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arXiv 2021年

作者： Molla, Jeta De León Chávez, Alejandro Ponce Hiraoka, Takayuki Ala-Nissila, Tapio Kivelä, Mikko Leskelä, Lasse Department of Applied Physics Aalto University Otakaari 1 Espoo02150 Finland Department of Mathematics and Systems Analysis Aalto University Otakaari 1 Espoo02150 Finland Department of Computer Science Aalto University Konemiehentie 2 Espoo02150 Finland Quantum Technology Finland Center of Excellence Department of Applied Physics Aalto University Otakaari 1 Espoo02150 Finland Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Loughborough LeicestershireLE11 3TU United Kingdom

We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering. © 2021, CC BY-NC-ND.

关键词： Vaccines

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Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks 23

Single perceptron at 12 GigaOPs based on a microcomb for ver...

引用

Smart Photonic and Optoelectronic Integrated Circuits XXIII 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T.G. Chu, S.T. Little, B.E. Morandotti, R. Mitchell, A. Hicks, D.G. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Hong Kong Xi'An Institute of Optics and Precision Mechanics Precision Mechanics CAS Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X1S2 Canada Adjunct Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China Bioinformatics Division Walter and Eliza Hall Institute of Medical Research ParkvilleVIC3052 Australia

ISBN: (数字)9781510642164

ISBN: (纸本)9781510642157

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs- A single neuron perceptron-by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection- A chieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. © 2021 SPIE.

关键词： Data handling

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HoneyCar: A framework to configure honeypot vulnerabilities on the internet of vehicles

arXiv

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arXiv 2021年

作者： Panda, Sakshyam Rass, Stefan Moschoyiannis, Sotiris Liang, Kaitai Loukas, George Panaousis, Emmanouil The School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom The LIT Secure and Correct Systems Lab Johannes Kepler University Linz Linz Austria The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom The Cyber Security Group Delft University of Technology Delft Netherlands

The Internet of Vehicles (IoV), whereby interconnected vehicles communicate with each other and with road infrastructure on a common network, has promising socioeconomic benefits but also poses new cyber-physical threats. Data on vehicular attackers can be realistically gathered through cyber threat intelligence using systems like honeypots. Admittedly, configuring honeypots introduces a trade-off between the level of honeypot-attacker interactions and any incurred overheads and costs for implementing and monitoring these honeypots. We argue that effective deception can be achieved through strategically configuring the honeypots to represent components of the IoV and engage attackers to collect cyber threat intelligence. In this paper, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar builds upon a repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) data within the National Vulnerability Database (NVD) to compute optimal honeypot configuration strategies. By taking a game-theoretic approach, we model the adversarial interaction as a repeated imperfect-information zero-sum game in which the IoV network administrator chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability of the IoV to exploit under uncertainty. Our investigation is substantiated by examining two different versions of the game, with and without the re-configuration cost to empower the network administrator to determine optimal honeypot configurations. We evaluate HoneyCar in a realistic use case to support decision makers with determining optimal honeypot configuration strategies for strategic deployment in *** Codes 68M25 © 2021, CC BY-NC-SA.

关键词： Game theory

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LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor

arXiv

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arXiv 2020年

作者： Munir, Farzeen Azam, Shoaib Jeon, Moongu Lee, Byung-Geun Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Modern vehicles are equipped with various driver-assistance systems, including automatic lane keeping, which prevents unintended lane departures. Traditional lane detection methods incorporate handcrafted or deep learning-based features followed by postprocessing techniques for lane extraction using frame-based RGB cameras. The utilization of frame-based RGB cameras for lane detection tasks is prone to illumination variations, sun glare, and motion blur, which limits the performance of lane detection methods. Incorporating an event camera for lane detection tasks in the perception stack of autonomous driving is one of the most promising solutions for mitigating challenges encountered by frame-based RGB cameras. The main contribution of this work is the design of the lane marking detection model, which employs the dynamic vision sensor. This paper explores the novel application of lane marking detection using an event camera by designing a convolutional encoder followed by the attention-guided decoder. The spatial resolution of the encoded features is retained by a dense atrous spatial pyramid pooling (ASPP) block. The additive attention mechanism in the decoder improves performance for high dimensional input encoded features that promote lane localization and relieve postprocessing computation. The efficacy of the proposed work is evaluated using the DVS dataset for lane extraction (DET). The experimental results show a significant improvement of 5.54% and 5.03% in F1 scores in multiclass and binary-class lane marking detection tasks. Additionally, the intersection over union (IoU) scores of the proposed method surpass those of the best-performing state-of-the-art method by 6.50% and 9.37% in multiclass and binary-class tasks, respectively. © 2020, CC BY.

