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

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

Site-Specific Glycoprofiles of HDL-Associated ApoE are Correlated with HDL Functional Capacity and Unaffected by Short-Term Diet.

Since high-density lipoprotein (HDL) glycoprofiles are associated with HDL functional capacity, we set out to determine whether diet can alter the glycoprofiles of key HDL-associated proteins, including ApoE, a potent driver of chronic disease risk. Ten healthy subjects consumed a fast food (FF) and a Mediterranean (Med) diet for 4 days in randomized order, with a 4-day wash-out between treatments. A multiple reaction monitoring method was used to characterize the site-specific glycoprofiles of HDL proteins, and HDL functional capacity was analyzed. We describe for the first time that ApoE has 7 mucin-type O-glycosylation sites, which were not affected by short-term diet. The glycoprofiles of other HDL-associated proteins were also unaffected, except that a disialylated ApoC-III glycan was enriched after Med diet, and a nonsialylated ApoC-III glycan was enriched after FF diet. Twenty-five individual glycopeptides were significantly correlated with cholesterol efflux capacity and 21 glycopeptides were correlated with immunomodulatory capacity. Results from this study indicate that the glycoprofiles of HDL-associated proteins including ApoE are correlated with HDL functional capacity but generally unaffected by diet in the short term, except ApoC-III sialylation. These results suggest that HDL protein glycoprofiles are affected by both acute and long-term factors and may be useful for biomarker discovery

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}

Robust efficiency and actuator saturation explain healthy heart rate control and variability

The correlation of healthy states with heart rate variability (HRV) using time series analyses is well documented. Whereas these studies note the accepted proximal role of autonomic nervous system balance in HRV patterns, the responsible deeper physiological, clinically relevant mechanisms have not been fully explained. Using mathematical tools from control theory, we combine mechanistic models of basic physiology with experimental exercise data from healthy human subjects to explain causal relationships among states of stress vs. health, HR control, and HRV, and more importantly, the physiologic requirements and constraints underlying these relationships. Nonlinear dynamics play an important explanatory role––most fundamentally in the actuator saturations arising from unavoidable tradeoffs in robust homeostasis and metabolic efficiency. These results are grounded in domain-specific mechanisms, tradeoffs, and constraints, but they also illustrate important, universal properties of complex systems. We show that the study of complex biological phenomena like HRV requires a framework which facilitates inclusion of diverse domain specifics (e.g., due to physiology, evolution, and measurement technology) in addition to general theories of efficiency, robustness, feedback, dynamics, and supporting mathematical tools