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Electrical Insulation Conference and Electrical Manufacturing & Coil Winding Conference

作者： Peng Zhang Bo Qi Qipeng Chen Zhihai Rong Chengrong Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing P.R. China Global Energy Interconnection Research Institute Co. Ltd Advanced Computing and Big Data Laboratory of SGCC Beijing China

ISBN: (纸本)9781538641798

As the transformation equipment of electrical energy, transformer is the key equipment in power system. When it comes to breakdown, there will be a great loss. Dissolved gas analysis (DGA) online testing is an important indicator of transformer health assessment which is widely used in insolation testing as it is sensitive to discharge defect. Nowadays, most of researches on DGA online testing analysis are aimed at the faults diagnosis. However, in some conditions, the fault may develop very rapidly. The operation and maintenance personnel don't have enough time to figure the problem out before the breakdown occur. This problem can be solved by forecasting the DGA on-line testing data effectively. With the reveal of deterioration trend, the serious failure will be avoided and the reliability of transformer will be improved. This paper proposes a short term trend forecast method based on online data optimization for dissolved gas in oil, which is a time series forecast. This method is made up of five parts: data optimization, related gases selection, the orders selection, model parameters estimation and model checking, multi-step forecast. With the field interference and DGA online testing device status error, the DGA online data's quality can't be assured. To improve the accuracy of forecast model, the transformer online testing data needs to be optimized in first step, including Pauta criterion removing for singular value and linear interpolation for missing data. The second step, select related gases, as different gases have strong relationship. The third step is to build forecaster model based on Auto-Regressive and Moving Average Model (ARMA), using Akaike information criterion (AIC) to select the model orders. The forth step, estimate the unknown parameters by least square method. After that, the model should be verified by residual error testing to make sure the effective information of the time series is fully extracted. The final step, use the forecast model

关键词： Predictive models Gases Oil insulation Time series analysis Mathematical model Monitoring data models

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： Computerized tomography

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Euclid preparation. Simulations and nonlinearities beyond ΛCDM. 2. Results from non-standard simulations

arXiv

引用

arXiv 2024年

作者： Rácz, G. Breton, M.-A. Fiorini, B. Le Brun, A.M.C. Winther, H.-A. Sakr, Z. Pizzuti, L. Ragagnin, A. Gayoux, T. Altamura, E. Carella, E. Pardede, K. Verza, G. Koyama, K. Baldi, M. Pourtsidou, A. Vernizzi, F. Adame, A.G. Adamek, J. Avila, S. Carbone, C. Despali, G. Giocoli, C. Hernández-Aguayo, C. Hassani, F. Kunz, M. Li, B. Rasera, Y. Yepes, G. Gonzalez-Perez, V. Corasaniti, P.-S. García-Bellido, J. Hamaus, N. Kiessling, A. Marinucci, M. Moretti, C. Mota, D.F. Piga, L. Pisani, A. Szapudi, I. Tallada-Crespí, P. Aghanim, N. Andreon, S. Baccigalupi, C. Bardelli, S. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Cardone, V.F. Carretero, J. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. De Lucia, G. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Gillis, B. Gómez-Alvarez, P. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Ilić, S. Jahnke, K. Jhabvala, M. Joachimi, B. Keihänen, E. Kermiche, S. Kilbinger, M. Kitching, T. Kubik, B. Kurki-Suonio, H. Lilje, P.B. Lindholm, V. Lloro, I. Mainetti, G. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Meylan, G. Moresco, M. Moscardini, L. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Salvignol, J.-C. Sánchez, A.G. Sapone, D. Sartoris, B. Schirmer, M. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics University of Helsinki P.O. Box 64 00014 Finland Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés Barcelona08193 Spain Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190 France Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom School of Physics and Astronomy Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern Oslo0315 Norway Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Dipartimento di Fisica "G. Occhialini" Università degli Studi di Milano Bicocca Piazza della Scienza 3 Milano20126 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16

The Euclid mission will measure cosmological parameters with unprecedented precision. To distinguish between cosmological models, it is essential to generate realistic mock observables from cosmological simulations that were run in both the standard Λ-cold-dark-matter (ΛCDM) paradigm and in many non-standard models beyond ΛCDM. We present the scientific results from a suite of cosmological N-body simulations using non-standard models including dynamical dark energy, k-essence, interacting dark energy, modified gravity, massive neutrinos, and primordial non-Gaussianities. We investigate how these models affect the large-scale-structure formation and evolution in addition to providing synthetic observables that can be used to test and constrain these models with Euclid data. We developed a custom pipeline based on the Rockstar halo finder and the nbodykit large-scale structure toolkit to analyse the particle output of non-standard simulations and generate mock observables such as halo and void catalogues, mass density fields, and power spectra in a consistent way. We compare these observables with those from the standard ΛCDM model and quantify the deviations. We find that non-standard cosmological models can leave significant imprints on the synthetic observables that we have generated. Our results demonstrate that non-standard cosmological N-body simulations provide valuable insights into the physics of dark energy and dark matter, which is essential to maximising the scientific return of Euclid. © 2024, CC BY.

