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A deep learning approach to geographical candidate selection through toponym matching

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arXiv 2020年

作者： Ardanuy, Mariona Coll Hosseini, Kasra Nanni, Federico van Strien, Daniel Krause, Amrey McDonough, Katherine Alan Turing Institute British Library Queen Mary University of London United Kingdom Edinburgh Parallel Computing Centre United Kingdom

Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously recognized toponym. While it has traditionally received little attention, candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors) and assess its performance in the context of geographical candidate selection in English and Spanish. Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

Unravel the rotational and translational behavior of a single squirmer in flexible polymer solutions at different Reynolds numbers

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arXiv 2025年

作者： Zhou, Yuan Qi, Kai De Corato, Marco Stratford, Kevin Pagonabarraga, Ignacio 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Batochime Avenue Forel 2 Lausanne1015 Switzerland University of Zaragoza Zaragoza Spain Edinburgh Parallel Computing Centre The University of Edinburgh The King’s Buildings EdinburghEH9 3JZ United Kingdom Departament de Física de la Matèria Condensada Universitat de Barcelona C. Martí Franquès 1 Barcelona08028 Spain Universitat de Barcelona Barcelona08028 Spain

Microorganisms, such as bacteria and algae, thrive in complex environments and their behavior in fluids holds significant importance for various medical and industrial applications. By conducting Lattice Boltzmann (LB) simulations, the transport and rotational properties of a generic squirmer are investigated in solutions embedded with flexible polymer chains at different Reynolds numbers. The interplay of activity and heterogeneously distributed polymers have profound influences on these properties. Remarkable enhancements of up to three orders of magnitude in the rotational motion, along with apparent decays in self-propelling velocities, are observed for squirmers with nonzero active stresses. These extraordinary phenomena stem from the squirmer-polymer mechanical and hydrodynamic interactions. Specifically, polymer wrapping occurs in front of a pusher, while numerous polymers are absorbed in the rear of a puller. Both mechanisms enhance the rotational motion and simultaneously impede translations through forces and torques arising from direct contacts or asymmetric local flows induced by polymers. The source dipole flow fields generated by a neutral swimmer rapidly advect polymers to the rear, leaving no apparent impacts on its rotational and transport properties. The influences of Reynolds number Re (Re=0.8 and 0.04) and squirmer-polymer boundary conditions (no-slip and repulsive) on the dynamics are addressed. In short, the no-slip boundary condition results in more profound effects on both rotational and translational properties at Re = 0.8. However, at Re = 0.04, the disparity between the two boundary conditions diminishes due to the heightened fluid viscous drag, which impedes direct contacts between squirmers and polymers. Our results reveal the relevance of system heterogeneity and highlight the essential role of squirmer-polymer mechanical and hydrodynamic interactions in shaping the behavior of swimmers in viscoelastic fluids. These findings offer valua

关键词： Reynolds number

Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration

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Using Machine Learning to Reduce Ensembles of Geological Mod...

IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)

作者： Anna Roubickova Nick Brown Oliver Brown Edinburgh Parallel Computing Centre EPCC The University of Edinburgh

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such data reduction techniques are required to reduce this set down to a much smaller yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. The result of this work is an approach which enables us to describe the entire state space using only 0.5% of the models, along with a series of lessons learnt. The techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the HPC community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.

关键词： Computational modeling Oils Market research Data models Rocks Reservoirs

Charlie and the CryptoFactory: Towards Secure and Trusted Manufacturing Environments

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Charlie and the CryptoFactory: Towards Secure and Trusted Ma...

Mediterranean Electrotechnical Conference (MELECON)

作者： Antonis Michalas Tamas Kiss Network and Information Security Group Tampere University Tampere Finland Research Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London U.K.

