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arXiv 2021年

作者： Wang, Qizhou Liu, Feng Han, Bo Liu, Tongliang Gong, Chen Niu, Gang Zhou, Mingyuan Sugiyama, Masashi Department of Computer Science Hong Kong Baptist University Hong Kong DeSI Lab Australian Artificial Intelligence Institute University of Technology Sydney Australia TML Lab School of Computer Science Faculty of Engineering The University of Sydney Australia PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of MoE Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China McCombs School of Business The University of Texas at Austin United States Graduate School of Frontier Sciences The University of Tokyo Japan

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighting adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such a probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrated that PMs are reliable and PM-based reweighting methods outperformed state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability

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Hyperspectral image classification using Uniform Manifold Approximation and Projection with fusion deep learning network

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Applied Soft Computing 2025年 180卷

作者： Chen Lou Mohammed A.A. Al-qaness Sike Ni Dalal Al-Alimi Robertas Damaševičius Saeed Hamood Alsamhi College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua 321004 China Zhejiang Institute of Photoelectronics Jinhua 321004 China Faculty of Engineering Sana’a University Sana’a 12544 Yemen Centre of Real Time Computer Systems Kaunas University of Technology Kaunas 51368 Lithuania Insight Centre for Data Analytics University of Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 Republic of Korea

Hyperspectral images (HSI) are crucial for remote sensing applications as they provide detailed spectral information that accurately identifies and analyzes various materials and land features. HSI classification faces challenges due to large data volumes and high dimensionality. Dimensionality reduction techniques help address these problems by simplifying model computation, reducing redundancy, and improving feature selection. However, traditional methods struggle to capture nonlinear structures and local–global relationships in hyperspectral data. We propose a new multi-feature fusion classification model called the Uniform Manifold Approximation and Projection (UMAP) and Simple Attention Module (SimAM) mechanism fusion network (UMAPSAMFN). The main workflow of the model consists of several steps. The network uses UMAP to map high-dimensional HSI data into a low-dimensional space while maintaining a local and global structure. The feature data are sent to the convolutional neural network (CNN) and graph convolutional network (GCN) modules to capture pixel-level features and contextual information. These data are separately processed through multi-head attention modules to enhance the ability to represent feature information. Finally, the processed data are jointly fed into a fusion module with an attention mechanism to boost feature information and achieve deep modeling results. The experimental results on three benchmark HSI datasets show that UMAPSAMFN consistently exhibits the highest classification accuracy, with overall classification accuracies of 93.28%, 96.13%, and 97.73% on the Indian Pines, Pavia University, and Salinas datasets, respectively.

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On values of the higher derivatives of the Barnes zeta function at non-positive integers

arXiv

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arXiv 2020年

作者： Sakane, Shinpei Aoki, Miho Department of Information Systems Design and Data Science Graduate School of Natural Science and Technology Shimane University 1060 Nishikawatsu Matsue Shimane690-8504 Japan Department of Mathematics Interdisciplinary Faculty of Science and Engineering Shimane University 1060 Nishikawatsu Matsue Shimane690-8504 Japan

Let x be a complex number which has a positive real part, and w1, . . ., wN be positive rational numbers. We show that wsζN (s, x | w1, . . ., wN) can be expressed as a finite linear combination of the Hurwitz zeta functions over Q(x), where ζN (s, x | w1, . . ., wN) is the Barnes zeta function and w is a positive rational number explicitly determined by w1, . . ., wN . Furthermore, we give generalizations of Kummer’s formula on the gamma function and Koyama-Kurokawa’s formulae on the multiple gamma functions, and an explicit formula for the values at non-positive integers for higher order derivatives of the Barnes zeta function in the case that x is a positive rational number, involving the generalized Stieltjes constants and the values at positive integers of the Riemann zeta function. Our formulae also makes it possible to calculate an approximation in the case that w1, . . ., wN and x are positive real *** Codes 11M41, 11M06, 11M35, 33B15 Copyright © 2020, The Authors. All rights reserved.

关键词： Rational functions

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Ethical Aspects of Computational Modelling in science, Decision Support and Communication 15th

Ethical Aspects of Computational Modelling in Science, Decis...

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15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Adopting Feature-Weighted and Scale Adaptive for Real-Time Visual Tracking

Adopting Feature-Weighted and Scale Adaptive for Real-Time V...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Dong, Qiujie He, Xuedong Ge, Haiyan Liu, Qin Liu, Yanting Li, Ajuan Zhang, Yuren Han, Aifu Zhou, Shengzong Chinese Academy of Sciences Fujian Institute of Research on the Structure of Matter Fuzhou China School of Data Science North University of China Taiyuan China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China College of Electrical and Electronic Engineering Shandong University of Technology Zibo China

ISBN: (纸本)9781728146911

Considering poor accuracy and robustness of single-feature object tracking, researchers have proposed numerous multiple-feature integration for tracking, including deep features and traditional hand-crafted features. In this paper, we propose a feature-weighted method of Histogram of Oriented Gradient (HOG) and color names (CN) based on correlation filters. Both features are adopted independently to obtain detection response maps, which are subsequently normalized. Finally, Euclidean distance between normalized map and desired output, which is activated by a negative exponential function, is regarded as the weight of the corresponding response map. Although using feature-weighted correlation filters for tracking has achieved great improvement in accuracy and robustness, many conventional trackers have not solved the problem of scale variation and the current mainstream scale estimation methods have existed the deficiencies of computational redundancy and fixed scale-factor. In view of this problem, firstly adopting three scale-factor to roughly judge the direction of target scale variation, and then the optimal scale-factor is acquired cyclically in this direction. The methods we proposed based on ECO-HC are tested in OTB2013, OTB100 and TC128 datasets and the results demonstrate the effectiveness of our proposed methods. © 2019 IEEE.

关键词： Exponential functions

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Better, Not Just More: data-Centric Machine Learning for Earth Observation

arXiv

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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.

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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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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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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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