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IEEE International Symposium on Biomedical Imaging

作者： Yi Pan Hanqi Jiang Junhao Chen Yiwei Li Huaqin Zhao Yifan Zhou Peng Shu Zihao Wu Zhengliang Liu Dajiang Zhu Xiang Li Yohannes Abate Tianming Liu School of Computing University of Georgia Athens GA USA Department of Computer Science and Engineering University of Texas at Arlington TX USA Department of Radiology Massachusetts General Hospital and Harvard Medical School MA USA Department of Physics and Astronomy University of Georgia Athens GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial neural networks (ANNs), predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. SNNs are more energy-efficient and excel at processing spatio-temporal data than ANNs. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in medical image analysis.

关键词： Image analysis Neuromorphic engineering Computational modeling Spiking neural networks Medical services Transformers Energy efficiency Data models Convolutional neural networks Medical diagnostic imaging

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Intelligent 5G Evolution: AI-Augmented Hybrid Model with Network Slicing and Autonomous Self-Healing for Future-Ready Connectivity

Intelligent 5G Evolution: AI-Augmented Hybrid Model with Net...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Purushotham Endla Ramya S Pure Anita Soni C. Andal D. Jagadeeswari K. Nirmala Devi Department of Physics School of Sciences and Humanities SR University Warangal Telangana India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad CSE Department IES University Bhopal Dr. MGR Educational and Research Institute Chennai Department of ECE Sri muthukumaran Institute of Technology Deartment of Computer Applications Madanapalle Institute of Technology & Science

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The increasing demand for dynamic, efficient, and resilient network management in 5G systems has driven advancements in AI-enhanced architectures. This research proposes an AI-Augmented Hybrid 5G Model that integrates dynamic network slicing, self-healing capabilities, and AI-driven predictive analytics to optimize network performance and reliability. The hybrid model employs supervised and unsupervised machine learning algorithms, achieving a fault detection accuracy of 95.2% and traffic demand prediction accuracy of 92.8%. Key innovations include dynamic resource allocation for enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communication (mMTC). Self-healing capabilities reduced fault recovery times to an average of 300 ms, significantly improving system uptime to 99.8%. Compared to traditional 5G systems, the model demonstrates a 50% reduction in latency, a 28.6% improvement in resource efficiency, and an 80% faster slice reconfiguration. These results highlight the model’s potential to revolutionize 5G network management, ensuring reliability and adaptability for diverse applications. The study concludes by exploring the applicability of the hybrid model in future 6G networks and cross-domain orchestration.

关键词： Adaptation models Accuracy 5G mobile communication Network slicing Fault detection Ultra reliable low latency communication Predictive models Resource management Reliability Predictive analytics

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Hyperedge overlap drives synchronizability of systems with higher-order interactions

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Physical Review E 2025年第3期111卷 034302-034302页

作者： Santiago Lamata-Otín Federico Malizia Vito Latora Mattia Frasca Jesús Gómez-Gardeñes Department of Condensed Matter Physics University of Zaragoza 50009 Zaragoza Spain GOTHAM Laboratory Institute of Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza 50018 Zaragoza Spain Department of Physics and Astronomy University of Catania 95125 Catania Italy School of Mathematical Sciences Queen Mary University of London London E1 4NS United Kingdom Complexity Science Hub Vienna A-1080 Vienna Austria Department of Electrical Electronics and Computer Science Engineering University of Catania 95125 Catania Italy

The microscopic organization of dynamical systems coupled via higher-order interactions plays a pivotal role in understanding their collective behavior. In this paper, we introduce a framework for systematically investigating the impact of the interaction structure on dynamical processes. Specifically, we develop an hyperedge overlap matrix whose elements characterize the two main aspects of the microscopic organization of higher-order interactions: the inter-order hyperedge overlap (nondiagonal matrix elements) and the intra-order hyperedge overlap (encapsulated in the diagonal elements). In this way, the first set of terms quantifies the extent of superposition of nodes among hyperedges of different orders, while the second focuses on the number of nodes in common between hyperedges of the same order. Our findings indicate that large values of both types of hyperedge overlap hinder synchronization stability, and that the larger is the order of interactions involved, the more important is their role. Our findings also indicate that the two types of overlap have qualitatively distinct effects on the dynamics of coupled chaotic oscillators. In particular, large values of intra-order hyperedge overlap hamper synchronization by favoring the presence of disconnected sets of hyperedges, while large values of inter-order hyperedge overlap hinder synchronization by increasing the number of shared nodes between groups converging on different trajectories, without necessarily causing disconnected sets of hyperedges.

