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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu 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 Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China 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

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

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Fed3Scale: A cloud-edge-client tri-scale collaborative semi-supervised hierarchical federated learning framework

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Knowledge-Based systems 2025年 325卷

作者： Luqi Wang Shanchen Pang Zhiyuan Zhao Xiao He Kuijie Zhang Haiyuan Gui Nuanlai Wang College of Computer Science and Technology Qingdao Institute of Software China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Chemical Safety China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China

In federated learning (FL) architectures, clients can train models locally without uploading raw data, thereby preserving data privacy. However, challenges such as heterogeneous computational capabilities, non-independent and identically distributed (non-IID) data, and a scarcity of labeled data limit the performance of FL in practical applications. Traditional two-tier FL architectures struggle to effectively address these issues, leading to suboptimal model training performance. To tackle these challenges, we propose a cloud–edge-client tri-scale semi-supervised FL framework (Fed3Scale). This framework leverages the computational capabilities of the cloud, edge servers (ES), and client devices, significantly mitigating the impact of heterogeneous computing power on model training. By uploading only feature-level data and incorporating a homomorphic encryption loss function, the framework further ensures data privacy. Additionally, to address data heterogeneity and the scarcity of labeled data, we introduce an aggregated semi-supervised learning (AggSSL) strategy: ES perform supervised training with their associated clients while engaging in unsupervised model aggregation with other ES, and jointly conduct semi-supervised training with the cloud server (CS). Experimental results show that compared to the best existing baseline method, Fed3Scale achieves a 5.77% improvement in accuracy, providing an efficient and reliable solution for intelligent task processing in heterogeneous clients. Our code is available at https://***/luqiwangupc/Fed3Scale .

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Retrospective study of malignant phyllodes tumors of the breast: Younger age, prior fibroadenoma surgery, malignant heterologous elements and surgical margins may predict recurrence

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Breast 2021年 57卷 62-70页

作者： Li, Yang Song, Yixuan Lang, Ronggang Shi, Lu Gao, Shuang Liu, Hong Wang, Ping The Second Surgical Department of Breast Cancer Tianjin Medical University Cancer Institute & Hospital National Clinical Research Center for Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer Tianjin China Department of Radiation Oncology Tianjin Medical University Cancer Institute & Hospital National Clinical Research Center for Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer Tianjin China Department of Breast Cancer Pathology and Research Laboratory Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center of Cancer Key Laboratory of Breast Cancer Prevention and Therapy Tianjin Medical University Ministry of Education Key Laboratory of Cancer Prevention and Therapy State Key Laboratory of Breast Cancer Research Tianjin China College of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China

Purpose: The potential recurrence rate of malignant phyllodes tumors (MPTs) of the breast is high, and the prognostic factors are still unclear. We therefore aim to study the factors affecting the outcome of MPTs. Methods: A retrospective review of MPT patients treated from 2006 to 2020 at our institution was conducted. Univariate and multivariate Cox proportional hazard models were used to examine the influence of different variables on RFS. Moreover, significant prognostic factors were combined to construct the nomogram to predict the probability of relapse occurring in MPT patients. The 5-year and 10-year RFS rates were estimated using the Kaplan–Meier method. Results: During the study period, 188 MPT patients were identified. The presence of malignant heterologous elements was observed in 23 (12.2%) patients with MPT, and the patients with malignant heterologous elements who received chemotherapy had longer RFS, which could reduce the risk of recurrence (p = 0.022). Recurrence occurred in 56/188 (29.8%) patients, of whom 47 experienced local recurrence and 11 experienced distant metastases. The 5-year and 10-year cumulative RFS rates were 77.5% and 70.1%, respectively. Age (p = 0.041), fibroadenoma surgery history (p = 0.004), surgical margins (p = 0.001) and malignant heterologous elements (p < 0.001) were independent risk factors for postoperative RFS. Subsequently, a nomogram was built, with a C-index of 0.64 (95% CI: 0.629–0.661), to predict the risk of recurrence. Conclusion: The results of this study showed that younger age, fibroadenoma surgery history, malignant heterologous elements and surgical margins <1 cm predict a higher incidence of recurrence in MPT patients. Patients with malignant heterologous elements treated with chemotherapy could have a reduced risk of recurrence. © 2021 The Authors

关键词： Age Fibroadenoma surgery history Malignant heterologous elements Malignant phyllodes tumors Recurrence-free survival Surgical margins

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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EC-GAN: Inferring brain effective connectivity via generative adversarial networks 34

EC-GAN: Inferring brain effective connectivity via generativ...

引用

34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Liu, Jinduo Ji, Junzhong Xun, Guangxu Yao, Liuyi Huai, Mengdi Zhang, Aidong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Computer Science and Engineering SUNY at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9781577358350

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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Multi-Weather Full-Body Triboelectric Garments for Personalized Moisture Management and Water Energy Acquisition

SSRN

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SSRN 2023年

作者： Kang, Tian Ma, Wanwan Guo, Yinben Zhou, Jie Zhang, Yihe Ji, Tianyi Gong, Wei School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai201620 China Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles Donghua University Shanghai201620 China College of Electronics and Information Engineering Sichuan University Chengdu610064 China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Shanghai201620 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore

The occurrence of extreme events (such as heatwaves, heavy precipitation) is unprecedented in the observed record and will increase with increasing global warming, leading to severe human health issues. Wearing suitable garments in different weather is one of the solutions for people to ease their physical discomfort in an energy-saving way. However, only one piece of traditional clothes hardly satisfies the demand of the changing weather. Herein, a multi-weather full-body triboelectric garment with a one-way moisture transport structure is presented to keep people feeling cool in the scorching weather while capturing droplet energy in rainy situations. A trilayer progressive wetting structure was designed to effectively transport sweat from the inner layer near the skin to the outer layer to keep the skin dry and comfortable in the muggy air, with a unidirectional transmission index of 1025%. When it starts to rain, the rain droplets quickly roll down the textile surface and generate triboelectric energy with the hydrophobic surface and conductive MXene layer inside. More than that, the scalable fabrication process makes it possible to obtain the textile with enough areas for making a whole garment, which is much more practical in real application scenarios. © 2023, The Authors. All rights reserved.

