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38th International Cosmic Ray Conference, ICRC 2023

作者： Fujii, Shingo Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-8585 Japan Institute of Physics Academia Sinica 128 Sec. 2 Academia Rd. Na

KAGRA, the gravitational wave telescope located in Japan, has started the 4th international observing run called O4 with LIGO and Virgo. The accuracy of parameter estimation for gravitational wave source objects depends on the accuracy of calibration. LIGO, Virgo, and KAGRA use the same calibration method called the photon calibrator (Pcal) method. In the Pcal system, we will obtain the precise displacement of a mirror induced by a well-calibrated radiation pressure of a laser beam which is injected into the mirror. As the uncertainty of the Pcal system is primarily limited by the uncertainty of the laser power measurement, we use integrating sphere (IS) power meters, which are considered to have good accuracy and precision, for the laser power measurement. However, during our calibration measurements in O3GK, we found that there were unidentified systematic errors in the laser power measurement. Therefore, we investigated the incident direction dependence and polarization dependence of the IS power meter. According to our study, the angular dependence differs between two types of ISs, namely GPS (General Purpose Spheres) and LPM (Laser Power Measurement Spheres), which are utilized in our calibration system. The angular dependence of the GPS type was greater than that of the LPM type. The GPS measurement showed an approximate 8.4% increase from the value obtained with perpendicular incidence at maximum. In contrast, the LPM measurement showed a 0.2% increase from the value obtained with perpendicular incidence at maximum. As to polarization dependence, the effect of that on the LPM type was within the range of -0.06% to 0.06%. From this study, we found a possibility that the angle dependence was able to explain the uncertainty observed in O3GK. In this proceeding, we report the angular and polarization dependence of ISs and consider the effect on the uncertainty of the KAGRA Pcal system. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Spheres

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Elastic mitral valve silicone replica made from 3D-printable molds offer advanced surgical training

Elastic mitral valve silicone replica made from 3D-printable...

引用

Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Engelhardt, Sandy Sauerzapf, Simon Al-Maisary, Sameer Karck, Matthias Preim, Bernhard Wolf, Ivo De Simone, Raffaele Faculty for Computer Science Mannheim University of Applied Sciences Mannheim Germany Department of Simulation and Graphics Magdeburg University Magdeburg Germany Departement of Cardiac Surgery Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783662580950

Reconstructive mitral valve surgeries are demanding cardiac surgeries that are conducted in the mid-age of a surgical career at the earliest. Receiving years of training in patients is required to gain the experiences and skills necessary for surgical success. On top of that, the number of such surgeries is limited per hospital, therefore other means of training should be offered to facilitate a steep learning curve. Within the scope of this work, we equipped an existing physical simulator with patient-specific flexible replica of the mitral valve. We developed software to automatically produce a 3D-printable casting mold for silicone material, as this mimics properties of the valve tissue in terms of stitching and cutting. We show the feasibility of the approach and the usefulness of these models is evaluated by an experienced cardiac surgeon, successfully conducting reconstructive surgery on pathological silicone valves. © Springer-Verlag GmbH Deutschland 2018.

关键词： Silicones

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Experimental and computational study of ecofriendly synthesis of silver nanoparticles from natural extracts: Self-controlled nucleation and growth, and colorimetric detection of heavy metal ions

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Surfaces and Interfaces 2025年 68卷

作者： Wonglakhon, Tanakorn Jommala, Nareerat Laksee, Sakchai Nuengmatcha, Prawit Ninwong, Benjawan Zahn, Dirk Thepchuay, Yanisa Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat 80160 Thailand Research Center for Theoretical Simulation and Applied Research in Bioscience and Sensing Walailak University Nakhon Si Thammarat 80160 Thailand Center of Excellence in Nanomaterials Chemistry and Department of Chemistry Faculty of Science and Technology Nakhon Si Thammarat Rajabhat University Nakhon Si Thammarat 80280 Thailand Nuclear Technology Research and Development Center Thailand Institute of Nuclear Technology (Public Organization) Nakhon Nayok 26120 Thailand Lehrstuhl für Theoretische Chemie / Computer Chemie Centrum Friedrich-Alexander-Universität Erlangen-Nürnberg Nägelsbachstraße 25 Erlangen 91052 Germany Flow Innovation-Research for Science and Technology Laboratories (First Labs) Thailand

We present an ecofriendly synthesis of silver nanoparticles (AgNPs) using the husk extract of Nypa fruticans, a largely unexploited biowaste, for effective colorimetric detection of heavy metal ions in aqueous solutions. Our one-step synthesis produces well-dispersed, highly stable AgNPs with an average size of ∼4 nm, as determined by transmission electron microscopy. The compounds in the extract act as both reducing and stabilizing agents, facilitating self-controlled nucleation and growth, as supported by molecular simulations. The synthesized AgNPs were further characterized using UV–visible spectroscopy, energy dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and zeta potential measurements. Under optimized conditions, the AgNPs exhibit high selectivity toward Fe²⁺ ions, producing a distinct dark brown color that can be observed by the naked eye, while showing almost no response to other tested heavy metal ions. The calibration curve for the Fe²⁺ detection showed excellent linearity (r² = 0.9959) with a detection limit of 0.24 ppm. Additionally, the AgNPs demonstrated excellent (> 94 %) Fe²⁺ recovery in real water samples. Through quantum chemical calculations and molecular dynamics simulations, we provided atomic-level insights into the mechanisms of Fe²⁺ detection. This present work thus highlights the potential of biomass extracts for green nanomaterial synthesis and environmental applications. © 2025 Elsevier B.V.

关键词： AgNPs Colorimetric detection Heavy metal ions Molecular dynamics Nypa fruticans

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Deep learning of preconditioners for conjugate gradient solvers in urban water related problems

arXiv

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arXiv 2019年

作者： Sappl, Johannes Seiler, Laurent Harders, Matthias Rauch, Wolfgang Unit of Environmental Engineering Faculty of Engineering Sciences Universität Innsbruck Technikerstraße 13 Innsbruck6020 Austria Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21 A Innsbruck6020 Austria

Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. Preconditioners further enhance the rate of convergence but hitherto only handcrafted ones requiring expert knowledge have been used. We propose an innovative approach employing Machine Learning, in particular a Convolutional Neural Network, to unassistedly design preconditioning matrices specifically for the problem at hand. Based on an in-depth case study in fluid simulation we are able to show that our learned preconditioner is able to improve the convergence rate even beyond well established methods like incomplete Cholesky factorization or Algebraic MultiGrid. Copyright © 2019, The Authors. All rights reserved.

关键词： Conjugate gradient method

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Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

arXiv

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arXiv 2019年

作者： Gsaxner, Christina Roth, Peter M. Wallner, Jürgen Egger, Jan Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz Styria Austria

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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Towards a Deadline-Based simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

Towards a Deadline-Based Simulation Experimentation Framewor...

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simulation Winter Conference

作者： Anastasia Anagnostou Simon J. E. Taylor Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gregoire Gesmier Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing Faculty of Science and Technology University of Westminster London UK Hungarian Academy of Science Institute for Computer Science and Control (MTA SZTAKI) Budapest HUNGARY

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework.

关键词： Containers Cloud computing Computational modeling Monitoring Measurement Runtime Tools

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