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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Augmented Reality for Presenting Local Wisdom: Sumatera Utara Traditional House

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Journal of Physics: Conference Series 2019年第1期1235卷

作者： M F Syahputra R Aulia O S Sitompul U Andayani D Abdullah S Sriadhi R F Nanuru M D Utama R T Manurung H Djanggih E Mouw Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Faculty of Law Universitas Tompotika Luwuk Banggai Indonesia Faculty of Social and Human Sciences Universitas Halmahera Indonesia

Augmented Reality is a technology that allows users to see the real world, with virtual objects placed on it or combined with the real world. Augmented Reality itself can be applied to various things, including as an educational media. In the field of education, technology can be used a culture introduction to society. One part of Indonesian culture is traditional houses. Traditional house is a building that symbolizes the culture of the local community. In North Sumatra itself, there are several traditional houses that have different forms. The use of Vuforia SDK in the construction of this application can display augmented objects in the form of 3-dimentional objects and also provide information on the house. This application will run on the Android mobile platform to make it easier for users to access it. In addition, users can choose their own objects to be used as markers to make the application use much easier.

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Predictive modeling of exercise response in CVD patients under rehabilitation

Predictive modeling of exercise response in CVD patients und...

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Joint Conference of the European Medical and Biological engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical engineering and Medical Physics, NBC 2107

作者： Filos, Dimitris Triantafyllidis, A. Manolios, V. Livitckaia, K. Claes, J. Buys, R. Cornelissen, V. Kouidi, E. Maglaveras, N. Chouvarda, I. Centre for Research and Technology Hellas Institute of Applied Biosciences Thessaloniki Greece Lab of Computing Medical Informatics & Biomedical lmaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Katholieke Universiteit Leuven Research Group for Cardiovascular and Respiratory Rehabilitation Belgium Laboratory of Sports Medicine Aristotle University of Thessaloniki Thermi Greece Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

ISBN: (纸本)9789811051210

Exercise-based rehabilitation plays a key role for patients with cardiovascular disease (CVD) in improving their well-being and reducing their symptoms. Monitoring and assessing the exercise response at an individual level is critical toward achieving better health outcomes. 15 exercise sessions performed by 5 CVD patients and 9 sessions from 3 regularly active individuals were monitored, and heart rate (HR) data were acquired. A model based on the HR dynamics during exercising at different intensities was built, and simulations were performed to assess performance in different scenarios of exercise selection. Our results show that the application of simple rules in exercise selection, which consider both the HR and the beneficial HR zones of individuals, can lead to beneficial execution of exercise programs (%time spent in beneficial HR zones: 60.6±27.5 for CVD patients). Personalized guidance during exercise has the potential to significantly contribute in the beneficial execution of exercise-based cardiac rehabilitation programs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Patient rehabilitation

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Robotics research: The 16th international symposium ISRR 16th

Robotics research: The 16th international symposium ISRR

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16th International Symposium of Robotics Research, ISRR 2013

作者： Inaba, Masayuki Corke, Peter Department of Creative Informatics The University of Tokyo Tokyo Japan School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD Australia

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Recurrent dynamic neural network model for myoelectric-based control of a prosthetic hand 10

Recurrent dynamic neural network model for myoelectric-based...

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10th Annual International Systems Conference, SysCon 2016

作者： Teban, Teodor-Adrian Precup, Radu-Emil Voisan, Emil-Ioan De Oliveira, Thiago Eustaquio Alves Petriu, Emil M. Department of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781467395182

This paper discusses a novel nonlinear autoregressive network with exogenous inputs (NARX) neural network (NN) model of the nonlinear dynamic mechanisms occurring in the myoelectric-based control of prosthetic hand fingers. The experimental results demonstrate the good performance of the NN architecture in terms of output response and mean squared error. The comparison of experimental results also shows that the proposed NARX recurrent NN outperforms a linear recurrent NN with the same training algorithm. © 2016 IEEE.

