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arXiv 2022年

作者： Xu, Wei Yang, Zhaohui Ng, Derrick Wing Kwan Levorato, Marco Eldar, Yonina C. Debbah, Mérouane The National Mobile Communications Research Lab Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The School of Electrical Engineering and Telecommunications The University of New South Wales Australia The Department of Computer Science University of California IrvineCA92697 United States The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The Technology Innovation Institute The Mohamed Bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi9639 United Arab Emirates

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning" and "learning for communications." The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic data interchange

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Simulated annealing for optimization of graphs and sequences

arXiv

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arXiv 2021年

作者： Liu, Xianggen Li, Pengyong Meng, Fandong Zhou, Hao Zhong, Huasong Zhou, Jie Mou, Lili Song, Sen College of Computer Science Sichuan University Chengdu610065 China School of Computer Science and Technology Xidian University Xi'an710071 China Laboratory for Brain and Intelligence Department of Biomedical Engineering Tsinghua University Beijing100084 China Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Pattern Recognition Center WeChat AI Tencent Inc Beijing100084 China ByteDance AI Lab Beijing100098 China MMU KuaiShou Inc Beijing100085 China EdmontonT6G 2R3 Canada

Optimization of discrete structures aims at generating a new structure with the better property given an existing one, which is a fundamental problem in machine learning. Different from the continuous optimization, the realistic applications of discrete optimization (e.g., text generation) are very challenging due to the complex and long-range constraints, including both syntax and semantics, in discrete structures. In this work, we present SAGS, a novel Simulated Annealing framework for Graph and Sequence optimization. The key idea is to integrate powerful neural networks into metaheuristics (e.g., simulated annealing, SA) to restrict the search space in discrete optimization. We start by defining a sophisticated objective function, involving the property of interest and pre-defined constraints (e.g., grammar validity). SAGS searches from the discrete space towards this objective by performing a sequence of local edits, where deep generative neural networks propose the editing content and thus can control the quality of editing. We evaluate SAGS on paraphrase generation and molecule generation for sequence optimization and graph optimization, respectively. Extensive results show that our approach achieves state-of-the-art performance compared with existing paraphrase generation methods in terms of both automatic and human evaluations. Further, SAGS also significantly outperforms all the previous methods in molecule generation. © 2021, CC BY-NC-ND.

关键词： Simulated annealing

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Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony

arXiv

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arXiv 2025年

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions—first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © 2025, CC BY-NC-SA.

关键词： Neurons

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Diffusive topological transport in spatiotemporal thermal lattices

arXiv

引用

arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

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Reciprocity of thermal diffusion in time-modulated systems

arXiv

引用

arXiv 2021年

作者： Li, Jiaxin Li, Ying Cao, Pei-Chao Zheng, Xu Peng, Yu-Gui Li, Baowen Zhu, Xue-Feng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China School of Physics and Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Engineering City University of New York New YorkNY10031 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States

The reciprocity principle governs the symmetry in transmission of electromagnetic and acoustic waves, as well as the diffusion of heat between two points in space, with important consequences for thermal management and energy harvesting. There has been significant recent interest in materials with time-modulated properties, which have been shown to efficiently break reciprocity for light, sound, and even charge diffusion. Quite surprisingly, here we show that, from a practical point of view, time modulation cannot generally be used to break reciprocity for thermal diffusion. We establish a theoretical framework to accurately describe the behavior of diffusive processes under time modulation, and prove that thermal reciprocity in dynamic materials is generally preserved by the continuity equation, unless some external bias or special material is considered. We then experimentally demonstrate reciprocal heat transfer in a time-modulated device. Our findings correct previous misconceptions regarding reciprocity breaking for thermal diffusion, revealing the generality of symmetry constraints in heat transfer, and clarifying its differences from other transport processes in what concerns the principles of reciprocity and microscopic reversibility. © 2021, CC BY.

关键词： Energy harvesting

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Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications

arXiv

引用

arXiv 2021年

作者： Wei, Li Huang, Chongwen Guo, Qinghua Yang, Zhaohui Zhang, Zhaoyang Alexandropoulos, George C. Debbah, Mérouane Yuen, Chau Pillar Singapore University of Technology and Design Singapore487372 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China International Joint Innovation Center Zhejiang University Haining314400 China Zhejiang-Singapore Innovation and AI Joint Research Lab Hangzhou310027 China Zhejiang Laboratory Hangzhou31121 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Optimizing the size and electronic effects of core-shell heterostructures via well-constructed Ru clusters encapsulated in N-doped carbon layers

引用

Chinese Chemical Letters 2024年

作者： Min Jie Wang Jiao Yang Lishan Peng Yongjie Bai Zehui Liu Xiaoliang Yang Huijuan Lu Bingjie Zhou Ningtao Jiang Guoxu He Han-Ming Zhang Liwei Mi Yonghui Deng School of Chemistry and Environmental Engineering Pingdingshan University Pingdingshan 467000 China Key Laboratory of Rare Earths Chinese Academy of Sciences； Ganjiang Innovation Academy Chinese Academy of Sciences Ganzhou 341119 China China Automotive Technology and Research Center Co. Ltd. Tianjin 300300 China Department of Electrical and Computer Engineering North Dakota State University Fargo 58102 United States School of Materials Science and Engineering Hebei University of Science and Technology Shijiazhuang 050018 China Yaoshan laboratory Pingdingshan University Pingdingshan 467000 China Department of Chemistry State Key Laboratory of Molecular Engineering of Polymers Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Collaborative Innovation Center of Chemistry for Energy Material (iChEM) Fudan University Shanghai 200433 China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

The design and development of high-performance electrocatalysts for the hydrogen evolution reaction (HER) are essential for advancing the hydrogen economy. The electronic structure and core size of an electrocatalyst are pivotal for determining the intrinsic activity of the catalytic sites. Interfacial engineering, particularly the formation of well-controlled core-shell heterostructures, has emerged as a promising strategy, although significant challenges remain. Here, we present a series of Ru@NC heterostructures with size-controlled Ru cores encapsulated in N-doped graphene layers. Among these, Ru@NC-3h, with the best holistic effects, has superior durability and mass activity 7.03 times that of Pt/C. This high performance is attributed to the open porous structure, which enhances active site exposure and mass transfer, and the optimized adsorption and desorption of reaction intermediates by the strengthened hetero-interfacial interaction between the smaller Ru cores and thin N-doped shells. Attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) reveals reinforced interfacial water interaction and reduced hydrogen adsorption. Density functional theory (DFT) calculations indicate that the size effect promotes interfacial H 2 O adsorption, whereas the electronic effect governs *H adsorption to collectively accelerate the HER kinetics. This novel strategy, introduced to regulate heterostructures through size and electronic effects, offers significant potential for various energy material applications.

关键词： Hydrogen evolution reaction Interfacial engineering Core-shell heterostructures Holistic effects Density functional theory

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Cross-media user profiling with joint textual and social user embedding 27

Cross-media user profiling with joint textual and social use...

引用

27th International Conference on Computational Linguistics, COLING 2018

作者： Wang, Jingjing Li, Shoushan Jiang, Mingqi Wu, Hanqian Zhou, Guodong NLP Lab. School of Computer Science and Technology Soochow University China Collaborative Innovation Center of Novel Software Technology and Industrialization School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9781948087506

In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Embeddings

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