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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Linear quadratic regulator design for multi-input systems with a distributed cooperative strategy

arXiv

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arXiv 2021年

作者： Duan, Peihu Duan, Zhisheng He, Lidong Shi, Ling Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

In this paper, a cooperative Linear Quadratic Regulator (LQR) problem is investigated for multi-input systems, where each input is generated by an agent in a network. The input matrices are different and locally possessed by the corresponding agents respectively, which can be regarded as different ways for agents to control the multi-input system. By embedding a fully distributed information fusion strategy, a novel cooperative LQR-based controller is proposed. Each agent only needs to communicate with its neighbors, rather than sharing information globally in a network. Moreover, only the joint controllability is required, which allows the multi-input system to be uncontrollable for every single agent or even all its neighbors. In particular, only one-time information exchange is necessary at every control step, which significantly reduces the communication consumption. It is proved that the boundedness (convergence) of the controller gains is guaranteed for time-varying (timeinvariant) systems. Furthermore, the control performance of the entire system is ensured. Generally, the proposed controller achieves a better trade-off between the control performance and the communication overhead, compared with the existing centralized/decentralized/consensus-based LQR controllers. Finally, the effectiveness of the theoretical results is illustrated by several comparative numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllers

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Patterning of 2D second harmonic generation active arrays in ferroelectric nematic fluids.

arXiv

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arXiv 2024年

作者： Lovšin, M. Petelin, A. Berteloot, B. Osterman, N. Aya, S. Huang, M. Drevenšek-Olenik, I. Mandle, R.J. Neyts, K. Mertelj, A. Sebastian, N. Jožef Stefan Institute Ljubljana Slovenia University of Ljubljana Faculty of Mathematics and Physics Ljubljana Slovenia Liquid Crystals and Photonics Group ELIS Department Ghent University Ghent Belgium School of Emergent Soft Matter South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices South China University of Technology Guangzhou China School of Physics and Astronomy University of Leeds Leeds United Kingdom School of Chemistry University of Leeds Leeds United Kingdom State Key Lab of Advanced Displays and Optoelectronics Technologies Hong Kong University of Science and Technology Hong Kong

Ferroelectric nematic liquid crystals exhibit unique non-linear optical properties, with the potential to become transformative materials for photonic applications. A promising direction relies on the fabrication of tailored polar orientational patterns via photoalignment, thus shaping the non-linear optical susceptibility through thin slabs of the ferroelectric fluid. Here, we explore the fabrication of 2D periodic SHG active arrays in ferroelectric nematic fluids, for different materials, cell thicknesses and motifs. Based on polarizing optical microscopy observations in combination with optical simulations, second harmonic generation microscopy and interferometry, the 3D structure of the motifs are revealed. Two different 2D periodic patterns are explored, showing that the balance between flexoelectric and electrostatic energy can lead to different domain structures, an effect which is rooted in the difference between the flexoelectric properties of the materials. It is shown that by combining the surface-inscribed alignment with different spontaneous degrees of twist, 2D SHG active arrays can be obtained in the micrometre scale, in which adjacent areas exhibit maximum SHG signals at opposite angles. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic generation

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Influence of Room-Temperature Oxidation on the Stability of Reactively Sputtered Tan Thin Films for Sheet Resistors

SSRN

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SSRN 2023年

作者： Zhang, Mingzhen Wang, Yue Song, Shangzhi Guo, Ruiqiang Zhang, Wenbin Li, Chengming Wei, Junjun Yang, Ronggui Jiang, Puqing School of Power and Energy Engineering Huazhong University of Science and Technology 1037 Luoyu Road Hubei Wuhan430074 China Institute for Advanced Materials and Technology University of Science and Technology Beijing No.30 Xueyuan Road Haidian District Beijing100083 China Shunde Graduate School University of Science and Technology Beijing No.2 Zhihui Road Shunde District Foshan528399 China Thermal Science Research Center Shandong Institute of Advanced Technology Shandong Jinan250103 China Department of Computer Science Michigan Technological University HoughtonMI49931 United States State Key Laboratory of Coal Combustion School of Power and Energy Engineering Huazhong University of Science and Technology 1037 Luoyu Road Hubei Wuhan430074 China

Reactively sputtered tantalum nitride (TaN) thin films are used extensively in high-precision chip resistors because of their near-zero temperature coefficients of resistance (TCR). Passivation is usually necessary to ensure the long-term stability of the films. However, room-temperature oxidation of TaN films is often unavoidable before the passivation of the resistor devices. The effect of room-temperature oxidation on the stability and properties of TaN thin films for use as resistors is still unclear. This work systematically studies room-temperature oxidation of reactively sputtered TaN thin films with different nitrogen contents (i.e., nitrogen flow ratio during film deposition). Results suggest that among different nitrogen flow ratios of 2%, 3%, 5%, and 7%, the films sputtered with a 3% N2 flow ratio are dominated by the Ta2N phase and have the most stable structure and properties, with the TCR, resistance, and thermal conductivity being unaffected by air exposure. Comparatively, the films prepared with other N2 contents are easily subject to room-temperature oxidation and result in apparent degradation of TCR. The influence of room-temperature oxidation also manifests as a reduction in the lattice thermal conductivities of the films. The films' electrical resistances, however, are little affected by room-temperature oxidation. © 2023, The Authors. All rights reserved.

