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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (ai) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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FUTURE-ai: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (ai) research for healthcare, the deployment and adoption of ai technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare ai. To increase adoption in the real world, it is essential that ai technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-ai framework as the first international consensus guideline for trustworthy ai in healthcare. Methods: The FUTURE-ai consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including ai scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable ai through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-ai framework was established based on six guiding principles for trustworthy ai in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy ai. The recommendations cover the entire lifecycle of healthcare ai, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-ai is a structured, risk-informed framework which provides guidance for constructing healthcare ai tools that will be trusted, deployed and adopted in real-world clinical practice. researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare ai. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

arXiv

引用

arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

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Dynamic neural network approach to human emotion: an analysis based on sliding time windows

Dynamic neural network approach to human emotion: an analysi...

引用

IEEE International Conference on Cognitive Informatics

作者： Jingqi Wang Gen Shi Ning Ma Yang Sun Xuesong Li Jie Sui School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Echocardiography Big Data and Engineering Research Center Beijing Children&#x2019 s Hospital Capital Medical University Beijing China School of Educational Science Shenyang Normal University Shenyang China School of Psychology University of Aberdeen Aberdeen UK

ISBN: (纸本)9781665421201

Emotion is a key motivational factor of a person strivings for health and well-being. Understanding neural networks supporting different types of emotion bears far-reaching implications for mental health. Recent studies suggest that emotional processing is associated with a large number of brain regions. However, the precise functional connectivity (FC) of these regions in investigations of emotional processing are largely unknown. To address this issue, we recruited 359 participants who completed emotional-related measures including the Positive and Negative Affect Schedule (PANAS) the Self-Compassion Scale, while scanned with resting-state functional magnetic resonance images (fMRI). Here, we proposed a novel psychological characteristics analysis framework by using a dynamic sliding window method to characterize the nature of resting-state functional connectivity in the human brain, in relation to the static FC method. The comparison results showed that the dynamic FC method produced the better performance, compared to the static FC method. The global network analyses across all 6 possible connectivity matrices further demonstrated that the dynamically hemispheric asymmetry best predicted emotional processing. The dynamic FC method was evaluated on the three emotional labels - positive emotion, negative emotion, self-compassion and the best prediction performance was consistently observed in the dynamically hemispheric asymmetric FC.

关键词： Schedules Computational modeling Magnetic resonance Network analyzers Mental health Machine learning Functional magnetic resonance imaging

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

引用

arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Applying the FaiR Principles to computational workflows

arXiv

引用

arXiv 2024年

作者： Wilkinson, Sean R. Aloqalaa, Meznah Belhajjame, Khalid Crusoe, Michael R. de Paula Kinoshita, Bruno Gadelha, Luiz Garijo, Daniel Ragnar Gustafsson, Ove Johan Juty, Nick Kanwal, Sehrish Khan, Farah Zaib Köster, Johannes Gehlen, Karsten Petersvon Pouchard, Line Rannow, Randy K. Soiland-Reyes, Stian Soranzo, Nicola Sufi, Shoaib Sun, Ziheng Vilne, Baiba Wouters, Merridee A. Yuen, Denis Goble, Carole Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science University of Manchester Manchester United Kingdom LAMSADE PSL Paris Dauphine University Paris France Berlin Germany Earth Sciences Barcelona Supercomputing Center Barcelona Spain Heidelberg Germany Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Australian BioCommons University of Melbourne MelbourneVIC Australia ParkvilleVIC Australia University Medicine Essen University of Duisburg-Essen Essen Germany Data Management Department Deutsches Klimarechenzentrum GmbH Hamburg Germany Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Silverdraft Supercomputing BoiseID United States Informatics Institute University of Amsterdam Amsterdam Netherlands Earlham Institute Norwich United Kingdom Center for Spatial Information Science and Systems Department of Geography and Geoinformation Science George Mason University FairfaxVA United States Bioinformatics Group Riga Stradins University Riga Latvia School of Clinical Medicine University of New South Wales KensingtonNSW Australia Ontario Institute for Cancer Research TorontoON Canada

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FaiR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FaiR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FaiR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FaiR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community. © 2024, CC BY.

