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Google COVID-19 community mobility reports: Insights from multi-criteria decision making

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arXiv 2020年

作者： da Silva, Gabriela Cavalcante Oliveira, Sabrina Wanner, Elizabeth F. Bezerra, Leonardo C.T. Brazil Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Engineering Dept. CEFET-MG Brazil Computer Science Group Aston University United Kingdom

Social distancing (SD) has been critical in the fight against the novel coronavirus disease (COVID-19). To aid SD monitoring, many technology companies have made available mobility data, the most prominent example being the community mobility reports (CMR) provided by Google. Given the wide range of research fields that have been drawing insights from CMR data, there has been a rising concern for methodological discussion on how to use them. Indeed, Google recently released their own guidelines, concerning the nature of the place categories and the need for calibrating regional values. In this work, we discuss how measures developed in the field of multi-criteria decision making (MCDM) might benefit researchers analyzing this data. Concretely, we discuss how Pareto dominance and performance measures adopted in MCDM enable the mobility evaluation for (i) multiple categories for a given time period and (ii) multiple categories over multiple time periods. We empirically demonstrate these approaches conducting both a region- and country-level analysis, comparing some of the most relevant outbreak examples from different continents. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Generative pre-training for paraphrase generation by representing and predicting spans in exemplars

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arXiv 2020年

作者： Bui, Tien-Cuong Le, Van-Duc To, Hai-Thien Cha, Sang Kyun Dept. Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Graduate School of Data Science Seoul National University Seoul Korea Republic of

Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems. Despite some encouraging results, recent methods either confront the problem of favoring generic utterance or need to retrain the model from scratch for each new dataset. This paper presents a novel approach to paraphrasing sentences, extended from the GPT-2 model. We develop a template masking technique, named first-order masking, to masked out irrelevant words in exemplars utilizing POS taggers. So that, the paraphrasing task is changed to predicting spans in masked templates. Our proposed approach outperforms competitive baselines, especially in the semantic preservation aspect. To prevent the model from being biased towards a given template, we introduce a technique, referred to as second-order masking, which utilizes Bernoulli distribution to control the visibility of the first-order-masked template’s tokens. Moreover, this technique allows the model to provide various paraphrased sentences in testing by adjusting the second-order-masking level. For scale-up objectives, we compare the performance of two alternatives template-selection methods, which shows that they were equivalent in preserving semantic information. © 2020, CC-BY-NC-SA.

关键词： computational linguistics

Service selection using predictive models and monte-carlo tree search

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arXiv 2020年

作者： Laschet, Cliff Winands, Mark H.M. op den Buijs, Jorn Pauws, Steffen Philips Research Dept. of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Collaborative Care Solutions Philips Research Eindhoven Netherlands Dept. of Communication and Cognition Tilburg University Tilburg Netherlands Amazon Germany

This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future. Copyright © 2020, The Authors. All rights reserved.

关键词： Hospitals

The advantage of conditional meta-learning for biased regularization and fine-tuning

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arXiv 2020年

作者： Denevi, Giulia Pontil, Massimiliano Ciliberto, Carlo Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genova Italy Computer Science Dept. University College of London London United Kingdom Electrical and Electronic Engineering Dept. Imperial College of London London United Kingdom

Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks’ target vectors are all close to a common meta-parameter vector. However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks’ distribution cannot be captured by a single meta-parameter vector. We address this limitation by conditional meta-learning, inferring a conditioning function mapping task’s side information into a meta-parameter vector that is appropriate for that task at hand. We characterize properties of the environment under which the conditional approach brings a substantial advantage over standard meta-learning and we highlight examples of environments, such as those with multiple clusters, satisfying these properties. We then propose a convex meta-algorithm providing a comparable advantage also in practice. Numerical experiments confirm our theoretical findings. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Deep reinforcement learning for iot networks: age of information and energy cost tradeoff

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arXiv 2020年

作者： Wu, Xiongwei Li, Xiuhua Li, Jun Ching, P.C. Poor, H. Vincent Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong Dept. of Electrical Engineering Princeton University Princeton United States School of Big Data & Software Engineering Chongqing University Chongqing China School of Electronic & Optical Engineering Nanjing University of Science and Technology Nanjing China