关键词： Resistors

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Dual Functional Mesoporous Silica Colloidal Electrolyte for Lithium-Oxygen Batteries

SSRN

引用

SSRN 2022年

作者： Mu, Hailiang Zhuge, Xiangqun Ren, Guogang Luo, Kun Ding, Zhengping Ren, Yurong Luo, Zhihong Bayati, Maryam Xu, Ben Bin Liu, Xiaoteng Changzhou Key Laboratory of Intelligent Manufacturing and Advanced Technology for Power Battery School of Materials Science ＆ Engineering Changzhou University Changzhou213164 China Department of Engineering and Technology School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire HatfieldAL10 9AB United Kingdom Guangxi Key Laboratory of Optical and Electronic Materials and Devices Guilin University of Technology Guilin541004 China Department of Mechanical and Construction Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom

Dual functional mesoporous silica (mSiO2) colloidal electrolytes are designed for lithium-oxygen batteries (LOBs) for the purpose of protecting lithium anode and accelerating cathode reaction kinetics. It suppresses the side reactions of Li stripping/plating and enhances the cycling life performance of the LOB by relieving the attack of superoxide intermediates. The co-deposition of mesoporous mSiO2 and Li2O2 also effectively accelerated the decomposition of the discharge product by promoting the mass transfer at the cathode. Testing results show that the battery life of LOB using mSiO2 with a concentration of 80 mg L-1 in the colloidal electrolyte has been extended from 55 to 328 cycles compared with the one using conventional LiClO4/DMSO electrolyte. The rate performance and full-discharge capacity are also significantly enhanced. We discovered that the as-synthesized mSiO2 has a special well-ordered hexagonal mesoporous structure, with a high specific surface area of 1016.30 m2 g-1, which can form a stable colloid after mixing with 1.0 M LiClO4/DMSO. This is a meaningful investigation of suppressing side reactions using non-aqueous electrolytes, and the design principle also impacts research on other types of lithium metal batteries. © 2022, The Authors. All rights reserved.

关键词： Electrolytes

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Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals

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Control Theory and technology 2019年第1期17卷 99-111页

作者： Meng WANG Jianbin QIU Gang FENG Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong China State Key Laboratory of Robotics and Systems & Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China

This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.

关键词： Takagi-Sugeno (T-S) fuzzy affine systems event-triggered scheme, piecewise Lyapunov-Krasovskii functional, state estimation

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Distributed mirror descent algorithm with bregman damping for nonsmooth constrained optimization

arXiv

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arXiv 2021年

作者： Chen, Guanpu Li, Weijian Xu, Gehui Hong, Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Automation University of Science and Technology of China Anhui Hefei China Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

To solve distributed optimization efficiently with various constraints and nonsmooth functions, we propose a distributed mirror descent algorithm with embedded Bregman damping, as a generalization of conventional distributed projection-based algorithms. In fact, our continuous-time algorithm well inherits good capabilities of mirror descent approaches to rapidly compute explicit solutions to the problems with some specific constraint structures. Moreover, we rigorously prove the convergence of our algorithm, along with the boundedness of the trajectory and the accuracy of the solution. © 2021, CC0.

关键词： Mirrors

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A Pose Estimation Method for Fisheye Cameras Based on the EPnP Algorithm 11th

A Pose Estimation Method for Fisheye Cameras Based on the...

引用

11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Wang, Chenxiao Wang, Biao Ding, Yulong Xiao, Dunhui Chen, Ben M. Zhang, Keping Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Tongji University Shanghai China Department of Mechanical and Automation Engineering the Chinese University of Hong Kong China State Grid Nanjing Lishui District Power Supply Jiangsu Nanjing China

ISBN: (纸本)9789819647521

Fisheye cameras offer a wider field of view compared to traditional pinhole cameras. This paper presents a pose estimation method for fisheye cameras based on the EPnP algorithm. A unified projection model is used to analyze the imaging process, and it is shown to be applicable to fisheye cameras. A virtual pinhole camera is constructed, where the image captured by the fisheye camera is mapped onto the virtual camera’s image. The resulting virtual 2D reference points are then used as input to the EPnP algorithm for estimating the fisheye camera’s pose. The mapping error from the fisheye image to the virtual camera image is analyzed, revealing that the process does not amplify pixel error when extracting 2D reference points. It is recommended to select points near the image’s central focus as 2D reference points. Experimental results demonstrate that the proposed method is both reliable and efficient. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pinhole cameras

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How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling

arXiv

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arXiv 2022年

作者： Cahyawijaya, Samuel Wilie, Bryan Lovenia, Holy Zhong, MingQian Zhong, Huan Ip, Nancy Y. Fung, Pascale Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Division of Life Science State Key Laboratory of Molecular Neuroscience Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong Hong Kong Center for Neurodegenerative Diseases Hong Kong Science Park Hong Kong Hong Kong

Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA. However, the applicability of these methods to real clinical use cases is hindered, due to the limitation of pre-trained LMs in processing long textual data with thousands of words, which is a common length for a clinical note. In this work, we explore long-range adaptation from such LMs with Longformer, allowing the LMs to capture longer clinical notes context. We conduct experiments on three n2c2 challenges datasets and a longitudinal clinical dataset from Hong Kong Hospital Authority electronic health record (EHR) system to show the effectiveness and generalizability of this concept, achieving 10% F1-score improvement. Based on our experiments, we conclude that capturing a longer clinical note interval is beneficial to the model performance, but there are different cut-off intervals to achieve the optimal performance for different target variables. © 2022, CC BY-NC-SA.

关键词： Modeling languages

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

引用

arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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