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arXiv 2024年

作者： Fornari, Fabrizio Compagnucci, Ivan De Donato, Massimo Callisto Bertrand, Yannis Beyel, Harry H. Carrión, Emilio Franceschetti, Marco Groher, Wolfgang Grüger, Joscha Kilic, Emre Koschmider, Agnes Leotta, Francesco Li, Chiao-Yun Lugaresi, Giovanni Malburg, Lukas Mangler, Juergen Mecella, Massimo Pastor, Oscar Riss, Uwe Seiger, Ronny Serral, Estefania Torres, Victoria Valderas, Pedro University of Camerino School of Science and Technology Computer Science Department Via Madonna delle Carceri 7 Camerino62032 Italy Gran Sasso Science Institute Computer Science Department Viale Francesco Crispi 7 L’Aquila67100 Italy University of St.Gallen Institute of Computer Science Rosenbergstrasse 30 St.Gallen9000 Switzerland University of Bayreuth Business Informatics and Process Analytics Wittelsbacherring 8 Bayreuth95444 Germany Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven Sankt Augustin53757 Germany RWTH Aachen University Ahornstraße 55 Aachen52074 Germany Mercadona Tech Mercadona Plaza de Amèrica 2 Valencia46004 Spain PROS Research Center - VRAIN Institute Universitat Politècnica de València Camino de Vera Valencia46022 Spain TU Dortmund University Leonhard-Euler-Str. 5 Dortmund44227 Germany KU Leuven Warmoesberg 26 Brussels1000 Belgium Sapienza Università di Roma DIAG via Ariosto 25 Rome00185 Italy KU Leuven Faculty of Engineering Technology Celestijnenlaan 300 Leuven3001 Belgium Branch Trier University Behringstraße 21 Trier54296 Germany Artificial Intelligence and Intelligent Information Systems Trier University Universitätsring 15 Trier54296 Germany OST Eastern Switzerland University of Applied Sciences Rosenbergstrasse 59 St. Gallen9001 Switzerland TUM School of Computation Information and Technology Boltzmannstraße 3 Garching bei München85748 Germany

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay. © 2024, CC BY-NC-ND.

关键词： Virtual corporation

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Versatile Method of engineering the Band Alignment and the Electron Wavefunction Hybridization of Hybrid Quantum Devices

arXiv

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arXiv 2023年

作者： Li, Guoan Shi, Xiaofan Lin, Ting Yang, Guang Rossi, Marco Badawy, Ghada Zhang, Zhiyuan Shi, Jiayu Qian, Degui Lu, Fang Gu, Lin Wang, An-Qi Tong, Bingbing Li, Peiling Lyu, Zhaozheng Liu, Guangtong Qu, Fanming Dou, Ziwei Pan, Dong Zhao, Jianhua Zhang, Qinghua Bakkers, Erik P.A.M. Nowak, Michal P. Wójcik, Pawel Lu, Li Shen, Jie Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China AGH University of Krakow Academic Centre for Materials and Nanotechnology al. A. Mickiewicza 30 Krakow30-059 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 Krakow30-059 Poland Department of Applied Physics Eindhoven University of Technology Eindhoven5600 MB Netherlands State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences P. O. Box 912 Beijing100083 China Beijing National Center for Electron Microscopy Laboratory of Advanced Materials Department of Materials Science and Engineering Tsinghua University Beijing100084 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Songshan Lake Materials Laboratory Dongguan523808 China Beijing Academy of Quantum Information Sciences Beijing100193 China Center of Materials Science and Optoelectronics Engineering CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China

Hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the integration of the properties of both materials, which relies on the interface details and the resulting coupling strength and wavefunction hybridization. However until now, none of the experiments have reported good control of band alignment of the interface, as well as its tunability to the coupling and hybridization. Here, the interface is modified by inducing specific argon milling while maintaining its high-quality, e.g., atomic connection, which results in a large induced superconducting gap and ballistic transport. By comparing with Schrödinger-Poisson calculations, it is proven that this method can vary the band bending/coupling strength and the electronic spatial distribution. In the strong coupling regime, the coexistence and tunability of crossed Andreev reflection and elastic co-tunnelling—key ingredients for the Kitaev chain—are confirmed. This method is also generic for other materials and achieves a hard and huge superconducting gap in Pb-InSb devices. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems. Copyright © 2023, The Authors. All rights reserved.

关键词： III-V semiconductors

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Quantum Mixed State Compiling

arXiv

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arXiv 2022年

作者： Ezzell, Nic Ball, Elliott M. Siddiqui, Aliza U. Wilde, Mark M. Sornborger, Andrew T. Coles, Patrick J. Holmes, Zoë Department of Physics & Astronomy University of Southern California Los AngelesCA90089 United States Information Sciences Los Alamos National Laboratory Los AlamosNM87545 United States Physics Department Lancaster University LancasterLA1 4YB United Kingdom Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Quantum Science Center Oak RidgeTN37931 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States LausanneCH-1015 Switzerland

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state;the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states. © 2022, CC BY.

