A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach

Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

The complex detection background and lesion features make the automatic detection of dermoscopy image lesions face many challenges. The previous solutions mainly focus on using larger and more complex models to improve the accuracy of detection, there is a lack of research on significant intra-class differences and inter-class similarity of lesion features. At the same time, the larger model size also brings challenges to further algorithm application; In this paper, we proposed a lightweight skin cancer recognition model with feature discrimination based on fine-grained classification principle. The propose model includes two common feature extraction modules of lesion classification network and a feature discrimination network. Firstly, two sets of training samples (positive and negative sample pairs) are input into the feature extraction module (Lightweight CNN) of the recognition model. Then, two sets of feature vectors output from the feature extraction module are used to train the two classification networks and feature discrimination networks of the recognition model at the same time, and the model fusion strategy is applied to further improve the performance of the model, the proposed recognition method can extract more discriminative lesion features and improve the recognition performance of the model in a small amount of model parameters; In addition, based on the feature extraction module of the proposed recognition model, U-Net architecture, and migration training strategy, we build a lightweight semantic segmentation model of lesion area of dermoscopy image, which can achieve high precision lesion area segmentation end-to-end without complicated image preprocessing operation; The performance of our approach was appraised through widespread experiments comparative and feature visualization analysis, the outcome indicates that the proposed method has better performance than the start-of-the-art deep learning-based approach on the ISBI 2016 skin lesion analysis towards melanoma detection challenge dataset

A Novel Image Classification Approach for Maize Diseases Recognition

Background: The spot, streak and rust are the most common diseases in maize, all of which require effective methods to recognize, diagnose and handle. This paper presents a novel image classification approach to the high accuracy recognition of these maize diseases. Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence of image noise and irrelevant background, so that the area of maize diseases could be effectively extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix (GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining their texture, shape and color features. Finally, Support Vector Machine (SVM) classification method is used to identify three diseases. Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed method. Conclusion: Our future work will focus on the investigation of how to use the new classification methods in dimensional and large scale data to improve the recognizing performance and how to use other supervised feature selection methods to improve the accuracy further

Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems

Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely DCRNet. Specifically, the dilated convolutions are used to enhance the receptive field (RF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, the proposed DCRNet can achieve almost the state-of-the-arts (SOTA) performance with much lower floating point operations (FLOPs). The open source code and checkpoint of this work are available at https://github.com/recusant7/DCRNet.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Alterations in the gut microbiota and serum metabolomics of spontaneous cholestasis caused by loss of FXR signal in mice

Background: Farnesoid X receptor (FXR) is a key metabolic target of bile acids (BAs) and is also a target for drugs against several liver diseases. However, the contribution of FXR in the pathogenesis of cholestasis is still not fully understood. The purpose of this study is to provide a comprehensive insight into the metabolic properties of FXR-involved cholestasis in mice.Materials and methods: In this study, an alpha-naphthylisothiocyanate (ANIT)-induced cholestasis mouse model and FXR−/− mice were established to investigate the effect of FXR on cholestasis. The effect of FXR on liver and ileal pathology was evaluated. Simultaneously, Untargeted metabolomics combined with 16s rRNA gene sequencing analysis was applied to reveal the involvement of FXR in the pathogenesis of cholestasis.Results: The results showed that ANIT (75 mg/kg) induced marked cholestasis in WT and FXR −/− mice. It is noteworthy that FXR−/− mice developed spontaneous cholestasis. Compared with WT mice, significant liver and ileal tissue damage were found. In addition, 16s rRNA gene sequencing analysis revealed gut microbiota dysbiosis in FXR−/− mice and ANIT-induced cholestasis mice. Differential biomarkers associated with the pathogenesis of cholestasis caused by FXR knockout were screened using untargeted metabolomics. Notably, Lactobacillus_ johnsonii_FI9785 has a high correlation with the differential biomarkers associated with the pathogenesis and progression of cholestasis caused by FXR knockout.Conclusion: Our results implied that the disorder of the intestinal flora caused by FXR knockout can also interfere with the metabolism. This study provides novel insights into the FXR-related mechanisms of cholestasis

Cytotoxicity of Botulinum Neurotoxins Reveals a Direct Role of Syntaxin 1 and SNAP-25 in Neuron Survival

Botulinum neurotoxins (BoNT/A-G) are well-known to act by blocking synaptic vesicle exocytosis. Whether BoNTs disrupt additional neuronal functions has not been addressed. Here we report that cleavage of syntaxin 1 (Syx 1) by BoNT/C and cleavage of SNAP-25 by BoNT/E both induce degeneration of cultured rodent and human neurons. Furthermore, although SNAP-25 cleaved by BoNT/A can still support neuron survival, it has reduced capacity to tolerate additional mutations and also fails to pair with syntaxin isoforms other than Syx 1. Syx 1 and SNAP-25 are well-known for mediating synaptic vesicle exocytosis, but we found that neuronal death is due to blockage of plasma membrane recycling processes that share Syx 1/SNAP-25 for exocytosis, independent of blockage of synaptic vesicle exocytosis. These findings reveal neuronal cytotoxicity for a subset of BoNTs and directly link Syx 1/SNAP-25 to neuron survival as the prevalent SNARE proteins mediating multiple fusion events on neuronal plasma membranes

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature