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RIS-assisted SATINs with RSMA and DRL: A Trade-off between Spectral, Secrecy, and Energy Efficiency

IEEE Transactions on Communications 2025年

作者： Wu, Min Guo, Kefeng Li, Xingwang Lin, Zhi Yang, Liang Tsiftsis, Theodoros A. Yuen, Chau Beihang University Institute of Artificial Intelligence Beijing100191 China Beihang University Bejing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China Space Engineering University School of Space Information Beijing101407 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China National University of Defense Technology College of Electronic Engineering China Anhui Province Key Laboratory of Electronic Restriction Hefei230037 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Nanyang Technological University School of Electrical and Electronics Engineering 50 Nanyang Ave 639798 Singapore

Given the rapid growth of diverse communication demands, future large-scale satellite-aerial-terrestrial integrated networks (SATINs) need to simultaneously provide services to users while guaranteeing spectral efficiency, secrecy and energy efficiency. This paper addresses the problem of maximising secrecy energy efficiency (SEE) in SATINs, which can accurately describe the effective trade-off between security, spectral efficiency and transmit power. Particularly, we investigate a secure beamforming (BF) scheme in cognitive SATINs that employs rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) in the presence of multiple eavesdroppers (Eves) in a UAV-aided secondary network (SN). To optimize the SEE for secondary vehicle users while satisfying the constraints of primary users (PUs), we utilize deep reinforcement learning (DRL) to address the coupling between different optimized parameters based on the improved long short-term memory proximal policy optimization (LSTM-PPO) algorithm. The main innovation of this paper is to design a sophisticated reward function, action space, and state space according to each constraint to speed up the convergence. In addition, simulation results show that the proposed DRL-based optimization scheme exhibits significant advantages in terms of SEE compared with benchmark schemes, validating the effectiveness of this work. © 1972-2012 IEEE.

关键词： Reinforcement learning

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Smooth quasi-developable surfaces bounded by smooth curves

arXiv

引用

arXiv 2019年

作者： Bo, Pengbo Zheng, Yujian Zhang, Caiming School of Computer Science and Technology Harbin Institute of Technology Weihai Weihai264209 Shandong Co-Innovation Center of Future Intelligent Computing Yantai264025 Shandong Province Key Lab of Digital Media Tachnology Shandong University of Finance and Economics Jinan250061

Computing a quasi-developable strip surface bounded by design curves finds wide industrial applications. Existing methods compute discrete surfaces composed of developable lines connecting sampling points on input curves which are not adequate for generating smooth quasi-developable surfaces. We propose the first method which is capable of exploring the full solution space of continuous input curves to compute a smooth quasi-developable ruled surface with as large developability as possible. The resulting surface is exactly bounded by the input smooth curves and is guaranteed to have no self-intersections. The main contribution is a variational approach to compute a continuous mapping of parameters of input curves by minimizing a function evaluating surface developability. Moreover, we also present an algorithm to represent a resulting surface as a B-spline surface when input curves are B-spline curves. Copyright © 2019, The Authors. All rights reserved.

关键词： Curve fitting

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First digit law from Laplace transform

arXiv

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arXiv 2019年

作者： Cong, Mingshu Li, Congqiao Ma, Bo-Qiang School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing100871 China Collaborative Innovation Center of Quantum Matter Beijing China Center for High Energy Physics Peking University Beijing100871 China FinTech and Blockchain Laboratory Department of Computer Science University of Hong Kong Hong Kong Hong Kong

The occurrence of digits 1 through 9 as the leftmost nonzero digit of numbers from real-world sources is distributed unevenly according to an empirical law, known as Benford’s law or the first digit law. It remains obscure why a variety of data sets generated from quite different dynamics obey this particular law. We perform a study of Benford’s law from the application of the Laplace transform, and find that the logarithmic Laplace spectrum of the digital indicator function can be approximately taken as a constant. This particular constant, being exactly the Benford term, explains the prevalence of Benford’s law. The slight variation from the Benford term leads to deviations from Benford’s law for distributions which oscillate violently in the inverse Laplace space. We prove that the whole family of completely monotonic distributions can satisfy Benford’s law within a small bound. Our study suggests that Benford’s law originates from the way that we write numbers, thus should be taken as a basic mathematical knowledge. Copyright © 2019, The Authors. All rights reserved.