关键词： Recurrent neural networks

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Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2

Research Square

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Research Square 2020年 rs3rs-100914页

作者： Sun, Mengying Shankar, Rama Ko, Meehyun Chang, Christopher Daniel Yeh, Shan-Ju Li, Shilong Liu, Ke Zhou, Guoli Xing, Jing VanVelsen, Austin VanVelsen, Tyler Paithankar, Shreya Feng, Benjamin Y. Young, Krista Strug, Michael Turco, Lauren Wang, Zichen Schadt, Eric Chen, Rong Li, Xiaohong Oskotsky, Tomiko Sirota, Marina Glicksberg, Benjamin S. Nadkarni, Girish N. Moeser, Adam J. Li, Li Kim, Seungtaek Zhou, Jiayu Chen, Bin Department of Pediatrics and Human Development Michigan State University Grand RapidsMI United States Department of Computer Science and Engineering Michigan State University East LansingMI United States Department of Pharmacology and Toxicology Michigan State University Grand RapidsMI United States College of Osteopathic Medicine Michigan State University East LansingMI United States Biomedical Research Informatics Core Clinical & Translational Sciences Institute Michigan State University East LansingMI United States Van Andel Research Institute Grand RapidsMI United States Sema4 StamfordCT United States Department of Genetics and Genomic Sciences The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine Mount SinaiNY United States Department of Obstetrics and Gynecology Spectrum Health Grand RapidsMI United States Department of Obstetrics Gynecology and Reproductive Biology Michigan State University Grand RapidsMI United States Emergency Medicine Residency Spectrum Health Grand RapidsMI United States Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Zoonotic Virus Laboratory Institut Pasteur Korea Seongnam Korea Republic of Large Animal Clinical Sciences and Department of Physiology Michigan State University East LansingMI United States Department of Pediatrics and Bakar Computational Health Sciences Institute University of California San FranciscoCA United States Department of Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States The Hasso Plattner Institute of Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New YorkNY United States The Charles Bronfman Institute of Personalized Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY United States

Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the gene expression of viral entry proteins ACE2 and TMPRSS2, and host transcriptional responses to SARS-CoV-2 through large-scale analysis of genomic and clinical data. We first compiled 220,000 human gene expression profiles from three databases and completed the meta-information through machine learning and manual annotation. Large scale analysis of these profiles indicated that male samples show higher expression levels of ACE2 and TMPRSS2 than female samples, especially in the older group (>60 years) and in the kidney. Subsequent analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present distinct expression changes. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Together, this study suggests biological differences in the kidney between sexes may contribute to sex disparity in COVID-19. © 2020, CC BY.

关键词： COVID-19

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Control System of a Semi-Autonomous Mobile Robot

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IFAC-PapersOnLine 2016年第25期49卷 460-469页

作者： Škrabánek, Pavel Vodika, Pavel Yildirim-Yayilgan, Sule Faculty of Electrical Engineering and Informatics University of Pardubice Studentská 95 Pardubice532 10 Czech Republic Faculty of Computer Science and Media Technology Norwegian University of Science and Technology postboks 191 GjøvikNO-2802 Norway

In this paper, a control system, which has been designed for a semi-autonomous mobile robot, is presented. The robot has been developed as a part of a teaching aid. The teaching aid is aimed at practice of a path-planning theory. The main purpose of the robot within the teaching aid is physical execution of a path-plan scheduled by a high-level control system. The execution of path-plans is required to be accomplished by the robot autonomously, hence the ‘semi-autonomous’ appellation. The robot is based on an Arduino UNO microcontroller board. The robot acquires information about his workspace via reflectance sensors, encoders, and a magnetometer sensor. Since all these sensors provide only very limited information about the workspace, all the acquired data has to be used with utmost effectiveness. Thus, processing of the sensor data and multi-sensor integration is also considered in this paper. © 2016

关键词： computer control systems Control theory Data integration Data processing Intelligent robots Leveling (machinery) Machine design Mobile robots Motion planning Navigation Robot programming Autonomous Mobile Robot High level control systems Limited information Magnetometer sensors Microcontroller boards Multi sensor integrations Reflectance sensors Teaching aids

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Model predictive control with state estimation for freeway systems

Model predictive control with state estimation for freeway s...

