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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Designing the Application of Security Text Messages Into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End Of File (EOF) Method

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： A M H Pardede Anggi Pramana Muhammad Zarlis Akbar Iskandar Rosida Tiurma Manurung S Sriadhi Citra Kurniawan Abdurrozzaq Hasibuan Nurintan Asyiah Siregar Ayu Esteka Sari Indrya Mulyaningsih Teguh Hidayat Iskandar Alam Ibrahim Tri Listyorini Edy Winarno Tulus STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Informatics Engineering Universitas Muhammadiyah Sorong Sorong Indonesia Department of English Education Universitas Muhammadiyah Sorong Sorong Indonesia Department of Informatics Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data.

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engineering chirality at wafer scale with ordered carbon nanotube architectures

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Nature communications 2023年第1期14卷 7380页

作者： Jacques Doumani Minhan Lou Oliver Dewey Nina Hong Jichao Fan Andrey Baydin Keshav Zahn Yohei Yomogida Kazuhiro Yanagi Matteo Pasquali Riichiro Saito Junichiro Kono Weilu Gao Department of Electrical and Computer Engineering Rice University Houston TX USA. Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA. Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA. Carbon Hub Rice University Houston TX USA. Department of Chemical and Biomolecular Engineering Rice University Houston TX USA. J.A. Woollam Co. Inc. Lincoln NE USA. Smalley-Curl Institute Rice University Houston TX USA. Department of Physics Tokyo Metropolitan University Tokyo Japan. Department of Chemistry Rice University Houston TX USA. Department of Materials Science and NanoEngineering Rice University Houston TX USA. Department of Physics Tohoku University Sendai Japan. Department of Physics National Taiwan Normal University Taipei Taiwan. Department of Physics and Astronomy Rice University Houston TX USA. Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA. weilu.gao@utah.edu. Carbon Hub Rice University Houston TX USA. weilu.gao@utah.edu.

Creating artificial matter with controllable chirality in a simple and scalable manner brings new opportunities to diverse areas. Here we show two such methods based on controlled vacuum filtration - twist stacking and mechanical rotation - for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and large circular dichroism (CD). By controlling the stacking angle and handedness in the twist-stacking approach, we maximize the CD response and achieve a high deep-ultraviolet ellipticity of 40 ± 1 mdeg nm. Our theoretical simulations using the transfer matrix method reproduce the experimentally observed CD spectra and further predict that an optimized film of twist-stacked CNTs can exhibit an ellipticity as high as 150 mdeg nm, corresponding to a g factor of 0.22. Furthermore, the mechanical rotation method not only accelerates the fabrication of twisted structures but also produces both chiralities simultaneously in a single sample, in a single run, and in a controllable manner. The created wafer-scale objects represent an alternative type of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures and can be used to explore chiral phenomena and develop chiral photonic and optoelectronic devices.

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Erratum to: An approach to the research on ion and water properties in the interphase between the plasma membrane and bulk extracellular solution

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The journal of physiological sciences : JPS 2018年第1期68卷 101页

作者： Hiroshi Hibino Madoka Takai Hidenori Noguchi Seishiro Sawamura Yasufumi Takahashi Hideki Sakai Hitoshi Shiku Department of Molecular Physiology Niigata University School of Medicine 1-757 Asahimachi-dori Chuo-ku Niigata Niigata 951-8510 Japan. hibinoh@med.niigata-u.ac.jp. Center for Transdisciplinary Research Niigata University Niigata Niigata 950-2181 Japan. hibinoh@med.niigata-u.ac.jp. AMED-CREST AMED Niigata Japan. hibinoh@med.niigata-u.ac.jp. Department of Bioengineering School of Engineering The University of Tokyo Tokyo Japan. International Center for Materials Nanoarchitectonics and Global Research Center for Environment and Energy Based on Nanomaterials Science National Institute for Materials Science Tsukuba Ibaraki Japan. Department of Molecular Physiology Niigata University School of Medicine 1-757 Asahimachi-dori Chuo-ku Niigata Niigata 951-8510 Japan. Division of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Kanazawa 920-1192 Japan. Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency (JST) Saitama 332-0012 Japan. Department of Pharmaceutical Physiology Graduate School of Medicine and Pharmaceutical Sciences University of Toyama Toyama Japan. Graduate School of Engineering Tohoku University Sendai 980-8579 Japan.

