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14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016

作者： Nozawa, Yuya Endo, Masaki Ehara, Yo Hirota, Masaharu Yokoyama, Syohei Ishikawa, Hiroshi Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Division of Core Manufacturing Polytechnic University Tokyo Japan National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Information Engineering National Institute of Technology Oita College Oita Japan Faculty of Informatics Shizuoka University Hamamatsu Japan

ISBN: (纸本)9783319606743

The importance of tourism information such as tourism purposes and tourist behavior continues to increase. However, obtaining precise tourist information such as the tourist destination and tourism period is difficult, as is applying that information to actual tourism marketing. We propose a method to classify Twitter user into tourist behavior and tourism purposes, extracting related information from Twitter posts. Our experiments demonstrated a 0.65 F-score for multi-class classification, showing accuracy for inferring tourist behavior and tourism purposes. © Springer International Publishing AG 2017.

关键词： Social networking (online)

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Scaffold-free tissue engineering for injured joint surface restoration

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Journal of Experimental Orthopaedics 2018年第1期5卷 1-14页

作者： Shimomura, Kazunori Ando, Wataru Fujie, Hiromichi Hart, David A. Yoshikawa, Hideki Nakamura, Norimasa Medicine for Sports and Performing Arts Department of Health and Sport Sciences Osaka University Graduate School of Medicine 2-2 Yamadaoka Suita City 565-0871 Osaka Japan Department of Orthopaedic Surgery Osaka University Graduate School of Medicine 2-2 Yamadaoka Suita City 565-0871 Osaka Japan Division of Human Mechatronics Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino City 191-0065 Tokyo Japan McCaig Institute for Bone & Joint Health University of Calgary 3330 Hospital Drive Northwest Calgary T2N 4N1 AB Canada Institute for Medical Science in Sports Osaka Health Science University 1-9-27 Tenma Kita-ku Osaka City 530-0043 Osaka Japan Center for Advanced Medical Engineering and Informatics Osaka University 2-2 Yamadaoka Suita City 565-0871 Osaka Japan

Articular cartilage does not heal spontaneously due to its limited healing capacity, and thus effective treatments for cartilage injuries has remained challenging. Since the first report by Brittberg et al. in 1994, autologous chondrocyte implantation (ACI) has been introduced into the clinic. Recently, as an alternative for chondrocyte-based therapy, mesenchymal stem cell (MSC)-based therapy has received considerable research attention because of the relative ease in handling for tissue harvest, and subsequent cell expansion and differentiation. In this review, we discuss the latest developments regarding stem cell-based therapies for cartilage repair, with special focus on recent scaffold-free approaches. © 2018, The Author(s).

关键词： Cartilage repair Mesenchymal stem cell Scaffold-free

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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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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Formation and temporal evolution of social groups during coffee breaks 1

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5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014

作者： Atzmueller, Martin Ernst, Andreas Krebs, Friedrich Scholz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Research Center for Information System Design University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Center for Environmental Systems Research University of Kassel Wilhelmshöher Allee 47 Kassel34117 Germany Department Energy Informatics and Information Systems Fraunhofer Institute for Wind Energy and Energy System Technology Königstor 59 Kassel34119 Germany

ISBN: (数字)9783319290096

ISBN: (纸本)9783319290089

Group formation and evolution are prominent topics in social contexts. This paper focuses on the analysis of group evolution events in networks of face-to-face proximity. We first analyze statistical properties of group evolution, e.g., individual activity and typical group sizes. After that, we define a set of specific group evolution events. These are analyzed in the context of an academic conference, where we provide different patterns according to phases of the conference. Specifically, we investigate group formation and evolution using real-world data collected at the LWA 2010 conference utilizing the Conferator system, and discuss patterns according to different phases of the conference. © Springer International Publishing Switzerland 2016.

关键词： Computers

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Visualizing high-risk paths using geo-tagged social data for disaster mitigation 9

Visualizing high-risk paths using geo-tagged social data for...

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9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016

作者： Kanno, Masaki Ehara, Yo Hirota, Masaharu Yokoyama, Shohei Ishikawa, Hiroshi Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino-shi Tokyo Japan National Institute of Advanced Industrial Science and Technology AIST Tokyo Waterfront BIO-IT Research Building 2-4-7 Aomi Koto-ku Japan Department of Information Engineering National Institute of Technology Oita College 1666 Maki Oita-shi Oita Japan Faculty of Informatics Shizuoka University 3-5-1 Johoku Naka-ku Hamamatsu-shi Shizuoka Japan Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino-shi Tokyo Japan

ISBN: (纸本)9781450345866

The 2011 Tohoku earthquake and tsunami showed the importance of smooth evacuation to nearby safe evacuation facilities. Smooth evacuation from crowded areas like train stations is difficult because people facing disaster often cannot find good paths to the nearest safe places and their evacuation may create congestion, which again hinders smooth evacuation. For smooth evacuation, the safety of roads used for evacuation needs to be evaluated. This evaluation requires 1) geographical characteristics of roads such as the risk of collapse of neighboring buildings and road width and 2) crowdedness of roads at the moment of disaster. While previous studies considered the former, the latter information has not been well studied because the crowdedness depends on the demographics of a city, which is quite dynamic and difficult to measure. For example, daytime and nighttime populations of a city differ greatly. This paper proposes a method that measures demographic snapshots of a city from time-and geo-stamped micro-blog posts and visualizes high-risk evacuation roads on the basis of geographical characteristics and demographics. Our method enabled visualization of high-risk evacuation roads on a per-hour basis. We also qualitatively analyze and discuss the visualization results. © ACM 2016.

