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Simple and high-Precision Hamiltonian Simulation by Compensating Trotter Error with Linear Combination of Unitary Operations

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PRX Quantum 2025年第1期6卷 010359-010359页

作者： Pei Zeng Jinzhao Sun Liang Jiang Qi Zhao Pritzker School of Molecular Engineering The University of Chicago Illinois 60637 USA Clarendon Laboratory University of Oxford Parks Road Oxford OX1 3PU United Kingdom Blackett Laboratory Imperial College London London SW7 2AZ United Kingdom QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Trotter and linear combination of unitary (LCU) operations are two popular Hamiltonian simulation methods. The Trotter method is easy to implement and enjoys good system-size dependence endowed by commutator scaling, while the LCU method admits high-accuracy simulation with a smaller gate cost. We propose Hamiltonian simulation algorithms using LCU to compensate Trotter error, which enjoy both of their advantages. By adding few gates after the Kth-order Trotter formula, we realize a better time scaling than 2Kth-order Trotter. Our first algorithm exponentially improves the accuracy scaling of the Kth-order Trotter formula. For a generic Hamiltonian, the estimated gate counts of the first algorithm can be 2 orders of magnitude smaller than the best analytical bound of fourth-order Trotter formula. In the second algorithm, we consider the detailed structure of Hamiltonians and construct LCU for Trotter errors with commutator scaling. Consequently, for lattice Hamiltonians, the algorithm enjoys almost linear system-size dependence and quadratically improves the accuracy of the Kth-order Trotter. For the lattice system, the second algorithm can achieve 3 to 4 orders of magnitude higher accuracy with the same gate costs as the optimal Trotter algorithm. These algorithms provide an easy-to-implement approach to achieve a low-cost and high-precision Hamiltonian simulation.

关键词： Size distribution

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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Driving functions and traces of the Loewner equation

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science China Mathematics 2014年第8期57卷 1615-1624页

作者： WU HaiHua DONG XinHan College of Mathematics and Computer Science Key Laboratory of High Performance Computing and Stochastic Information ProcessingHunan Normal University

We consider the chordal Loewner differential equation in the upper half-plane,the behavior of the driving functionλ(t)and the generated hull Kt when Kt approachesλ(0)in a fixed direction or in a *** the case that the hull Kt is generated by a simple curveγ(t)withγ(0)=0,we prove some sharp relations ofλ(t)/√t andγ(t)/√t as t→0 which improve the previous work.

关键词： Loewner equation hull half-plane capacity driving function

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On Rank Factorizations and Factor Prime Factorizations for Multivariate Polynomial Matrices

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Journal of Systems science & Complexity 2018年第6期31卷 1647-1658页

作者： GUAN Jiancheng LI Weiqing OUYANG Baiyu College of Mathematics and Computer Science Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China)Hunan Normal UniversityChangsha 410081China Department of Mathematics Xiangnan UniversityChenzhou 423000China College of Mathematics and Computer Science Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China)Hunan Normal UniversityChangsha 410081China

In this paper,rank factorizations and factor left prime factorizations are *** authors prove that any polynomial matrix with full row rank has factor left prime *** for a class of polynomial matrices,the authors give an algorithm to decide whether they have rank factorizations or factor left prime factorizations and compute these factorizations if they exist.

关键词： Factor prime factorizations multivariate polynomial matrices rank factorizations

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Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Liu, Ziheng Zhu, Yiyang Shi, Enyu Xu, Bokai Yuen, Chau Niyato, Dusit Debbah, Mérouane Jin, Shi Ai, Bo Shen, Xuemin School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing jiaotong University Beijing100044 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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Utterance as A Bridge: Few-shot Joint Learning of Empathy Detection and Empathy Intent Classification

Utterance as A Bridge: Few-shot Joint Learning of Empathy De...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liting Jiang Di Wu Zhe Li Shuangyong Song Yanbing Li Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multi-Lingual Information Technology Urumqi China Institute of Artificial Intelligence (TeleAI) China Telecom Corp Ltd Beijing China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong China Department of Electrical Engineering Stanford University USA Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Empathy detection (ED) and empathy intent classification (EIC) aim to identify the empathy direction expressed in user utterances and the underlying empathy intent behind them. Previous studies show that facilitating information transfer between tasks can enhance model performance. However, the interaction between ED and EIC in few-shot learning remains underexplored. To this end, we identify the challenges in jointly training ED and EIC in a few-shot setting: establishing effective information transfer between them and improving the model’s generalization capability. We propose a novel model called USB. For information transfer, the interactive module maps empathy and empathy intent labels through utterances to model task correlations. For generalization capability, after capturing empathy and empathy intent representations with an adaptive fusion module, we introduce a multi-level contrastive learning strategy to optimize representations at task and label levels, enhancing generalization. Experimental results on two public datasets show that our model outperforms all baselines.

关键词： Training Bridges Adaptation models Intent recognition Focusing Contrastive learning Signal processing Universal Serial Bus Few shot learning Speech processing

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Numerical Analysis of the Evolution of Hydrodynamic Instabilities and Vortex Generation Associated With Collisionless Shocks in Laser-Produced Plasmas

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Contributions to Plasma Physics 2025年

作者： Mizuta, A. Jao, C.-S. Kurochi, S. Nikaido, F. Sakai, K. Ohnishi, N. Chen, Y.-C. Liu, Y.-L. Watamura, N. Minami, T. Suzuki, H. Hoshi, T. Terachi, Y. Nakano, A. Kuramitsu, Y. Saitama Japan Saitama Japan Department of Physics National Cheng Kung University Tainan Taiwan Graduate School of Engineering Osaka University Osaka Japan National Institute for Fusion Science Gifu Japan Department of Aerospace Engineering Tohoku University Sendai Japan National Center for High-Performance Computing National Applied Research Laboratories Taiwan Institute of Space and Plasma Sciences National Cheng Kung University Taiwan Osaka School of International Public Policy Osaka University Osaka Japan Graduate School of Information Science and Technology Osaka University Osaka Japan Institute of Laser Engineering Osaka University Osaka Japan Kyoto Japan

We numerically studied laboratory experiments that generate turbulence by colliding asymmetric supersonic counterstreaming plasmas driven by laser-produced plasma. After the interaction of these two flows, the resulting reflection shocks and contact discontinuity exhibit curvatures due to the asymmetry, driving the growth of hydrodynamic instabilities such as Kelvin-Helmholtz and Richtmyer–Meshkov instabilities. This leads to the development of turbulence in the shocked plasma. We numerically calculate proton radiographs through this plasma, assuming an electric field via quasi-static force balance in the equation of motion of electrons. Using neural networks, we demonstrate the reconstruction of unknown electric fields in the plasma from the synthetic proton radiography data. © 2025 Wiley-VCH GmbH.

关键词： Laser produced plasmas

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PFS3F: Probabilistic Fusion of Superpixel-wise and Semantic-aware Structural Features for Hyperspectral Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Ying Duan, Puhong Liang, Lianhui Kang, Xudong Li, Jun Plaza, Antonio Hunan University of Technology College of Railway Transportation Zhuzhou412007 China Hunan University School of Robotics Changsha410082 China Guangxi University School of Electrical Engineering Nanning530004 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing the School of Computer Science Wuhan430078 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura CaceresE-10071 Spain

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets. ©1991-2012 IEEE.

关键词： Superpixels

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