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Opacity of Nondeterministic Transition Systems: A (Bi)Simulation Relation Approach

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Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

Combining Lattice-Based Planning and Path Optimization in autonomous Heavy Duty Vehicle Applications

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Combining Lattice-Based Planning and Path Optimization in Au...

2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Oliveira, Rui Cirillo, Marcello Mårtensson, Jonas M. Wahlberg, Bo Department of Automatic Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Scania Autonomous Transport Solutions Sodertalje Sweden

ISBN: (纸本)9781538644522

Lattice-based motion planners are an established method to generate feasible motions for car-like vehicles. However, the solution paths can only reach a discretized approximation of the intended goal pose. Moreover, they can be optimal only with respect to the actions available to the planner, which can result in paths with excessive steering. These drawbacks have a negative impact when used in real systems. In this paper we address both drawbacks by integrating a steering method into a state-of-the-art lattice-based motion planner. Un- like previous approaches, in which path optimization happens in an a posteriori step after the planner has found a solution, we propose an interleaved execution of path planning and path optimization. The proposed approach can run in real-time and is implemented in a full-size autonomous truck, and we show experimentally that it is able to greatly improve the quality of the solutions provided by a lattice planner. © 2018 IEEE.

关键词： Motion planning

Quantum repeaters: From quantum networks to the quantum internet

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arXiv 2022年

作者： Azuma, Koji Economou, Sophia E. Elkouss, David Hilaire, Paul Jiang, Liang Lo, Hoi-Kwong Tzitrin, Ilan NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation 3-1 Morinosato-Wakamiya Kanagawa Atsugi243-0198 Japan Department of Physics Virginia Tech BlacksburgVA24061 United States QuTech Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan Quandela SAS 10 Boulevard Thomas Gobert Palaiseau91120 France Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Quantum Bridge Technologies Inc. 100 College Street TorontoONM5G 1L5 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Center for Quantum Information and Quantum Control Department of Physics Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Department of Physics University of Toronto Toronto Canada

A quantum internet is the holy grail of quantum information processing, enabling the deployment of a broad range of quantum technologies and protocols on a global scale. However, numerous challenges exist before the quantum internet can become a reality. Perhaps the most crucial of these is the realization of a quantum repeater, an essential component in the long-distance transmission of quantum information. As the analog of a classical repeater, extender, or booster, the quantum repeater works to overcome loss and noise in the quantum channels comprising a quantum network. Here, we review the conceptual frameworks and architectures for quantum repeaters, as well as the experimental progress towards their realization. We also discuss the various near-term proposals to overcome the limits to the communication rates set by point-to-point quantum communication. Finally, we overview how quantum repeaters fit within the broader challenge of designing and implementing a quantum internet. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information

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arXiv 2021年

作者： Huang, Jiahao Ding, Weiping Lv, Jun Yang, Jingwen Dong, Hao Del Ser, Javier Xia, Jun Ren, Tiaojuan Wong, Stephen T. Yang, Guang College of Information Science and Technology Zhejiang Shuren University Hangzhou310015 China National Heart and Lung Institute Imperial College London London United Kingdom School of Information Science and Technology Nantong University Nantong226019 China School of Computer and Control Engineering Yantai University Yantai264005 China Department of Prosthodontics Peking University School Hospital of Stomatology Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Derio48160 Spain Bilbao48013 Spain Department of Radiology Shenzhen Second People’s Hospital The First Affiliated Hospital of Shenzhen University Health Science Center Shenzhen China Systems Medicine and Bioengineering Department Houston Methodist Cancer Center HoustonTX77030 United States Department of Radiology Houston Methodist Hospital Weill Cornell Medicine HoustonTX77030 United States Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

Time-Varying Convex Optimization: Time-Structured Algorithms and Applications

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arXiv 2020年

作者： Simonetto, Andrea Dall’Anese, Emiliano Paternain, Santiago Leus, Geert Giannakis, Georgios B. Optimization and Control Group of IBM Research Ireland Dublin Ireland College of Engineering and Applied Science University of Colorado BoulderCO80309 United States Department of Electrical and Systems Engineering University of Pennsylvania Faculty of Electrical Mathematics and Computer Science Delft University of Technology Delft2628CD Netherlands Digital Technology Center University of Minnesota MinneapolisMN55455 United States

Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. The present paper reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods, and unveils open challenges in application domains of broad interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exempify wide engineering relevance of analytical tools and pertinent theoretical foundations. Copyright © 2020, The Authors. All rights reserved.

