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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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Self-Propelled Detachment upon Coalescence of Surface Bubbles

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Physical Review Letters 2021年第23期127卷 235501-235501页

作者： Pengyu Lv Pablo Peñas Hai Le The Jan Eijkel Albert van den Berg Xuehua Zhang Detlef Lohse State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science Beijing Innovation Center for Engineering Science and Advanced Technology College of Engineering Peking University Beijing 100871 People’s Republic of China Physics of Fluids group Faculty of Science and Technology Max Planck—University of Twente Center for Complex Fluid Dynamics MESA+Institute and J. M. Burgers Centre for Fluid Dynamics University of Twente P.O. Box 217 7500 AE Enschede Netherlands BIOS Lab-on-a-Chip group Faculty of Electrical Engineering Max Planck—University of Twente Center for Complex Fluid Dynamics Mathematics and Computer Science MESA+Institute University of Twente P.O. Box 217 7500 AE Enschede Netherlands Department of Chemical & Materials Engineering University of Alberta Edmonton Alberta T6G1H9 Canada Max Planck Institute for Dynamics and Self-Organization 37077 Göttingen Germany

The removal of microbubbles from substrates is crucial for the efficiency of many catalytic and electrochemical gas evolution reactions in liquids. The current work investigates the coalescence and detachment of bubbles generated from catalytic decomposition of hydrogen peroxide. Self-propelled detachment, induced by the coalescence of two bubbles, is observed at sizes much smaller than those determined by buoyancy. Upon coalescence, the released surface energy is partly dissipated by the bubble oscillations, working against viscous drag. The remaining energy is converted to the kinetic energy of the out-of-plane jumping motion of the merged bubble. The critical ratio of the parent bubble sizes for the jumping to occur is theoretically derived from an energy balance argument and found to be in agreement with the experimental results. The present results provide both physical insight for the bubble interactions and practical strategies for applications in chemical engineering and renewable energy technologies like electrolysis.

关键词： Drops & bubbles

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Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

arXiv

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology

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Integrating simulation and numerical analysis in the evaluation of generalized stochastic petri nets

Integrating simulation and numerical analysis in the evaluat...

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作者： Zimmermann, Armin Hotz, Thomas Department of Computer Science and Automation Systems and Software Engineering Group Technische Universität Ilmenau Helmholtzplatz 5 Ilmenau98693 Germany Institute for Mathematics Group for Probability Theory and Mathematical Statistics Technische Universität Ilmenau Weimarer Straße 25 Ilmenau98693 Germany

The standard existing performance evaluation methods for discrete-state stochastic models such as Petri nets either generate the reachability graph followed by a numerical solution of equations or use some variant of simulation. Both methods have characteristic advantages and disadvantages depending on the size of the reachability graph and type of performance measure. This article proposes a hybrid performance evaluation algorithm for the steady-state solution of Generalized Stochastic Petri Nets that integrates elements of both methods. It automatically adapts its behavior depending on the available size of main memory and number of model states. As such, the algorithm unifies simulation and numerical analysis in a joint framework. It is proved to result in an unbiased estimator whose variance tends to zero with increasing simulation time. The article extends earlier results with an algorithm variant that starts with a small maximum number of particles and increases them during the run to increase the efficiency in cases that are rapidly solved by regular simulation. The algorithm's applicability is demonstrated through case studies, including an example where it outperforms the standard methods. © 2019 Copyright held by the owner/author(s).

