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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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Cost allocation problems on highways with grouped users

arXiv

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arXiv 2023年

作者： Gómez-Rodríguez, Marcos Davila-Pena, Laura Casas-Méndez, Balbina Department of Mathematics Faculty of Computer Science Universidade da Coruña Campus de Elviña A Coruña15071 Spain MODESTYA Research Group Department of Statistics Mathematical Analysis and Optimization Faculty of Mathematics Universidade de Santiago de Compostela Campus Vida Santiago de Compostela15782 Spain Department of Analytics Operations and Systems Kent Business School University of Kent Canterbury CT2 7PE United Kingdom CITMAga MODESTYA Research Group Department of Statistics Mathematical Analysis and Optimization Faculty of Mathematics Universidade de Santiago de Compostela Campus Vida Santiago de Compostela15782 Spain

One of the practical applications of cooperative transferable utility games involves determining the fee structure for users of a given facility, whose construction or maintenance costs need to be recouped. In this context, certain efficiency and equity criteria guide the considered solutions. This paper analyzes how to allocate the fixed costs of a highway among its users through tolls, considering that different classes of vehicles or travelers utilize the service. For this purpose, we make use of generalized highway games with a priori unions that represent distinct user groups, such as frequent travelers or truckers, who, due to enhanced bargaining power, often secure reductions in their fares in real-world scenarios. In particular, the Owen value, the coalitional Tijs value, and a new value termed the Shapley-Tijs value, are axiomatically characterized. Additionally, straightforward formulations for calculating these values are provided. Finally, the proposed methodology is applied to actual traffic data from the AP-9 highway in Spain. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Preface:Special Issue on Combinatorial Optimization

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Journal of the Operations Research Society of China 2019年第3期7卷 395-397页

作者： Xiao-Dong Hu Guo-Hui Lin Li-Ying Kang Guo-Chuan Zhang Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science University of AlbertaEdmontonAB T6G 2R3Canada Department of Mathematics Shanghai UniversityShanghai 200444China College of Computer Science and Technology Zhengjiang UniversityHouzhou 310027China

Combinatorics is,as defined by MathWorld,the branch of mathematics that studies the enumeration,combination and permutation of sets of elements and the mathemat-ical relations that characterize their *** optimization not only enumerates the sets studied,but also has the goal of finding the member of the set in an efficient manner that optimizes an objective *** optimization is an emerging field at the forefront of combinatorics,theoretical computer science and operations research that has various applications in many disciplines of science,engineering,industry,economy,military and so on.

关键词： properties. function. optimization

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CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

CARDIOCARE: An integrated platform for the management of eld...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： G. Karanasiou L. Koumakis S. Sfakianakis G. Manikis G. Kalliatakis A. Antoniades L. Lakkas D. Mauri C. Cipolla K. Mazzocco A. Papakonstantinou G. Filippatos A. Constantinidou B. Šeruga C. Conti A. Bucur E. Pacella K. Marias M. Tsiknakis D.I. Fotiadis Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Crete Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Stremble Ventures Ltd Limassol Cyprus 2nd Department of Cardiology School of Medicine University of Ioannina Department of Medical Oncology University of Ioannina Greece Cardiology Division European Institute of Oncology Milan Italy Department for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital and the Department of Oncology-Pathology Karolinska Institute Stockholm Sweden Department of Cardiology National and Kapodistrian University of Athens School of Medicine Attikon University Hospital Athens Greece Bank of Cyprus Oncology Centre Bank of Cyprus Oncology Centre Nicosia Cyprus Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Istituto di Management Sanitario S.r.l Milano Italy Philips Research Europe Eindhoven Netherlands European Society of Cardiology Brussels Belgium Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute Foundation for Research and Technology – Hellas University Campus of Ioannina Greece

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced *** Relevance— Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

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Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels

arXiv

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arXiv 2024年

作者： Gebhard, Timothy D. Wildberger, Jonas Dax, Maximilian Kofler, Annalena Angerhausen, Daniel Quanz, Sascha P. Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany ETH Zurich Institute for Particle Physics & Astrophysics Wolfgang-Pauli-Strasse 27 Zurich8093 Switzerland ELLIS Institute Tübingen Maria-von-Linden-Straße 2 Tübingen72076 Germany Max Planck Institute for Gravitational Physics Albert Einstein Institute Am Mühlenberg 1 Potsdam14476 Germany ETH Zurich Department of Earth and Planetary Sciences Sonneggstrasse 5 Zurich8092 Switzerland ETH Zurich Department of Computer Science Universitätsstrasse 6 Zurich8092 Switzerland

