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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

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Max-Min Fairness in IRS-Aided Multi-Cell MISO systems with Joint Transmit and Reflective Beamforming

arXiv

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arXiv 2020年

作者： Xie, Hailiang Xu, Jie Liu, Ya-Feng School of Information Engineering Guangdong University of Technology Guangzhou510006 China Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with a set of multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming vectors at the BSs and the reflective beamforming vector at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS. To solve the non-convex min-weighted-SINR maximization problem, we first present an exact-alternating-optimization approach to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program (SOCP) and semi-definite relaxation (SDR), respectively. However, the exact-alternating-optimization approach has high computational complexity, and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization approach, in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation (SCA). In addition, to further reduce the computational complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Via numerical results, it is shown that the proposed three designs achieve significantly increased min-weighted-SINR values, as compared with benchmark schemes without the IRS or with random re

关键词： Beamforming

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Unobtrusive monitoring of learners’ game interactions to identify their dyslexia level

Unobtrusive monitoring of learners’ game interactions to id...

引用

International Conference on Advanced Learning Technologies (ICALT)

作者： Ahmed Tlili Roua Najjar Fathi Essalmi Mohamed Jemni Maiga Chang Ronghuai Huang Ting-Wen Chang Smart Learning Institute of Beijing Normal University China Research laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE) Tunis higher school of engineering (ENSIT) University of TUNIS Tunisia School of Computing and Information Systems Athabasca University Athabasca Canada

ISBN: (数字)9781728160900

ISBN: (纸本)9781728160917

Several research studies have highlighted that the traditional method of identifying dyslexia within learners is time consuming, expensive and might not be effective as some people acquired the skills to hide their disability. Particularly, no tool or method was reported in the Arab region (22 countries) that could help identify dyslexia within Arab learners. Therefore, this paper presents a developed and validated educational game to implicitly identify the level of dyslexia within learners based on their game play traces. The game was played by twenty-six children within a private school for special education with the supervision of experts from a private center for learners with disabilities. The obtained results showed that the accuracy level of identifying learners with dyslexia with the use of the game is high. Additionally, the experts reported a favorable perception and high technology acceptance degree towards the game. The findings of this research could enhance the educational technology field by providing an educational game design for implicit identification of dyslexia level.

关键词： Games Tools Interviews Decoding Instruments Encoding Writing

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Multi-label Classification with High-rank and High-order Label Correlations

arXiv

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Unlocking Integrated Wireless Powered Sensing and Communication Networks using Reconfigurable Intelligent Surface

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IEEE Transactions on Wireless Communications 2025年

作者： Zhu, Zhengyu Guo, Kaixuan Chu, Zheng Mi, De Mu, Junsheng Muhaidat, Sami Wong, Kai-Kit Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks 710126 China University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China Next-Generation Internet of Everything Laboratory Ningbo315100 China Birmingham City University College of Computing BirminghamB4 7XG United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Khalifa University KU6G Research Center Department of Computer and Communication Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes. © 2002-2012 IEEE.

关键词： Lagrange multipliers

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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

arXiv

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arXiv 2021年

作者： Wang, Qizhou Han, Bo Liu, Tongliang Niu, Gang Yang, Jian Gong, Chen Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering The University of Sydney Australia Japan Department of Computing Hong Kong Polytechnic University Hong Kong

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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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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Predicting extreme events from data using deep machine learning: when and where

arXiv

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arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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Demonstrating a long-coherence dual-rail erasure qubit using tunable transmons

arXiv

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arXiv 2023年

作者： Levine, H. Haim, A. Hung, J.S.C. Alidoust, N. Kalaee, M. DeLorenzo, L. Wollack, E.A. Arrangoiz-Arriola, P. Khalajhedayati, A. Sanil, R. Moradinejad, H. Vaknin, Y. Kubica, A. Hover, D. Aghaeimeibodi, S. Alcid, J.A. Baek, C. Barnett, J. Bawdekar, K. Bienias, P. Carson, H.A. Chen, C. Chen, L. Chinkezian, H. Chisholm, E.M. Clifford, A. Cosmic, R. Crisosto, N. Dalzell, A.M. Davis, E. D’Ewart, J.M. Diez, S. D’Souza, N. Dumitrescu, P.T. Elkhouly, E. Fang, M.T. Fang, Y. Flammia, S. Fling, M.J. Garcia, G. Gharzai, M.K. Gorshkov, A.V. Gray, M.J. Grimberg, S. Grimsmo, A.L. Hann, C.T. He, Y. Heidel, S. Howell, S. Hunt, M. Iverson, J. Jarrige, I. Jiang, L. Jones, W.M. Karabalin, R. Karalekas, P.J. Keller, A.J. Lasi, D. Lee, M. Ly, V. MacCabe, G. Mahuli, N. Marcaud, G. Matheny, M.H. McArdle, S. McCabe, G. Merton, G. Miles, C. Milsted, A. Mishra, A. Moncelsi, L. Naghiloo, M. Noh, K. Oblepias, E. Ortuno, G. Owens, J.C. Pagdilao, J. Panduro, A. Paquette, J.-P. Patel, R.N. Peairs, G. Perello, D.J. Peterson, E.C. Ponte, S. Putterman, H. Refael, G. Reinhold, P. Resnick, R. Reyna, O.A. Rodriguez, R. Rose, J. Rubin, A.H. Runyan, M. Ryan, C.A. Sahmoud, A. Scaffidi, T. Shah, B. Siavoshi, S. Sivarajah, P. Skogland, T. Su, C.-J. Swenson, L.J. Sylvia, J. Teo, S.M. Tomada, A. Torlai, G. Wistrom, M. Zhang, K. Zuk, I. Clerk, A.A. Brandão, F.G.S.L. Retzker, A. Painter, O. AWS Center for Quantum Computing PasadenaCA91125 United States Institute of Applied Physics The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Racah Institute of Physics The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Centre for Engineered Quantum Systems School of Physics The University of Sydney SydneyNSW2006 Australia Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Department of Physics California Institute of Technology PasadenaCA91125 United States Laboratory of Applied Physics Kavli Nanoscience Institute California Institute of Technology PasadenaCA91125 United States

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a "dual-rail qubit" consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon T1 errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability perasure = 2.19(2) × 10−3 per gate while the residual errors are ∼ 40 times lower. We further demonstrate mid-circuit detection of erasure errors while introducing Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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