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates could lead to considerable energy cost and challenge the lifetime of IoT sensors. To address this issue, we adopt the Age of Information (AoI) to quantify data freshness and propose an online cache update scheme to obtain an effective tradeoff between the average AoI and energy cost. Specifically, we first develop a characterization of transmission energy consumption at IoT sensors by incorporating a successful transmission condition. Then, we model cache updating as a Markov decision process to minimize average weighted cost with judicious definitions of state, action, and reward. Since user preference towards content items is usually unknown and often temporally evolving, we therefore develop a deep reinforcement learning (DRL) algorithm to enable intelligent cache updates. Through trial-and-error explorations, an effective caching policy can be learned without requiring exact knowledge of content popularity. Simulation results demonstrate the superiority of the proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Li, Xiuhua Wang, Xiaofei Sheng, Zhengguo Hu, Chunqiang Leung, Victor C.M. School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science and Technology Tianjin University Tianjin China Dept. of Engineering and Design University of Sussex Falmer Brighton United Kingdom Dept. Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (纸本)9781450371582

Content requests from mobile users are explosively increasing due to the growing popularity of mobile social activities. However, current mobile network infrastructure cannot effectively support the generated massive mobile data traffic while maintaining consistent and acceptable network performances. To deal with such a serious challenge, caching popular content at the edges of mobile networks has been recognized as a promising solution. In this paper, we introduce the content caching technique in Fog-Cloud based radio access networks for offering joint Fog-enabled and Cloud-enabled data services. Then we propose a collaborative hierarchical Fog-Cloud caching framework based on predicting content popularity with the Auto Regressive model. Our objective is to explore the maximum capacity of the considered network infrastructure by effectively offloading network traffic. Trace-driven evaluations demonstrate the effectiveness of our proposed caching mechanism in terms of traffic offload. © 2019 Association for Computing Machinery.

关键词： Fog

Lessons Learned in Performing a Trustworthy AI and Fundamental Rights Assessment

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arXiv 2024年

作者： Boonstra, Marjolein Bruneault, Frédérick Chakraborty, Subrata Faber, Tjitske Gallucci, Alessio Hickman, Eleanore Kema, Gerard Kim, Heejin Kooiker, Jaap Hildt, Elisabeth Lamadé, Annegret Mathez, Emilie Wiinblad Möslein, Florian Pathuis, Genien Sartor, Giovanni Steege, Marijke Stocco, Alice Tadema, Willy Tuimala, Jarno van Vledder, Isabel Vetter, Dennis Vetter, Jana Westerlund, Magnus Zicari, Roberto V. Rijks ICT Gilde Ministry of the Interior and Kingdom Relations Netherlands Université du Québec à Montréal and Philosophie Collège André-Laurendeau Canada School of Science & Technology University of New England NSW Australia Province of Friesland Netherlands Z-Inspection® Initiative Utrecht Netherlands Graduate School of Data Science Seoul National University Seoul Korea Republic of University of Bristol School of Law Centre for Private and Commercial Law UK Young Academy United Kingdom Illinois Institute of Technology United States L3S Research Center Hannover Germany Institute for Law and Regulation of Digitalisation Philipps-University Marburg Germany Hanseatic Higher Regional Court Germany Z-Inspection® Initiative Geneva Switzerland Philipps-University Marburg Germany Institute for Law and Regulation of Digitalisation Centre Responsible Digitality Germany Innovation managerNetherlands Netherlands Cirsfid-Alma AI Department of Law University of Bologna Italy European University Institute of Florence Italy Environmental Sciences Informatics and Statistics Dept. Ca’ Foscari University Venezia Italy Finland Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Germany Marine Holobiomics Lab Justus Liebig University Giessen Germany ARCADA University of Applied Sciences Finland Kristiania University College Oslo Norway Graduate School of Data Science Seoul National University Korea Republic of