关键词： Data Analytics

The 1995-2018 global evolution of the network of amicable and hostile relations among nation-states

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arXiv 2020年

作者： Askarisichani, Omid Singh, Ambuj K. Bullo, Francesco Friedkin, Noah E. Department of Computer Science University of California Santa BarbaraCA93106 United States Center for Control Dynamical Systems and Computation University of California Santa BarbaraCA93106 United States Department of Mechanical Engineering University of California Santa BarbaraCA93106 United States Department of Sociology University of California Santa BarbaraCA93106 United States

We draw on the data collected by the Integrated Crisis Early Warning System on millions of international and regional public news stories, and this system's indicators of the orientation toward a specific nation-state. We construct the networks of international amicable and hostile relations among nation-states that occur in specific time-periods in order to study the global evolution of the network of such international appraisals. Our analysis presents evidence of an evolution of the structure of this network and a model of the probabilistic micro-dynamics of the alterations of international appraisals during the 1995-2018 span of the available data. Our research provides empirical findings on long-standing debates in the interdisciplinary field of work on Structural Balance Theory. Also remarkably, we find that the trajectory of the Frobenius norm of sequential transition probabilities, which govern the evolution of international appraisals among nations, dramatically stabilizes. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

Offset-Free MPC for Resource Sharing on a Nonlinear SCARA Robot⁎

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IFAC-PapersOnLine 2018年第20期51卷 265-272页

作者： Bianchi, M. van der Maas, A. Maljaars, E. Heemels, W.P.M.H. Department of Mechanical Engineering Control System Technology Group Eindhoven University of Technology Eindhoven5600MB Netherlands Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L'Aquila L'Aquila67100 Italy

High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill of materials. A cost saving alternative could be to strive for a reduction in the hardware components needed, in combination with advanced motion control, to still meet the desired specifications. Particularly, in this paper, the possibility is analyzed to allow for resource sharing among several actuators. This results in a switched system, for which we develop a real-time MPC algorithm for optimization of both the input and the switching signals. This implementation applies to a fairly general class of nonlinear systems and uses a novel offset-free formulation in velocity form for LTV prediction models, to realize good tracking performance under the resource sharing constraints. We provide a proof of concept for this MPC solution on a high fidelity model of an industrial SCARA robot, where it is proposed to use a single amplifier to serve two actuators. The MPC solution is compared to heuristically switched LTI controllers, and the potential of the proposed approach is shown in simulations. © 2018

关键词： computer systems programming

Expertise and confidence explain how social influence evolves along intellective tasks

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arXiv 2020年

作者： Askarisichani, Omid Huang, Elizabeth Y. Sato, Kekoa S. Friedkin, Noah E. Bullo, Francesco Singh, Ambuj K. Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Mechanical Engineering University of California Santa BarbaraCA93106 United States Center for Control Dynamical Systems and Computation University of California Santa BarbaraCA93106 United States Department of Sociology University of California Santa BarbaraCA93106 United States

Discovering the antecedents of individuals’ influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate’s expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time. We demonstrate the model’s accuracy in predicting individuals’ influence and provide analytical results on its asymptotic behavior for the case with identically performing individuals. Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals. Using message contents, message times, and individual correctness collected during tasks, we are able to accurately predict individuals’ self-reported influence over time. Extensive experiments verify the accuracy of the proposed models compared to baselines such as structural balance and reflected appraisal model. While the neural networks model is the most accurate, the dynamical model is the most interpretable for influence prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting