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Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions

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arXiv 2022年

作者： Ukpabi, Dandison Karjaluoto, Heikki Bötticher, Astrid Nikiforova, Anastasija Petrescu, Dragoş Schindler, Paulina Valtenbergs, Visvaldis Lehmann, Lennard Yakaryilmaz, Abuzer University of Jyväskylä Jyväskylä University School of Business and Economics Finland Friedrich-Schiller-University Jena Department of Political Science Jena Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia University of Bucharest Faculty of Political Science Bucharest Romania Friedrich-Schiller-University Jena Project "Spheres of Freedom and Protection of Liberty in the Digital State" Jena Germany University of Latvia Faculty of Social Sciences Riga Latvia University of Latvia Faculty of Computing Riga Latvia QWorld Association Tallinn Estonia

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. Since this is an emerging and highly technical domain, most of the existing studies focus heavily on the technical aspects of QC, but our study highlights its practical and social uses cases, which are needed for the increased interest of governments. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC, identifies possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion. © 2022, CC BY.

关键词： Commerce

Rubik: A hierarchical architecture for efficient graph learning

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arXiv 2020年

作者： Chen, Xiaobing Wang, Yuke Xie, Xinfeng Hu, Xing Basak, Abanti Liang, Ling Yan, Mingyu Deng, Lei Ding, Yufei Du, Zidong Chen, Yunji Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

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arXiv 2023年

作者： Dorosti, Tina Schultheiss, Manuel Hofmann, Felix Thalhammer, Johannes Kirchner, Luisa Urban, Theresa Pfeiffer, Franz Schaff, Florian Lasser, Tobias Pfeiffer, Daniela Chair of Biomedical Physics Department of Physics School of Natural Sciences Technical University of Munich Bavaria Garching85748 Germany Munich Institute of Biomedical Engineering Technical University of Munich Bavaria Garching85748 Germany Department of Diagnostic and Interventional Radiology School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Bavaria Munich81675 Germany Institute for Advanced Study Technical University of Munich Bavaria Garching85748 Germany Computational Imaging and Inverse Problems Department of Computer Science School of Computation Information and Technology Technical University of Munich Bavaria Garching85748 Germany

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7,194 contrast-enhanced CT images (3,597 with COPD;3,597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.2021) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline ‘full-range’ window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower,upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range. © 2023, CC BY.

关键词： Convolutional neural networks

Design of Vision Guidance Transferring System for Automobile Hub Manufacturing

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Design of Vision Guidance Transferring System for Automobile...

作者： Zhi Zeng Yongfu Zhou Guosheng Zhong School of Computer Science and Engineering Huizhou University Institute of Electronic and Information Engineering Heyuan Polytechnic School of Architecture and Civil Engineering Huizhou University

In the era of industrial 4.0 intelligent manufacturing, improving product quality and reducing labor costs are important issues in the transformation and upgrading of manufacturing industry. For Automobile Hub manufacturing, first the whole design of software and hardware based on visual guidance is carried out;then, from the perspective of equipment communication and control, the communication between industrial robot and PC host computer is implemented by using Modbus RTU communication protocol, and PLC control frequency converter is selected to complete the control of transmission unit, and finally, the parameters of camera geometric model are obtained by software identification during the wheel hub to be transferred Now the camera is calibrated, so that the offset can be calculated further to adjust the robot's attitude, and finally the whole process of visual guidance and load transfer can be completed through image recognition. The practice shows that the visual guidance design scheme has been successfully applied to the automatic production line of automobile hub, which improves the production efficiency of the factory and further reduces the cost.

关键词： Industrial Robot Automobile Hub Loading Vision Guidance

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

Array relocation approach for radial scanning algorithms on multi-GPU systems: Total viewshed problem as a case study

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arXiv 2020年

作者： Sanchez, A.J. Romero, L.F. Bandera, G. Tabik, S. Department of Computer Architecture University of Malaga Malaga Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

In geographic information systems, Digital Elevation Models (DEMs) are commonly processed using radial scanning based algorithms. These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to radio signals, sound waves, and human eyesight. However, radial scanning algorithms imply a large number of accesses to 2D arrays, which despite being regular, results in poor data locality. This paper proposes a new methodology, termed sDEM, which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms. In particular, sDEM applies a data restructuring technique prior to accessing the memory and performing the computation. In order to demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study. Sequential, parallel, single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm. Experiments show that sDEM achieves an acceleration rate of up to 827.3 times for the best multi-GPU execution approach with respect to the best multi-core implementation. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveying

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

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arXiv 2022年

作者： Guo, Zhuoqiang Lu, Denghui Yan, Yujin Hu, Siyu Liu, Rongrong Tan, Guangming Sun, Ninghui Jiang, Wanrun Liu, Lijun Chen, Yixiao Zhang, Linfeng Chen, Mohan Wang, Han Jia, Weile Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture ICT Chinese Academy of Sciences Beijing China HEDPS CAPT College of Engineering Peking University Beijing China AI for Science Institute Beijing China Osaka University Osaka Japan Princeton University Princeton United States DP Technology Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 − 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an © 2022, CC BY-NC-SA.

