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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar for Human Pose Estimation 12

A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar f...

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12th International electrical engineering Congress, iEECON 2024

作者： Phaisiri, Kritthee Kittiyanpunya, Chainarong Shutimarrungson, Nattakarn Boonpoonga, Akkarat Akkaraekthalin, Prayoot Kaemarungsi, Kamol King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand Rajamangala University Of Technology Rattanakosin Faculty Of Engineering Department Of Mechatronics Engineering Nakhon Pathom Thailand Mahasarakham Business School Mahasarakham University Department Of Business Computer Maha Sarakham Thailand Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand National Electronics And Computer Technology Center National Science And Technology Development Agency Pathum Thani Thailand

ISBN: (纸本)9798350383591

This paper presents human pose estimation using millimeter-wave (mmWave) radar technology capable of penetrating obstacles. Operating within the frequency modulated continuous wave (FMCW) at 60 - 64 GHz, our radar system generates signals conducive to accurate human pose representation. Our data processing framework focuses on producing point cloud data, effectively capturing each human movement. Results are visualized through a 2D graph, with the z-axis representing time. Experimental data, collected from various activities conducted behind different walls, underwent thorough analysis, comparing point cloud positions with actual human postures. The outcomes affirm the effectiveness of mmWave radar systems, positioned behind obstacles, in accurately extracting human postures. © 2024 IEEE.

关键词： Frequency modulation

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Nonlinearity of optoacoustic signals and a new contrast mechanism for imaging

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光：科学与应用（英文版） 2025年第5期14卷 1420-1432页

作者： Jaber Malekzadeh-Najafabadi Jaya Prakash Daniel Razansky Jorge Ripoll Vipul Gujrati Vasilis Ntziachristos Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Department of Instrumentation and Applied Physics Indian Institute of ScienceBengaluruIndia Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology Faculty of MedicineUniversity of ZurichZurichSwitzerland Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Department of Bioengineering and Aerospace Engineering Universidad Carlos Ⅲ de MadridMadridSpain Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunichGermany

Optoacoustic signals behave nonlinearly at light fluences above a few mJ/cm2,which may affect the interpretation and quantification of *** has been proposed that optoacoustic nonlinearity arises from the heat-induced formation of nanobubbles or changes in local thermo-physical ***,such explanations are only valid at much higher fluences than typically used in biomedical optoacoustic imaging(＞20 mJ/cm2)or in the presence of materials with high absorption coefficients such as gold *** propose herein that electromagnetic permittivity changes in response to photon absorption are major source of optoacoustic signal nonlinearity at low *** provide theoretical and experimental evidence that supports this postulation and show that optoacoustic pressure responses due to permittivity changes,which are function of thermally excited third-order nonlinear susceptibility,can explain the nonlinear behavior of the optoacoustic *** different materials exhibit different thermally excited third-order nonlinear susceptibility,this property could function as a new contrast mechanism that can identify the sensitivity of a substance's dielectric constant to photon-induced temperature ***,we propose an imaging method based on nonlinear optoacoustic signals that exploits this newly identified contrast *** findings may have far-reaching implications for improving the accuracy of optoacoustics and utilizing the proposed new contrast mechanism would advance our understanding of cellular and tissue functionality.

关键词： mechanism contrast imaging nonlinearity optoacoustic signals

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Adaptive nonsingular integral-type dynamic terminal sliding mode synchronizer for disturbed nonlinear systems and its application to secure communication systems

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JVC/Journal of Vibration and Control 2023年第11-12期29卷 2566-2578页

作者： Vaseghi, Behrouz Mobayen, Saleh Din, Sami ud Hashemi, Seyedeh Somayeh Vu, Mai The Department of Electrical and Computer Engineering Abhar Branch Islamic Azad University Abhar Iran Department of Electrical Engineering University of Zanjan Zanjan Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Electrical Engineering Namal University Mianwali Mianwali Pakistan School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of

This study proposes an adaptive nonsingular integral dynamic terminal sliding mode tracker/synchronizer for disturbed nonlinear systems along with its usage in safe communication systems. The convergence of the closed-loop structure under unknown uncertainty and disturbances is guaranteed via Lyapunov analysis. Furthermore, a parameter-tuning method is planned to approximate the upper bound of uncertainty and disturbance terms, since this latter is typically unknown in practice. The proposed approach is used to design a digital secure transmission scheme according to the chaotic systems. The effectiveness of the suggested approach is validated using computer simulations on a benchmark example of chaotic system. The obtained outcomes clearly confirm the ability of the planned control approach enables to attain the desired tracking/synchronizing performance despite the disturbances. Additionally, when implemented to the data encryption of a communication system, the proposed control and secure communication techniques enabled the complete and secure retrieval of the original digital sequences. © The Author(s) 2022.

关键词： Chaotic systems

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COVICT: an IoT based architecture for COVID-19 detection and contact tracing

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7381-7398页

作者： Wahid, Mirza Anas Bukhari, Syed Hashim Raza Daud, Ahmad Awan, Saeed Ehsan Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Islamabad Pakistan Department of Electrical and Computer Engineering Air University Islamabad Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 Uni-versity Road Section 3 Douliou ROC Yunlin64002 Taiwan

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world’s population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION 12

OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Saberi, Amir Hossein Movasaghinia, Mohammad Hosein Najafi, Amir Motahari, Seyed Abolfazl Heidari, Alireza Khalaj, Babak H. Department of Electrical Engineering Department of Computer Engineering Sharif Center for Information Systems and Data Science Iran Sharif Institute for Convergence Science & Technology Sharif University of Technology Tehran Iran

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where in addition to the m independent and labeled samples from the true distribution, a set of n (usually with n m) out of domain and unlabeled samples are given as well. Using only the labeled data, it is known that the generalization error can be bounded by ∝ (d/m)1/2. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared to ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Polynomial approximation

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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A Bayesian Approach to Online Planning 41

A Bayesian Approach to Online Planning

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41st International Conference on Machine Learning, ICML 2024

作者： Greshler, Nir Eli, David Ben Rabinovitz, Carmel Guetta, Gabi Gispan, Liran Zohar, Guy Tamar, Aviv General Motors Advanced Technical Center Israel Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

The combination of Monte Carlo tree search and neural networks has revolutionized online planning. As neural network approximations are often imperfect, we ask whether uncertainty estimates about the network outputs could be used to improve planning. We develop a Bayesian planning approach that facilitates such uncertainty quantification, inspired by classical ideas from the meta-reasoning literature. We propose a Thompson sampling based algorithm for searching the tree of possible actions, for which we prove the first (to our knowledge) finite time Bayesian regret bound, and propose an efficient implementation for a restricted family of posterior distributions. In addition we propose a variant of the Bayes-UCB method applied to trees. Empirically, we demonstrate that on the ProcGen Maze and Leaper environments, when the uncertainty estimates are accurate but the neural network output is inaccurate, our Bayesian approach searches the tree much more effectively. In addition, we investigate whether popular uncertainty estimation methods are accurate enough to yield significant gains in planning. Our code is available at: https://***/nirgreshler/bayesian-online-planning. Copyright 2024 by the author(s)

关键词： Intelligent systems

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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IEEE Transactions on Automation Science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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