Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

Abstract: A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Abstract: The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edge-cloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process.

Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks

Abstract: Discovering evolving communities in dynamic networks is essential to important applications such as analysis for dynamic web content and disease progression. Evolutionary clustering uses the temporal smoothness framework that simultaneously maximizes the clustering accuracy at the current time step and minimizes the clustering drift between two successive time steps. In this paper, we propose two evolutionary nonnegative matrix factorization (ENMF) frameworks for detecting dynamic communities. To address the theoretical relationship among evolutionary clustering algorithms, we first prove the equivalence relationship between ENMF and optimization of evolutionary modularity density. Then, we extend the theory by proving the equivalence between evolutionary spectral clustering and ENMF, which serves as the theoretical foundation for hybrid algorithms. Based on the equivalence, we propose a semi-supervised ENMF (sE-NMF) by incorporating a priori information into ENMF. Unlike the traditional semi-supervised algorithms, a priori information is integrated into the objective function of the algorithm. The main advantage of the proposed algorithm is to escape the local optimal solution without increasing time complexity. The experimental results over a number of artificial and real world dynamic networks illustrate that the proposed method is not only more accurate but also more robust than the state-of-the-art approaches.

Hyperspectral Unmixing via Deep Convolutional Neural Networks

Abstract: Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this letter, an end-to-end HU method is proposed based on the convolutional neural network (CNN). The proposed method uses a CNN architecture that consists of two stages: the first stage extracts features and the second stage performs the mapping from the extracted features to obtain the abundance percentages. Furthermore, a pixel-based CNN and cube-based CNN, which can improve the accuracy of HU, are presented in this letter. More importantly, we also use dropout to avoid overfitting. The evaluation of the complete performance is carried out on two hyperspectral data sets: Jasper Ridge and Urban. Compared with that of the existing method, our results show significantly higher accuracy.

Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns

Abstract: Abnormal DNA methylation is an important epigenetic regulator involving tumorigenesis. Deciphering cancer common and specific DNA methylation patterns is essential for us to understand the mechanisms of tumor development. The Cancer Genome Atlas (TCGA) project provides a large number of samples of different cancers that enable a pan-cancer study of DNA methylation possible. Here we investigate cancer common and specific DNA methylation patterns among 5480 DNA methylation profiles of 15 cancer types from TCGA. We first define differentially methylated CpG sites (DMCs) in each cancer and then identify 5450 hyper- and 4433 hypomethylated pan-cancer DMCs (PDMCs). Intriguingly, three adjacent hypermethylated PDMC constitute an enhancer region, which potentially regulates two tumor suppressor genes BVES and PRDM1 negatively. Moreover, we identify six distinct motif clusters, which are enriched in hyper- or hypomethylated PDMCs and are associated with several well-known cancer hallmarks. We also observe that PDMCs relate to distinct transcriptional groups. Additionally, 55 hypermethylated and 7 hypomethylated PDMCs are significantly associated with patient survival. Lastly, we find that cancer-specific DMCs are enriched in known cancer genes and cell-type-specific super-enhancers. In summary, this study provides a comprehensive investigation and reveals meaningful cancer common and specific DNA methylation patterns.