关键词： Laplace transforms

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Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

arXiv

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical absorption spectra of hexagonal boron nitride

引用

Physical Review B 2020年第4期102卷 045117-045117页

作者： Tong Shen Xiao-Wei Zhang Honghui Shang Min-Ye Zhang Xinqiang Wang En-Ge Wang Hong Jiang Xin-Zheng Li State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontier Science Center for Nano-optoelectronics and School of Physics Peking University Beijing 100871 China International Center for Quantum Materials and School of Physics Peking University Beijing 100871 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100871 China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Collaborative Innovation Center of Quantum Matter Peking University Beijing 100871 China Ceramic Division Songshan Lake Lab Institute of Physics Chinese Academy of Sciences Guangdong 523808 China School of Physics Liaoning University Shenyang 110036 China

We report ab initio band diagram and optical absorption spectra of hexagonal boron nitride (h-BN), focusing on unravelling how the completeness of the basis set for GW calculations and electron-phonon interactions (EPIs) impact on them. The completeness of the basis set, an issue which was seldom discussed in previous optical spectra calculations of h-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based GW calculations, the quasiparticle direct and fundamental indirect band gaps are widened by ∼0.2 eV, giving values of 6.81 eV and 6.25 eV, respectively at the GW0 level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around K in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of ∼6.00 eV. This accuracy is acceptable to an ab initio description of excited states with no fitting parameter.

关键词： Bethe-Salpeter equation Electron-phonon coupling Excitons GW method

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Author Correction: Africa needs context-relevant evidence to shape its clean energy future

引用

Nature Energy 2024年 907页

作者： Yacob Mulugetta Lucas Somavilla Croxatto Meron Tesfamichael Abdulmutalib Yussuff Youba Sokona Philipp A. Trotter Samuel Fankhauser Jessica Omukuti Bjarne Steffen Edo Abraham Jean-Paul Adam Mekalia Paulos Aklilu Yohannes Hailu Linus Mofor S. Nadia Ouedraogo Lawrence Agbemabiese Churchill Agutu Florian Egli Abdulrasheed Isah Tobias S. Schmidt Olakunle Alao Wikus Kruger Peter Twesigye Bothwell Batidzirai Getachew Bekele Anteneh G. Dagnachew Ogunlade Davidson Fatima Denton E. Ogheneruona Diemuodeke Gebrekidan Gebresilassie Eshetu Mulualem Gebreslassie Mamadou Goundiam Haruna Kachalla Gujba Adam D. Hawkes Stephanie Hirmer Helen Hoka Mark Howells Daniel Kammen Francis Kemausuor Ismail Khennas Ifeoma Malo Minette Nago Destenie Nock Chukwumerije Okereke Benedict Probst Maria Schmidt Carlos Shenga Mohamed Sokona Jan Christoph Steckel Sebastian Sterl Bernard Tembo Julia Tomei Jim Watson Harald Winkler Department of Science Technology Engineering & Public Policy University College London London UK Responsible Technology Institute Department of Computer Science University of Oxford Oxford UK Groupe de Reflection et d’Initiatives Novatrices Bamako Mali Schumpeter School of Business and Economics University of Wuppertal Wuppertal Germany Smith School of Enterprise and the Environment University of Oxford Oxford UK Institute for Science Innovation and Society (INSIS) University of Oxford Oxford UK Climate Finance and Policy Group ETH Zurich Zurich Switzerland Department of Water Management Delft University of Technology Delft the Netherlands United Nations Economic Commission for Africa Addis Ababa Ethiopia Center for Energy & Environmental Policy University of Delaware Newark DE USA Energy and Technology Policy Group ETH Zurich Zurich Switzerland Kigali Collaborative Research Centre Kigali Rwanda IIPP Institute for Innovation and Public Purpose University College London London UK Institute of Science Technology and Policy ETH Zurich Zurich Switzerland Power Futures Lab Graduate School of Business University of Cape Town Cape Town South Africa African Union Development Agency (AUDA-NEPAD) Midrand Johannesburg South Africa School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia PBL Netherlands Environmental Assessment Agency the Hague the Netherlands Utrecht University Utrecht the Netherlands University of Sierra Leone Freetown Sierra Leone United Nations University–Institute for Natural Resources in Africa (UNU–INRA) Accra Ghana Department of Mechanical Engineering University of Port Harcourt Choba Nigeria Institute for Power Electronics and Electrical Drives RWTH Aachen University Aachen Germany Center of Energy Ethiopian Institute of Technology Mekelle University Mekelle Ethiopia Institute of Engineering University Grenoble Alpes Grenoble France GIZ — Africa–EU Energy Partnership (AEEP) Addis