关键词： Disasters

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EBSCAN: An entanglement-based algorithm for discovering dense regions in large geo-social data streams with noise 8

EBSCAN: An entanglement-based algorithm for discovering dens...

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8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015

作者： Yokoyama, Shohei Bogárdi-Mészöly, Ágnes Ishikawa, Hiroshi Faculty of Informatics Shizuoka University Hamamatsu Japan Department of Automation and Applied Informatics Hungary Faculty of Informatics Budapest University of Technology and Economics Budapest Shizuoka University Hamamatsu Japan Faculty of System Design Tokyo Metropolitan University Tokyo Japan

ISBN: (纸本)9781450339759

The remarkable growth of social networking services on global positioning system (GPS)-enabled handheld devices has produced enormous amounts of georeferenced big data. Given a large spatial dataset, the challenge is to effectively discover dense regions from the dataset. Dense regions might be the most attractive area in a city or the most dangerous zone of a town. A solution to this problem can be useful in many applications, including marketing, tourism, and social research. Density-based clustering methods, such as DBSCAN, are often used for this purpose. Nevertheless, current spatial clustering methods emphasize density while neglecting human behavior derived from geographical features. In this paper, we propose EBSCAN, which is based on the novel idea of an entanglement-based approach. Our method considers not only spatial information but also human behavior derived from geographical features. Another problem is that competing methods such as DBSCAN have two input parameters. Thus, it is difficult to determine optimal values. EBSCAN requires only a single intuitive parameter, tooFar, to discover dense regions. Finally, we evaluate the effectiveness of the proposed method using both toy examples and real datasets. Our experimentally obtained results reveal the properties of EBSCAN and show that it is >10 times faster than the competitor.

关键词： Global positioning system

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A new pragmatic approach to solve the problems of vector optimization with uncertain parameters

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IOP Conference Series: Materials Science and Engineering 2016年第1期124卷

作者： V I Reizlin A A Nefedova Department of Informatics and System Design Tomsk Polytechnic University 30 Lenina ave. Tomsk 634050 Russia

In this paper, we consider the method for solving problems of multi-criteria optimization with mathematical models that contain a lot of variables. The values of these variables are not regulated by a decision-maker. We introduce a new concept of 'tolerance of the decision variant'. This concept is similar to such concepts as stability, survivability, etc.

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Flight control systems of a quad tilt rotor Unmanned Aerial Vehicle for a large attitude change

Flight control systems of a quad tilt rotor Unmanned Aerial ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Atsushi Oosedo Satoko Abiko Shota Narasaki Atsushi Kuno Atsushi Konno Masaru Uchiyama Department of Mechanical Systems and Design Tohoku University Sendai Japan Division of System Science and Informatics Hokkaido University Sapporo Hokkaido Japan

ISBN: (纸本)9781479969241

Quad tilt rotor Unmanned Aerial Vehicle (UAV) solves the problem of underacuated system in general quadrotor UAV. The quad tilt rotor UAV can control position and attitude independently by tilting directions of propellers. However, the flight control system in a wide range of attitudes has not been discussed yet, e.g. a UAV flying and hovering with a 90 [°] pitch angle and can flip over when the range of the tilting motor rotates wide enough. In this paper, we present the attitude transition flight control system for pitch angles ranging 0 [°] to 90 [°] since flight condition with a 90 [°] pitch angle significantly differs from that in a conventional quadrotor UAV flight, and then adequate control system and sufficient experimental validation are necessary for stable flight in a wide range of attitude conditions.

关键词： Rotors Attitude control Propellers Torque Switches Mathematical model

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Forwarding mechanism using prioritized forwarders for opportunistic routing

Forwarding mechanism using prioritized forwarders for opport...

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Taku Yamazaki Ryo Yamamoto Takumi Miyoshi Takuya Asaka Yoshiaki Tanaka Department of Communications and Computer Engineering Waseda University Shinjuku-ku Tokyo Japan Global Information and Telecommunication Institute Waseda University Shinjuku-ku Tokyo Japan Graduate School of Informatics and Engineering The University of Electro-Communications Chofu-shi Tokyo Japan College of Systems Engineering and Science Shibaura Institute of Technology Saitama-shi Saitama Japan Faculty of System Design Tokyo Metropolitan University Hino-shi Tokyo Japan Waseda Daigaku Shinjuku-ku Tokyo JP

ISBN: (纸本)9781467390576

In ad hoc networks, backoff-based OR (opportunistic routing) protocols, which autonomously selects a forwarder among the potential forwarders based on a random backoff time, have been proposed. However, each potential forwarder must wait for the backoff time to avoid packet collisions among receivers. In addition, the terminal density strongly affects the performance since the backoff-based OR protocols improve the performance by using multiple terminals. In this paper, we propose a novel OR protocol called PRIOR (prioritized forwarding for opportunistic routing). In PRIOR, a terminal called prioritized forwarder, which forwards packets without using a backoff time, is selected among neighbours. In addition, we propose a hop-by-hop retransmission control that performs the retransmission terminal selection on the basis of a neighbour relation with the PFs. Moreover, we introduce an explicit acknowledgement mechanism on the basis of the difference of hop counts to alleviate the bad effect of the retransmission control in dense environments. Finally, we evaluate PRIOR in comparison with conventional backoff-based OR protocols in computer simulation.

关键词： Receivers Routing Ad hoc networks Routing protocols Maintenance engineering Wireless communication

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