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Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance-and decomposition-based approaches, for many-objective optimization. Our major purpose is to exploit the merits from both of dominance-and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The performance of our proposed method is validated and compared with four state-of-the-art algorithms on a number of unconstrained benchmark problems with up to fifteen objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.

Abstract:In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods. for nuclei detection of breast cancer histopathology images," IEEE Trans.

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The development of highly active, universal, and stable inexpensive electrocatalysts/cocatalysts for hydrogen evolution reaction (HER) by morphology and structure modulations remains a great challenge. Herein, a simple self-template strategy was developed to synthesize hollow Co-based bimetallic sulfide (MxCo3-xS4, M = Zn, Ni, and Cu) polyhedra with superior HER activity and stability. Homogenous bimetallic metal-organic frameworks are transformed to hollow bimetallic sulfides by solvothermal sulfidation and thermal annealing. Electrochemical measurements and density functional theory computations show that the combination of hollow structure and homoincorporation of a second metal significantly enhances the HER activity of Co3S4. Specifically, the homogeneous doping in Co3S4 lattice optimizes the Gibbs free energy for H* adsorption and improves the electrical conductivity. Impressively, hollow Zn0.30Co2.70S4 exhibits electrocatalytic HER activity better than most of the reported nobel-metal-free electrocatalysts over a wide pH range, with overpotentials of 80, 90, and 85 mV at 10 mA cm(-2) and 129, 144, and 136 mV at 100 mA cm(-2) in 0.5 M H2SO4, 0.1 M phosphate buffer, and 1 M KOH, respectively. It also exhibits photocatalytic HER activity comparable to that of Pt cocatalyst when working with organic photosensitizer (Eosin Y) or semiconductors (TiO2 and C3N4). Furthermore, this catalyst shows excellent stability in the electrochemical and photocatalytic reactions. The strategy developed here, i.e., homogeneous doping and self-templated hollow structure, provides a way to synthesize transition metal sulfides for catalysis and energy conversion.

Influences from solvents on charge storage in titanium carbide MXenes. (2019) Nature Energy, 4 (3). 241-248.

Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large-scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter-and intra-class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-theart approaches on our DIOR dataset to establish a baseline for future research.