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arXiv 2023年

作者： Roscher, Ribana Rußwurm, Marc Gevaert, Caroline Kampffmeyer, Michael dos Santos, Jefersson A. Vakalopoulou, Maria Hänsch, Ronny Hansen, Stine Nogueira, Keiller Prexl, Jonathan Tuia, Devis Data Science for Crop Systems Group Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich52428 Germany Remote Sensing Group University of Bonn Niebuhrstr. 1a Bonn53113 Germany Laboratory of Geo-information Science and Remote Sensing Wageningen University Droevendaalsesteeg 3 Wageningen Gelderland6708 PB Netherlands Department of Earth Observation Science Faculty ITC University of Twente Drienerlolaan 5 Enschede Overijssel7522 NB Netherlands Department of Physics and Technology UiT The Arctic University of Norway Klokkargårdsbakken 35 Tromsø9019 Norway Department of Computer Science University of Sheffield Sheffield City Centre 211 Portobello SheffieldS1 4DP United Kingdom Münchner Str. 20 Oberpfaffenhofen-Wessling82234 Germany Computing Science and Mathematics University of Stirling StirlingFK9 4LA United Kingdom Department of Aerospace Engineering University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 Neubiberg Bavaria85579 Germany Ronquos 86 Sion1951 Switzerland

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many methods has already saturated on these datasets. We argue that a shift from a model-centric view to a complementary data-centric perspective is necessary for further improvements in accuracy, generalization ability, and real impact on end-user applications. Furthermore, considering the entire machine learning cycle—from problem definition to model deployment with feedback—is crucial for enhancing machine learning models that can be reliable in unforeseen situations. This work presents a definition as well as a precise categorization and overview of automated data-centric learning approaches for geospatial data. It highlights the complementary role of data-centric learning with respect to model-centric in the larger machine learning deployment cycle. We review papers across the entire geospatial field and categorize them into different groups. A set of representative experiments shows concrete implementation examples. These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches. © 2023, CC BY.

关键词： Deep learning

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The benefits of a dual-lane zone picking system with buffer-free conveyor lanes: An analytical approach

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Computers & Industrial engineering 2025年 207卷

作者： Thao Huong Tran-Vo Young Myoung Ko Soondo Hong Major in Industrial Data Science and Engineering Department of Industrial Engineering Pusan National University Busan 46241 South Korea Department of Industrial Systems Engineering Faculty of Mechanical Engineering Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City 700000 Vietnam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City 700000 Vietnam Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang Gyeongsangbuk 37673 South Korea Department of Industrial Engineering Pusan National University Busan 46241 South Korea

The increasing prevalence of small-footprint warehouses in urban areas necessitates a system that accommodates both fast order processing and space-saving requirements. This study introduces a Dual-lane Zone Picking system (DZP) featuring dual buffer-free conveyor lanes to improve picking performance in such warehouses. We develop an analytical model for a two-zone DZP, incorporating zone passing, a notable feature in sequential zone picking systems. We fit phase-type distributions for tote interarrival times and tote picking times, model the system as a Continuous-Time Markov Chain with a block-form transition generator, and apply the matrix geometric method to obtain the system’s stationary distribution and performance metrics. Numerical experiments demonstrate that DZP considerably reduces mean flow time and yields higher maximum throughput compared to traditional single-lane zone picking systems, especially in two-zone systems with high zone-passing probabilities. The results highlight the potential of the DZP to improve order picking efficiency and provide valuable insights into operational control.

关键词： Continuous-Time Markov Chain Dual-lane zone picking system Facilities planning and design Sequential zone picking

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Charge-Sign Dependent Cosmic-Ray Modulation Observed with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2023年第21期130卷 211001-211001页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka E. Berti G. Bigongiari W. R. Binns M. Bongi P. Brogi A. Bruno J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol G. A. de Nolfo K. Ebisawa A. W. Ficklin H. Fuke S. Gonzi T. G. Guzik T. Hams K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo K. Kobayashi K. Kohri H. S. Krawczynski J. F. Krizmanic P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake A. A. Moiseev M. Mori N. Mori H. M. Motz K. Munakata S. Nakahira J. Nishimura S. Okuno J. F. Ormes S. Ozawa L. Pacini P. Papini B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi P. Spillantini F. Stolzi S. Sugita A. Sulaj M. Takita T. Tamura T. Terasawa S. Torii Y. Tsunesada Y. Uchihori E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida W. V. Zober Department of Physics University of Florence Via Sansone 1 - 50019 Sesto Fiorentino Italy INFN Sezione di Firenze Via Sansone 1 - 50019 Sesto Fiorentino Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo 162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 - 56127 Pisa Italy Department of Physics and McDonnell Center for the Space Sciences Washington University One Brookings Drive Saint Louis Missouri 63130-4899 USA Heliospheric Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Sciences and Technology NASA/GSFC Greenbelt Maryland 20771 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori 036

We present the observation of a charge-sign dependent solar modulation of galactic cosmic rays (GCRs) with the Calorimetric Electron Telescope onboard the International Space Station over 6 yr, corresponding to the positive polarity of the solar magnetic field. The observed variation of proton count rate is consistent with the neutron monitor count rate, validating our methods for determining the proton count rate. It is observed by the Calorimetric Electron Telescope that both GCR electron and proton count rates at the same average rigidity vary in anticorrelation with the tilt angle of the heliospheric current sheet, while the amplitude of the variation is significantly larger in the electron count rate than in the proton count rate. We show that this observed charge-sign dependence is reproduced by a numerical “drift model” of the GCR transport in the heliosphere. This is a clear signature of the drift effect on the long-term solar modulation observed with a single detector.