关键词： Cameras

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Specialty optical fibers for advanced sensing applications

引用

Opto-Electronic science 2023年第2期2卷 1-26页

作者： Huanhuan Liu Dora Juan Juan Hu Qizhen Sun Lei Wei Kaiwei Li Changrui Liao Bozhe Li Cong Zhao Xinyong Dong Yuhan Tang Yihong Xiao Gerd Keiser Perry Ping Shum Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#21-01Connexis South TowerSingapore 138632Singapore School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Bionic Engineering of Ministry of Education Jilin UniversityChangchun 130022China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things Guangdong and Hong Kong Joint Research Centre for Optical Fibre SensorsShenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Guangdong Provincial Key Laboratory of Information Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Department of Electrical&Computer Engineering Boston UniversityBoston 02215USA Pengcheng Laboratory Shenzhen 518055China

Optical fiber technology has changed the world by enabling extraordinary growth in world-wide communications and *** rapid development and wide deployment of optical fiber sensors are driven by their excellent sensing performance with outstanding flexibility,functionality,and ***,the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing *** paper overviews recent developments in specialty optical fibers and their sensing *** specialty optical fibers are reviewed based on their innovations in special structures,special materials,and technologies to realize lab in/on a *** overview of sensing applications in various fields is *** prospects and emerging research areas of specialty optical fibers are also discussed.

关键词： specialty optical fibers photonic crystal fiber multifunctional multi-material fibers lab in/on fiber

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On geometry of information flow for causal inference

arXiv

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arXiv 2020年

作者： Surasinghe, Sudam Bollt, Erik M. Department of Mathematics Clarkson University PotsdamNY13699 United States Electrical and Computer Engineering C3S2 Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699 United States

Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts, and scientists from many other fields. This paper takes the perspective of information flow, which includes the Nobel prize winning work on Granger-causality, and the recently highly popular transfer entropy, these being probabilistic in nature. Our main contribution will be to develop analysis tools that will allow a geometric interpretation of information flow as a causal inference indicated by positive transfer entropy. We will describe the effective dimensionality of an underlying manifold as projected into the outcome space that summarizes information flow. Therefore contrasting the probabilistic and geometric perspectives, we will introduce a new measure of causal inference based on the fractal correlation dimension conditionally applied to competing explanations of future forecasts, which we will write GeoCy→x. This avoids some of the boundedness issues that we show exist for the transfer entropy, Ty→x. We will highlight our discussions with data developed from synthetic models of successively more complex nature: these include the Hénon map example, and finally a real physiological example relating breathing and heart rate function. Copyright © 2020, The Authors. All rights reserved.

关键词： Philosophical aspects

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Control and decision method for overtaking problem in autonomous vehicles 16

Control and decision method for overtaking problem in autono...

引用

16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018

作者： Hegedűs, Tamás Németh, Balázs Bede, Zsuzsanna Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Stoczek utca. 2 BudapestH-1111 Hungary Systems and Control Laboratory Institute for Computer Science and Control Kende utca 13-17 BudapestH-1111 Hungary

In this paper we deal with an overtaking and lane change strategy for autonomous vehicles. The algorithm is divided into two main layers. The upper layer is responsible for the decision making (e.g lane changing). Before decision-making we predict the motion of other participants and divide the road into discrete sets in each interval, then we compute the probability of occupancy. Considering the state of the motion of ego vehicle we can compute the probability of collision. After assigning a graph points to discrete elements, the probability of collision is ordered to the graph edges, then we determine the route with the smallest cost. The decision-making algorithm is not able to control the vehicle, so it is necessary to design a traceable trajectory for the vehicle. Since we have a series of points to be reached for the future, this can be used as a reference vector for the trajectory design algorithm. One Model Predicitve Controller (MPC) is responsible for the design of the trajectory so we can obtain limitations to several states to ensure comfort and security. Extending the algorithm, we can compute the longitudinal movement as well. In this paper the decision-making and the trajectory planning algorithm is presented, and the whole model validated in CarMaker. © 2019 Budapest University of Technology and Economics. All rights reserved.

关键词： Decision making

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Tera-OP/s Neuromorphic Processing with Kerr Microcombs

Tera-OP/s Neuromorphic Processing with Kerr Microcombs

引用

Photonics in Switching and Computing, PSC 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T. Chu, S.T. Little, B.E. Morandotti, R. Hicks, D.G. Mitchell, A. Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi'an China INRS-Ãnergie Matriaux et Tlcommunications 1650 Boulevard Lionel-Boulet VarennesQubecJ3X 1S2 Canada Walter and Eliza Hall Institute of Medical Research Bioinformatics Division ParkvilleVictoria3052 Australia

We report optical neuromorphic processors based on Kerr micro-combs that are programmable, scalable and ultra-high speed. We demonstrate a perceptron at 11.9Giga-OPS at 8 bits, or 95.2 Gbps, and a convolutional accele...

ISBN: (纸本)9781557528209

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Sampled training and node inheritance for fast evolutionary neural architecture search

arXiv

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arXiv 2020年

作者： Zhang, Haoyu Jin, Yaochu Cheng, Ran Hao, Kuangrong Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive. To address this issue, this paper proposes a new evolutionary framework for fast ENAS based on directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted to generate offspring individuals and their fitness is directly evaluated without training. To enhance the feature processing capability of the evolved neural networks, we also encode a channel attention mechanism in the search space. We evaluate the proposed algorithm on the widely used datasets, in comparison with 26 state-of-the-art peer algorithms. Our experimental results show the proposed algorithm is not only computationally much more efficiently, but also highly competitive in learning performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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