关键词： Synchronization

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution

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Radiation Oncology 2025年第1期20卷 1-18页

作者： Hou, Zhen Qin, Lang Gu, Jiabing Liu, Zidong Liu, Juan Zhang, Yuan Gao, Shanbao Zhu, Jian Li, Shuangshuang The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Jiangsu Nanjing 210000 China The School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing 210000 China Department of Radiology The First Affiliated Hospital with Nanjing Medical University Jiangsu Nanjing 210000 China Department of Radiotherapy Technology Center The Affiliated Hospital of Qingdao University Shandong Qingdao 266001 China The Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Jiangsu Nanjing 210096 China Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Shandong Jinan 250000 China Shandong Provincial Key Medical and Health Laboratory of Pediatric Cancer Precision Radiotherapy (Shandong Cancer Hospital) Shandong Jinan 250000 China Centre de Recherche en Information BioMedicale Sino-Français Nanjing China

Purpose: Accurate pre-treatment dose prediction is essential for efficient radiotherapy planning. Although deep learning models have advanced automated dose distribution, comprehensive multi-tumor analyses remain scarce. This study assesses deep learning models for dose prediction across diverse tumor types, combining objective and subjective evaluation methods. Methods and materials: We included 622 patients with planning data across various tumor sites: nasopharyngeal carcinoma (n = 29), esophageal carcinoma (n = 82), left-sided breast carcinoma (n = 107), right-sided breast carcinoma (n = 95), cervical carcinoma treated with radical radiotherapy (n = 84), postoperative cervical carcinoma (n = 122), and rectal carcinoma (n = 103). Dose predictions were generated using U-Net, Flex-Net, and Highres-Net models, with data split into training (60%), validation (20%), and testing (20%) sets. Quantitative comparisons used normalized dose difference (NDD) and dose-volume histogram (DVH) metrics, and qualitative assessments by radiation oncologists were performed on the testing set. Results: Predicted and clinical doses correlated well, with NDD values under 3% for tumor targets in nasopharyngeal, breast, and postoperative cervical cancer. Qualitative assessments revealed that U-Net, Flex-Net, and Highres-Net achieved the highest accuracy in cervical radical, breast/rectal/postoperative cervical, and nasopharyngeal/esophageal cancers, respectively. Among the test cases (n = 123), 53.7% were deemed clinically acceptable and 32.5% required minor adjustments. The “Best Selection” approach, combining strengths of all three models, raised clinical acceptance to 62.6%. Conclusion: This study demonstrates that automated dose prediction can provide a robust starting point for rapid plan generation. Leveraging model-specific strengths through the “Best Selection” approach enhances prediction accuracy and shows potential to improve clinical efficiency across multiple tumor types. ©

关键词： Deep learning Dose prediction Multi-tumor Radiotherapy

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Qubit-state purity oscillations from anisotropic transverse noise

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Physical Review A 2025年第3期111卷 032420-032420页

作者： David A. Rower Kotaro Hida Lamia Ateshian Helin Zhang Junyoung An Max Hays Sarah E. Muschinske Christopher M. McNally Samuel C. Alipour-Fard Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Cambridge Massachusetts 02139 USA

We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed laboratory-frame axis and observe oscillations in the purity at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on the noise anisotropy, orientation, and power spectral density, using a low-frequency fluxonium qubit. Our results elucidate the impact of transverse noise anisotropy on qubit decoherence and may be useful for disentangling charge and flux noise in superconducting quantum circuits.

关键词： Qubits

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Event-triggered consensus control with dynamic agents and communication delays in heterogeneous multi-agent systems

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Alexandria engineering Journal 2025年 128卷 1-11页

作者： Asad Khan Muhammad Awais Javeed Waqar Ul Hassan Yubin Zhong Sidra Ahmed Azmat Ullah Khan Niazi Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou University Guangzhou 510006 PR China School of Transportation Southeast University Nanjing Jiangsu 211189 PR China Department of Mathematics and Statistics The University of Lahore Sargodha 40100 Pakistan School of Mathematics and Information Sciences Guangzhou University Guangzhou 510006 PR China Department of Physics The University of Lahore Sargodha 40100 Pakistan

This paper investigates the consensus problem in discrete multi-agent systems characterized by both first-order and second-order agent dynamics, addressing the challenges posed by input delays and disturbances. To optimize data exchange and information sharing, an innovative event-triggered control system with communication delays is developed for the consensus control strategy. The materials employed in this study include a diverse set of heterogeneous agents designed to mirror real-world dynamics and delay characteristics. The methods involve the formulation of the consensus condition using Linear Matrix Inequalities (LMIs) and Kronecker product techniques, complemented by Lyapunov functional approaches for rigorous system stability analysis. The uniquely distributed control mechanism relies solely on local neighbor communication, enhancing its practical applicability. The theoretical framework is validated through comprehensive simulations, demonstrating its robustness and effectiveness in achieving consensus among agents under the specified dynamic conditions.

