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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Hexagonal Convolutional Neural network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Gao Fa Zhang Kai Hu Xuefeng Cui Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs).

关键词： Visualization Convolution Supervised learning Interference Spatial databases Graph neural networks Convolutional neural networks

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A Recommendation Method of Educational Resources Based on Knowledge Structure

A Recommendation Method of Educational Resources Based on Kn...

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International Conference on Computer and Communications (ICCC)

作者： Ronghua Shi Lei Mao Chao Hu Sixiang Li Information and Network Center Central South University Changsha China School of Information Science and Engineering Central South University Changsha China Key Laboratory of Medical Information Research of Hunan Province Central South University Changsha China School of Software Central South University Changsha China

ISBN: (纸本)9781538683408

With the development of online learning platform, network education resources become more and more rich. It makes online learners easier to choose the time, place and content of learning. Because of the huge number of educational re-sources in learning platform, it is difficult for learners to find learning re-sources quickly and efficiently. The user-based collaborative filtering recommendation algorithm is improved by knowledge structure. A new method of education resource recommendation is brought up in this paper. In this method, not only the recommendation result accords with the learner's interest, but also accords with the existing knowledge system of the learner. It better meets the learner's individualized learning needs and improves the learning efficiency. In this paper, the experiment was carried out by the method of controlled experiment. The experimental results are evaluated through two aspects of correlation score and learning process score, and good experimental results have been obtained.

关键词： Education Collaboration Recommender systems Knowledge based systems Internet Prediction algorithms

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Deep learning based joint resource scheduling algorithms for hybrid MEC networks

arXiv

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arXiv 2019年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and Business Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UEs information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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A Novel Lncrna, Lncery, Interacts with Wdr82 to Regulate Erythroid Differentiation By Promoting Globin Gene Transcription

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BLOOD 2020年 136卷 2-2页

作者： Yang, Shangda Sun, Guohuan Wu, Peng Kuang, Yijin Chen, Cong Zheng, Zhaofeng Gu, Quan Zhu, Caiying Gou, Fanglin Yang, Zining Zhao, Xiangnan Yang, Liu Lu, Shihong Lihong, Shi Lv, Xue Dong, Fang Shi, Lihong Ng, Lai Guang Liu, Jing Cheng, Hui Cheng, Tao Institute of Hematology & Blood Diseases Hospital State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Science & Peking Union Medical College Tianjin China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China University of Pittsburgh School of Medicine Tianjin China Molecular Biology Research Center Center for Medical Genetics Hunan Province Key Laboratory of Basic and Applied Hematology School of Life Sciences Central South University Changsha China State Key Laboratory of Experimental Hematology Tianjin China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Science & Peking Union Medical College Tianjin China State Key Laboratory of Hematology Institute of Hematology & Blood Diseases Hospital Tianjin China Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China Singapore Immunology Network Agency for Science Technology and Research Biopolis Singapore Department of Hematology Third Xiangya Hospital of Central South University Changsha China

Hematopoietic differentiation is controlled by both genetic and epigenetic regulators. Long non-coding RNAs (lncRNAs) have been demonstrated to be important for normal hematopoiesis, but their function in erythropoiesis needs to be further explored. Here, we profiled the transcriptome of 17 murine hematopoietic cell populations by deep sequencing and identified a novel lncRNA, that was highly expressed in erythroid-related progenitors and erythrocytes. For this reason, we named it lncEry. We also identified a novel lncEry isoform, which was the principal transcript and has not been reported before. Furthermore, we found that nearly 90% of lncEry molecules localized to the nucleus. Next, we performed knockdown and knockout assays to study the function of lncEry, and found that lncEry depletion impaired erythroid differentiation. RNA sequencing analysis showed that lncEry depletion decreased the expression of erythrocyte homeostasis or differentiation related genes, including globin genes, thus indicating its important role in regulating erythroid differentiation. Mechanistically, we performed RNA-pulldown assays and found that lncEry could interact with Wdr82, a component of the Set1A histone H3-Lys4 methyltransferase complex. In addition, a series of molecular assays indicated that lncEry could stabilize the localization of Set1A/Wdr82 complex to facilitate H3K4me3 on the promoter region of globin genes and participate in regulating erythropoiesis. These findings identify lncEry as an important player in the transcriptional regulation of globin genes to coordinate erythropoiesis. Disclosures No relevant conflicts of interest to declare.

