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arXiv 2020年

作者： Aydin, Orhun Utku Taha, Abdel Aziz Hilbert, Adam Khalil, Ahmed A. Galinovic, Ivana Fiebach, Jochen B. Frey, Dietmar Madai, Vince Istvan Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Research Studio Data Science Research Studios Austria Salzburg Austria Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University United Kingdom

Average Hausdorff Distance (AVD) is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, AVD is used to compare ground truth images with segmentation results allowing their ranking. We identified, however, a ranking bias of AVD making it less suitable for segmentation ranking. To mitigate this bias, we present a modified calculation of AVD that we have coined balanced AVD (bAVD). To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using AVD and bAVD. We calculated the Kendall-rank-correlation-coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by bAVD had a significantly higher average correlation (0.969) than those of AVD (0.847). In 200 total rankings, bAVD misranked 52 and AVD misranked 179 segmentations. Our proposed evaluation measure, bAVD, alleviates AVDs ranking bias making it more suitable for rankings and quality assessment of segmentations. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

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Innovative Developments in HCI and Future Trends

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International Journal of Automation and computing 2017年第1期14卷 10-20页

作者： Mohammad S.Hasan Hongnian Yu Faculty of Computing Engineering and Sciences Staffordshire University Faculty of Science and Technology Bournemouth University

The recent developments in technology have made noteworthy positive impacts on the human-computer interaction （HCI）. It is now possible to interact with computers using voice commands, touchscreen, eye movement, hand gesture, etc. This paper compiles some of the innovative HCI progresses in various areas, e.g., specialised input/output devices, virtual or augmented reality, wearable technology, etc. It also identifies some future research directions.

关键词： Human-computer interaction （HCI） virtual reality augmented reality haptic feedback controller smart-home.

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Identifying topology in power networks in the absence of breaker status sensor signals 19

Identifying topology in power networks in the absence of bre...

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19th IEEE Mediterranean Eletrotechnical Conference, MELECON 2018

作者： Oliveira, Rui Bessa, Ricardo Iranda, Vladimiro M. INESC TEC Portugal Faculty of Engineering University of Porto Porto Portugal INESC TEC Institute for Systems and Computing Engineering Technology and Science Porto Portugal INESC TEC Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9781538637388

This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms the conjecture that the knowledge of the topology of a network is hidden in the electric measurement values in the network. A test case is presented with computing speed accelerated by using a GPU (graphics processing unit). The comparison with a previous model illustrates the superiority of the novel approach. © 2018 IEEE.

关键词： Topology

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Wireless Powering Internet of Things with UAVs: Challenges and Opportunities

arXiv

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arXiv 2020年

作者： Liu, Yalin Dai, Hong-Ning Wang, Qubeijian Imran, Muhammad Guizani, Nadra Faculty of Information Technology Macau University of Science and Technology Macau China Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong School of Cyber Security Northwestern Polytechnical University China The School of Engineering Information Technology & Physical Sciences Federation University Australia Brisbane Australia The School of Electrical and Computer Engineering Washington State University United States

Unmanned aerial vehicles (UAVs) have the potential to overcome the deployment constraint of Internet of Things (IoT) in remote or rural area. Wirelessly powered communications (WPC) can address the battery limitation of IoT devices through transferring wireless power to IoT devices. The integration of UAVs and WPC, namely UAV-enabled Wireless Powering IoT (Ue-WPIoT) can greatly extend the IoT applications from cities to remote or rural areas. In this article, we present a state-of-theart overview of Ue-WPIoT by first illustrating the working flow of Ue-WPIoT and discussing the challenges. We then introduce the enabling technologies in realizing Ue-WPIoT. Simulation results validate the effectiveness of the enabling technologies in Ue-WPIoT. We finally outline the future directions and open issues. © 2020, CC BY-SA.