关键词： Decentralized consensus Heterogeneous agents Lyapunov stability Linear matrix inequality (LMI) Time communication delay and disturbances

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All-electrical perpendicular switching of chiral antiferromagnetic order

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Nature Materials 2025年 1-7页

作者： Zheng, Zhenyi Jia, Lanxin Zhang, Zhizhong Shen, Qia Zhou, Guowei Cui, Zhiwei Ren, Lizhu Chen, Zhiteng Jamaludin, Nur Fadilah Zhao, Tieyang Xiao, Rui Zhang, Qihan Du, Yi Liu, Liang Gradečak, Silvija Novoselov, Kostya S. Zhao, Weisheng Xu, Xiaohong Zhang, Yue Chen, Jingsheng Department of Materials Science and Engineering National University of Singapore Singapore Singapore MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Physics Beihang University Beijing China Institute for Functional Intelligent Materials National University of Singapore Singapore Singapore Chongqing Research Institute National University of Singapore Chongqing China Suzhou Research Institute National University of Singapore Jiangsu China

Antiferromagnets as active components in devices have been attracting attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and fast memory devices. Although the switching of antiferromagnetic (AFM) order by current-induced spin–orbit torque (SOT) has already been realized, field-free SOT-induced bidirectional switching of AFM order in perpendicular geometry has been elusive. Here we experimentally demonstrate field-free perpendicular switching of the magnetic octupole moct in chiral AFM Mn3Sn by combining in-plane and out-of-plane SOTs generated by two-dimensional van der Vaals WTe2. The out-of-plane SOT breaks the in-plane inversion symmetry and leads to deterministic bidirectional switching of moct in polycrystalline Mn3Sn even without a fixed perpendicular moct easy axis. The switching ratio reaches up to 80% compared to 20–30% in polycrystalline Mn3Sn and the critical current density is reduced by an order of magnitude to around 1 MA cm−2. Our work promotes the development of chiral AFM devices toward practical application. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

关键词： Antiferromagnetism

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Epitaxial high-K barrier AlBN/GaN HEMTs

arXiv

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arXiv 2025年

作者： Savant, Chandrashekhar Nguyen, Thai-Son Nomoto, Kazuki Vishwakarma, Saurabh Ma, Siyuan Dhar, Akshey Chen, Yu-Hsin Casamento, Joseph Smith, David J. Xing, Huili Grace Jena, Debdeep Department of Materials Science and Engineering Cornell University IthacaNY14853 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Arizona State University TempeAZ85287 United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States

We report a polarization-induced 2D electron gas (2DEG) at an epitaxial AlBN/GaN heterojunction grown on a SiC substrate. Using this 2DEG in a long conducting channel, we realize ultra-thin barrier AlBN/GaN high electron mobility transistors that exhibit current densities > 0.25 A/mm, clean current saturation, a low pinch-off voltage of -0.43 V, and a peak transconductance of ∼0.14 S/mm. Transistor performance in this preliminary realization is limited by the contact resistance. Capacitance-voltage measurements reveal that introducing ∼7 % B in the epitaxial AlBN barrier on GaN boosts the relative dielectric constant of AlBN to ΕrAlBN ∼ 16, higher than the AlN dielectric constant of ΕrAlN ∼ 9. Epitaxial high-K barrier AlBN/GaN HEMTs can thus extend performance beyond the capabilities of current GaN transistors. © 2025, CC BY-NC-ND.

关键词： Gallium nitride

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engineering nonlinear activation functions for all-optical neural networks via quantum interference

arXiv

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arXiv 2025年

作者： Xu, Xinzhe Canora, Ruben Alaeian, Hadiseh Du, Shengwang Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

All-optical neural networks (AONNs) harness the wave nature of light to achieve unparalleled speed and energy efficiency for artificial intelligence tasks, outperforming their electronic counterparts. Despite their potential, the development of deep AONNs is constrained by the high optical power demands of conventional nonlinear optical processes, which limits scalability. This work introduces a novel low-power nonlinear optical activation function scheme based on a three-level quantum medium driven by two laser fields. Unlike traditional single-input, single-output activations, our design offers two-port optical nonlinear activation functions with both self- and cross-nonlinearities, making them great candidates for multi-input, multi-output networks. The approach allows precise control of nonlinear optical behavior, achieving sigmoid and rectified linear unit (ReLU)functions at ultralow power levels (≈ 17µW per neuron). Our theoretical and numerical analysis demonstrates the feasibility of constructing large-scale, deep AONNs with millions of neurons powered by less than 100 W of optical power. This advancement represents a significant step toward scalable, high-speed, and energy-efficient AONNs for next-generation AI hardware. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear optics

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