关键词： Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles

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Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

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Image and Vision Computing 2025年 161卷

作者： Oladosu Oyebisi Oladimeji Abdullah Al-Zubaer Imran Xiaoqin Wang Saritha Unnikrishnan Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Faculty of Engineering and Design Atlantic Technological University Sligo F91YW50 Ireland Health and Biomedical Strategic Research Centre Atlantic Technological University Sligo Ireland Department of Computer Science University of Kentucky Lexington KY USA Department of Radiology Markey Cancer Center University of Kentucky College of Medicine Lexington KY USA

Breast cancer is the leading cause of death from cancer among women globally. According to the World Health Organization (WHO), early detection and treatment can significantly reduce surgeries and improve survival rates. Since deep learning emerged in 2012, it has garnered significant research interest in breast cancer, particularly for diagnosis, treatment, prognosis, and survival prediction. This review specifically focuses on the application of deep learning to breast image analysis (MRI, mammogram, and ultrasound) with a particular emphasis on radiologist involvement in the evaluation process. Studies published between 2019 and 2024 in the Scopus database will be reviewed. We further explore radiologists’ perspectives on the clinical readiness of artificial intelligence (AI) for breast image analysis. By analyzing insights from published articles, we will discuss the challenges, limitations, and future directions for this evolving field. While the review highlights the promise of deep learning in breast image analysis, it also acknowledges critical issues that must be addressed before widespread clinical integration can be achieved.

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Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012]

Franklin Open

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Franklin Open 2024年 9卷

作者： Xingyue Liu Kaibo Shi Kun Zhou Shiping Wen Yiqian Tang Lin Tang School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 PR China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 PR China Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 PR China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 PR China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

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Precise Measurement of the Cosmic-Ray Electron and Positron Spectrum with CALET on the International Space Station 37

Precise Measurement of the Cosmic-Ray Electron and Positron ...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Torii, Shoji Akaike, Yosui Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Buckley, J.H. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. Ebisawa, K. Ficklin, A.W. Fuke, H. Gonzi, S. Guzik, T.G. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. de Nolfo, G.A. Okuno, S. Ormes, J.F. Ospina, N. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo Japan Department of Physics University of Florence Via Sansone 1 Sesto Fiorentino50019 Italy INFN Sezione di Florence Via Sansone 1 Sesto Fiorentino50019 Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa277-8582 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 Pisa56127 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Department of Physics Catholic University of America WashingtonDC20064 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA/GSFC GreenbeltMD20771 United States Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Kanagawa Yokohama221-

Primary objectives of the CALET (CALorimetric Electron Telescope) mission are to search for possible nearby cosmic-ray sources and dark matter signatures with the precise measurement of the electron and positron (all-electron) spectrum. The instrument, consisting of a charge detector, an imaging calorimeter and a total absorption calorimeter, is optimized to measure the all-electron spectrum well into the TeV region with a thick calorimeter of 30 radiation length with fine shower-imaging capability. Due to the excellent energy resolution (a few % above 10 GeV) and the outstanding e/p separation (∼ 105), CALET achieves optimal performance for a detailed search for structures in the energy spectrum. CALET has been accumulating scientific data for more than five years without any major interruption, and the statistics of observed electron events has increased more than double since the latest publication in 2018. In this paper we will present precise measurements of the all-electron spectrum up to several TeV, as obtained with the high statistics data, and we will briefly discuss about its interpretation. © Copyright owned by the author(s).

关键词： Electrons

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Intrablock interleaving for Batched network coding with blockwise adaptive recoding

arXiv

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arXiv 2021年

作者： Yin, Hoover H.F. Ng, Ka Hei Zhong, Allen Z. Yeung, Raymond W. Yang, Shenghao Chan, Ian Y.Y. The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Limited The Department of Physics The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Economics The Chinese University of Hong Kong Hong Kong

Batched network coding (BNC) is a low-complexity solution to network transmission in multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets belonging to the same batch instead of just forwarding them. A recoding scheme that may generate more recoded packets for batches of a higher rank is also called adaptive recoding. Meanwhile, in order to combat burst packet loss, the transmission of a block of batches can be interleaved. Stream interleaving studied in literature achieves the maximum separation among any two consecutive packets of a batch, but permutes packets across blocks and hence cannot bound the buffer size and the latency. To resolve the issue of stream interleaver, we design an intrablock interleaver for adaptive recoding that can preserve the advantages of using a block interleaver when the number of recoded packets is the same for all batches. We use potential energy in classical mechanics to measure the performance of an interleaver, and propose an algorithm to optimize the interleaver with this performance measure. Our problem formulation and algorithm for intrablock interleaving are also of independent interest. © 2021, CC BY-NC-ND.

