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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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Matryoshka-caged gold nanorods： Synthesis, plasmonic properties, and catalytic activity

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Nano Research 2016年第2期9卷 415-423页

作者： Wei Xiong Debabrata Sikdar Lim Wei Yap Pengzhen Guo Malin Premaratne Xinyong Li Wenlong Cheng Department of Chemical Engineering Monash University Clayton VIC 3800 Australia The Melbourne Centre for Nanofabrication 151 Wellington Road Clayton VIC 3168 Australia Key Laboratory of Industrial Ecology and Environmental Engineering and State Key Laboratory of Fine Chemicals School of Environmental Sciences and Technology Dalian University of Technology Dalian 116024 China Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton VIC 3800 Australia

Matryoshka-caged gold nanorods （mCGNRs） were successfully synthesized by alternating between a seed-mediated silver-coating method and galvanic replacement reactions （GRRs）. As the number of matryoshka layers of the mCGNRs increased, the plasmon resonance peak broadened and was red-shifted, and the catalytic activity towards the reduction of 4-nitrophenol （4-NTP） increased. When mCGNRs with 6 layers were used as nanocatalysts in the reduction of 4-nitrophenol, the reaction rate coefficient was 5.2- and 3.7-times higher than that of the gold-nanorod- and caged-gold-nanorod-catalyzed reductions of 4-nitrophenol, respectively. In addition, the surface-plasmon-resonance-based absorption of light enhanced the catalytic performance of the mCGNRs. With the support of a polyurethane foam, the mCGNRs synthesized in this study can be applied as recydable heterogeneous catalysts for the reduction of 4-nitrophenol.

关键词： matryoshka-caged gold nanorods galvanic replacement reaction 4-nitrophenol catalysis surface plasmon resonance

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Motion trajectory design and tracking control for underactuated Furata pendulum system

Motion trajectory design and tracking control for underactua...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ancai Zhang Jianlong Qiu Chaomin Luo Chengdong Yang Zhenxing Li School of Automation and Electrical Engineering Linyi University Linyi Shandong China Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong (Linyi University) Linyi Shandong China Department of Electrical and Computer Engineering University of Detroit Mercy MI USA School of Information Science and Engineering Linyi University Linyi Shandong China

This paper discusses the motion trajectory design and tracking control problem for an underactuated mechanical system called Furuta pendulum. Firstly, the dynamic equations of a Furuta pendulum system are given. And the dynamic properties of this mechanical system are analyzed. Secondly, we construct a trajectory for the Furuta pendulum in its motion space based on the analysis properties. Afterwards, a tracking controller is designed to track the constructed motion trajectory. This guarantees the stabilization of the Furuta pendulum from an initial point to an objective point to be achieved. To demonstrate the validity of our proposed theoretical analysis results, a numerical example is presented finally.

关键词： Trajectory Tracking Mathematical model Mechanical systems Aerospace electronics Dynamics

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A coalition formation approach to coordinated task allocation in heterogeneous uav networks

arXiv

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arXiv 2017年

作者： Afghah, Fatemeh Zaeri-Amirani, Mohammad Razi, Abolfazl Chakareski, Jacob Bentley, Elizabeth School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Electrical & Computer Engineering University of Alabama TuscaloosaAL United States Air Force Research Laboratory RomeNY United States

The problem of adversary target detection and the subsequent task completion using a heterogeneous network of resource-constrained UAVs is considered. No prior knowledge about locations and required resources to identify these targets is available to the UAVs. In the proposed leader-follower coalition formation model, the UAV that first locates a target serves as the coalition leader and selects a group of follower UAVs to complete the task associated with the identified target. The goal of the coalition formation is to complete the designated tasks with minimal resource utilization. Another role of coalition members is to make the ground station aware of the detected adversary target by forwarding its signal to the station via a distributed cooperative relaying scheme. We also propose a reputation-based mechanism for coalition formation to monitor the cooperative behavior of the UAVs over the course of time and exclude potentially untrustworthy UAVs. Simulation results show the efficiency of the proposed method in forming optimal coalitions compared to alternative methods.1. Copyright © 2017, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Correction to: Imaging features of immune-mediated genitourinary disease

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Neurochemical research 2019年第7期44卷 1780页

作者： Ryan A Kerekes Rodrigo A P Martins Denise Davis Mahmut Karakaya Shaun Gleason Michael A Dyer Measurement Science and Systems Engineering Division Oak Ridge National Laboratory MS 6075 1 Bethel Valley Rd. Oak Ridge TN 37831 USA. kerekesra@ornl.gov. Instituto de Biofı ́ sica Carlos Chagas Filho Universidade Federal do Rio de Janeiro CCS Bloco G Cidade Universitária Rio de Janeiro RJ 21941-900 Brazil. Department of Developmental Neurobiology St. Jude Children's Research Hospital Memphis TN 38105 USA. Department of Computer Science and Electrical Engineering University of Tennessee Knoxville TN 37996 USA. Measurement Science and Systems Engineering Division Oak Ridge National Laboratory MS 6075 1 Bethel Valley Rd. Oak Ridge TN 37831 USA. Department of Ophthalmology University of Tennessee Health Sciences Center Memphis TN 38105 USA. Howard Hughes Medical Institute Baltimore MD USA.

The authors regret that they neglected to cite their conference report on the technical part of a 'preliminary study' presented at, and published in, the Biomedical Sciences and engineering Conference (BSEC), 2010, May 25-26 (Fully automated segmentation and characterization of the dendritic trees of retinal horizontal neurons -DOI: 10.1109/BSEC.2010.5510843 ), as it related to the larger dataset presented as validation of the method in the Neurochemical Research article (Automated Tracing of Horizontal Neuron Processes During Retinal Development- Neurochem Res. 2011 Apr;36(4):583-93). This resulted in the lack of transparency on the re-use and duplication of introductory text, which should have been cited. No figures or tables were reproduced, but rather larger confirmatory data and different set of results were reported. Appropriate authors were cited in both papers.

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Flexible multiple base station association and activation for downlink heterogeneous networks

arXiv

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arXiv 2017年

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

This letter shows that the flexible association of possibly multiple base stations (BSs) to each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the turning-off of the BSs at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research

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IEEE Transactions on Cognitive Communications and Networking 2016年第2期2卷 110-128页

作者： Bhattarai, Sudeep Park, Jung-Min Gao, Bo Bian, Kaigui Lehr, William Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061-0131 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Network Computing and Information Systems Peking University Beijing100871 China Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions-i.e., across frequencies, time, location, users, uses, and networks. Realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies;a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges. © 2015 IEEE.

关键词： Spectroscopy

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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State Estimation for BAM Neural Networks with Mixed Time Delays

State Estimation for BAM Neural Networks with Mixed Time Del...

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IEEE International Conference on Automation Science and engineering

作者： Guoquan Liu Chaomin Luo Jianlong Qiu Xianxi Luo Jiakai Ding Jiangxi Province Engineering University of Technology Nanchang China Department of Electrical and Computer Engineering University of Detroit Mercy Michigan USA School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong (Linyi University) Linyi Shandong Province China Jiangxi Province Engineering East China University of Technology Nanchang China School of mechanical and electronic engineering East China University of Technology Nanchang China

This paper is concerned with the state estimation problem of BAM neural networks with mixed time delays. By constructing a suitable Lyapunov-Krasovskii functional (LKF), a new criterion is obtained so that the BAM neural networks error system is asymptotically stable. A simulation example is given to demonstrate the effectiveness of the proposed method.

关键词： LKF concerned estimation

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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