关键词： Internet of things

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CFRP-wrapped hollow steel tubes under axial impact loading 16th

CFRP-wrapped hollow steel tubes under axial impact loading

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16th International Symposium on Tubular Structures, ISTS 2017

作者： Batuwitage, C. Fawzia, S. Thambiratnam, D.P. Tafsirojjaman, T. Al-Mahaidi, R. Elchalakani, M. Science and Engineering Faculty Queensland University of Technology Brisbane Australia Department of Civil and Construction Engineering Swinburne University of Technology Australia School of Civil Environmental and Mining Engineering Faculty of Engineering Computing and Mathematics University of Western Australia Australia

ISBN: (纸本)9780815381341

CFRPs are widely used in the automobile industry and civil engineering applications. However, the structural behaviour of CFRP-wrapped steel hollow sections under axial impact loading is not yet fully understood. This paper evaluates the effect of impact mass on the axial impact behaviour of CFRP-wrapped steel hollow square sections. FE models were developed using LS-DYNA explicit finite element code and validated using existing experimental data. The axial impact behaviour of the tubes was evaluated by studying the responses to peak impact force, axial deflection and energy absorption. Appropriate results are presented and the trends discussed. © 2018 Taylor & Francis Group, London.

关键词： Automotive industry

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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Erratum to: Several Articles in JETP Letters

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JETP Letters 2023年第11期116卷 857-858页

作者： Bogomolov, Yu. V. Alekseev, V. V. Levanova, O. A. Maiorov, A. G. Malakhov, V. V. Yazynin, S. G. Kataev, A. L. Molokoedov, V. S. Maksimovskaya, A. A. Ruzhickiy, V. I. Klenov, N. V. Bakurskiy, S. V. Kupriyanov, M. Yu. Soloviev, I. I. Abdullaev, N. A. Amiraslanov, I. R. Aliev, Z. S. Jahangirli, Z. A. Sklyadneva, I. Yu. Alizade, E. G. Aliyeva, Y. N. Otrokov, M. M. Zverev, V. N. Mamedov, N. T. Chulkov, E. V. Yaroslavl State University Yaroslavl Russia National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) Moscow Russia Institute for Nuclear Research of the Russian Academy of Science Moscow Russia Research Computing Center Moscow State University Moscow Russia Moscow Center for Fundamental and Applied Mathematics Moscow Russia Faculty of Physics Moscow State University Moscow Russia Dukhov All-Russian Research Institute of Automatics Moscow Russia Skobeltsyn Institute of Nuclear Physics Moscow State University Moscow Russia Institute of Physics Azerbaijan National Academy of Sciences Baku Azerbaijan Baku State University Baku Azerbaijan Tomsk State University Tomsk Russia Centro de Fisica de Materiales (CFM-MPC) Centro Mixto CSIC-UPV/EHU Donostia-San Sebastián Spain IKERBASQUE Basque Foundation for Science Bilbao Spain Institute of Solid State Physics Russian Academy of Sciences Chernogolovka Russia St. Petersburg State University St. Petersburg Russia Departamento de Polímeros y Materiales Avanzados: Física Química y Tecnología Facultad de Ciencias Químicas Universidad del Pais Vasco UPV/EHU San Sebastián/Donostia Spain

An Erratum to this paper has been published: https://***/10.1134/S0021364022380039

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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era

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Scientific reports 2025年第1期15卷 19630页

作者： Kaleem Razzaq Malik Muhammad Sajid Ahmad Almogren Tauqeer Safdar Malik Ali Haider Khan Ayman Altameem Ateeq Ur Rehman Seada Hussen Department of Computer Science Air University Islamabad 44230 Pakistan. Chair of Cyber Security Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia. ahalmogren@ksu.edu.sa. Department of Information and Communication Technology Bahauddin Zakariya University Multan 60800 Punjab Pakistan. Department of Software Engineering Faculty of Computer Science Lahore Garrison University Lahore 54000 Punjab Pakistan. College of Computer Science Beijing University of Technology Beijing 100124 China. Chair of Cyber Security Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia. School of Computing Gachon University Seongnam-si 13120 Republic of Korea. 202411144@gachon.ac.kr. Department of Electrical Power Adama Science and Technology University Adama 1888 Ethiopia. seada.hussen@aastu.edu.et.

