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arXiv 2020年

作者： Yu, Liang Sun, Yi Xu, Zhanbo Shen, Chao Yue, Dong Jiang, Tao Guan, Xiaohong College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210003 China Systems Engineering Institute Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China college of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Wuhan National Laboratory for Optoelectronics School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Photonic Modes Prediction via Multi-Modal Diffusion Model

arXiv

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arXiv 2024年

作者： Sun, Jinyang Chen, Xi Wang, Xiumei Zhu, Dandan Zhou, Xingping Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of AI Education Shanghai East China Normal University Shanghai200333 China Institute of Quantum Information and Technology Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell's equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the Multi-Modal Diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces Multi-Modal deep learning to construct complex mapping between structural information and light field as high-dimensional vectors, and generates light field images based on this mapping. © 2024, CC BY.

关键词： Maxwell equations

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Deep learning for the design of non-Hermitian topolectrical circuits

arXiv

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arXiv 2024年

作者： Chen, Xi Sun, Jinyang Wang, Xiumei Jiang, Hengxuan Zhu, Dandan Zhou, Xingping College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of AI Education Shanghai East China Normal University Shanghai200333 China Institute of Quantum Information and Technology Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalues non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Multiple energy scales and anisotropic energy gap in the charge-density-wave phase of kagome superconductor CsV3Sb5

arXiv

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arXiv 2021年

作者： Nakayama, Kosuke Li, Yongkai Kato, Takemi Liu, Min Wang, Zhiwei Takahashi, Takashi Yao, Yugui Sato, Takafumi Department of Physics Faculty of Science Tohoku University Sendai980-8578 Japan Tokyo102-0076 Japan School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan WPI Research Center Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Tohoku University Sendai980-8577 Japan

Kagome metals AV3Sb5 (A = K, Rb, and Cs) exhibit superconductivity at 0.9-2.5 K and charge-density wave (CDW) at 78-103 K. key electronic states associated with the CDW and superconductivity remain elusive. Here, we investigate low-energy excitations of CsV3Sb5 by angle-resolved photoemission spectroscopy. We found an energy gap of 50-70 meV at the Dirac-crossing points of linearly dispersive bands, pointing to an importance of spin-orbit coupling. We also found a signature of strongly Fermi-surface and momentum-dependent CDW gap characterized by the larger energy gap of maximally 70 meV for a band forming a saddle point around the M point, the smaller (0-18 meV) gap for a band forming massive Dirac cones, and a zero gap at the Γ/A-centered electron pocket. The observed highly anisotropic CDW gap which is enhanced around the M point signifies an importance of scattering channel connecting the saddle points, laying foundation for understanding the nature of CDW and superconductivity in AV3Sb5 © 2021, CC BY.

关键词： Charge density

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

arXiv

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arXiv 2022年

作者： Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Biao, Jiang Yuhan, Ding Zhang, Li Wan Xiaoyue, Feng Sijing, Chen Tianheng, Zhong Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang University of Science and Technology of China China Shanghai Ai Laboratory China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Macau China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology China School of Artificial Intelligence Xidian University China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Harbin Institute of Technology China Changsha Research Institute of Mining and Metallurgy China Tianjin University China University of Wurzburg Computer Vision Lab Germany Couger Inc. Japan School of Statistics and Data Science Nankai University China Founding Minds Software India Department of Electronics and Communication Srm University Fudan University China Shenyang Aerospace University China Nanjing University of Science and Technology China

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Comparison of Research on Social Media in China and Foreign Countries Based on Bibliometric Analysis

引用

Journal of Physics: Conference Series 2020年第1期1616卷

作者： Peng Yunan Wang Zongshui Feng Yueyang Yang shengqin School of Economics and Management Beijing Information Science and Technology University Beijing 100192 China Beijing Key Lab of Green Development and Decision Based on Big Data Beijing 100192 China

This paper aims to compare the similarities and differences of Chinese and foreign research on social media. Cluster analysis, co-word analysis and social network analysis have been used to compare the research trends, content relevance and network structure of research on social media. (1) Similarities: Social media is a hot topic for scholars both in China and foreign countries; the development of social network technology leads to many new areas; social media research tends to study privacy, commerciality and service; hotspots of social media research in China and foreign countries both include social media, platform and social network. (2) Difference: Chinese research mainly focuses on big data, Wechat and Weibo, while foreign research mainly focuses on Facebook, Twitter and other kinds of social network media; Chinese research is clustered while foreign research is extensive; Chinese research pays more attention on macroscopic perspectives, such as media convergence, big data and libraries; foreign research tends to microcosmic perspectives, such as emotional analysis, crisis information dissemination, social media marketing.

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Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG

arXiv

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arXiv 2024年

作者： Chen, Jiarong Wu, Wanqing Liu, Tong Hong, Shenda National Institute of Health Data Science Peking University Beijing100191 China School of Biomedical Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Department of Micro/Nano Electronics and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin300211 China Institute of Medical Technology Health Science Center of Peking University Beijing100191 China Institute for Artificial Intelligence Peking University Beijing100871 China

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410. © 2024, CC BY.

关键词： Mean square error

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Deep User Modeling for Content-based Event Recommendation in Event-based Social networks

Deep User Modeling for Content-based Event Recommendation in...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

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Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

arXiv

引用

arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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Correction to: a network method to identify the dynamic changes of the data flow with spatio-temporal feature

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Applied Intelligence 2021年第5期52卷 5594-5594页

作者： Wang, Li-Na Tan, Gui-Min Zang, Chen-Rui Science College Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot China Inner Mongolia Branch China Unicom Hohhot China

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