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DOUBLE PHASE IMPLICIT OBSTACLE PROBLEMS WITH CONVECTION AND MULTIVALUED MIXED BOUNDARY VALUE CONDITIONS

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arXiv 2021年

作者： Zeng, Shengda Rǎdulescu, Vicenţiu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Faculty of Applied Mathematics Agh University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper we consider a mixed boundary value problem with a nonhomogeneous, nonlinear differential operator (called double phase operator), a nonlinear convection term (a reaction term depending on the gradient), three multivalued terms and an implicit obstacle constraint. Under very general assumptions on the data, we prove that the solution set of such implicit obstacle problem is nonempty (so there is at least one solution) and weakly compact. The proof of our main result uses the Kakutani-Ky Fan fixed point theorem for multivalued operators along with the theory of nonsmooth analysis and variational methods for pseudomonotone operators. Copyright © 2021, The Authors. All rights reserved.

关键词： Fixed point arithmetic

Quasimonoenergetic Proton Bunch Generation by Dual-Peaked Electrostatic-Field Acceleration in Foils Irradiated by an Intense Linearly Polarized Laser

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Physical Review Letters 2010年第6期105卷 065003-065003页

作者： H. B. Zhuo (卓红斌) Z. L. Chen (陈正林) W. Yu (余玮) Z. M. Sheng (盛政明) M. Y. Yu (郁明阳) Z. Jin (金展) R. Kodama (兒玉了祐) Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 People’s Republic of China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 People’s Republic of China Department of Physics Shanghai Jiao Tong University Shanghai 200240 People’s Republic of China Beijing National Laboratory for Condensed Matter Physics Institute of Physics CAS Beijing 100080 People’s Republic of China Institute for Fusion Theory and Simulation Zhejiang University Hangzhou 310027 People’s Republic of China Institute for Theoretical Physics I Ruhr University Bochum D-44780 Germany Institute of Laser Engineering Osaka University 2-6 Yamada-oka Suita Osaka 565-0871 Japan

It is found that stable proton acceleration from a thin foil irradiated by a linearly polarized ultraintense laser can be realized for appropriate foil thickness and laser intensity. A dual-peaked electrostatic field, originating from the oscillating and nonoscillating components of the laser ponderomotive force, is formed around the foil surfaces. This field combines radiation-pressure acceleration and target normal sheath acceleration to produce a single quasimonoenergetic ion bunch. A criterion for this mechanism to be operative is obtained and verified by two-dimensional particle-in-cell simulation. At a laser intensity of ∼5.5×1022 W/cm2, quasimonoenergetic GeV proton bunches are obtained with ∼100 MeV energy spread, less than 4° spatial divergence, and ∼50% energy conversion efficiency from the laser.

关键词： Germanium compounds

Corrections to "Efficient Adaptive Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications" [W. Duan, J. Tao, and Y. Rosa Zheng, IEEE J. Ocean. Eng ., 2017, DOI: 10.1109/JOE.2017.2707285]

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IEEE Journal of Oceanic Engineering 2017年第3期42卷 752-752页

作者： W. Duan J. Tao Y. R. Zheng Qualcomm Technologies Inc. San Diego CA USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Equation In [1], (6) of the above named work is corrected.

关键词： MIMO Adaptive equalizers Underwater communication Underwater acoustics Least mean squares methods

Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

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Application of Deep Artificial Neural Network to Model Chara...

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

DOUBLE PHASE OBSTACLE PROBLEMS WITH MULTIVALUED CONVECTION AND MIXED BOUNDARY VALUE CONDITIONS

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arXiv 2021年

作者： Zeng, Shengda Radulescu, Vicent Iu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania China-Romania Research Center in Applied Mathematics Craiova Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper, we consider a mixed boundary value problem with a double phase partial differential operator, an obstacle effect and a multivalued reaction convection term. Under very general assumptions, an existence theorem for the mixed boundary value problem under consideration is proved by using a surjectivity theorem for multivalued pseudomonotone operators together with the approximation method of Moreau-Yosida. Then, we introduce a family of the approximating problems without constraints corresponding to the mixed boundary value problem. Denoting by S the solution set of the mixed boundary value problem and by Sn the solution sets of the approximating problems, we establish the following convergence relation ∅ =6 w- lim sup Sn = s- lim sup Sn ⊂ S, n→∞ n→∞ where w-lim supn→∞ Sn and s-lim supn→∞ Sn stand for the weak and the strong Kuratowski upper limit of Sn, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Mathematical operators

Self adaptation of chimera states

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arXiv 2018年

作者： Yao, Nan Huang, Zi-Gang Ren, Hai-Peng Grebogi, Celso Lai, Ying-Cheng Department of Applied Physics Xi’an University of Technology Xi’an710048 China School of Life Science and Technology Xi’an Jiao Tong University Xi’an710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex Systems and Mathematical Biology King’s College University of Aberdeen AberdeenAB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self adaptation. The behavior can be exploited for controlled generation of chimera states with their coherent component placed in any desired spatial region of the system. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

Anisotropic and Isotropic Implicit Obstacle Problems with Nonlocal Terms and Multivalued Boundary Conditions

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SSRN

SSRN 2022年

作者： Zeng, Shengda Gasinski, Leszek Radulescu, Vicentiu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Department of Mathematics Pedagogical University of Cracow Podchorazych 2 Cracow30-084 Poland Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania China-Romania Research Center in Applied Mathematics Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper we study an anisotropic implicit obstacle problem driven by the $(p(\cdot),q(\cdot))$-Laplacian and an isotropic implicit obstacle problem involving a nonlinear convection term (a reaction term depending on the gradient) which contain several interesting and challenging untreated problems. These two implicit obstacle problems have both highly nonlinear and nonlocal functions and three multivalued terms where two of them are appearing on the boundary and the other one is formulated in the domain. Under very general assumptions on the data, we develop general frameworks to examine the nonemptiness and compactness of the set of weak solutions to the problems under consideration. The proofs of our main results use the theory of nonsmooth analysis, Tychonoff's fixed point theorem for multivalued operators, the theory of pseudomonotone operators and variational approach. © 2022, The Authors. All rights reserved.

关键词： Anisotropy

Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software