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Observation of quantum strong Mpemba effect

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arXiv 2024年

作者： Zhang, Jie Xia, Gang Wu, Chun-Wang Chen, Ting Zhang, Qian Xie, Yi Su, Wen-Bo Wu, Wei Qiu, Cheng-Wei Chen, Ping-Xing Li, Weibin Jing, Hui Zhou, Yan-Li Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Mechanism and technology of Quantum Information Changsha410073 China Hefei National Laboratory Anhui Hefei230088 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha410081 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Physics and Astronomy University of Nottingham NottinghamNG7 2RD United Kingdom Centre for the Mathematics and Theoretical Physics of Quantum Non-equilibrium Systems University of Nottingham NottinghamNG7 2RD United Kingdom College of Science National University of Defense Technology Changsha410073 China

An ancient and counterintuitive phenomenon known as the Mpemba effect showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we experimentally study the strong Mpemba effect in a single trapped ion system in which an exponentially accelerated relaxation in time is observed by preparing an optimal quantum initial state with no excitation of the slowest decaying mode. Also, we demonstrate that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the systems. Our work provides an efficient strategy to engineer the dynamics of open quantum system, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

Adapting ClusTree for more challenging data stream environments

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Adapting ClusTree for more challenging data stream environme...

作者： Zgraja, Jakub Moulton, Richard Hugh Gama, João Kasprzak, Andrzej Woźniak, Michal Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland Department of Electrical and Computer Engineering Queen's University KingstonON Canada Laboratory of Artificial Intelligence and Decision Support and Faculty of Economics University of Porto Porto Portugal

Data stream mining seeks to extract useful information from quickly-arriving, infinitely-sized and evolving data streams. Although these challenges have been addressed throughout the literature, none of them can be considered 'solved.' We contribute to closing this gap for the task of data stream clustering by proposing two modifications to the well-known ClusTree data stream clustering algorithm: pruning unused branches and detecting concept drift. Our experimental results show the difficulty in tackling these aspects of data stream mining and the sensitivity of stream mining algorithms to parameter values. We conclude that further research is required to better equip stream learners for the data stream clustering task. © 2019 - IOS Press and the authors. All rights reserved.

关键词： Clustering algorithms

Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Online reinforcement learning control by direct heuristic dynamic programming: from time-driven to event-driven

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arXiv 2020年

作者： Zhao, Qingtao Si, Jennie Sun, Jian Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate dynamic programming (ADP) and reinforcement learning (RL) algorithms, the direct heuristic dynamic programming (dHDP) has been shown an effective tool as demonstrated in solving several complex learning control problems. It continuously updates the control policy and the critic as system states continuously evolve. It is therefore desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. Toward this goal, we propose a new event-driven dHDP. By constructing a Lyapunov function candidate, we prove the uniformly ultimately boundedness (UUB) of the system states and the weights in the critic and the control policy networks. Consequently we show the approximate control and cost-to-go function approaching Bellman optimality within a finite bound. We also illustrate how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

Community detection for gossip dynamics with stubborn agents

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arXiv 2020年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl Henrik Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden

We consider a community detection problem for gossip dynamics with stubborn agents in this paper. It is assumed that the communication probability matrix for agent pairs has a block structure. More specifically, we assume that the network can be divided into two communities, and the communication probability of two agents depends on whether they are in the same community. Stability of the model is investigated, and expectation of stationary distribution is characterized, indicating under the block assumption, the stationary behaviors of agents in the same community are similar. It is also shown that agents in different communities display distinct behaviors if and only if state averages of stubborn agents in different communities are not identical. A community detection algorithm is then proposed to recover community structure and to estimate communication probability parameters. It is verified that the community detection part converges in finite time, and the parameter estimation part converges almost surely. Simulations are given to illustrate algorithm performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Population dynamics

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

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arXiv 2022年

作者： Placed, Julio A. Strader, Jared Carrillo, Henry Atanasov, Nikolay Indelman, Vadim Carlone, Luca Castellanos, José A. Universidad de Zaragoza C/María de Luna 1 Zaragoza50018 Spain The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Genius Sports One Plaza Avenida El Poblado #5A - 113 North Tower 2nd & 10th Flr Medellín AN Colombia The Department of Electrical and Computer Engineering UC San Diego 9500 Gilman Drive La Jolla CA92093-0411 United States The Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa32000 Israel

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics. © 2022, CC BY.

关键词： Reinforcement learning

An IoT Edge Computing System Architecture and its Application

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An IoT Edge Computing System Architecture and its Applicatio...

2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

Compositional Synthesis of Symbolic Models for Infinite Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1868-1873页

作者： Abdalla Swikir Navid Noroozi Majid Zamani Department of Electrical and Computer Engineering Technical University of Munich Germany Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Computer Science Department University of Colorado BoulderUSA. Institute of Informatics Ludwig Maximilian University Munich Germany

In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.

关键词： Compositionality interconnected systems small-gain condition infinite networks symbolic models

Low density majority codes and the problem of graceful degradation

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arXiv 2019年

作者： Roozbehani, Hajir Polyanskiy, Yury Laboratory of Information and Decision Systems MIT Department of Electrical Engineering and Computer Science MIT

We study a problem of constructing codes that transform a channel with high bit error rate (BER) into one with low BER (at the expense of rate). Our focus is on obtaining codes with smooth ("graceful") input-output BER curves (as opposed to threshold-like curves typical for long error-correcting codes). This paper restricts attention to binary erasure channels (BEC) and contains three contributions. First, we introduce the notion of Low Density Majority Codes (LDMCs). These codes are non-linear sparse-graph codes, which output majority function evaluated on randomly chosen small subsets of the data bits. This is similar to Low Density Generator Matrix codes (LDGMs), except that the XOR function is replaced with the majority. We show that even with a few iterations of belief propagation (BP) the attained input-output curves provably improve upon performance of any linear systematic code. The effect of non-linearity bootstraping the initial iterations of BP, suggests that LDMCs should improve performance in various applications, where LDGMs have been used traditionally. Second, we establish several two-point converse bounds that lower bound the BER achievable at one erasure probability as a function of BER achieved at another one. The novel nature of our bounds is that they are specific to subclasses of codes (linear systematic and non-linear systematic) and outperform similar bounds implied by the area theorem for the EXIT function. Third, we propose a novel technique for rigorously bounding performance of BP-decoded sparse-graph codes (over arbitrary binary input-symmetric channels). This is based on an extension of ***’s lemma to the χ2-information and a new characterization of the extremality under the less noisy order. Copyright © 2019, The Authors. All rights reserved.

关键词： Belief propagation