关键词： application programs

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基于大语言模型的糖尿病管理:潜力与展望

引用

science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(ai),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative ai that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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MDKAT: Multimodal Decoupling with Knowledge Aggregation and Transfer for Video Emotion Recognition

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Jian Wang, Chenglong Guo, Lin Zhao, Shuchang Wang, Dandan Zhang, Shiqing Zhao, Xiaoming Yu, Jun Wang, Yaowei Yang, Yi Ma, Siwei Tian, Qi Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Hangzhou Dianzi University School of Computer Science Hangzhou310018 China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310027 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Huawei Cloud & AI Shenzhen518129 China

Multimodal Emotion Recognition (MER) leverages multiple input signals to identify the expressed emotions in user-generated data. Currently, effectively addressing both modality heterogeneity and homogeneity on MER tasks is a challenging issue due to the diversity of multimodal inputs in videos. To address this issue, this work proposes an efficient Multimodal Decoupling Method with Knowledge Aggregation and Transfer (MDKAT) for robust multimodal feature learning in emotional videos. MDKAT is consisted of three key steps: modality-independent feature extraction, modality-specific feature extraction, and multi-loss integration for decoupling. In these three steps, four crucial modules are individually designed to improve different aspects of multimodal learning on MER tasks, including a Cross-modal Feature Fusion (CFF) module for enhancing modality-independent features, an Adaptive Masked Self-Attention (AMSA) module for feature refinement, a Knowledge Aggregation (KA) module for ensuring the semantic similarity of modality-independent features, and a Knowledge Transfer (KT) module for balancing the strengths of different modalities. Experimental results on the typical CMU-MOSI and CMU-MOSEI datasets show that MDKAT obtains superior performance over state-of-the-art methods, demonstrating the effectiveness of MDKAT on MER tasks. © 1991-2012 IEEE.

关键词： Feature extraction

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Cryogenic in-memory computing using tunable chiral edge states

arXiv

引用

arXiv 2022年

作者： Liu, Yuting Lee, Albert Qian, Kun Zhang, Peng He, Haoran Ren, Zheyu Cheung, Shun Kong Li, Yaoyin Zhang, Xu Ma, Zichao Xiao, Zhihua Yu, Guoqiang Wang, Xin Liu, Junwei Wang, Zhongrui Wang, Kang L. Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong School of Material Science and Engineering Harbin Institute of Technology Shenzhen518055 China Device Research Laboratory Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Road 999077 Hong Kong Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong Beijing National Laboratory for Condensed Matter Physics Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China Department of Physics The City University of Hong Kong 999077 Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology The Hong Kong University of Science and Technology Hong Kong ACCESS AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong

Cryogenic electronics have become essential in reducing the number of input/output ports to the quantum chips and generating multiplexed reading and control pulses for scalable quantum computation 1-5. A demanding request for cryogenic electronics is to support efficient machine learning algorithms, which have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control 6,7. However, current digital cryogenic electronics suffer from the von Neumann bottleneck, where the physical separation of computation and memory units causes a huge amount of energy and delay when processing data-intensive programs. Energy-efficient hardware implementation of machine learning algorithms for quantum computation requires nonvolatile and electrically-programmable devices, memristors, working at cryogenic temperatures that enable in-memory computing 8,9. Magnetic topological insulators are promising candidates due to their tunable magnetic order by electrical currents with high energy efficiency 10-15. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a chiral edge state-based cryogenic in-memory computing scheme. On the one hand, the chiral edge state can be tuned from left-handed to right-handed chirality through spin-momentum locked topological surface current injection. On the other hand, the chiral edge state exhibits giant and bipolar anomalous Hall resistance, which facilitates the electrical readout. The memristive switching and reading of the chiral edge state exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks based on magnetic topological memristors demonstrate a software-level accuracy and lower energy consumption for image recogniti

关键词： Topology

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