This report shares the experiences, results and lessons learned in conducting a pilot project "Responsible use of AI '' in cooperation with the Province of Friesland, Rijks ICT Gilde- part of the Ministry of the Interior and Kingdom Relations (BZK) (both in The Netherlands) and a group of members of the Z-Inspection® Initiative. The pilot project took place from May 2022 through January 2023. During the pilot, the practical application of a deep learning algorithm from the province of Fryslân was assessed. The AI maps heathland grassland by means of satellite images for monitoring nature reserves. Environmental monitoring is one of the crucial activities carried on by society for several purposes ranging from maintaining standards on drinkable water to quantifying the CO2 emissions of a particular state or region. Using satellite imagery and machine learning to support decisions is becoming an important part of environmental monitoring. The main focus of this report is to share the experiences, results and lessons learned from performing both a Trustworthy AI assessment using the Z-Inspection® process and the EU framework for Trustworthy AI, and combining it with a Fundamental Rights assessment using the Fundamental Rights and Algorithms Impact Assessment (FRAIA) as recommended by the Dutch government for the use of AI algorithms by the Dutch public authorities. © 2024, CC BY-NC-SA.

关键词： Deep learning

Optimal Encoding and Decoding Algorithms for the RAID-6 Liberation Codes

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Optimal Encoding and Decoding Algorithms for the RAID-6 Libe...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Zhijie Huang Hong Jiang Zhirong Shen Hao Che Nong Xiao Ning Li Dept. of Computer Science and Engineering University of Texas at Arlington Arlington USA School of Informatics Xiamen University Xiamen China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

RAID-6 is gradually replacing RAID-5 as the dominant form of disk arrays due to its capability of tolerating concurrent failures of any two disks, as well as the case of encountering an uncorrectable read error during recovery. Implementing a RAID-6 system relies on some erasure coding schemes, and so far the most representative solutions are EVENODD codes [1], RDP codes [2] and Liberation codes [3], none of which has emerged as a clear "all-around" winner. In this paper, we are interested in revealing the undiscovered potential of the Liberation codes, since these codes have the following attractive features: (a) they have the best update performance, (b) they have better scalability, and (c) they are open-sourced and publicly available, as well as the following drawbacks: fair encoding performance and, more importantly, relatively poor decoding performance. Specificly, we present novel optimal encoding and decoding algorithms for the Liberation codes by introducing an alternative, geometric presentation of these codes. The proposed algorithms completely eliminate redundant computations during the encoding and decoding procedures by extracting and reusing common expressions between the two types of parity constraints, and do not involve any matrix operations on which the original algorithms are based. Our experiment results show that compared with the original solution, the proposed encoding and decoding algorithms reduce the number of XOR's by up to 16 percent and 15 ~20 percent respectively, and the encoding and decoding throughputs are increased by 22.3 percent and at most 155 percent respectively. Moreover, the encoding complexity reaches the theoretical lower bound, while the decoding complexity is also very close to the theoretical lower bound.

关键词： Encoding Decoding Arrays Complexity theory Measurement Standards Reed-Solomon codes

Modeling boundary conditions in high-order, nodal, time-domain finite element methods 23

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Modeling boundary conditions in high-order, nodal, time-doma...

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Pind, Finnur Jeong, Cheol-Ho Hesthaven, Jan S. Engsig-Karup, Allan P. Strømann-Andersen, Jakob Henning Larsen Denmark Acoustic Technology Dept. of Electrical Engineering Technical University of Denmark Denmark Chair of Computational Mathematics and Simulation Science École polytechnique fédérale de Lausanne Switzerland Scientific Computing Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783939296157

Accurate modeling of boundary conditions is an important aspect in room acoustic simulations. It has been shown that the acoustics of rooms is not only dependent on the frequency characteristic of the complex boundary impedance, but also on the angle dependent properties of the impedance ("extended reaction"). This paper presents a computationally efficient method for modeling local-reaction (LR) and extended-reaction (ER) boundary conditions in high-order, nodal, time-domain finite element methods, such as the spectral element method (SEM) or the discontinuous Galerkin finite element method (DGFEM). The frequency and angle dependent boundary impedance is mapped to a multipole model and formulated in differential form. The solution of the boundary differential equations comes with minimal computational cost. In the ER model, wave splitting is applied at the boundary to separate the incident and reflected parts of the sound field. The directional properties of the incident sound field are determined from the incident particle velocity and the boundary conditions are adjusted continuously according to the wave angle of incidence. The accuracy of the boundary condition model is assessed by comparing simulations against measurements, where a significantly improved match between simulations and measurements is found when the ER model is used. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： *fpin@ Extended-reaction High-order numerical methods Sound absorption Wave-based room acoustic simulations