The article An approach to the research on ion and water properties in the interphase between the plasma membrane and bulk extracellular solution, written by Hiroshi Hibino, Madoka Takai, Hidenori Noguchi, Seishiro Sawamura, Yasufumi Takahashi, Hideki Sakai and Hitoshi Shiku, was originally published Online First without open access.

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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THE CALLING(SM) NETWORK - A GLOBAL WIRELESS COMMUNICATION-SYSTEM

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international JOURNAL OF SATELLITE COMMUNICATIONS 1994年第1期12卷 45-61页

作者： TUCK, EF PATTERSON, DP STUART, JR LAWRENCE, MH Calling Communications Corporation. 1900 West Garvey Ave South. Suite 200 West Covina CA 91790 USA. Chairman of Calling Communications Corporation. He is also the Managing Director of Kinship Venture Management Inc. the general partner of Kinship Partners 11 and a General Partner of Boundary the general partner of The Boundary Fund. As a venture capitalist he has founded or participated in founding several telecommunications companies including Calling Communications Corporation Magellan Systems Corporation manufactures of Global Positioning System receivers Applied Digital Access manufacturer of DS-3 test access and network performance monitoring equipment Endgate Technology Corporation specialists in satellite phased array antennas and Poynting Systems Corporation. now a division of Reliance Corporation manufacturers of fibre optic transport equipment. He was a founder of Kebby Microwave Corporation where he invented the first solid-state. frequency-modulated commercial microwave link system. The company was acquired by ITT Corporation where he rose to the position of V.P. and Technical Director of ITT North America Telecommunications Inc. Subsequently he was V.P. of Marketing and Engineering at American Telecommunications Inc. (ATC). He was founding Director of American Telecom Inc. a joint venture between ATC and Fujitsu and has served on more than 20 boards of directors including those of three public companies. He has authored articles on microwave engineering and telephone signalling and was a contributor to Reference Data For Radio Engineers. He is a graduate of the University of Missouri at Rolla where he was later awarded an honorary Professional degree and serves on its Academy of Electrical Engineering. Mr Tuck is a Senior Member of the IEEE a Fellow of the Institution of Engineers (Australia) a Professional Member of the AIAA and a registered professional engineer in three states. More than 25 years of experience in the telecommunications industry where he has been responsibl

There is a very large demand for basic telephone service in developing nations, and remote parts of industrialized nations, which cannot be met by conventional wireline and cellular systems. This is the world's largest unserved market. We describe a system which uses recent advances in active phased arrays, fast-packet switching technology, adaptive routeing, and light spacecraft technology, in part based on the work of the Jet Propulsion Laboratory and on recently-declassified work done on the Strategic Defense Initiative, to make it possible to address this market with a global telephone network based on a large low-Earth-orbit constellation of identical satellites. A telephone utility can use such a network to provide the same modern basic and enhanced telephone services offered by telephone utilities in the urban centres of fully-industrialized nations. Economies of scale permit capital and operating costs per subscriber low enough to provide a service to all subscribers, regardless of location, at prices comparable to the same services in urban areas of industrialized nations, while generating operating profits great enough to attract the capital needed for its construction. The bandwidth needed to support the capacity needed to gain these economies of scale requires that the system use K(alpha)-band frequencies. This choice of frequencies places unusual constraints on the network design, and in particular forces the use of a large number of satellites. Global demand for basic and enhanced telephone service is great enough to support at least three networks of the size described herein. The volume of advanced components, and services such as launch services, required to construct and replace these networks is sufficient to propel certain industries to market leadership positions in the early 21st Century.

关键词： LOW EARTH ORBIT SATELLITE COMMUNICATIONS FAST PACKET SWITCHING EARTH FIXED CELLS ADAPTIVE ROUTEING LIGHT SPACECRAFT K(A) BAND PHASED-ARRAY ANTENNAS

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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MODELING AND CONTROL OF ELEVATORS BY STATECHARTS

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Asian Journal of Control 2008年第2期6卷

