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Transverse-momentum-dependent wave functions of the pion from lattice QCD

Physical Review D 2024年第9期109卷 L091503-L091503页

作者： Min-Huan Chu Jin-Chen He Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Hai-Tao Shu Yushan Su Ji-Hao Wang Wei Wang Yi-Bo Yang Jun Zeng Jian-Hui Zhang Qi-An Zhang INPAC Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China School of Physics Beihang University Beijing 102206 China

We present a lattice QCD calculation of the transverse-momentum-dependent wave function (TMDWF) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks with lattice spacing a=0.121 fm from the MILC collaboration, and one with 2+1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS collaboration with a=0.098 fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWF simulated on the lattice, we extract the TMDWF. The results for both lattice ensembles are compatible and a comparison with a phenomenological parametrization is made. Our studies provide a first attempt of ab initio calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

关键词： Lattice QCD Transverse momentum dependent distribution Pions

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Human intention recognition for human aware planning in integrated warehouse systems

arXiv

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

引用

arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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ANN Based Sensor and Actuator Fault Detection in Nuclear Reactors

ANN Based Sensor and Actuator Fault Detection in Nuclear Rea...

引用

International Conference on control, Mechatronics and Automation (ICCMA)

作者： Shohan Banerjee Jiamei Deng Chris Gorse Vineet Vajpayee Victor Becerra S. R. Shimjith School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom School of Energy and Electronic Engineering University of Portsmouth Portsmouth United Kingdom Reactor Control System Design Section Bhabha Atomic Research Centre and Homi Bhabha National Institute Mumbai India

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data-driven methods, and signal-based methods. In practical applications, model-based methods are very difficult to achieve. Thus, various data-driven methods and signal- based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation.

关键词： Artificial neural networks Coolants Fault detection Inductors Temperature sensors Actuators Training

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Collaborative Scheduling of computing Tasks for Edge computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

引用

Chinese Automation Congress (CAC)

作者： Yue Liu Gang Xiong Fenghua Zhu Shichao Chen Liguo Zhang Xudong Liu School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and the Cloud Computing Center Chinese Academy of Sciences Beijng China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation and Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verily the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network.

关键词： Analytical models Scheduling algorithms Computational modeling Collaboration Scheduling Delays Servers

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

引用

arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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Game Theoretic Potential Field for Autonomous Water Surface Vehicle Navigation Using Weather Forecasts

Game Theoretic Potential Field for Autonomous Water Surface ...

引用

American control Conference

作者： Evan Krell Luis Rodolfo Garcia Carrillo Scott A. King Joao P. Hespanha Control of Robots and Autonomous Agents Laboratory (CORAL) Texas A&M University Corpus Christi CORAL and the Unmanned Systems Laboratory (TAMUCC-USL) School of Engineering & Computing Sciences Texas A&M University Corpus Christi Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Imperfect weather forecasts complicate robot planning. A conservative motion planning algorithm is developed to address uncertainty in autonomous boat missions. Dynamic Programming generates an optimal action for every location and game theory strategically handles uncertainty. Experimental results using Massachusetts Bay forecasts show the robot is able minimize the cost of worst-case weather.

关键词： Robots Planning Force Games Heuristic algorithms Weather forecasting

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Calibration of Agent-Based Model Using Reinforcement Learning

Calibration of Agent-Based Model Using Reinforcement Learnin...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Bing Song Gang Xiong Songmin Yu Peijun Ye Xisong Dong Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China Fraunhofer Institute for Systems and Innovation Research Breslauer Germany

In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity.

关键词： Learning systems Microscopy Digital twin Sociology Reinforcement learning Mathematical models Calibration

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

引用

IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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LPI-Based OFDM Radar Waveform Design in a Cooperative Radar-Communications System

LPI-Based OFDM Radar Waveform Design in a Cooperative Radar-...

引用

2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Shi, Chenguang Wang, Yijie Wang, Fei Salous, Sana Zhou, Jianjiang Ministry of Education Nanjing210016 China Science and Technology on Electro-Optic Control Laboratory Luoyang471009 China School of Engineering and Computing Sciences Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781728123455

This paper investigates the problem of low probability of intercept (LPI)-based orthogonal frequency division multiplexing (OFDM) radar waveform design in a cooperative radar-communications system, where the communications base station (CBS) is able to receive and process the radar transmissions. The primary objective of the proposed strategy is to minimize the radar transmitted power while guaranteeing a specified target time delay estimation accuracy. The expression for Cramér-Rao lower bound (CRLB) is analytically derived and utilized as a performance metric for target time delay estimation performance. Numerical simulation results are provided to demonstrate that the total transmitted power of radar system can be considerably decreased for the cooperative scenario. © 2019 IEEE.

关键词： Time delay

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