检索结果-内蒙古大学图书馆

arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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The chemical evolution of the solar neighbourhood for planet-hosting stars

arXiv

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arXiv 2023年

作者： Pignatari, Marco Trueman, Thomas C.L. Womack, Kate A. Gibson, Brad K. Côté, Benoit Turrini, Diego Sneden, Christopher Mojzsis, Stephen J. Stancliffe, Richard J. Fong, Paul Lawson, Thomas V. Keegans, James D. Pilkington, Kate Passy, Jean-Claude Beers, Timothy C. Lugaro, Maria ELKH Konkoly Thege M. út 15–17 BudapestH-1121 Hungary CSFK MTA Centre of Excellence Konkoly Thege Miklós út 15-17. BudapestH-1121 Hungary E. A. Milne Centre for Astrophysics Department of Physics and Mathematics University of Hull HU6 7RX United Kingdom INAF - Turin Astrophysical Observatory Via Osservatorio 20 Pino Torinese10025 Italy INAF - Institute of Space Astrophysics and Planetology Via Fosso del Cavaliere 100 Rome00133 Italy ICSC National Research Centre for High Performance Computing Big Data and Quantum Computing Via Magnanelli 2 Casalecchio di Reno40033 Italy Department of Astronomy and McDonald Observatory The University of Texas AustinTX78712 United States NuGrid Collaboration Joint Institute for Nuclear Astrophysics Center for the Evolution of the Elements United States Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Pázmány Péter sétány 1/c BudapestH-1117 Hungary H.H. Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom Centre of Excellence for Data Science Artificial Intelligence and Modelling University of Hull HU6 7RX United Kingdom Astrophysics Group Lennard-Jones Laboratories Keele University KeeleST5 5BG United Kingdom School of Physical Sciences The Open University Milton KeynesMK7 6AA United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Department of Physics and Astronomy University of Notre Dame Notre DameIN46556 United States ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A Budapest1117 Hungary School of Physics and Astronomy Monash University VIC3800 Australia

Theoretical physical-chemical models for the formation of planetary systems depend on data quality for the Sun’s composition, that of stars in the solar neighbourhood, and of the estimated "pristine" compositions for stellar systems. The effective scatter and the observational uncertainties of elements within a few hundred parsecs from the Sun, even for the most abundant metals like carbon, oxygen and silicon, are still controversial. Here we analyse the stellar production and the chemical evolution of key elements that underpin the formation of rocky (C, O, Mg, Si) and gas/ice giant planets (C, N, O, S). We calculate 198 galactic chemical evolution (GCE) models of the solar neighbourhood to analyse the impact of different sets of stellar yields, of the upper mass limit for massive stars contributing to GCE (Mup) and of supernovae from massive-star progenitors which do not eject the bulk of the iron-peak elements (faint supernovae). Even considering the GCE variation produced via different sets of stellar yields, the observed dispersion of elements reported for stars in the Milky Way disk is not reproduced. Among others, the observed range of super-solar [Mg/Si] ratios, sub-solar [S/N], and the dispersion of up to 0.5 dex for [S/Si] challenge our models. The impact of varying Mup depends on the adopted supernova yields. Thus, observations do not provide a constraint on the Mup parametrization. When including the impact of faint supernova models in GCE calculations, elemental ratios vary by up to 0.1-0.2 dex in the Milky Way disk;this modification better reproduces observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Dispersions

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Petri Net Control Method for Pipe-line Systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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Multi-Thread Concurrent Compression Algorithm for Genomic Big data

Multi-Thread Concurrent Compression Algorithm for Genomic Bi...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Yimu Ji Shuai Chen Haichang Yao Houzhi Fang Kui Li Shangdong Liu Zhengyuan Xie Kairui Wang School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC.

关键词： Bioinformatics Genomics Compression algorithms Instruction sets Telecommunications Computer science Concurrent computing

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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aNMM: Ranking short answer texts with attention-based neural matching model

arXiv

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arXiv 2018年

作者： Yang, Liu Ai, Qingyao Guo, Jiafeng Bruce Croft, W. Center for Intelligent Information Retrieval University of Massachusetts AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Flickr group recommendation with auxiliary information in heterogeneous information networks

Flickr group recommendation with auxiliary information in he...

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作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS College of Computer Science and Technology Zhejiang University Hangzhou China

Over the past few years, the appropriate utilization of user communities or image groups in social networks (i.e., Flickr or Facebook) has drawn a great deal of attention. In this paper, we are particularly interested in recommending preferred groups to users who may favor according to auxiliary information. In real world, the images captured by mobile equipments explicitly record a lot of contextual information (e.g., locations) about users generating images. Meanwhile, several words are employed to describe the particular theme of each group (e.g., "Dogs for Fun Photos" image group in Flickr), and the words may mention particular entities as well as their belonging categories (e.g., "Animal"). In fact, the group recommendation can be conducted in heterogeneous information networks, where informative cues are in general multi-typed. Motivated by the assumption that the auxiliary information (visual features of images, mobile contextual information and entity-category information of groups in this paper) in heterogeneous information networks will boost the performance of the group recommendation, this paper proposes to combine auxiliary information with implicit user feedback for group recommendation. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user–user similarity via visual features and heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation approaches. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Non-negative matrix factorization

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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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Radio detection of an elusive millisecond pulsar in the Globular Cluster NGC 6397

arXiv

引用

arXiv 2022年

作者： Zhang, Lei Ridolfi, Alessandro Blumer, Harsha Freire, Paulo C.C. Manchester, Richard N. McLaughlin, Maura Kremer, Kyle Cameron, Andrew D. Zhang, Zhiyu Behrend, Jan Burgay, Marta Buchner, Sarah Champion, David J. Chen, Weiwei Dai, Shi Feng, Yi Fu, Xiaoting Guo, Meng Hobbs, George Keane, Evan F. Kramer, Michael Levin, Lina Li, Xiangdong Ni, Mengmeng Pan, Jingshan Padmanabh, Prajwal V. Possenti, Andrea Ransom, Scott M. Tsai, Chao-Wei Krishnan, Vivek Venkatraman Wang, Pei Zhang, Jie Zhi, Qijun Zhang, Yongkun Li, Di National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia SelargiusI-09047 Italy Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Max-Planck Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia TAPIR California Institute of Technology PasadenaCA91125 United States The Observatories of the Carnegie Institution for Science PasadenaCA91101 United States Swinburne University of Technology Mail H74 PO Box 218 VIC3122 Australia School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210093 China South African Radio Astronomy Observatory Fir Street Black River Park Cape Town7925 South Africa School of Science Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China National Supercomputing Center in Jinan Qilu University of Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China School of Physics Trinity College Dublin College Green Dublin Ireland Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom HannoverD-30167 Germany MonserratoI-09042 Italy NRAO 520 Edgemont Road CharlottesvilleVA22903 United States College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Guizhou Provincial Key Laboratory of Radio

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740−5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyanga), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing binaries in globular clusters (GCs) and consistent with that of the coincident X-ray source U18, previously suggested to be a ‘hidden MSP’. Our XMM-Newton observations during NGC 6397B’s radio quiescent epochs detected no X-ray flares. NGC 6397B is either a transitional MSP or an eclipsing binary in its initial stage of mass transfer after the companion star left the main sequence. The discovery of NGC 6397B potentially reveals a subgroup of extremely faint and heavily obscured binary pulsars, thus providing a plausible explanation to the apparent dearth of binary neutron stars in core-collapsed GCs as well as a critical constraint on the evolution of GCs. Copyright © 2022, The Authors. All rights reserved.

关键词： Mass transfer

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