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SSRN 2024年

作者： Mittenentzwei, Sarah Garrison, Laura A. Budich, Beatrice Lawonn, Kai Dockhorn, Alexander Preim, Bernhard Meuschke, Monique Department of Simulation and Graphics University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Informatics University of Bergen Thormøhlens gate 55 Bergen5020 Norway Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07743 Germany Institute of Artificial Intelligence Leibniz University Hannover30167 Germany

Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. In the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Stable Diffusion, a deep learning text-to-image model, is a crucial component of our pipeline for converting data into visual representations of characters. This reduces the time and artistic expertise required to create characters that accurately reflect the underlying data, even in time-oriented studies such as cohort studies. We evaluate the characters in a crowd-sourced study according to their authenticity to the underlying data, resemblance across different time steps, and compare static images with AI-driven animations. Study participants were able to recognize many aspects of the underlying data in our characters. Adjusting the weight and age of a character remains a challenge, as many facial features tend to change that make the character almost unrecognizable. While study participants preferred the static images, our evaluation shows the potential of animating the characters. © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification 18

Continual Learning of a Time Series Model Using a Mixture of...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Glomb, Przemyslaw Cholewa, Michal Foszner, Pawel Bularz, Jakub Institute of Theoretical and Applied Informatics Polish Academy of Sciences Batycka 5 Gliwice44-100 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Aiut Sp. Z O.o. ul. Wyczókowskiego 113 Gliwice44-109 Poland

ISBN: (纸本)9788396744784

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification. © 2023 Polish Information Processing Society.

关键词： Hidden Markov models

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Visual Analysis of Point Cloud Neighborhoods via Multi-Scale Geometric Measures

Visual Analysis of Point Cloud Neighborhoods via Multi-Scale...

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42nd Annual Conference on European Association for computer graphics, EUROgraphics 2021

作者： Ritter, M. Schiffner, D. Harders, M. Interactive Graphics and Simulation Group The Department of Computer Science University of Innsbruck Austria DIPF Leibniz Institute for Research and Information Education Frankfurt Germany Airborne Hydromapping GmbH Innsbruck Austria

ISBN: (纸本)9783038681335

Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computer graphics or remote sensing. Algorithms typically operate in local neighborhoods of point sets, for computing physical states, surface reconstructions, etc. We present a visualization technique based on multi-scale geometric features of such point clouds. We explore properties of different choices on the underlying weighted co-variance neighborhood descriptor, illustrated on different point set geometries and for varying noise levels. The impact of different weighting functions and tensor centroids, as well as point set features and noise levels becomes visible in the rotation-invariant feature images. We compare to a curvature based scale space visualization method and, finally, show how features in real-world LiDAR data can be inspected by images created with our approach in an interactive tool. In contrast to the curvature based approach, with our method line structures are highlighted over growing scales, with clear border regions to planar or spherical geometric structures. © 2021 The Author(s).

关键词： Data visualization

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Towards narrative medical visualization

arXiv

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arXiv 2021年

作者： Meuschke, Monique Garrison, Laura Smit, Noeska Bruckner, Stefan Lawonn, Kai Preim, Bernhard Department of Simulation and Graphics University of Magdeburg Germany Institute of Computer Science University of Jena Germany

Narrative visualization aims to communicate scientific results to a general audience and garners significant attention in various applications. Merging exploratory and explanatory visualization could effectively support a non-expert understanding of scientific processes. Medical research results, e.g., mechanisms of the healthy human body, explanations of pathological processes, or avoidable risk factors for diseases, are also interesting to a general audience that includes patients and their relatives. This paper discusses how narrative techniques can be applied to medical visualization to tell data-driven stories about diseases. We address the general public comprising people interested in medicine without specific medical background knowledge. We derived a general template for the narrative medical visualization of diseases. Applying this template to three diseases selected to span bone, vascular, and organ systems, we discuss how narrative techniques can support visual communication and facilitate understanding of medical data. Other scientists can adapt our proposed template to inform an audience on other diseases. With our work, we show the potential of narrative medical visualization and conclude with a comprehensive research agenda. © 2021, CC BY-NC-ND.

关键词： Visualization

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Generalised tangent stabilised nonlinear elasticity: An automated framework for controlling material and geometric instabilities

arXiv

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arXiv 2024年

作者： Poya, Roman Ortigosa, Rogelio Gil, Antonio J. Kim, Theodore Bonet, Javier Meshing Framework Simulation & Test Solutions Siemens Digital Industries Software Cambridge United Kingdom Multiphysics Simulation and Optimization Lab Technical University of Cartagena Cartagena Murcia Spain Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus United Kingdom Computer Graphics Group Yale School of Engineering & Applied Science Yale University New HavenCT United States Barcelona Spain