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

引用

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Ultrastrong magnon-magnon coupling dominated by antiresonant interactions

arXiv

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arXiv 2020年

作者： Makihara, Takuma Hayashida, Kenji Noe, G. Timothy Li, Xinwei Peraca, Nicolas Marquez Ma, Xiaoxuan Jin, Zuanming Ren, Wei Ma, Guohong Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Turchinovich, Dmitry Cao, Shixun Bamba, Motoaki Kono, Junichiro Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Division of Applied Physics Graduate School Faculty of Engineering Hokkaido University Sapporo Hokkaido060-8628 Japan Department of Physics International Center of Quantum and Molecular Structures and Materials Genome Institute Shanghai University Shanghai200444 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology 516 JunGong Road Shanghai200093 China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Institute for Materials Research Tohoku University Sendai Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany Department of Physics Kyoto University Sakyo-ku Kyoto606-8502 Japan PRESTO Japan Science and Technology Agency Saitama332-0012 Japan Department of Materials Science and NanoEngineering Rice University HoustonTX United States

Exotic quantum vacuum phenomena are predicted in cavity quantum electrodynamics (QED) systems with ultrastrong light-matter interactions. Their ground states are predicted to be vacuum squeezed states with suppressed quantum fluctuations. The source of such phenomena is antiresonant terms in the Hamiltonian, yet antiresonant interactions are typically negligible compared to resonant interactions in light-matter systems. We report an unusual, ultrastrongly coupled matter-matter system of magnons that is analytically described by a unique Hamiltonian in which the relative importance of resonant and antiresonant interactions can be easily tuned and the latter can be made vastly dominant. We found a novel regime where vacuum Bloch-Siegert shifts, the hallmark of antiresonant interactions, greatly exceed analogous frequency shifts from resonant interactions. Further, we theoretically explored the system's ground state and calculated up to 5.9 dB of quantum fluctuation suppression. These observations demonstrate that magnonic systems provide an ideal platform for exploring exotic quantum vacuum phenomena predicted in ultrastrongly coupled light-matter systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

引用

arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Automated speech analysis for detection of cognitive and emotional changes

引用

Alzheimer's & Dementia 2021年第S5期17卷

作者： Alexandra König Inez H.G.B. Ramakers Nicklas Linz Radia Zeghari Philippe Robert CoBTeK (Cognition Behaviour Technology) Research Lab University Côte d’azur Nice France National Institute for Research in Computer Science and Automation (INRIA) Sophia Antipolis France School for Mental Health & Neuroscience Maastricht Netherlands Maastricht University Maastricht Netherlands Ki elements UG Saarbrücken Germany Université Côte d’Azur Cognition Behaviour Technology Lab (CoBTeK) Nice France Association Innovation Alzheimer Nice France Association IA Nice France

Background Many aspects of language and speech seem affected with increased dementia risk. Recently speech analysis has become mature enough to provide completely automatically extracted fine-grained features highly sensitive to early cognitive and affective changes. Method Several studies were performed during which over 150 speech samples of patients at different stages (controls, Mild Cognitive Impairment, Dementia) were collected at the clinic as well as remotely (over the phone and video-conference system). Subsamples of patients with apathy and depression were included. The spoken features extracted from the recordings were compared against data from classical assessment tools and manual annotations. Result Comparison to reference measures show firstly, highly accuracies in demonstrating speech differences based on clinical diagnosis (up to 90%) and secondly, significant correlations between automatically extracted and manually annotated measures ( r = 0.9). Conclusion Speech and language features represent a promising biomarker candidate as they can be automatically and remotely extracted even over the telephone. With the help of advanced machine learning and different computational techniques the most significant markers can be identified as early indicator for screening.

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