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

The modernization that stems from Industry 4.0 started populating the manufacturing sector with networked devices, complex sensors, and a significant proportion of physical actuation components. However, new capabilities in networked cyber-physical systems demand more complex infrastructure and algorithms and often lead to new security flaws and operational risks that increase the attack surface area exponentially. The interconnected nature of Industry 4.0-driven operations and the pace of digital transformation mean that cyberattacks can have far more extensive effects than ever before. Based on that, the core ideas of this paper are driven by the observation that cybersecurity is one of the key enablers of Industry 4.0. Having this in mind, we propose CryptoFactory - a forward-looking design of a layered-based architecture that can be used as a starting point for building secure and privacy-preserving smart factories. CryptoFactory aims to change the security outlook in smart manufacturing by discussing a set of fundamental requirements and functionality that modern factories should support in order to be resistant to both internal and external attacks. To this end, CryptoFactory first focuses on how to build trust relationships between the hardware devices in the factory. Then, we look on how to use several cryptographic approaches to allow IoT devices to securely collect, store and share their data while we also touch upon the emerging topic of secure and privacy-preserving communication and collaboration between manufacturing environments and value chains. Finally, we look into the problem of how to perform privacy-preserving analytics by leveraging Trusted Execution Environments and the promising concept of Functional Encryption.

关键词： Cryptography Manufacturing Industries Cloud computing Hardware Computer security

Bridging HPC Communities through the Julia Programming Language

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arXiv 2022年

作者： Churavy, Valentin Godoy, William F. Bauer, Carsten Ranocha, Hendrik Schlottke-Lakemper, Michael Räss, Ludovic Blaschke, Johannes Giordano, Mosè Schnetter, Erik Omlin, Samuel Vetter, Jeffrey S. Edelman, Alan Massachussetts Institute of Technology United States Oak Ridge National Laboratory United States Paderborn Center for Parallel Computing Paderborn University Germany Department of Mathematics University of Hamburg Germany Applied and Computational Mathematics RWTH Aachen University Germany University of Stuttgart Germany ETH Zurich Switzerland Birmensdorf Switzerland National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Centre for Advanced Research Computing University College London Gower Street LondonWC1E 6BT United Kingdom Perimeter Institute 31 Caroline St. N. WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States ETH Zurich Switzerland

The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.g., Python, R, MATLAB) are used for data analysis and interactive computing. Julia's rapid growth in language capabilities, package ecosystem, and community make it a promising universal language for HPC. This paper presents the views of a multidisciplinary group of researchers from academia, government, and industry that advocate for an HPC software development paradigm that emphasizes developer productivity, workflow portability, and low barriers for entry. We believe that the Julia programming language, its ecosystem, and its community provide modern and powerful capabilities that enable this group's objectives. Crucially, we believe that Julia can provide a feasible and less costly approach to programming scientific applications and workflows that target HPC facilities. In this work, we examine the current practice and role of Julia as a common, end-to-end programming model to address major challenges in scientific reproducibility, data-driven AI/machine learning, co-design and workflows, scalability and performance portability in heterogeneous computing, network communication, data management, and community education. As a result, the diversification of current investments to fulfill the needs of the upcoming decade is crucial as more supercomputing centers prepare for the exascale era. © 2022, CC BY.

关键词： Productivity

Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Innovations in Simulation: Experiences With Cloud-Based Simulation Experimentation

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Innovations in Simulation: Experiences With Cloud-Based Simu...

Simulation Winter Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Warwickshire UK

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

关键词： Technological innovation Cloud computing Computational modeling Optimization

Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

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Towards a Deadline-Based Simulation Experimentation Framewor...

2019 Winter Simulation Conference, WSC 2019

作者： Anagnostou, Anastasia Taylor, Simon J. E. Tijjani Abubakar, Nura Kiss, Tamas Deslauriers, James Gesmier, Gregoire Terstyanszky, Gabor Kacsuk, Peter Kovacs, Jozsef Brunel University London Modelling Simulation Group UxbridgeUB8 3PH United Kingdom University of Westminster Centre for Parallel Computing Faculty of Science and Technology LondonW1W 6UW United Kingdom Hungarian Academy of Science Budapest1111 Hungary

ISBN: (纸本)9781728132839

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework. © 2019 IEEE.

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Foundational Competencies and Responsibilities of a Research Software Engineer

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： Computer aided software engineering