Tangent stabilised large strain isotropic elasticity was recently proposed by Poya et al. [1] wherein by working directly with principal stretches the entire eigenstructure of constitutive and geometric/initial stiffness terms were found in closed-form, giving fresh insights into exact convexity conditions of highly non-convex functions in discrete settings. Consequently, owing to these newly found tangent eigenvalues an analytic tangent stabilisation was proposed (for common non-convex strain energies that exhibit material and/or geometric instabilities) bypassing incumbent numerical approaches routinely used in nonlinear finite element analysis. This formulation appears to be extremely robust for quasi-static simulation of complex deformations even with no load increments and time stepping while still capturing instabilities (similar to dynamic analysis) automatically in ways that are infeasible for path-following techniques in practice. In this work, we generalise the notion of tangent stabilised elasticity to virtually all known invariant formulations of nonlinear elasticity. We show that, closed-form eigen-decomposition of tangents is easily available irrespective of invariant formulation or integrity basis. In particular, we work out closed-form tangent eigensystems for isotropic Total Lagrangian deformation gradient (F)-based and right Cauchy-Green (C)-based as well as Updated Lagrangian left Cauchy-Green (b)-based formulations and present their exact convexity conditions postulated in terms of their corresponding tangent and initial stiffness eigenvalues. In addition, we introduce the notion of geometrically stabilised polyconvex large strain elasticity for models that are materially stable but exhibit geometric instabilities for whom we construct their initial stiffness in a spectrally-decomposed form analytically. We further extend this framework to the case of transverse isotropy where once again, closed-form tangent eigensystems are found for common tran

关键词： Stiffness

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A Distributed Approximation Algorithm for the Total Dominating Set Problem 1

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18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024

作者： Wang, Limin Zhang, Zhao Du, Donglei Mao, Yaping Zhang, Xiaoyan School of Mathematical Sciences Henan Institute of Science and Technology Xinxiang453003 China College of Mathematics and Computer Sciences Zhejiang Normal University Zhejiang321004 China Faculty of Management University of New Brunswick Fredericton E3B 5A3 New Brunswick Canada School of Mathematics and Statistics Qinghai Normal University Qinghai810008 China Academy of Plateau Science and Sustainability Xining Qinghai810008 China School of Mathematical Science and Institute of Mathematics Key Laboratory of Ministry of Education Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Jiangsu210023 China

ISBN: (数字)9789819777983

ISBN: (纸本)9789819777976

Given a network graph G=(V,E), a subset T⊆V is said to be a total dominating set (TDS) if every v∈V is adjacent to at least one node in T. In this paper, we present a distributed algorithm for the TDS problem via the LP relaxation techniques. For a positive integer k and maximum degree Δ, the proposed algorithm outputs a fractional total dominating set of expected size O(kΔ2k)|TDSOPT|. The distributed algorithm runs in O(k2) communication rounds, and the algorithm use messages of size O(logΔ). The fractional solution is then rounded to obtain an integer total dominating set for the original problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Consensus algorithm

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Deep Learning and Network Analysis:Classifying and Visualizing Geologic Hazard Reports

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Journal of Earth science 2024年第4期35卷 1289-1303页

作者： Wenjia Li Liang Wu Xinde Xu Zhong Xie Qinjun Qiu Hao Liu Zhen Huang Jianguo Chen School of Geography and Information Engineering China University of GeosciencesWuhan430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan430078China School of Computer Science China University of GeosciencesWuhan430078China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen518034China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Wuhan Geomatics Institute Wuhan430074China Faculty of Earth Resources China University of GeosciencesWuhan430074China

If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the *** such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed *** text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse *** deficiencies are most obviously exposed in long text ***,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long ***,utilizing a softmax layer to automatically extract text features and classify geohazards without manual *** latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize *** proposed method is useful in enabling the machine-assisted interpretation of text-based ***,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.

关键词： geologic hazard network analysis latent dirichlet allocation text classification deep learning

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Dynamic task allocation algorithm based on D-NSGA3 21

Dynamic task allocation algorithm based on D-NSGA3

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2nd International Conference on Control, Robotics and Intelligent System, CCRIS 2021

作者： Zhou, Jing Zhao, Xiaozhe Xu, Zhen Peng, Siyun Lin, Zhong Faculty of Management and Economics Dalian University of Technology and Operation Software and Simulation Institute Dalian Navy Academy China Faculty of Management and Economics Dalian University of Technology China School of Computer Science and Technology Wuhan University of Technology China Operation Software and Simulation Institute Dalian Navy Academy China

ISBN: (纸本)9781450390453

Task allocation is a key part of unmanned aerial vehicle (UAV) swarm. Although a large number of solving algorithms have been developed, there are few technologies that support task allocation algorithms in dynamic environments. Obviously, this is not in accord with the actual situation. The battlefield is changing rapidly, which may lead to the failure of the allocated tasks and the inability to allocate new tasks. In order to deal with this problem, this paper improves the original D-NSGA3 algorithm to adapt to the dynamic environment. The experimental results show that, compared with the original static algorithm, the proposed algorithm has better effect in solving the task allocation problem of high-dimensional multi-objective agent based on maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing the consumption cost, and minimizing the time cost. © 2021 ACM.

关键词： Multi agent systems

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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Synthetic Data for Face Recognition: Current State and Future Prospects

arXiv

引用

arXiv 2023年

作者： Boutros, Fadi Struc, Vitomir Fierrez, Julian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia School of Engineering Universidad Autonoma de Madrid Madrid Spain Department of Computer Science TU Darmstadt Darmstadt Germany

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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