关键词： Dark energy

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Fault Range Analysis of Information and Communications Assets in Power Grid: A Graph data Perspective

Fault Range Analysis of Information and Communications Asset...

引用

2017 International Conference on Applied Mathematics, Modelling and Statistics Application（AMMSA2017）

作者： Bo Chai Siyan Liu Jiangpeng Dai Ting Zhao Aihua Zhou Kunlun Gao Advanced Computing and Big Data Laboratory Global Energy Interconnection Research Institute State Grid Corporation of China

In this paper, information and communication assets in the power grid are modeled as graph data. A graph database, Neo4j, is chosen to capturing the connections that could help to analyze fault ranges. Based on breadth-first search, recursion terminations are designed to obtain the fault range from the failed nodes. Two cases are given in the paper, and more cases will be further considered in the future work. In final, we provide the analysis results and shows the advantage of graph databases.

关键词： graph data fault range analysis Neo4j breadth-first search

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Research on Loop Detection of Power Grid Equipment Based on Graph database

Research on Loop Detection of Power Grid Equipment Based on ...

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2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE2017)

作者： Hongbin Qiu Aihua Zhou Kunlun Gao Jiangpeng Dai Bo Chai Bo Zhang Advanced Computing and Big Data Laboratory Global Energy Interconnection Research Institute State Grid Corporation of China

this paper presents a method of power grid loop detection based on graph database. The topology concept is introduced to extract the network relationship from the power grid equipment. The devices and power lines in the power grid correspond to the vertices in the topological graph, and the connection relation between the grid devices corresponds to the edges in the topological graph. The topological graph data model is generated by establishing the edges of connected vertices and stored in graph database. The loop detection of power grid equipment is divided into ordinary topological loop detection and multi-power loop detection, using the degree reduce algorithm and union-find sets to achieve the ordinary loop detection, and using the breadth first search algorithm to achieve the multipower loop detection. For large-scale power grid, the efficiency of power grid loop detection based on graph database can be 10 times higher than relational database.

关键词： topological graph graph database loop detection power grid

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Virtual reality learning environments for vocational education: a comparative study with conventional instructional media on two-stroke engine

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IOP Conference Series: Materials Science and Engineering 2020年第1期979卷

作者： F Sholichin N M Suaib D A Irawati Sutiman M Solikin A Yudantoko A Yudianto I W Adiyasa A J Sihes H A Sulaiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Automotive Engineering of Education Faculty of Engineering Universitas Negeri Yogyakarta School of Education Faculty of Social Science and Humanities Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka

Virtual reality technology has been adopted as a modern learning media in vocational education. Nowadays, the practical learning needs complete equipment and tools to achieve the goal study. However, the conventional media requirement is a challenge for academician for learning process, specifically in automotive and machine areas. Virtual reality provides a solution for this problem, but still needs proper analysis on the impact of usability between conventional and VR media to which each learning objective is achieved. The case study used in this research is a two-stroke engine, which is currently a rare item in the real world. We replicated this two-stroke engine in the developed VR system using Unity game engine and Oculus quest. A comparison on the usability effectiveness on conventional and virtual reality methods was conducted using cognitive walkthrough, which follows the four steps of learning objective training. The result shows that three out of four learning objectives are similar with conventional objective training. One of the process on preparation for disassembly machine has slight difference with conventional training; faulty engine due from environment elements (liquid, rusty iron) was not included.

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Scientific data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

引用

Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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CommuteShare: A Ridesharing Service for Daily Commuters Using Cross-Domain Urban big data

CommuteShare: A Ridesharing Service for Daily Commuters Usin...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaoliang Fan Chang Xu Fang Tang Jianzhong Qi Xiao Liu Longbiao Chen Cheng Wang Digital Fujian Institute of Urban Traffic Big Data Research Xiamen University Xiamen China Software School Xiamen University Xiamen China Xiamen Branch Industrial Bank Co. LTD Xiamen China School of Computing and Information Systems University of Melbourne Melbourne Australia School of Information Technology Deakin University Melbourne Australia

Existing ridesharing services have focused on on-demand trip matching, which resembles traditional taxi dispatching. This may encourage more private vehicles on the road, which aggravate traffic congestions in peak hours rather than alleviating them. We propose CommuteShare, a novel ridesharing service for daily commuters that encourages long-term ridesharing among commuters with similar commuting patterns, to increase the traffic efficiency in peak hours. We first identify commuting private vehicles (CPVs) from traffic records and model their commuting patterns. We then design a dynamic model to formulate the intention level of a CPV driver to offer a ride based on the spatio-temporal convenience and dynamic traffic conditions. Based on the commuting patterns of the CPVs and the dynamic model of the CPV drivers, we propose a ridesharing algorithm to compute ridesharing matches among CPVs. We perform extensive experiments on three real-world cross-domain urban big datasets from a major city of China. Experimental results show that, using the proposed CommuteShare service, over 5,300 private vehicles can be reduced daily on average during morning peak hours, with a reduction of 7-minute average waiting time for the riders.

关键词： Accidents Feature extraction Traffic congestion Meteorology Public transportation Vehicle dynamics Roads

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