关键词： Fabrication

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Comprehensive Mechanism of Co Electroreduction on Dacs Anchored on N-Doped Graphene

SSRN

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SSRN 2022年

作者： Liu, Di Kong, Youchao Zhao, Jia Ai, Haoqiang Wu, Rucheng Bai, Haoyun Leong, Chon Chio Lo, Kin Ho Wang, Shuangpeng Ip, Weng Fai Lin, Sen Pan, Hui Institute of Applied Physics and Materials Engineering University of Macau China State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry Fuzhou University Fuzhou350002 China Department of Electromechanical Engineering Faculty of Science and Technology University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China Department of Physics and Chemistry Faculty of Science and Technology University of Macau China

Carbon neutralization has drawn increasing attention for realizing the carbon cyclization and reducing the greenhouse effect. Although the C1 products, such as CO, can be achieved with a high Faraday efficiency, the targeted production of C2 fuels as well as the mechanism have not been systematically investigated. In this work, we carry out a first-principles study to screen dual-atom catalysts (DACs) for producing C2 fuels through the electrocatalytic carbon monoxide reduction reaction (e-CORR). We find that e-CORR on DAC-Co can produce methanol and ethylene, while e-CORR on DAC-Cu produces methanol, acetate, and ethylene. Importantly, the C2 products can be obtained more easily than the C1 products because of lower limiting potentials. Furthermore, we show that the explicit solvent can enhance the adsorption and influence the protonation steps, which subsequently affects the protonation and dimerization behavior as well as the performance and selectivity of e-CORR on DACs. Furthermore, we demonstrate that the C-C coupling is easy to be formed and stabilized if the Integrated Crystal Orbital Hamilton Population (ICOHP) is low because of the low energy barrier. Our findings provide not only guidance on the design of novel catalysts for e-CORR, but insightful understanding on the mechanism. © 2022, The Authors. All rights reserved.

关键词： Carbon monoxide

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Offsite Evaluation of Localization systems: Criteria, systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Katsa: Knn Ameliorated Tree-Seed Algorithm for Complex Optimization Problems

SSRN

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SSRN 2023年

作者： Jiang, Jianhua Wu, Jiaqi Meng, Xianqiu Qian, Lize Luo, Jinmeng Li, Keqin Center for Artificial Intelligence Jilin University of Finance and Economics Changchun130117 China Jilin Province Key Laboratory of Fintech Jilin University of Finance and Economics Changchun130117 China College of Computer Science and Technology Jilin University China Institute of Energy Research Jiangxi Academy of Sciences Nanchang330096 China Department of Computer Science State University of New York New PaltzNY12561 United States

Tree-Seed Algorithm (TSA) is an outstanding algorithm for optimization problems, but it inevitably falls into the local optimum and has a low convergence speed in solving complex problems. This paper aims to address the above defects. Inspired by efficient learning from neighbors, a K-Nearest Neighbor (KNN) mechanism is adopted to enhance the tree or seed generation methods for achieving the balance between exploitation and exploration. The proposed algorithm is named the KNN Ameliorated Tree-Seed Algorithm (KATSA). First, inspired by the KNN mechanism, based on the best tree, the search space is divided into best and non-best neighbor areas. Based on this division approach, the proposed seed generation strategy has a precise heuristic, and the convergence speed can be accelerated. Second, the proposed seed generation and tree migration methods integrate the proposed dynamic regulation mechanism, which reduces the possibility of falling into a local optimum. Third, the proposed feedback mechanism can effectively balance exploration and exploitation. With the enhancement from the KNN mechanism, KATSA can converge to the global optima more effectively during the search process. The results obtained from IEEE CEC 2014 benchmark functions verify the excellent performance of the KATSA when compared with some recent variants, including STSA, EST-TSA, fb\_TSA and MTSA. In addition, GWO, PSO, BOA, BA, GA, LSHADE and RSA are also adopted for some benchmark comparative experiments. The applicability of the proposed KATSA is demonstrated by 3 real complex and constrained problems compared to TSA, fb\_TSA, LSHADE, RSA, GWO, ABC and PSO. The experimental results show that the proposed KATSA can obtain stable and best results on these complex problems. © 2023, The Authors. All rights reserved.

关键词： Swarm intelligence

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Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks

arXiv

引用

arXiv 2022年

作者： Gong, Shimin Cui, Leiyang Gu, Bo Lyu, Bin Hoang, Dinh Thai Niyato, Dusit The School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518055 China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China The School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. blueWe aim to minimize the average age-of-information (AoI) by adapting the nodes’ transmission scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming bluevector and the IRS’s phase shifting bluematrix. blueConsidering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to bluefor AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the blueinner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes. © 2022, CC BY.

关键词： Reinforcement learning

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