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Rényi relative entropy based monogamy of entanglement in tripartite systems

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arXiv 2024年

作者： Mannaï, Marwa Sati, Hisham Byrnes, Tim Radhakrishnan, Chandrashekar Center for Quantum and Topological Systems NYUAD Research Institute New York University Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates The Courant Institute for Mathematical Sciences NYU NY10012 United States New York University Shanghai NYU- ECNU Institute of Physics at NYU Shanghai 567 West Yangsi Road 200124 China State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai200062 China Department of Physics New York University New YorkNY10003 United States Department of Computer Science and Engineering New York University Shanghai 567 West Yangsi Road Pudong Shanghai200124 China

A comprehensive investigation of the entanglement characteristics is carried out on tripartite spin-1/2 systems, examining prototypical tripartite states, the thermal Heisenberg model, and the transverse field Ising model. The entanglement is computed using the Rényi relative entropy. In the traditional Rényi relative entropy, the generalization parameter α can take values only in the range 0 ≤ α ≤ 2 due to the requirements of joint convexity of the measure. To use the Rényi relative entropy over a wider range of α, we use the sandwiched form which is jointly convex in the regime 0.5 ≤ α ≤ ∞. In prototypical tripartite states, we find that GHZ states are monogamous, but surprisingly so are W states. On the other hand, star states exhibit polygamy, due to the higher level of purity of the bipartite subsystems. For spin models, we study the dependence of entanglement on various parameters such as temperature, spin-spin interaction, and anisotropy, and identify regions where entanglement is the largest. The Rényi parameter α scales the amount of entanglement in the system. The entanglement measure based on the traditional and the sandwiched Rényi relative entropies obey the Araki-Lieb-Thirring inequality. In the Heisenberg models, namely the XYZ, XXZ, and XY models, the system is always monogamous. However, in the transverse field Ising model, the state is initially polygamous and becomes monogamous with temperature and coupling. © 2024, CC BY.

关键词： Ising model

SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年 999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

A Communication-Efficient Decentralized Newton's Method with Provably Faster Convergence

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arXiv 2022年

作者： Liu, Huikang Zhang, Jiaojiao So, Anthony Man-Cho Ling, Qing The Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China The Pazhou Lab China

In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for solving it. The main challenges in designing such an algorithm come from three aspects: (i) mismatch between local gradients/ Hessians and the global ones;(ii) cost of sharing second-order information;(iii) tradeoff among computation and communication. To handle these challenges, we first apply dynamic average consensus (DAC) so that each node is able to use a local gradient approximation and a local Hessian approximation to track the global gradient and Hessian, respectively. Second, since exchanging Hessian approximations is far from communication-efficient, we require the nodes to exchange the compressed ones instead and then apply an error compensation mechanism to correct for the compression noise. Third, we introduce multistep consensus for exchanging local variables and local gradient approximations to balance between computation and communication. With novel analysis, we establish the globally linear (resp., asymptotically super-linear) convergence rate of the proposed method when m is constant (resp., tends to infinity), where m ≥ 1 is the number of consensus inner steps. To the best of our knowledge, this is the first super-linear convergence result for a communication-efficient decentralized Newton's method. Moreover, the rate we establish is provably faster than those of first-order methods. Our numerical results on various applications corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Newton-Raphson method

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

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arXiv 2023年

作者： Lin, Xuan Dai, Lichang Zhou, Yafang Yu, Zu-Guo Zhang, Wen Shi, Jian-Yu Cao, Dong-Sheng Zeng, Li Chen, Haowen Song, Bosheng Yu, Philip S. Zeng, Xiangxiang Xiangtan China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China College of Informatics Huazhong Agricultural University China The Northwestern Polytechnical University Xi’an China The Xiangya School of Pharmaceutical Sciences Central South University China AIDD Department of Yuyao Biotech Shanghai China Hunan University China The College of Information Science and Engineering Hunan University Changsha China Computer Science The University of Illinois Chicago United States

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

When brain-inspired AI meets AGI

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Meta-Radiology

Meta-Radiology 2023年第1期1卷

作者： Zhao, Lin Zhang, Lu Wu, Zihao Chen, Yuzhong Dai, Haixing Yu, Xiaowei Liu, Zhengliang Zhang, Tuo Hu, Xintao Jiang, Xi Li, Xiang Zhu, Dajiang Shen, Dinggang Liu, Tianming School of Computing The University of Georgia Athens 30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington 76019 United States MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China School of Automation Northwestern Polytechnical University Xi'an 710072 China Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston 02115 United States School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai 200230 China Shanghai Clinical Research and Trial Center Shanghai 201210 China

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI. © 2023 The Authors

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Physical-barrier detection based collective motion analysis

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Frontiers of computer science 2019年第2期13卷 426-436页

作者： Gaoqi HE Qi CHEN Dongxu JIANG Yubo YUAN Xingjian LU Department of Computer Science and Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Smart City Collaborative Innovation Center Shanghai Jiao Tong University Shanghai 200240 China

Collective motion is one of the most fascinating phenomena and mainly caused by the interactions between individuals. Physical-barriers, as the particular facilities which divide the crowd into different lanes, greatly affect the measurement of such interactions. In this paper we propose the physical-barrier detection based collective motion analysis (PDCMA) approach. The main idea is that the interaction between spatially adjacent pedestrians actually does not exist if they are separated by the physical-barrier. Firstly, the physical-barriers are extracted by two-stage clustering. The scene is automatically divided into several motion regions. Secondly, local region collectiveness is calculated to represent the interactions between pedestrians in each region. Finally, extensive evaluations use the three typical methods, i.e., the PDCMA, the Collectiveness, and the average normalized Velocity, to show the efficiency and efficacy of our approach in the scenes with and without physical barriers. Moreover, several escalator scenes are selected as the typical physical-barrier test scenes to demonstrate the performance of our approach. Compared with the current collective motion analysis methods, our approach better adapts to the scenes with physical barriers.

关键词： crowd behavior analysis collective motion physical-barrier detection two-stage clustering local region collectiveness

Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network

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Vehicle Check-In Data-driven POI Recommendation Based on Imp...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Yuwen Liu Jie Zhang Ruihan Dou Xiaokang Zhou Xiaolong Xu Shoujin Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University China Shanghai Meteorological Information and Technology Support Center China Faculty of Mathematics University of Waterloo Waterloo Canada Faculty of Data Science Shiga University Japan RIKEN Center for Advanced Intelligence Project RIKEN Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia School of Computer Science Qufu Normal University China

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations.

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Identification of switched linear systems: Persistence of excitation and numerical algorithms

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arXiv 2021年

作者： Mu, Biqiang Chen, Tianshi Cheng, Changming Bai, Er-Wei Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States

This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the minimum number of samples needed derived from our condition is much smaller than that reported in the literature. The secondary contribution of the paper concerns the numerical algorithm. Though the algorithm is not new, we show that our surrogate problem, relaxed from an integer optimization to a continuous minimization, has exactly the same solution as the original integer optimization, which is effectively solved by a block-coordinate descent algorithm. Moreover, an algorithm for handling unknown number of subsystems is proposed. Several numerical examples are illustrated to support theoretical analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Integer programming