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arXiv 2024年

作者： Shi, Yuan Evert, Bram Brown, Amy F. Tripathi, Vinay Sete, Eyob A. Geyko, Vasily Cho, Yujin DuBois, Jonathan L. Lidar, Daniel Joseph, Ilon Reagor, Matt Department of Physics Center for Integrated Plasma Studies University of Colorado Boulder BoulderCO80309 United States Rigetti Computing 775 Heinz Ave BerkeleyCA94710 United States Department of Physics & Astronomy Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Electrical & Computer Engineering Department of Chemistry University of Southern California Los AngelesCA90089 United States

Simulating plasma physics on quantum computers is difficult because most problems of interest are nonlinear, but quantum computers are not naturally suitable for nonlinear operations. In weakly nonlinear regimes, plasma problems can be modeled as wave-wave interactions. In this paper, we develop a quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems. We demonstrate our approach using two qubits on a superconducting device. Unlike a photonic device, a superconducting device does not naturally have the desired interactions in its native Hamiltonian. Nevertheless, Hamiltonian simulations can still be performed by decomposing required unitary operations into native gates. To improve experimental results, we employ a range of error mitigation techniques. Apart from readout error mitigation, we use randomized compilation to transform undiagnosed coherent errors into well-behaved stochastic Pauli channels. Moreover, to compensate for stochastic noise, we rescale exponentially decaying probability amplitudes using rates measured from cycle benchmarking. We carefully consider how different choices of product-formula algorithms affect the overall error and show how a trade-off can be made to best utilize limited quantum resources. This study provides an example of how plasma problems may be solved on near-term quantum computing platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Pre-Chirp-Domain Index Modulation for Full-Diversity Affine Frequency Division Multiplexing towards 6G

arXiv

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arXiv 2024年

作者： Liu, Guangyao Mao, Tianqi Xiao, Zhenyu Wen, Miaowen Liu, Ruiqi Zhao, Jingjing Basar, Ertugrul Wang, Zhaocheng Chen, Sheng The School of Electronic and Information Engineering The State Key Laboratory of CNS/ATM Beihang University Beijing100191 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Zhuhai519088 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronics Engineering Koç University Sariyer Istanbul34450 Turkey The Department of Electronic Engineering Tsinghua University Beijing100084 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

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Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

arXiv

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arXiv 2023年

作者： Zhang, Fan Mao, Tianqi Liu, Ruiqi Han, Zhu Chen, Sheng Wang, Zhaocheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China The MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communications simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communications and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a 'locally' perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Modeling the Performance of Early Fault-Tolerant Quantum Algorithms

arXiv

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arXiv 2023年

作者： Liang, Qiyao Zhou, Yiqing Dalal, Archismita Johnson, Peter Zapata Computing Inc. 100 Federal Street 20th Floor BostonMA02110 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Laboratory of Atomic and Solid State Physics Cornell University IthacaNY14853 United States Zapata Computing Canada Inc. TorontoONM5V 2Y1 Canada

Progress in fault-tolerant quantum computation (FTQC) has driven the pursuit of practical applications with early fault-tolerant quantum computers (EFTQC). These devices, limited in their qubit counts and fault-tolerance capabilities, require algorithms that can accommodate some degrees of error, which are known as EFTQC algorithms. To predict the onset of early quantum advantage, a comprehensive methodology is needed to develop and analyze EFTQC algorithms, drawing insights from both the methodologies of noisy intermediate-scale quantum (NISQ) and traditional FTQC. To address this need, we propose such a methodology for modeling algorithm performance on EFTQC devices under varying degrees of error. As a case study, we apply our methodology to analyze the performance of Randomized Fourier Estimation (RFE) [1], an EFTQC algorithm for phase estimation. We investigate the runtime performance and the fault-tolerant overhead of RFE in comparison to the traditional quantum phase estimation algorithm. Our analysis reveals that RFE achieves significant savings in physical qubit counts while having a much higher runtime upper bound. We anticipate even greater physical qubit savings when considering more realistic assumptions about the performance of EFTQC devices. By providing insights into the performance trade-offs and resource requirements of EFTQC algorithms, our work contributes to the development of practical and efficient quantum computing solutions on the path to quantum advantage. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Application-level Benchmarking of Quantum computers using Nonlocal Game Strategies

arXiv

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arXiv 2023年

作者： Furches, Jim Chehade, Sarah Hamilton, Kathleen Wiebe, Nathan Marrero, Carlos Ortiz College of Computing Georgia Institute of Technology AtlantaGA30332 United States Physical Detection Systems and Deployment Division Pacific Northwest National Laboratory RichlandWA99354 United States Quantum Computational Science Group Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States

In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games by performing joint measurements on a shared entangled state, but computing these strategies can be challenging. We present a variational quantum algorithm to compute quantum strategies for nonlocal games by encoding the rules of a nonlocal game into a Hamiltonian. We show how this algorithm can generate a short-depth optimal quantum strategy for a graph coloring game with a quantum advantage. This quantum strategy is then evaluated on fourteen different quantum hardware platforms to demonstrate its utility as a benchmark. Finally, we discuss potential sources of errors that can explain the observed decreased performance of the executed task and derive an expression for the number of samples required to accurately estimate the win rate in the presence of noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Generative AI for Space-Air-Ground Integrated Networks

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

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The Graph Structure of Chebyshev Permutation Polynomials over Ring pk

arXiv

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arXiv 2022年

作者： Li, Chengqing Lu, Xiaoxiong Tan, Kai Chen, Guanrong The School of Computer Science Xiangtan University Hunan Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the structure of the associated functional graph of Chebyshev permutation polynomials over a ring pk, with p being a prime number greater than three, where every number in the ring is considered as a vertex and the existing mapping relation between two vertices is regarded as a directed edge. Based on some new properties of Chebyshev polynomials and their derivatives, we disclose how the basic structure of the functional graph evolves with respect to parameter k. First, we present a complete and explicit form of the length of a path starting from any given vertex. Then, we show that the strong patterns of the functional graph that the number of cycles of any given length always remains constant as k increases. Moreover, we rigorously prove the rules on the elegant structure of the functional graph and verify them experimentally. Our results could be useful for studying the emergence of the complexity of a nonlinear map in digital computers and security analysis of its cryptographic *** Codes 94A55 Copyright © 2022, The Authors. All rights reserved.

关键词： Chebyshev polynomials

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Channel pruning via automatic structure search 29

Channel pruning via automatic structure search

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Lin, Mingbao Ji, Rongrong Zhang, Yuxin Zhang, Baochang Wu, Yongjian Tian, Yonghong Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University China School of Automation Science and Electrical Engineering Beihang University China Co. Ltd China School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9780999241165

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https://***/lmbxmu/ABCPruner. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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