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Large-scale three-dimensional (3D) seismic modeling is considered the foundation of imaging and inversion. Parallelization strategies are crucial to solving such a computationally intensive problem. In this paper, we focus on two factors, decomposition direction and decomposition dimension, that significantly affect the computational performance. The decomposition direction determines the cache hit ratio during register addressing, and the decomposition dimension influences the communication size. We thoroughly analyze these two factors by performing time-space domain staggered-grid finite-difference (SGFD) modeling with a set of decomposition strategies. Four metrics, including computation time, speedup ratio, strong scaling property, and memory usage, are introduced to evaluate the computational performance of each trial. After theoretical analysis and metrics testing, we conclude that the optimized domain decomposition strategy is: decomposing models at two dimensions, the decomposition directions are perpendicular to the fastest and the second fastest dimensions, here we refer the dimension in which data are continuously saved as the fastest dimension. Three examples further verify the feasibility and efficiency of the optimized parallel scheme. Considering that domain decomposition-based 3D seismic parallel simulation packages are seldom available in the public domain, we provide a program template for the optimized domain decomposition strategies as an open-source package.

A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored for interpreting localization and classification tasks while they ignored fine-grained features. Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN. In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape \& margin (LNSM) feature analysis with a CNN so that it can access rich discrete features. Secondly, by combining high-level convolutional features with SAM, we further propose a high-level feature enhancement scheme (HESAM) to localize LNSM features. Experiments on the LIDC-IDRI dataset indicate that 1) SAM captures more fine-grained and discrete attention regions than existing methods, 2) HESAM localizes more accurately on LNSM features and obtains the state-of-the-art predictive performance, reducing the false positive rate, and 3) we design and conduct a visually matching experiment which incorporates radiologists study to increase the confidence level of applying our method to clinical diagnosis.

Visual prostheses based on micro-electronic technologies and biomedical engineering have been demonstrated to restore vision to blind individuals. It is necessary to determine the minimum requirements to achieve useful artificial vision for image recognition. To find the primary factors in common object and scene images recognition and optimize the recognition accuracy on low resolution images using image processing strategies, we investigate the effects of two kinds of image processing methods, two common shapes of pixels (square and circular) and six resolutions (8x8, 16x16, 24x24, 32x32, 48x48 and 64x64). The results showed that the mean recognition accuracy increased with the number of pixels. The recognition threshold for objects was within the interval of 16x16 to 24x24 pixels. For simple scenes, it was between 32x32 and 48x48 pixels. Near the threshold of recognition, different image modes had great impact on recognition accuracy. The images with ''threshold pixel number and binarization-circular points'' produced the best recognition results.