关键词： Supercomputers

Wind Comfort Analysis Using an Immersed Boundary Method for Early-Stage Building Design

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SSRN

SSRN 2024年

作者： Mitkov, Radostin Hunger, Franziska Pantusheva, Mariya Naserentin, Vasilis Gösta, Alexander Mark, Andreas Petrova-Antonova, Dessislava Logg, Anders GATE Institute Sofia University "St. Kliment Ohridski" Sofia1113 Bulgaria Fraunhofer-Chalmers Centre for Industrial Mathematics Computational Engineering and Design Chalmers Science Park GothenburgSE-412 88 Sweden Department of Mathematical Sciences Chalmers University of Technology GothenburgSE-412 969 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki ThessalonikiGR- 54124 Greece RISE - Research Institutes of Sweden Architecture and spatial planning Drottning Kristinas Väg 61 StockholmSE-114 28 Sweden Liljewall Arkitekter Odinsplatsen 1 GothenburgSE-411 02 Sweden

This paper presents a thorough exploration of wind comfort dynamics in urban environments through the application of computational fluid dynamics simulations, with a specific emphasis on the Immersed Boundary Method. The study validates the Immersed Boundary Octree Flow solver, IBOFlow, against experimental data and then simulates the flow in a real-world case study of an area in Mölndal, Sweden. The research examines the impact of varying the number of simulated wind directions on the wind comfort maps, revealing that employing 16 wind directions is a reliable balance between computational efficiency and precision. Further, the investigation delves into the implications of using a simplified building geometry as available in early-stage design, demonstrating that computational simulations can provide reliable information about the pedestrian wind comfort early in the design process. While underscoring the significance of early-stage CFD simulations in informing design decisions, the paper concludes by advocating for a holistic approach. It emphasizes the relevance of simulation testing in later design iterations, particularly as architectural details continue to be refined. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

The COVID-19 Data Exchange Platform of the German University Medicine 32

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The COVID-19 Data Exchange Platform of the German University...

32nd Medical Informatics Europe Conference, MIE 2022

作者： Prokosch, Hans-Ulrich Bahls, Thomas Bialke, Martin Eils, Jürgen Fegeler, Christian Gruendner, Julian Haarbrandt, Birger Hampf, Christopher Hoffmann, Wolfgang Hund, Hauke Kampf, Marvin Kapsner, Lorenz A. Kasprzak, Piotr Kohlbacher, Oliver Krefting, Dagmar Mang, Jonathan M. Marschollek, Michael Mate, Sebastian Müller, Armin Prasser, Fabian Sass, Julian Semler, Sebastian Stenzhorn, Holger Thun, Sylvia Zenker, Sven Eils, Roland Chair of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Wetterkreuz 15 Erlangen91058 Germany Institute for Community Medicine University Medicine Greifswald Greifswald Germany Berlin Institute of Health at Charité-Universitätsmedizin Berlin Berlin Germany Gecko Institute Heilbronn University of Applied Sciences Heilbronn Germany Peter L. Reichertz Institute for Medical Informatics of Tu Braunschweig and Hannover Medical School Hannover Germany Medical Center for Information and Communication Technology Universitätsklinikum Erlangen Erlangen Germany Institute for Translational Bioinformatics University Medical Center Tübingen Germany Department of Medical Informatics University Medical Center Göttingen Göttingen Germany Tmf-Technology Methods and Infrastructure for Networked Medical Research Berlin Germany Institute for Medical Biometry Epidemiology und Medical Informatics Saarland University Medical Center Homburg Germany Commercial DirectorateInstitute for Medical Biometry Informatics & EpidemiologyDepartment of Anesthesiology and Intensive Care Medicine University Hospital Bonn Bonn Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany Department of Computer Science University of Tübingen Tübingen Germany Gesellschaft für Wissenschaftliche Datenverarbeitung MbH Göttingen Germany

ISBN: (纸本)9781643682846

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Electronic data interchange