Analysis of Impact Factor of Lightning Density in Hunan Province

In this paper, information from Hunan Province lightning monitoring and warning system platform is used and 14 sample points are selected, to analyze its average annual lightning density, and establish PLS model for statistical analysis to research the complex relationship formed between lightning density and altitude, aspect and geological structures. The results show that thunderstorms path, altitude, aspect, and shade have significant effects on lightning density distribution. Soil resistivity has a certain influence on this but overall it has relatively lesser effect

CNN or ViT? Revisiting Vision Transformers Through the Lens of Convolution

The success of Vision Transformer (ViT) has been widely reported on a wide range of image recognition tasks. The merit of ViT over CNN has been largely attributed to large training datasets or auxiliary pre-training. Without pre-training, the performance of ViT on small datasets is limited because the global self-attention has limited capacity in local modeling. Towards boosting ViT on small datasets without pre-training, this work improves its local modeling by applying a weight mask on the original self-attention matrix. A straightforward way to locally adapt the self-attention matrix can be realized by an element-wise learnable weight mask (ELM), for which our preliminary results show promising results. However, the element-wise simple learnable weight mask not only induces a non-trivial additional parameter overhead but also increases the optimization complexity. To this end, this work proposes a novel Gaussian mixture mask (GMM) in which one mask only has two learnable parameters and it can be conveniently used in any ViT variants whose attention mechanism allows the use of masks. Experimental results on multiple small datasets demonstrate that the effectiveness of our proposed Gaussian mask for boosting ViTs for free (almost zero additional parameter or computation cost). Our code will be publicly available at \href{https://github.com/CatworldLee/Gaussian-Mixture-Mask-Attention}{https://github.com/CatworldLee/Gaussian-Mixture-Mask-Attention}

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

High Efficiency Secondary Somatic Embryogenesis in Hovenia dulcis

Embryogenic callus was obtained from mature seed explants on medium supplemented with 2,4-dichlorophenoxyacetic acid. Primary somatic embryos (SEs) can only develop into abnormal plants. Well-developed SEs could be obtained through secondary somatic embryogenesis both in solid and liquid cultures. Temperature strongly affected induction frequency of secondary embryogenesis. Relatively high temperature (30∘C) and germinated SEs explants were effective for induction of secondary somatic embryos, and low temperature (20∘C) was more suitable for further embryo development, plantlet conversion, and transplant survival. Somatic embryos formed on agar medium had larger cotyledons than those of embryos formed in liquid medium. Supplementing 0.1 mg L−1 6-benzyladenine (BA) was effective for plant conversion; the rate of plant conversion was 43.3% in somatic embryos from solid culture and 36.5% in embryos from liquid culture. In vitro plants were successfully acclimatized in the greenhouse. The protocol established in this study will be helpful for large-scale vegetative propagation of this medicinal tree