The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today's digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data. Researchers have increasingly explored steganography as a reliable method for secure communication because it plays a crucial role in concealing and safeguarding sensitive information. This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. The proposed model achieved reconstruction accuracies of 96.2% using Xu Net and 95.7% with SR Net. By combining DCT with deep learning algorithms, the proposed approach overcomes the limitations of spatial domain methods, offering a more flexible and effective steganography solution. Furthermore, simulation results confirm that the proposed technique outperforms state-of-the-art methods across key performance metrics, inc

关键词： DCT Deep learning GAN Spatial domain Steganography

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Metalla-Carbaporphyrinoids Consisting of an Acyclic N-Confused Tetrapyrrole Analogue Served as Stable Near-Infrared-II Dyes

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Angewandte Chemie 2024年第23期136卷

作者： Dr. Biju Basumatary Hidetoshi Tsuruda Prof. Dr. Dariusz W. Szczepanik Jiyeon Lee Jaehyeok Ryu Dr. Shigeki Mori Kyo Yamagata Prof. Dr. Takayuki Tanaka Dr. Atsuya Muranaka Prof. Masanobu Uchiyama Prof. Dr. Jiwon Kim Prof. Dr. Masatoshi Ishida Prof. Dr. Hiroyuki Furuta Department of Applied Chemistry Graduate School of Engineering Kyushu University Fukuoka 819-0395 Japan Department of Theoretical Chemistry Jagiellonian University Faculty of Chemistry Gronostajowa 2 30-387 Krakow Poland School of Integrated Technology College of Computing Integrated Science and Engineering Division Underwood International College Integrative Biotechnology and Translational Medicine Graduate School Yonsei University Incheon 21983 Korea Advanced Research Support Center Ehime University Matsuyama 790-8577 Japan Department of Molecular Engineering Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8195 Japan Molecular Structure Characterization Unit RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan Graduate School of Pharmaceutical Sciences The University of Tokyo Tokyo 113-0033 Japan Department of Chemistry Graduate School of Sciences Tokyo Metropolitan University Hachioji 192-0397 Japan

We present herein the synthesis of novel pseudo -metalla-carbaporphyrinoid species ( 1M : M=Pd and Pt) achieved through the inner coordination of palladium(II) and platinum(II) with an acyclic N-confused tetrapyrrin analogue. Despite their tetrapyrrole frameworks being small, akin to well-known porphyrins, these species exhibit an unusually narrow HOMO–LUMO gap, resulting in an unprecedentedly low-energy absorption in the second near-infrared (NIR-II) region. Density functional theory (DFT) calculations revealed unique d π -p π -conjugated electronic structures involving the metal d π -ligand p π hybridized molecular orbitals of 1M . Magnetic circular dichroism (MCD) spectroscopy confirmed distinct electronic structures. Remarkably, the complexes feature an open-metal coordination site in the peripheral NN dipyrrin site, forming hetero-metal complexes ( 1Pd-BF 2 and 1Pt-BF 2 ) through boron difluoride complexation. The resulting hetero metalla-carbaporphyrinoid species displayed further redshifted NIR-II absorption, highly efficient photothermal conversion efficiencies ( η ; 62–65 %), and exceptional photostability. Despite the challenges associated with the theoretical and experimental assessment of d π -p π -conjugated metalla-aromaticity in relatively larger (more than 18π electrons) polycyclic ring systems, these organometallic planar tetrapyrrole systems could serve as potential molecular platforms for aromaticity-relevant NIR-II dyes.

关键词： porphyrinoid N-confusion near-infrared photothermal conversion metalla-(anti)aromaticity

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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