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The effectiveness of backward contact tracing in networks

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arXiv 2020年

作者： Kojaku, Sadamori Hébert-Dufresne, Laurent Mones, Enys Lehmann, Sune Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States DTU Compute Technical University of Denmark Kgs. Lyngby 2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen K1353 Denmark Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

Discovering and isolating infected individuals is a cornerstone of epidemic control1-7. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control8-15. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing-tracing to whom disease spreads, "backward" tracing-tracing from whom disease spreads-is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that-in terms of the number of prevented transmissions per isolation-case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest

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Plant-Environment Interactions

Plant-Environment Interactions 2024年第2期5卷 e10136页

作者： Kearsley, Elizabeth Verbeeck, Hans Stoffelen, Piet Janssens, Steven B. Yakusu, Emmanuel Kasongo Kosmala, Margaret De Mil, Tom Bauters, Marijn Kitima, Elasi Ramanzani Ndiapo, José Mbifo Chuda, Adelard Lonema Richardson, Andrew D. Wingate, Lisa Ilondea, Bhély Angoboy Beeckman, Hans van den Bulcke, Jan Boeckx, Pascal Hufkens, Koen Computational and Applied Vegetation Ecology Lab Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium BlueGreen Labs Melsele Belgium Meise Botanic Garden Meise Belgium Department of Biology Leuven Plant Institute KULeuven Leuven Belgium UGent-Woodlab (Laboratory of Wood Technology) Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium Service of Wood Biology Royal Museum for Central Africa Tervuren Belgium Faculté de gestion des ressources naturelles renouvelables Université de Kisangani Kisangani Democratic Republic Congo Department of Organismic and Evolutionary Biology Harvard University Cambridge MA United States CIBO Technologies Cambridge MA United States Forest is Life TERRA Teaching and Research Centre Gembloux Agro Bio-Tech University of Liège Gembloux Belgium Isotope Bioscience Laboratory - ISOFYS Department of Green Chemistry and Technology Ghent University Gent Belgium Research Group of Plants and Ecosystems (PLECO) Department of Biology University of Antwerp Wilrijk Belgium Institut National pour l'Etude et la Recherche Agronomiques-INERA Yangambi Democratic Republic Congo Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ United States INRAE UMR ISPA Villenave d'Ornon France Institut National pour l'Étude et la Recherche Agronomiques Kinshasa Democratic Republic Congo

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937–1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals. © 2024 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： leaf senescence leaf turnover phenology scaling tropical forest

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

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A cyber-enabled mission-critical system for post-flood respo...

作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford University Press. All rights reserved.

关键词： Cyber Physical System

Cellular automata DEVS: A modeling, simulation, and visualization environment 17

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Cellular automata DEVS: A modeling, simulation, and visualiz...

10th EAI International Conference on Simulation Tools and Techniques, SIMUTOOLS 2017

作者： Zhang, Chao Sarjoughian, Hessam S. Arizona Center for Integrative Modeling and Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781450363884

Cellular Automata (CA) models are represented as a collection of independent dynamical cells having some specific spatial relationship to each other. These tessellation automata can have simple to complex behaviors due to both individual cell behaviors as well as their interactions. Code debugging, supported by advanced software development tools, is needed for developing CAs owing their complex dynamics to cells that have non-trivial event handling and timing. As such, it is useful to debug models during simulation through step-by-step examination of any number of cells using rich control and visualization means. In this paper, we show the CA-DEVS framework where cell and Cellular Automata models are derived from atomic and coupled Parallel DEVS models. This framework uniquely supports visualizations using run-time generation of input, output, and state linear and superdense time trajectories as well as run-time spatial animation with playback. Multi-modal visualization capabilities allow examining behavior of any number of cells independent of any other cell. We describe some key parts of the architectural design of the CA-DEVS and highlight some ongoing and future research. © 2017 Copyright held by the owner/author(s).

关键词： Visualization

Global optimum encoding packet selection mechanism based on opportunistic network coding for wireless network retransmission

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 47-59页

作者： Wang Lian Peng Daiyuan Liang Hongbin School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computing Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Transportation and Logistics Southwest Jiaotong University Chengdu 610031 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85281-8809 USA

Packet loss cannot be avoided in wireless network due to wireless transmission medium particularity, therefore improving retransmission efficiency is meaningful to wireless transmission. The current retransmission packet selection mechanisms based on oppornistic network coding （ONC） face low retransmission efficiency and high computational complexity problems. To these problems, an optimized encoding packet selection mechanism based on ONC in wireless network retransmission （OONCR） is proposed. This mechanism is based on mutual exclusion packets and decoding gain concepts, and makes full use of ONC advantages. The main contributions of this scheme are to control the algorithm eomplexity of the maximum encoding packets selection effectively, avoid the redundancy encoding packets due to the overlapping among encoding packets, and take the encoding packet local and global optimization problem into consideration. Retransmission efficiency is evaluated according to the computational complexity, the throughput, the retransmission redundancy ratio, and the number of average retransmission. Under the various conditions, the number of average retransmission of OONCR is mainly lower than that of other typical retransmission packet selection schemes. The average retransmission redundancy ratios of OONCR are lower about 5%-40% compared with other typical schemes. Simultaneously the computational complexity of OONCR is comparatively lower than that of other typical schemes.

关键词： wireless network retransmission network coding throughput

Structured point cloud data analysis via regularized tensor regression for process modeling and optimization

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arXiv 2018年

作者： Yan, Hao Paynabar, Kamran Pacella, Massimo School of Computing Informatics & Decision Systems Engineering Arizona State University TempeAZ United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Dipartimento di Ingegneria dell'Innovazione University of Salento Lecce Italy

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point clouds are still a challenge. In this paper, we utilize multilinear algebra techniques and propose a set of tensor regression approaches to model the variational patterns of point clouds and to link them to process variables. The performance of the proposed methods is evaluated through simulations and a real case study of turning process optimization. Copyright © 2018, The Authors. All rights reserved.

关键词： Regression analysis

Unsupervised embedding of trajectories captures the latent structure of scientific migration

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arXiv 2020年

作者： Murray, Dakota Yoon, Jisung Kojaku, Sadamori Costas, Rodrigo Jung, Woo-Sung Milojević, Staša Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Network Science Institute Northeastern University BostonMA02115 United States Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern Institute on Complex Systems EvanstonIL60208 United States Centre for Science and Technology Studies Leiden University AX Leiden2300 Netherlands DST-NRF Centre of Excellence in Scientometrics and Science Technology and Innovation Policy Stellenbosch University Stellenbosch South Africa Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science. © 2020, CC BY.

关键词： Embeddings

New bounds on the maximum size of Sperner partition systems

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arXiv 2018年

作者： Chang, Yanxun Colbourn, Charles J. Gowty, Adam Horsley, Daniel Zhou, Junling Department of Mathematics Beijing Jiaotong University Beijing China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States School of Mathematical Sciences Monash University Vic Australia

An (n,k)-Sperner partition system is a collection of partitions of some n-set, each into k nonempty classes, such that no class of any partition is a subset of a class of any other. The maximum number of partitions in an (n,k)-Sperner partition system is denoted SP(n,k). In this paper we introduce a new construction for Sperner partition systems and use it to asymptotically determine SP(n,k) in many cases as nk becomes large. We also give a slightly improved upper bound for SP(n,k) and exhibit an infinite family of parameter sets (n,k) for which this bound is *** Codes 05D05 (Primary) Copyright © 2018, The Authors. All rights reserved.

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