Context. Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to atmospheric retrieval (e.g., nested sampling) are computationally expensive, a growing number of machine learning (ML) methods such as neural posterior estimation (NPE) have been proposed. Aims. We seek to make ML-based atmospheric retrieval (1) more reliable and accurate with verified results, and (2) more flexible with respect to the underlying neural networks and the choice of the assumed noise models. Methods. First, we adopt flow matching posterior estimation (FMPE) as a new ML approach to atmospheric retrieval. FMPE maintains many advantages of NPE, but provides greater architectural flexibility and scalability. Second, we use importance sampling (IS) to verify and correct ML results, and to compute an estimate of the Bayesian evidence. Third, we condition our ML models on the assumed noise level of a spectrum (i.e., error bars), thus making them adaptable to different noise models. Results. Both our noise level-conditional FMPE and NPE models perform on par with nested sampling across a range of noise levels when tested on simulated data. FMPE trains about 3 times faster than NPE and yields higher IS efficiencies. IS successfully corrects inaccurate ML results, identifies model failures via low efficiencies, and provides accurate estimates of the Bayesian evidence. Conclusions. FMPE is a powerful alternative to NPE for fast, amortized, and parallelizable atmospheric retrieval. IS can verify results, thus helping to build confidence in ML-based approaches, while also facilitating model comparison via the evidence ratio. Noise level conditioning allows design studies for future instruments to be scaled up, for example, in terms of the range of signal-to-noise ratios. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency modulation

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Sustainability through cognition aware safety systems - Next level human-machine-interaction

arXiv

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arXiv 2021年

作者： Mangler, Juergen Diwold, Konrad Etz, Dieter Rinderle-Ma, Stefanie Ferscha, Alois Reiner, Gerald Kastner, Wolfgang Bougain, Sebastien Pollak, Christoph Haslgrübler, Michael Technical University of Munich Faculty of Computer Science Munich Germany Johannes Kepler University Linz Faculty of Computer Science Linz Austria Technical University of Vienna Faculty of Computer Science Vienna Austria Vienna University of Economics and Business Department of Information Systems and Operations Management Institute for Production Management Center for Digital Production GmbH Vienna Austria Pro2Future GmbH Linz Austria Graz University of Technology Department of Electrical Engineering Institute of technical Informatics Graz Austria

Industrial Safety deals with the physical integrity of humans, machines and the environment when they interact during production scenarios. Industrial Safety is subject to a rigorous certification process that leads to inflexible settings, in which all changes are forbidden. With the progressing introduction of smart robotics and smart machinery to the factory floor, combined with an increasing shortage of skilled workers, it becomes imperative that safety scenarios incorporate a flexible handling of the boundary between humans, machines and the environment. In order to increase the well-being of workers, reduce accidents, and compensate for different skill sets, the configuration of machines and the factory floor should be dynamically adapted, while still enforcing functional safety requirements. The contribution of this paper is as follows: (1) We present a set of three scenarios, and discuss how industrial safety mechanisms could be augmented through dynamic changes to the work environment in order to decrease potential accidents, and thus increase productivity. (2) We introduce the concept of a Cognition Aware Safety System (CASS) and its architecture. The idea behind CASS is to integrate AI based reasoning about human load, stress, and attention with AI based selection of actions to avoid the triggering of safety stops. (3) And finally, we will describe the required performance measurement dimensions for a quantitative performance measurement model to enable a comprehensive (triple bottom line) impact assessment of CASS. Additionally we introduce a detailed guideline for expert interviews to explore the feasibility of the approach for given scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Accident prevention

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Assessing quality of decision reducts

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Procedia computer science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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Reduct-based ranking of attributes

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Procedia computer science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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Globally optimal stretching foliations of dynamical systems reveal the organizing skeleton of intensive instabilities

arXiv

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arXiv 2021年

作者： Balasuriya, Sanjeeva Bollt, Erik M. School of Mathematical Sciences University of Adelaide AdelaideSA5005 Australia Department of Electrical and Computer Engineering C3S2 The Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699 United States

Understanding instabilities in dynamical systems drives to the heart of modern chaos theory, whether forecasting or attempting to control future outcomes. Instabilities in the sense of locally maximal stretching in maps is well understood, and is connected to the concepts of Lyapunov exponents/vectors, Oseledec spaces and the Cauchy–Green tensor. In this paper, we extend the concept to global optimization of stretching, as this forms a skeleton organizing the general instabilities. The ‘map’ is general but incorporates the inevitability of finite-time as in any realistic application: it can be defined via a finite sequence of discrete maps, or a finite-time flow associated with a continuous dynamical system. Limiting attention to two-dimensions, we formulate the global optimization problem as one over a restricted class of foliations, and establish the foliations which both maximize and minimize global stretching. A classification of nondegenerate singularities of the foliations is obtained. Numerical issues in computing optimal foliations are examined, in particular insights into special curves along which foliations appear to veer and/or do not cross, and foliation behavior near singularities. Illustrations and validations of the results to the Hénon map, the double-gyre flow and the standard map are provided. © 2021, CC0.

关键词： Lyapunov methods

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DenseNet for Breast Tumor Classification in Mammographic Images 1st

DenseNet for Breast Tumor Classification in Mammographic Ima...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Jiménez Gaona, Yuliana Rodriguez-Alvarez, María José Espino-Morato, Hector Castillo Malla, Darwin Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n CP1101608 Loja Ecuador Valencia46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Waterloo Canada Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada Valencia46022 Spain

ISBN: (纸本)9783030881627

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification. © 2021, Springer Nature Switzerland AG.

关键词： Mammography

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