LMDs (liquid metal droplets), the concentration of electrolyte solution and the applied electric field on the movement behavior of LMDs experimentally. The research also explains the experimental phenomenon with an innovative modeling analysis, which combines pertinent forces (i.e., the driving force induced by the gradient of surface tension, the viscous friction between the droplet and its surrounding electrolyte, and the friction between the droplet and the substrate). The model is highly consistent with the rule that LMDs with a larger radius need smaller actuation voltage, and we can predict the critical voltages of LMDs with r = 2-4 mm through V = 30.62/r - 0.998, which is obtained by fitting the parameters. We also obtain the model V = [-66.2Vr/(259.7-17.7) + 1.253]r, which can predict the average velocity-voltage lines of LMDs with r = 3, 3.5 mm and V = 1-13 V. In addition, the velocity increases upon increasing the concentration of the electrolyte solution from 0.1 mol L to 0.3 mol L, and tends to be stable at more than 0.3 mol L owing to the saturation of the EDL (electrical double layer) charge density. Additionally, we discuss the phenomenon of elongation during movement that occurs upon increasing the size of the LMDs. If the size of the LMDs continues to increase, the reverse movement from the anode to the cathode can occur, and the phenomenon can also be explained by the model.

Feature selection aims to gain relevant features for improved classification performance and remove redundant features for reduced computational cost. How to balance these two factors is a problem especially when the categorical labels are costly to obtain. In this paper, we address this problem using semisupervised learning method and propose a max-relevance and min-redundancy criterion based on Pearson's correlation (RRPC) coefficient. This new method uses the incremental search technique to select optimal feature subsets. The new selected features have strong relevance to the labels in supervised manner, and avoid redundancy to the selected feature subsets under unsupervised constraints. Comparative studies are performed on binary data and multicategory data from benchmark data sets. The results show that the RRPC can achieve a good balance between relevance and redundancy in semisupervised feature selection. We also compare the RRPC with classic supervised feature selection criteria (such as mRMR and Fisher score), unsupervised feature selection criteria (such as Laplacian score), and semisupervised feature selection criteria (such as sSelect and locality sensitive). Experimental results demonstrate the effectiveness of our method.

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<abstract> <p>In this work, we use a within-host viral dynamic model to describe the SARS-CoV-2 kinetics in the host. Chest radiograph score data are used to estimate the parameters of that model. Our result shows that the basic reproductive number of SARS-CoV-2 in host growth is around 3.79. Using the same method we also estimate the basic reproductive number of MERS virus is 8.16 which is higher than SARS-CoV-2. The PRCC method is used to analyze the sensitivities of model parameters. Moreover, the drug effects on virus growth and immunity effect of patients are also implemented to analyze the model.</p> </abstract>