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American Control Conference

作者： A. Brandi A. Ferrara S. Sacone S. Siri C. Vivas F. Rubio the Dept. of Electrical Computer and Biomedical Engineering University of Pavia Italy the Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy the Dept. de Ingeniería de Sistemas y Automática Escuela Superior de Ingenieros Universidad de Sevilla Spain

ISBN: (纸本)9781509045839

The objective of the paper is the design of a ramp metering control scheme for freeway systems in which the traffic state estimation is also considered. The development of an approach for estimating the traffic densities is motivated by the fact that traffic flow measurements at some locations are in many cases the only available information obtained by sensors, whereas the knowledge of the traffic density is necessary for determining suitable ramp metering actions. A piecewise-affine version of the Asymmetric Cell Transmission Model is adopted and, on the basis of its operating mode and observability properties, a density estimation mechanism based on switched Luenberger observers is realized. The problem is formulated and solved resorting to Linear Matrix Inequalities. The ramp metering control action is then computed by applying a Model Predictive Control approach that uses state estimates in place of measurements. Some simulation results have been performed to test the effectiveness of the proposed approach.

关键词： estimation freeway systems

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#PrayForDad: Learning the semantics behind why social media users disclose health information 10

#PrayForDad: Learning the semantics behind why social media ...

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10th International Conference on Web and Social Media, ICWSM 2016

作者： Yin, Zhijun Chen, You Fabbri, Daniel Sun, Jimeng Malin, Bradley Department of Electrical Engineering and Computer Science School of Engineering Vanderbilt University United States Department of Biomedical Informatics School of Medicine Vanderbilt University United States School of Computational Science and Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781577357582

User-generated content in social media is increasingly acknowledged as a rich resource for research into health problems. One particular area of interest is in the semantics individuals evoke because they can influence when healthrelated information is disclosed. While there have been multiple investigations into why self-disclose occurs, much less is known about when individuals choose to disclose information about other people (e.g., a relative), which is a significant privacy concern. In this paper, we introduce a novel framework to investigate how semantics influence disclosure routines for 34 health issues. This framework begins with a supervised classification model to distinguish tweets that communicate personal health issues from confounding concepts (e.g., metaphorical statements that include a health-related keyword). Next, we annotate tweets for each health issue with linguistic and psychological categories (e.g. social processes, affective processes and personal concerns). Then, we apply a non-negative matrix factorization over a health issue-bylanguage category space. Finally, the factorized basis space is leveraged to group health issues into natural aggregations based around how they are discussed. We evaluate this framework with four months of tweets (over 200 million) and show that certain semantics correspond with whom a health mention pertains to. Our findings show that health issues related with family members, high medical cost and social support (e.g., Alzheimer's Disease, cancer, and Down syndrome) lead to tweets that are more likely to disclose another individual's health status, while tweets with more benign health issues (e.g., allergy, arthritis, and bronchitis) with biological processes (e.g., health and ingestion) and negative emotions are more likely to contain self-disclosures. © Copyright 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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Implementation of SIMON and SPECK lightweight block ciphers on programmable logic controllers

Implementation of SIMON and SPECK lightweight block ciphers ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Adrian-Vasile Duka Béla Genge Department of Electrical Engineering and Computer Science 1 N.Iorga St. Tîrgu Mureş Mureş Romania Department of Informatics 1 N.Iorga St. Tîrgu Mureş Mureş Romania

ISBN: (纸本)9781509058365

Programmable Logic Controllers (PLCs) are the key components of SCADA systems, controlling different industrial processes, while handling the communication with other systems via different communication protocols, which are subject to security attacks. The introduction of cryptography at the PLC's application level could represent a first step in providing the needed security for such systems. However, despite the recent technological progress, PLCs are still quite limited equipment in terms of processing performance, when compared to other computing systems (e.g., PCs). Consequently, running the traditional cryptographic algorithms at PLC level is time consuming and requires a lot of resources. In this context, the implementation of the recent lightweight cryptographic algorithms may provide more feasible solutions. This paper presents the PLC implementation aspects and the results for the SIMON and SPECK families of cryptographic algorithms, as tested on Phoenix Contact's ILC 350 PN controller.

关键词： Ciphers Encryption SCADA systems Organizations Servers

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