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At present, many works about magneto-hydrodynamics wave diagnostics in magnetic flux tubes are based on some pioneer works not considering the contributions of magnetic twist. Other works considered the effect on magneto-hydrodynamics waves, but the dispersion relationship they presented only gave the wave modes of m=0,1,2. The kink mode of m=-1 was absent. Therefore, in this work we would like to present a complete dispersion relationship that includes both magnetic twist and the wave mode of m=-1. Analogous to the m=+1 wave mode, the mode of m=-1 also exhibits the mode change at finite kr0, from body to surface mode. The phase speeds of this mode are usually less than those of m=+1 mode. The harmonic curves of m=+1/-1 modes in dispersion relationship diagrams are approximately symmetric in respect to a characteristic velocity, e.g. the tube velocity in flux tubes. Based on the present dispersion relationship, we revisit the issue of spiral wave patterns in sunspot and find that the magnetic twist has no great influence on their morphology in the frame of linear perturbation analysis., Comment: 14 pages, 8 figures, accepted for publication in Research in Astronomy and Astrophysics

Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on. Many algorithms try to predict model performance of the pruned sub-nets by introducing various evaluation methods. But they are either inaccurate or very complicated for general application. In this work, we present a pruning method called EagleEye, in which a simple yet efficient evaluation component based on adaptive batch normalization is applied to unveil a strong correlation between different pruned DNN structures and their final settled accuracy. This strong correlation allows us to fast spot the pruned candidates with highest potential accuracy without actually fine-tuning them. This module is also general to plug-in and improve some existing pruning algorithms. EagleEye achieves better pruning performance than all of the studied pruning algorithms in our experiments. Concretely, to prune MobileNet V1 and ResNet-50, EagleEye outperforms all compared methods by up to 3.8%. Even in the more challenging experiments of pruning the compact model of MobileNet V1, EagleEye achieves the highest accuracy of 70.9% with an overall 50% operations (FLOPs) pruned. All accuracy results are Top-1 ImageNet classification accuracy. Source code and models are accessible to open-source community https://github.com/anonymous47823493/EagleEye ., Comment: Accepted in ECCV 2020(Oral). Codes are available on https://github.com/anonymous47823493/EagleEye

Modern CNN are typically based on floating point linear algebra based implementations. Recently, reduced precision NN have been gaining popularity as they require significantly less memory and computational resources compared to floating point. This is particularly important in power constrained compute environments. However, in many cases a reduction in precision comes at a small cost to the accuracy of the resultant network. In this work, we investigate the accuracy-throughput trade-off for various parameter precision applied to different types of NN models. We firstly propose a quantization training strategy that allows reduced precision NN inference with a lower memory footprint and competitive model accuracy. Then, we quantitatively formulate the relationship between data representation and hardware efficiency. Our experiments finally provide insightful observation. For example, one of our tests show 32-bit floating point is more hardware efficient than 1-bit parameters to achieve 99% MNIST accuracy. In general, 2-bit and 4-bit fixed point parameters show better hardware trade-off on small-scale datasets like MNIST and CIFAR-10 while 4-bit provide the best trade-off in large-scale tasks like AlexNet on ImageNet dataset within our tested problem domain., Comment: Accepted by ARC 2018

We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks., Comment: 6 pages

In this paper, using the database of the university of Michigan Radio Astronomy Observatory (UMRAO) at three (4.8 GHz, 8 GHZ, and 14.5 GHz) radio frequencies, we studied the polarization properties for 47 BL Lacertae objects(38 radio selected BL Lacertae objects, 7 X-ray selected BL Lacertae, and two inter-middle objects (Mkn 421 and Mkn 501), and found that (1) The polarizations at higher radio frequency is higher than those at lower frequency, (2) The variability of polarization at higher radio frequency is higher than those at lower frequency, (3) The polarization is correlated with the radio spectral index, and (4) The polarization is correlated with core-dominance parameter for those objects with known core-dominance parameters suggesting that the relativistic beaming could explain the polarization characteristic of BL Lacs., Comment: 5 pages, 3 figures, 1 table. PASJ, in press