关键词： Petri nets

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Phase transition analysis for covariance based massive random access with massive MIMO

arXiv

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arXiv 2020年

作者： Chen, Zhilin Sohrabi, Foad Liu, Ya-Feng Yu, Wei The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper considers a massive random access problem in which a large number of sporadically active devices wish to communicate with a base station (BS) equipped with massive multiple-input multiple-output (MIMO) antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem for which the sample covariance matrix of the received signal is a sufficient statistic. The goal of this paper is to characterize the feasible set of problem parameters under which this covariance based approach is able to successfully recover the device activities in the massive MIMO regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing probability of detection error as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching, which relates the phase transition analysis to a recently derived scaling law. Further, we provide a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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An algorithm for computing the factor ring of an ideal in a Dedekind domain with finite rank

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science China mathematics 2018年第5期61卷 783-796页

作者： Dandan Huang Yingpu Deng Department of Cyber Space Security School of Software EngineeringJinling Institute of Technology Key Laboratory of Mathematics Mechanization National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

We give an algorithm for computing the factor ring of a given ideal in a Dedekind domain with finite rank, which runs in deterministic and polynomial time. We provide two applications of the algorithm:judging whether a given ideal is prime or prime power. The main algorithm is based on basis representation of finite rings which is computed via Hermite and Smith normal forms.

关键词： deterministic polynomial-time test Dedekind domains basis representation Hermite and Smith normal forms

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A manifold-based adversarial autoencoder with Fourier convolution for hyperspectral unmixing

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Applied Soft Computing 2025年 177卷

作者： Guo, Ziyang Xiao, Meixia Zhu, Fa Chen, Xingchi Shankar, Achyut Zamani, Mazdak Singh, Sushil Kumar Department of Artificial Intelligence Nanjing University of Information Science and Technology No. 129 of Ningliu Road Nanjing210044 China Research Center of Nonlinear Science School of Mathematical and Physical Sciences Wuhan Textile University Wuhan China College of Information Science and Technology & College of Artificial Intelligence Nanjing Forestry University Nanjing210037 China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen518000 China Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom State University of New York - Empire State University New York10016 United States Department of Computer Engineering Marwadi University Gujarat Rajkot India

Hyperspectral unmixing aims to decompose each subpixel into their pure endmembers and the corresponding proportions. But existing deep autoencoder-based hyperspectral unmixing methods often suffer from obstacles like endmember variability, local respective fields and insufficient use of inner structure. In the manuscript, we build a manifold-based Fourier adversarial autoencoder which regards generative adversarial mechanism as a utilization of prior information. This method combines manifold learning with adversarial autoencoder in order to promote the performance of hyperspectral unmixing. Specifically, firstly, in order to preserve local manifold structure, we add a discriminator to the autoencoder which uses the covariance matrices of a superpixel as real samples while covariance matrices of the abundance as fake samples;secondly, we add a regularization term of Laplacian eigenmap at the loss of autoencoder to in-depth abbreviate autoencoder solution space;thirdly, Fast Fourier Convolution modules are used to enhance multi-scale information fusion. At last, comparative experiments are conducted on three popular datasets, including Jasper, Urban4 and Samson, to validate the effectiveness of the proposed method. © 2025 Elsevier B.V.

关键词： Eigenvalues and eigenfunctions

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Generating Approximate Ground States of Molecules Using Quantum Machine Learning

arXiv

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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Hysteresis Control of the Pseudo Boost PFC Converter

Hysteresis Control of the Pseudo Boost PFC Converter

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Aleksandra Lekić-Vervoort Milovan Majstorović Leposava Ristić Dušan Stipanović Intelligent Electrical Power Grids (IEPG) Electrical Sustainable Energy (ESE) Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands The Department of Power Converters and Drive Systems University of Belgrade Belgrade Serbia Coordinated Science Laboratory University of Illinois Urbana IL USA

ISBN: (数字)9781728156354

ISBN: (纸本)9781728156361

In this paper, a control design for the the Pseudo Boost PFC converter with the application in the charger for electric vehicles, is formulated and verified. The rectifier ends up being energy efficient because due to containing smaller number of switches. The converter is controlled using a method involving the minimum Lyapunov function derivative and a hysteresis. This control provides an averaged value of the converter sine input current, and thus, decreasing the THD value. Theoretical results are complemented with the corresponding simulations and experimental waveforms.

关键词： Hysteresis Switches Lyapunov methods Inductors Mathematical model Electric vehicles

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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