SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages

Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual heterogeneity which typically exist in raw EEG signals of sleep data can significantly affect the classification performance of any machine learning algorithms. To solve these two problems, this paper develops a generative adversarial network (GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalanced classification of sleep stages. To alleviate class imbalance, we propose a new GAN (called EGAN) architecture adapted to the features of EEG signals for data augmentation. The generated samples for the minority classes are used in the training process. In addition, we design a cost-free ensemble learning strategy to reduce the model estimation variance caused by the heterogeneity between the validation and test sets, so as to enhance the accuracy and robustness of prediction performance. We show that the proposed method can improve classification accuracy compared to several existing state-of-the-art methods using three public sleep datasets.Comment: 20 pages, 6 figure

Single underwater image enhancement based on adaptive correction of channel differential and fusion

Clear underwater images are necessary in many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. In order to overcome the limitations of the available color correction and deblurring algorithms, this paper proposed a fusion-based image enhancement method for various water areas. We proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval. Subsequently, using these two methods, we took two images from a single underwater image as inputs of the fusion framework. Finally, we obtained a satisfactory underwater image. To validate the effectiveness of the experiment, we tested our method using public datasets. The results showed that the proposed method can adaptively correct color casts and significantly enhance the details and quality of attenuated underwater images

Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data

Structure Inheritance in Nanoparticle Ink Direct-Writing Processes and Crack-Free Nano-Copper Interconnects Printed by a Single-Run Approach

When nanoparticle conductive ink is used for printing interconnects, cracks and pores are common defects that deteriorate the electrical conductivity of the printed circuits. Influences of the ink solvent, the solid fraction of the ink, the pre-printing treatment and the sintering parameters on the interconnect morphology and conductivity were investigated. It was found that the impacts of all these factors coupled with each other throughout the whole procedure, from the pre-printing to the post-printing processes, and led to a structure inheritance effect. An optimum process route was developed for producing crack-free interconnects by a single-run direct-writing approach using home-made nano-copper ink. A weak gel was promoted in the ink before printing in the presence of long-chain polymers and bridging molecules by mechanical agitation. The fully developed gel network prevented the phase separation during ink extrusion and crack formations during drying. With the reducing agents in the ink and slow evaporation of the ink solvent, compact packing and neck joining of copper nanoparticles were obtained after a two-step sintering process. The crack-free interconnects successfully produced have a surface roughness smaller than 1.5 μm and the square resistances as low as 0.01 Ω/□

Human Papillomavirus Positivity in the Anal Canal in HIV-Infected and HIV-Uninfected Men Who Have Anal Sex with Men in Guangzhou, China: Implication for Anal Exams and Early Vaccination

Background. The epidemiology of HPV in men who have sex with men (MSM) in Guangzhou, China, had not been reported previously. Methods. HIV-infected and HIV-uninfected MSM were recruited from a Guangzhou-based MSM clinic in 2013. Sociodemographic characteristics and sexual behaviors were collected. An anal cytological sample was taken for HPV testing. Results. We recruited 79 HIV-infected and 85 HIV-uninfected MSM. The median age was 26 years in both groups. The positivities of anal HPV of any type (81.0% versus 48.2%), any high risk type (50.6% versus 27.1%), any low risk type (55.7% versus 31.8%), and any 9-valent vaccine type (74.7% versus 36.5%) were all significantly higher among HIV-infected compared to that among HIV-negative MSM (p for all < 0.05). The great majority of HPV-infected MSM were infected with 9-valent vaccine types (59 out of 64 HIV-infected and 31 out of 41 HIV-uninfected). Anal bacterial infections were associated with higher anal HPV positivity and greater number of anal HPV types. Conclusion. Sexually active MSM in Guangzhou, especially those infected with HIV, had high and multiple HPV detections. The majority of these cases were potentially preventable by HPV vaccine. Regular anal exams and early HPV vaccination are warranted in this population

Highly efficient V-Sb-O/SiO₂ catalyst with Sb atom-isolated VOx species for oxidative dehydrogenation of propane to propene

Silica supported vanadium–antimony mixed oxide catalyst (V–Sb–O/SiO₂) with the single ⎯⎯O⎯⎯Sb⎯⎯O⎯⎯V⎯⎯O⎯⎯Sb⎯⎯framework was prepared by a two-step impregnation method, in which SbCl₅ and NH₄VO₃ were introduced successively to generate monomeric VOx species isolated by Sb atoms. In the ODH of propane with O₂ as oxidant, the V–Sb–O/SiO₂ catalyst leads to high activity and high propene selectivity but depressed production of COx. At 600 °C, a 65.6% C₃H₆ selectivity with a 40.1% C₃H₈ conversion was obtained and then, a propene yield up to 26.3% was achieved on V–Sb–O/SiO₂ catalyst. This yield is comparable to that for V–Mg–O catalysts, SBA-15/MCM-41supported vanadia catalysts and vanadium-containing mesoporous silica materials