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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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Performance Modeling of Sawmills using Artificial Intelligence

Performance Modeling of Sawmills using Artificial Intelligen...

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2020 International Conference on Decision Aid sciences and Application, DASA 2020

作者： Shahi, Shashi K. Dia, Mohamed Choudhury, Salimur Research Group in Operations Analytics and Decision Sciences Laurentian University Department of Finance and Operations Faculty of Management SudburyONP3E 2C6 Canada Quantitative Methods and Operations Management Research Group in Operations Analytics and Decision Sciences Laurentian University Department of Finance and Operations Faculty of Management SudburyONP3E 2C6 Canada Faculty of Science and Environmental Studies Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada

ISBN: (纸本)9781728196770

The measurement capabilities of the Data Envelopment Analysis (DEA) models are used to train the Artificial Neural Network (ANN) models for the best performance modeling of the sawmills in Ontario. The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results. This study proposes an innovative ANN modeling approach that helps in continuous improvement of the forest industry working under an uncertain business environment. © 2020 IEEE.

关键词： Data envelopment analysis

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Bioelectrical brain activity can predict prosocial behavior

arXiv

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arXiv 2021年

作者： Kunavin, Mikhail A. Kozitsina, Tatiana S. Myagkov, Mikhail G. Kozhevnikova, Irina S. Pankov, Mikhail N. Sokolova, Ludmila V. Federal University Arkhangelsk Russia Moscow Institute of Physics and Technology National Research University 9 Institutskiy per. Dolgoprudny Moscow Region 141701 Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow119333 Russia Laboratory of Experimental Methods in Cognitive and Social Sciences Tomsk State University Tomsk Russia Department of Political Science University of Oregon EugeneOR United States Federal University Arkhangelsk Russia

Generally, people behave in social dilemmas such as proself and prosocial. However, inside social groups, people have a tendency to choose prosocial alternatives due to in-group favoritism. The bioelectrical activity of the human brain shows the differences between proself and prosocial exist even out of a socialized group. Moreover, a group socialization strengthens these differences. We used EEG System, "Neuron-Spectrum-4/EPM" (16 channels), to track the brain bioelectrical activity during decision making in laboratory experiments with the Prisoner's dilemma game and the short-term socialization stage. We compared the spatial distribution of the spectral density during the different experimental parts. The noncooperative decision was characterized by the increased values of spectral the beta rhythm in the orbital regions of prefrontal cortex. The cooperative choice, on the contrary, was accompanied by the theta-rhythm activation in the central cortex regions in both hemispheres and the high-frequency alpha rhythm in the medial regions of the prefrontal cortex. People who increased the cooperation level after the socialization stage was initially different from the ones who decreased the cooperation in terms of the bioelectrical activity. Well-socialized participants differed by increased values of spectral density of theta-diapason and decreased values of spectral density of beta-diapason in the middle part of frontal lobe. People who decreased the cooperation level after the socialization stage was characterized by decreased values of spectral density of alpha rhythm in the middle and posterior convex regions of both hemispheres. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Enhancing image quality in fast neutron-based range verification of proton therapy using a deep learning-based prior in LM-MAP-EM reconstruction

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Physics in medicine and biology 2025年第12期70卷 2025 Jun 5页

作者： Lena Marie Setterdahl Kyrre Skjerdal Hunter Nathaniel Ratliff Kristian Smeland Ytre-Hauge William R B Lionheart Sean Holman Helge Egil Seime Pettersen Francesco Blangiardi Danny Lathouwers Ilker Meric Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen Hordaland 5063 NORWAY. Department of Physics and Technology University of Bergen Allégaten 55 Bergen Hordaland 5007 NORWAY. Department of Mathematics The University of Manchester Alan Turing Building Upper Brook St Manchester England M13 9SR UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND. Department of Oncology and Medical Physics Haukeland Universitetssjukehus Jonas Lies veg 65 Postboks 1400 Bergen Hordaland 5021 NORWAY. Technology Methods and Systems Data Based Methods Fraunhofer Institute for Electronic Nano Systems ENAS Technologie-Campus 3 Chemnitz SN 09126 GERMANY. Department of Radiation Science and Technology Delft University of Technology Mekelweg 15 Delft 2629 JD NETHERLANDS. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen Hordaland 5063 NORWAY.

This study investigates the use of list-mode (LM) maximum a posteriori (MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification. Approach. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography (CT)-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional (2D) ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts. Main results. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM. Significance. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios. .

关键词： LM-MAP-EM Monte Carlo simulation fast neutron imaging generative adversarial network organic scintillators proton therapy range verification

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Artificial intelligence in medical imaging of the liver

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World Journal of Gastroenterology 2019年第6期25卷 672-682页

作者： Li-Qiang Zhou Jia-Yu Wang Song-Yuan Yu Ge-Ge Wu Qi Wei You-Bin Deng Xing-Long Wu Xin-Wu Cui Christoph F Dietrich Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine Department of Medical Ultrasound Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Department of Ultrasound Tianyou Hospital Affiliated to Wuhan University of Technology School of Mathematics and Computer Science Wuhan Textitle University Medical Clinic 2 Caritas-Krankenhaus Bad Mergentheim Academic Teaching Hospital of the University of Würzburg

Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

关键词： Liver Imaging Ultrasound Artificial intelligence Machine learning Deep learning

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Design android-based learning media using augmented reality technology to support ethnomathematics materials at junior high school

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AIP Conference Proceedings 2023年 070005页

作者： Tri Rochmadi Rino Richardo Ahmad Anis Abdullah Ariyadi Wijaya Nurkhamid Department of Information Systems Faculty of Computer Alma Ata University Indonesia Department of Mathematics Education Faculty of Teaching and Education Alma Ata University Indonesia Department of Mathematics Education Faculty of Mathematics and Natural Science Yogyakarta State University Indonesia Department of Electronic Engineering Education Faculty of Engineering Yogyakarta State University Indonesia