关键词： Principal component analysis

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The impact of generative artificial intelligence on socioeconomic inequalities and policy making

arXiv

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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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Valley population of donor states in highly strained silicon

arXiv

引用

arXiv 2021年

作者： Voisin, B. Ng, K.S.H. Salfi, J. Usman, M. Wong, J.C. Tankasala, A. Johnson, B.C. McCallum, J.C. Hutin, L. Bertrand, B. Vinet, M. Valanoor, N. Simmons, M.Y. Rahman, R. Hollenberg, L.C.L. Rogge, S. Silicon Quantum Computing SydneyNSW2052 Australia School of Physics Sydney The University of New South Wales SydneyNSW2052 Australia Centre for Quantum Computation and Communication Technology School of Physics The University of New South Wales SydneyNSW2052 Australia 5. Physikalisches Institut and Center for Integrated Quantum Science and Technology Universität Stuttgart Stuttgart70569 Germany Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Centre for Quantum Computation and Communication Technology School of Physics The University of Melbourne ParkvilleVIC3010 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3010 Australia School of Materials Science and Engineering The University of New South Wales SydneyNSW2052 Australia Electrical and Computer Engineering Department Purdue University West LafayetteIN United States Quantum Photonics Laboratory School of Engineering RMIT University MelbourneVIC3000 Australia Université Grenoble Alpes CEA LETI Grenoble38000 France

Strain is extensively used to controllably tailor the electronic properties of materials. In the context of indirect band-gap semiconductors such as silicon, strain lifts the valley degeneracy of the six conduction band minima, and by extension the valley states of electrons bound to phosphorus donors. Here, single phosphorus atoms are embedded in an engineered thin layer of silicon strained to 0.8% and their wave function imaged using spatially resolved spectroscopy. A prevalence of the out of plane valleys is confirmed from the real-space images, and a combination of theoretical modelling tools is used to assess how this valley repopulation effect can yield isotropic exchange and tunnel interactions in the xy-plane relevant for atomically precise donor qubit devices. Finally, the residual presence of in-plane valleys is evidenced by a Fourier analysis of both experimental and theoretical images, and atomistic calculations highlight the importance of higher orbital excited states to obtain a precise relationship between valley population and strain. Controlling the valley degree of freedom in engineered strained epilayers provides a new competitive asset for the development of donor-based quantum technologies in silicon. Copyright © 2021, The Authors. All rights reserved.

关键词： Landforms

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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

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Data science in science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength

Indoor Object Localization and Tracking Using Deep Learning ...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Guannan Liu Hsiao-Chun Wu Weidong Xiang Jinwei Ye Yiyan Wu Limeng Pu School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA University of Michigan Dearborn USA Communications Research Centre Ottawa Canada Center for Computation and Technology Louisiana State University Baton Rouge USA

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

This paper introduces a new indoor-localization approach using a deep-learning network for which the received signal-strength indicator (RSSI) is adopted as the radiofrequency fingerprint. In our proposed scheme, the RSSIs which are estimated by a channel-propagation emulator software, are adopted as the input features for deep learning. This approach is measurement-free and thus it is very cost-effective and convenient to users. A multilayer perceptron (MLP) is constructed to predict the location(s) of the mobile object(s). The time evolution of the predicted locations of an object will form the predicted trajectory thereby. Because deep-learning networks require tremendous training data to achieve good prediction accuracy, we propose to partition the indoor geometry of interest (ex., a room) into several zones. Preliminary simulation results demonstrate that the AUC (area under the receiver-operating characteristic curve) can reach up to 0.89 for a room partitioned into eight zones. Our proposed new indoor-localization scheme in this work can be a rare but promising localization technology, which is neither passive nor active as other existing prevalent localization methods.

关键词： Location awareness Deep learning Radio frequency Simulation Training data Software Trajectory

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Correction: PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第3期6卷 530-530页

作者： Seleson, Pablo Pasetto, Marco John, Yohan Trageser, Jeremy Reeve, Samuel Temple Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge USA Department of Mechanical and Aerospace Engineering University of California San Diego La Jolla USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta USA Center for Control Dynamical Systems and Computation University of California at Santa Barbara Harold Frank Hall Room 5119 Santa Barbara USA Engineering Sciences Center Sandia National Laboratories Albuquerque USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge USA

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Corrigendum to “Understanding the urban mobility community by taxi travel trajectory” [Commun. Nonlinear Sci. Numer. Simul. 101(2021) 105863/6]

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Communications in Nonlinear science and Numerical Simulation 2022年 104卷

作者： Wei-Peng Nie Zhi-Dan Zhao Shi-Min Cai Tao Zhou Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China Complexity Computation Laboratory Department of Computer Science School of Engineering Shantou University Shantou 515063 China Key Laboratory of Intelligent Manufacturing Technology (Ministry of Education) Shantou University Shantou 515063 China

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