关键词： Network coding

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Direct Measurement of the Cosmic-Ray Helium Spectrum from 40 GeV to 250 TeV with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2023年第17期130卷 171002-171002页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka E. Berti G. Bigongiari W. R. Binns M. Bongi P. Brogi A. Bruno J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol G. A. de Nolfo K. Ebisawa A. W. Ficklin H. Fuke S. Gonzi T. G. Guzik T. Hams K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo K. Kobayashi K. Kohri H. S. Krawczynski J. F. Krizmanic P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake A. A. Moiseev M. Mori N. Mori H. M. Motz K. Munakata S. Nakahira J. Nishimura S. Okuno J. F. Ormes S. Ozawa L. Pacini P. Papini B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi P. Spillantini F. Stolzi S. Sugita A. Sulaj M. Takita T. Tamura T. Terasawa S. Torii Y. Tsunesada Y. Uchihori E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida W. V. Zober Department of Physics University of Florence Via Sansone 1–50019 Sesto Fiorentino Italy INFN Sezione di Firenze Via Sansone 1–50019 Sesto Fiorentino Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo 162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3–56127 Pisa Italy Department of Physics and McDonnell Center for the Space Sciences Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Heliospheric Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Sciences and Technology NASA/GSFC Greenbelt Maryland 20771 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori 036-8561 J

We present the results of a direct measurement of the cosmic-ray helium spectrum with the CALET instrument in operation on the International Space Station since 2015. The observation period covered by this analysis spans from October 13, 2015, to April 30, 2022 (2392 days). The very wide dynamic range of CALET allowed for the collection of helium data over a large energy interval, from ∼40 GeV to ∼250 TeV, for the first time with a single instrument in low Earth orbit. The measured spectrum shows evidence of a deviation of the flux from a single power law by more than 8σ with a progressive spectral hardening from a few hundred GeV to a few tens of TeV. This result is consistent with the data reported by space instruments including PAMELA, AMS-02, and DAMPE and balloon instruments including CREAM. At higher energy we report the onset of a softening of the helium spectrum around 30 TeV (total kinetic energy). Though affected by large uncertainties in the highest energy bins, the observation of a flux reduction turns out to be consistent with the most recent results of DAMPE. A double broken power law is found to fit simultaneously both spectral features: the hardening (at lower energy) and the softening (at higher energy). A measurement of the proton to helium flux ratio in the energy range from 60 GeV/n to about 60 TeV/n is also presented, using the CALET proton flux recently updated with higher statistics.

关键词： Cosmic ray & astroparticle detectors Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

arXiv

引用

arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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Common and distinct quantitative characteristics of Chinese and Western music in terms of modes, scales, degrees and melody variations

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Interface 2023年第2-3期52卷

作者： Nan Nan Xiaohong Guan a Center for Art & Science and Presentation & Communication Frontier Institute of Science and Technology Xi'an Jiaotong University Xi'an People’s Republic of Chinab MOE KLINNS Lab Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an People’s Republic of China a Center for Art & Science and Presentation & Communication Frontier Institute of Science and Technology Xi'an Jiaotong University Xi'an People’s Republic of Chinab MOE KLINNS Lab Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an People’s Republic of Chinac Center for Intelligent and Networked Systems Tsinghua University Beijing People’s Republic of China

Music is in general mutually understood among different cultures worldwide but can sometimes vary by different styles. The Western music system (heptatonic system) and the Chinese music system (pentatonic system) have common and different characteristics in terms of modes, scales, degrees, and melody variations. In this paper, we show these characteristics through quantitative analysis and comparison and have identified two common characteristics of Chinese pentatonic and Western heptatonic music with respect to modes, scales, and degrees. Although the structures and profiles of the modes, scales, and degrees of both kinds of music are different, they both show a preference to bright modes and scales, and their preference to tonal centres and degrees are consistent with the generating orders via their primal tuning temperaments. Based on the structural characteristics of the musical scales of Chinese pentatonic and Western heptatonic music, we present three interval-dividing metrics to examine the melody variations quantitatively. We find that the melody variations of Chinese pentatonic and Western heptatonic music measured by the interval-dividing metrics both follow the power law, a physical-mathematical law existing in many natural and engineering systems. This study fills the gap in the quantitative analysis of Chinese pentatonic music and provides an approach to studying the common characteristics of different types of music.

关键词： Quantitative music analysis melody variations mode and scale Chinese pentatonic music power law Distribution music analysis theory databases

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