作者： Yi-Sheng Huang Sheng-Luen Chung Mu-Der Jeng Department of Aeronautical Engineering Chung Cheng Institute of Technology National Defense University Tashi Taoyuan 335 Taiwan R.O.C. Department of Electrical Engineering National Taiwan University of Science and Technology Taipei 106 Taiwan R.O.C. Department of Electrical Engineering National Taiwan Ocean University Kellung 202 Taiwan R.O.C. MuDer Jeng received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute Troy NY in 1992. Since August 1992 Dr. Jeng has been with National Taiwan Ocean University Keelung Taiwan where he is currently a full Professor at the Department of Electrical Engineering. His current research interests include Petri nets discrete event systems computer integrated manufacturing semiconductor factory automation embedded systems. Dr. Jeng is the author/co-author of over 120 book chapters journal papers and conference papers. Dr. Jeng received the Franklin V. Taylor Outstanding Paper Award from the IEEE Systems Man and Cybernetics Society in 1993. He was granted the Research Award by the National Science Council of Taiwan annually from 1994 to 2000. He is an Associate Editor for IEEE Transactions on Systems Man and Cybernetics-Part A IEEE Transactions on Robotics and Automation IEEE Transactions on Robotics and serves on the Editorial Board of International Journal of Computer Integrated Manufacturing. He has been a Guest Editor for eight leading journals. Dr. Jeng is the Chair of the Technical Committee on Discrete Event Systems of the IEEE SMC Society and the Founding Chair of the Technical Committee on Semiconductor Factory Automation of the IEEE Robotics and Automation Society. He served as the Exhibitions Chair of 2003 IEEE International Conference on Robotics and Automation and the Special Sessions Chair of 2004 IEEE International Conference on Networking Sensing and Control. He serves as a Program Co-Chair of 2005 IEEE International Conference on Networking Sensing and Control and the Organization Commit

Statechart has been utilized as a visual formalism for the modeling of complex and interactive systems for its illuminating features on describing properties of causality, concurrency, and synchronization. This paper presents the application of satechart to the modeling, design and implementation of an elevator system, whose system behavior involves aggregating complexity of state descriptions, and imposition of underlying control policy. Based on the operational flow of an elevator, we derive the associated statechart model by looking into the inherent hierarchical structure of the elevator. The advantage of the proposed approach is the clear presentation of system behavior in terms of conditions and events that cause the transitions in system dynamics. Implementation of the controlled elevator based on the modeled statechart is also presented.

关键词： Statechart interactive systems elevator

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Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

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Scientific data 2017年第1期4卷 170077-170077页

作者： Hesham Elhalawani Abdallah S.R. Mohamed Aubrey L. White James Zafereo Andrew J. Wong Joel E. Berends Shady AboHashem Bowman Williams Jeremy M. Aymard Aasheesh Kanwar Subha Perni Crosby D. Rock Luke Cooksey Shauna Campbell Yao Ding Clifton D. Fuller Stephen Y. Lai Elisabeta G. Marai David Vock Guadalupe M. Canahuate John Freymann Keyvan Farahani Jayashree Kalpathy-Cramer Department of Radiation Oncology University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Department of Clinical Oncology University of Alexandria Alexandria 21527 Egypt McGovern Medical School at University of Texas Health Science Center at Houston (UTHealth) Houston 77030 Texas USA University of Texas Health Science Center San Antonio 78229 Texas USA Department of Cardiology Harvard Medical School and Massachusetts General Hospital Boston 02115 Massachusetts USA Furman University Greenville 29613 South Carolina USA Abilene Christian University Abilene 79601 Texas USA Texas Tech University Health Sciences Center School of Medicine Lubbock 79905 Texas USA Columbia College of Physicians and Surgeons New York 10032 Massachusetts USA Texas Tech Health Sciences Center El Paso Paul L. Foster School of Medicine 79905 Texas USA Department of Radiation Oncology Cleveland Clinic Foundation Cleveland 44124 Ohio USA Department of Imaging Physic University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Medical Physics Program University of Texas Graduate School of Biomedical Sciences Houston 77030 Texas USA Department of Head and Neck Surgery University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Department of Computer Science University of Illinois at Chicago Chicago 60607 Illinois USA Department of Biostatistics University of Minnesota of Public Health Minneapolis 55455 Minnesota USA Department of Electrical & Computer Engineering University of Iowa Iowa City 52242 Iowa USA Leidos Biomedical Research Inc Frederick National Laboratory for Cancer Research Frederick 21701 Maryland USA The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins Medical Institutions Baltimore 21287 Maryland USA National Cancer Institute National Institutes of Health Bethesda 20892 Maryland USA Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging Massachusetts Ge

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that 'radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual *** public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.

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