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arXiv 2024年

作者： Chen, Chuang Sun, Xiao Liu, Zhi School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer and Network Engineering The University of Electro-Communications Chofu-shi Tokyo182-8585 Japan

Visual emotion analysis holds significant research value in both computer vision and psychology. However, existing methods for visual emotion analysis suffer from limited generalizability due to the ambiguity of emotion perception and the diversity of data scenarios. To tackle this issue, we introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework. Inspired by psychological research emphasizing the inseparability of the emotional exploration process from the interaction between individuals and their environment, UniEmoX integrates scene-centric and person-centric low-level image spatial structural information, aiming to derive more nuanced and discriminative emotional representations. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations more effectively. To the best of our knowledge, this is the first large-scale pretraining framework that integrates psychological theories with contemporary contrastive learning and masked image modeling techniques for emotion analysis across diverse scenarios. Additionally, we develop a visual emotional dataset titled Emo8. Emo8 samples cover a range of domains, including cartoon, natural, realistic, science fiction and advertising cover styles, covering nearly all common emotional scenes. Comprehensive experiments conducted on six benchmark datasets across two downstream tasks validate the effectiveness of UniEmoX. The source code is available at https://***/chincharles/u-emo. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Nonreciprocal Single-Photon Band Structure

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Physical Review Letters 2022年第20期128卷 203602-203602页

作者： Jiang-Shan Tang Wei Nie Lei Tang Mingyuan Chen Xin Su Yanqing Lu Franco Nori keyu Xia College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210023 China School of Physics Nanjing University Nanjing 210023 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing 210023 China Key Laboratory of Intelligent Optical Sensing and Manipulation (Nanjing University) Ministry of Education Nanjing 210023 China

We study a single-photon band structure in a one-dimensional coupled-resonator optical waveguide that chirally couples to an array of two-level quantum emitters (QEs). The chiral interaction between the resonator mode and the QE can break the time-reversal symmetry without the magneto-optical effect and an external or synthetic magnetic field. As a result, nonreciprocal single-photon edge states, band gaps, and flat bands appear. By using such a chiral QE coupled-resonator optical waveguide system, including a finite number of unit cells and working in the nonreciprocal band gap, we achieve frequency-multiplexed single-photon circulators with high fidelity and low insertion loss. The chiral QE-light interaction can also protect one-way propagation of single photons against backscattering. Our work opens a new door for studying unconventional photonic band structures without electronic counterparts in condensed matter and exploring its applications in the quantum regime.

关键词： Cavity quantum electrodynamics Light propagation, transmission & absorption Quantum description of light-matter interaction Topological effects in photonic systems

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How universal polynomial bases enhance spectral graph neural networks: heterophily, over-smoothing, and over-squashing 24

How universal polynomial bases enhance spectral graph neural...

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Proceedings of the 41st International Conference on Machine Learning

作者： Keke Huang Yu Guang Wang Ming Li Pietro Liò School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China and Shanghai AI Laboratory Shanghai China and School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge UK

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation.

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Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images

arXiv

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arXiv 2020年

作者： Che, Fengying Shi, Ruichuan Li, Shuqin Wu, Jian Li, Haoran Chen, Weixing Zhang, Hao Qu, Hui Li, Zhi Cui, Xiaoyu College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Liaoning Shenyang China Department of Medical Oncology The First Affiliated Hospital of China Medical University Liaoning110001 China Shenzhen College of Advanced Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Breast Surgery Liaoning Cancer Hospital and Institute Cancer Hospital of China Medical University Liaoning Shenyang110042 China

The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static *** results show that compared with static features, dynamic feature extraction can achieve higher robustness and accuracy for time-dependent tomographic images. We also found that the dynamic features that influence different clinical problems are also quite different. Copyright © 2020, The Authors. All rights reserved.

关键词： Tomography

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

arXiv

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arXiv 2024年

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network – UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Using entropy-driven amplifier circuit response to build nonlinear model under the influence of Lévy jump

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BMC bioinformatics 2022年第SUPPL 12期22卷 437页

作者： Hao Fu Hui Lv Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. lh8481@***. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China. lh8481@***. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. zhangq@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China. zhangq@***.

BACKGROUND:Bioinformatics is a subject produced by the combination of life science and computer science. It mainly uses computer technology to study the laws of biological systems. The design and realization of DNA circuit reaction is one of the important contents of bioinformatics. RESULTS:In this paper, nonlinear dynamic system model with Lévy jump based on entropy-driven amplifier (EDA) circuit response is studied. Firstly, nonlinear biochemical reaction system model is established based on EDA circuit response. Considering the influence of disturbance factors on the system, nonlinear biochemical reaction system with Lévy jump is built. Secondly, in order to prove that the constructed system conforms to the actual meaning, the existence and uniqueness of the system solution is analyzed. Next, the sufficient conditions for the end and continuation of EDA circuit reaction are certified. Finally, the correctness of the theoretical results is proved by numerical simulation, and the reactivity of THTSignal in EDA circuit under different noise intensity is verified. CONCLUSIONS:In EDA circuit reaction, the intensity of external noise has a significant impact on the system. The end of EDA circuit reaction is closely related to the intensity of Lévy noise, and Lévy jump has a significant impact on the nature of biochemical reaction system.

关键词： Entropy-driven amplifier Lyapunov function Lévy jump Nonlinear system model

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Deep high-order supervised hashing

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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

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Correction to: Emotion recognition based on physiological signals using brain asymmetry index and echo state network

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Neural computing and Applications 2018年第10期32卷 6415-6415页

作者： Ren, Fuji Dong, Yindong Wang, Wei School of Computer and Information Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

The authors quite agree that the AsI model is first developed by Panagiotis Petrantonakis from references [11] and [18] in their thesis.

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Unauthorized UAV Countermeasure for Low-Altitude Economy: Joint Communications and Jamming based on MIMO Cellular Systems

arXiv

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arXiv 2024年

作者： Li, Zhuoran Gao, Zhen Wang, Kuiyu Mei, Yikun Zhu, Chunli Chen, Lei Wu, Xiaomei Niyato, Dusit Beijing100081 China BIT Zhuhai519088 China The MIT Key Laboratory of Complex-Field Intelligent Sensing BIT Beijing100081 China Jinan250307 China Jiaxing314019 China The State Key Laboratory of CNS ATM & State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China China Tower Corporation Limited No.9 Don-gran North Street Haidian District Beijing100089 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Research Institue of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China

To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of this, this paper investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input multiple-output (MIMO) cellular systems. We first formulate a joint communication and jamming (JCJ) problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Therefore, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. Simulation codes are provided to reproduce the results in this paper: https://***/LiZhuoRan0. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas. Copyright © 2024, The Authors. All rights reserved.

关键词： Jamming

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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

arXiv

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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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