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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Signal sparse representation based on the peak transform and modulus maximum of wavelet coefficients

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Tamkang Journal of Science and Engineering 2011年第3期14卷 217-224页

作者： Cen, Yi-Gang Xin, Rui Chen, Xiao-Fang Cen, Li-Hui Hu, Ming-Xing Chen, Shi-Ming Institute of Information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China School of Information Science and Engineering Central South University Hunan Changsha 410083 China Centre for Medical Image Computing University College London United Kingdom School of Electrical and Electronic Engineering East China Jiao Tong University Jiangxi Nanchang 330013 China

It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements. This technique is known as "compressed sensing" or "compressive sampling" (CS). A basic requirement of CS is that a signal should be sparse or it can be sparsely represented in some orthogonal bases. Based on the Peak Transform (PT) and modulus maximum of wavelet coefficients, a new algorithm was proposed for the signals that are non-sparse themselves and can not be sparsely represented by wavelet transform such as the Linear Frequency Modulated signal. According to this algorithm, K-sparse wavelet coefficients can be obtained. For the peak sequence produced by the Peak Transform, value expansion approach of reversible watermarking is exploited such that the peak sequence can be embedded into the measurements of the signal, which avoids increasing additional points for transmission. By using the Peak Transform and modulus maximum, non-sparse wavelet coefficients can be transformed into K-sparse coefficients, which improves the reconstruction result of CS. Simulation results showed that our proposed algorithm achieved better performance comparing with the original CS algorithm.

关键词： Compressed sensing

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Machine learning prediction of higher heating value of biomass

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Biomass Conversion and Biorefinery 2021年第5期13卷 3659-3667页

作者： Dai, Zuocai Chen, Zhengxian Selmi, Abdellatif Jermsittiparsert, Kittisak Denić, Nebojša M. Nеšić, Zoran College of Mechanical and Electrical Engineering Hunan City University Yiyang China Key Laboratory Energy Monitoring and Edge Computing for Smart City of Hunan Province Yiyang China Department of Mechanical Engineering Columbia University New York USA Department of Civil Engineering College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Ecole Nationale d’Ingénieurs deTunis (ENIT) Civil Engineering Laboratory Tunis Tunisia Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Humanities and Social Sciences Duy Tan University Da Nang Vietnam MBA School Henan University of Economics and Law Zhengzhou China Faculty of Sciences and Mathematics University of Priština Kosovska Mitrovica Serbia Faculty of Technical Sciences Čačak University in Kragujevac Čačak Serbia

Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs’ influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.

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Tunable coupling of a quantum phononic resonator to a transmon qubit with flip-chip architecture

arXiv

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arXiv 2024年

作者： Ruan, Xinhui Li, Li Liang, Guihan Zhao, Silu Wang, Jia-Heng Bu, Yizhou Chen, Bingjie Song, Xiaohui Li, Xiang Zhang, He Wang, Jinzhe Zhao, Qianchuan Xu, Kai Fan, Heng Liu, Yu-Xi Zhang, Jing Peng, Zhihui Xiang, Zhongcheng Zheng, Dongning Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China CAS Center for Excellence in Topological Quantum Computation School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China

A hybrid system with tunable coupling between phonons and qubits shows great potential for advancing quantum information processing. In this work, we demonstrate strong and tunable coupling between a surface acoustic wave (SAW) resonator and a transmon qubit based on galvanic-contact flip-chip technique. The coupling strength varies from 2π×7.0 MHz to -2π×20.6 MHz, which is extracted from different vacuum Rabi oscillation frequencies. The phonon-induced ac Stark shift of the qubit at different coupling strengths is also shown. Our approach offers a good experimental platform for exploring quantum acoustics and hybrid systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

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