The era of the industrial revolution 4.0 has changed human life a lot because most of them use information and communication technology. However, from the many changes that occur, augmented reality technology becomes a solution and challenge to make interactive learning media, especially those that require visual media or require visiting places that are not accessible. Augmented reality is one of the technologies that is growing rapidly in the 4.0 era where the technology can present objects as if they are real in front of us using only Android. The use of android-based learning media using augmented reality technology in addition to helping in the interactive learning process is also a means of equal distribution of education in Indonesia where many schools are still lacking in terms of learning media infrastructure and the high cost of visiting museums. The method used in designing this media uses the ADDIE approach method, namely Analysis, Design, Develop, Implement and Evaluate with limitations without evaluation. The results of the research can produce android-based ethnomathematics learning media design. This means that this android-based learning media is affordable for all people because on average all junior high school students have an android smartphone.

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Human activity recognition based on dynamic Spatio-Temporal relations

arXiv

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arXiv 2020年

作者： Liu, Zhenyu Liu, Yan Tao, Zhenchao Yao, Yaqiang Zhu, Yuening Wang, Lei Feng, Yuhong Department of Science and Technology Teaching China University of Political Science and Law Beijing100088 China School of Computer and Science Technology University of Science and Technique of China Hefei Anhui230027 China First Affiliated Hospital University of Science and Technique of China Hefei Anhui230027 China School of Materials and Engineering Shanghai University Shanghai China Third People’s Hospital of Hefei Hefei Anhui230000 China School of Computer and Software Shenzhen University Shenzhen China

Human activity, which usually consists of several actions (sub-activities), generally covers interactions among persons and/or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatiotemporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatiotemporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Neural-symbolic argumentation mining: An argument in favour of deep learning and reasoning

arXiv

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arXiv 2019年

作者： Galassi, Andrea Kersting, Kristian Lippi, Marco Shao, Xiaoting Torroni, Paolo Department of Computer Science and Engineering University of Bologna Italy Department of Computer Science Centre for Cognitive Science TU Darmstadt Germany Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap towards performing advanced reasoning tasks. We illustrate how neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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The Definitions of Health Apps and Medical Apps From the Perspective of Public Health and Law: Qualitative Analysis of an Interdisciplinary Literature Overview

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JMIR mHealth and uHealth 2022年第10期10卷 e37980-e37980页

作者： Maaß, Laura Freye, Merle Pan, Chen-Chia Dassow, Hans-Henrik Niess, Jasmin Jahnel, Tina Department of Health Long-Term Care and Pensions Research Center on Inequality and Social Policy University of Bremen Bremen Germany Leibniz ScienceCampus Digital Public Health Bremen Germany Institute for Information Health and Medical Law University of Bremen Bremen Germany Unit of Lifestyle-Related Disorders Department of Epidemiological Methods and Etiological Research Leibniz Institute for Prevention and Epidemiology Bremen Germany Department of Prevention and Health Promotion Institute for Public Health and Care Research University of Bremen Bremen Germany Institute for Philosophy University of Bremen Bremen Germany Human Computer Interaction Research Group Institute of Computer Science University of St. Gallen St. Gallen Switzerland Department of Health Services Research Institute for Public Health and Care Research University of Bremen Bremen Germany

Background: The terms health app and medical app are often used interchangeably but do not necessarily mean the same thing. To better understand these terms and better regulate such technologies, we need distinct definitions of health and medical apps. Objective: This study aimed to provide an overview of the definitions of health and medical apps from an interdisciplinary perspective. We summarized the core elements of the identified definitions for their holistic understanding in the context of digital public health. methods: The legal frameworks for medical device regulation in the United States, the European Union, and Germany formed the basis of this study. We then searched 6 databases for articles defining health or medical apps from an interdisciplinary perspective. The narrative literature review was supported by a forward and backward snowball search for more original definitions of health and medical apps. A qualitative analysis was conducted on the identified relevant aspects and core elements of each definition. On the basis of these findings, we developed a holistic definition of health and medical apps and created a decision flowchart to highlight the differences between the 2 types. Results: The legal framework showed that medical apps could be regulated as mobile medical devices, whereas there is no legal term for health apps. Our narrative literature review identified 204 peer-reviewed publications that offered a definition of health and medical apps. After screening for original definitions and applying the snowball method, 11.8% (24/204) of the publications were included in the qualitative analysis. Of these 24 publications, 22 (88%) provided an original definition of health apps and 11 (44%) described medical apps. The literature suggests that medical apps are a part of health apps. To describe health or medical apps, most definitions used the user group, a description of health, the device, the legal regulation, collected data, or technological

关键词： DiGA digital care applications digital health digital health applications DiPA health app medical app mobile health mobile medical device mobile phone regulation snowball search

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Satellite-based InSAR Geodesy and Collocation with GNSS

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Procedia computer science 2024年 239卷 2329-2340页

作者： Juraj Papco Matus Bakon Lukas Kubica Gabika Belicova Branislav Droscak Martin Ferianc Martin Rovnak Antonio M. Ruiz Joaquim J. Sousa Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 813 62 Bratislava Slovakia insar.sk Ltd Konstantinova 3 080 01 Presov Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 080 01 Presov Slovakia Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro Quinta de Prados 5000-801 Vila Real Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 827 45 Bratislava Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Center for Advanced Studies in Earth Sciences Energy and Environment (CEACTEMA) University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Research Group RNM-282 Microgeodesia Jaén University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC 4200-465 Porto Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite Systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame.

关键词： InSAR geodesy collocation GNSS

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