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10th International Conference on Ubiquitous Information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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Cognitive Context Detection for Adaptive Automation

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2016年第1期60卷 223 - 227页

作者： Pujitha Mannaru Balakumar Balasingam Krishna Pattipati Ciara Sibley Joseph Coyne Dept. of Electrical and Computer Engineering University of Connecticut CT USA Warfighter Human Systems Integration Lab Naval Research Laboratory Washington DC USA

An important research challenge in Human Machine systems (HMS) is to create machines that are able to better understand human behavior so that the overall efficiency of the HMS can be enhanced through increased productivity and reduced safety risk. The research question posed in this paper is the following: Can an understanding of physiological behavior of humans be combined with statistical machine learning theory to develop predictive models that are able to accurately predict the cognitive difficulty experienced by humans? In this paper, we answer this question in the affirmative by demonstrating the use of two physiological measurements, pupil dilation and eye-gaze patterns, as indices of cognitive workload. Specifically, we demonstrate the possibility of cognitive context detection through machine learning and classification using eye-tracking data from NRL’s Supervisory Control Operations User Testbed (SCOUTTM), a flexible simulation environment that represents the tasks that a future UAS operator would engage in, while controlling multiple UAS.

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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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Analysis and Design of Coupling Capacitors for Contactless Capacitive Power Transfer systems

Analysis and Design of Coupling Capacitors for Contactless C...

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Transportation Electrification Conference and Expo

作者： Deepak Rozario Najath Abdul Azeez Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory UOIT-Automotive Center of Excellence (UOIT-ACE) Department of Electrical Computer and Software Engineering Fac

ISBN: (纸本)9781509004041

This paper is focused on the analysis and design of the capacitive plates of the capacitive wireless power transfer system. The coupling structure resembles a loosely coupled capacitor. The electric field coupling interface is the most vital part of the wireless technology. The coupling interface determines the capacitance of the coupling capacitors, maximum power transfer, frequency and overall efficiency of the system. The paper is dedicated to understand the behaviour of the system with respect to the structural construction, frequency of operation and factors that affect the power transfer of the system. The various arrangements and configurations of the capacitive power transfer (CPT) are discussed. The important factors affects the value of coupling capacitance are discussed. Based on results obtained from the analysis, a design guide line is laid out for the construction of the coupling capacitors. The design guidelines addresses critical issues such as operational voltage, temperature stability and selection of the dielectric material. The construction of the system is simple, rugged and inexpensive, which makes it a great contender among the other wireless technology.

关键词： Capacitor circuits chargers energy storage modelling surface charging transportation

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A Modified Resonant Converter for Wireless Capacitive Power Transfer systems used in Battery Charging Applications

A Modified Resonant Converter for Wireless Capacitive Power ...

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Transportation Electrification Conference and Expo

ISBN: (纸本)9781509004041

The paper outlines a capacitive power transfer (CPT) system for higher power applications. The paper summarizes the operating principle, power electronics requirement and modeling of a basic CPT system. The high frequency converter is the drive force of the contactless system. The high frequency converters found in literature are discussed with their advantages and their drawbacks. With the knowledge of the converters used, an existing topology is modified to fit the capacitive interface of the capacitive power transfer system. The modified LLC resonant converters has several advantages over the traditional converters used for contactless CPT .The converter has a very high power density and can be used for very high power applications. One of the greatest advantages the modified LLC resonant converter is the ability to work off resonance and the inherent ability to buck or boost as per its gain plot. The converter exhibits efficiencies greater than 90%. The design of the proposed topology along with its advantages over the existing topologies for small distance applications are listed.

关键词： Capacitive Circuits Wireless Power Transfer Power Electronics Surface Charging Transportation

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Publisher Correction: Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly

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Nature communications 2019年第1期10卷 2459页

作者： Peer Aramillo Irizar Sascha Schäuble Daniela Esser Marco Groth Christiane Frahm Steffen Priebe Mario Baumgart Nils Hartmann Shiva Marthandan Uwe Menzel Jule Müller Silvio Schmidt Volker Ast Amke Caliebe Rainer König Michael Krawczak Michael Ristow Stefan Schuster Alessandro Cellerino Stephan Diekmann Christoph Englert Peter Hemmerich Jürgen Sühnel Reinhard Guthke Otto W Witte Matthias Platzer Eytan Ruppin Christoph Kaleta Research Group Medical Systems Biology Institute of Experimental Medicine Christian-Albrechts-University Kiel D-24105 Kiel Germany. Jena University Language and Information Engineering Lab Friedrich-Schiller-University Jena D-07743 Jena Germany. GerontoSys JenAge Consortium D-07745 Jena Germany. Genome Analysis Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Hans Berger Department of Neurology Jena University Hospital D-07747 Jena Germany. Systems Biology and Bioinformatics Group Leibniz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute D-07745 Jena Germany. Biology of Ageing Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Molecular Genetics Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Imageing Facility Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Integrated Research and Treatment Center Center for Sepsis Control and Care (CSCC) Jena University Hospital D-07747 Jena Germany. Network Modeling Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute D-07745 Jena Germany. Institute for Medical Informatics and Statistics Christian-Albrechts-University Kiel D-24105 Kiel Germany. Energy Metabolism Laboratory Swiss Federal Institute of Technology (ETH) Zurich Schwerzenbach/Zürich CH-8603 Switzerland. Department of Bioinformatics Friedrich-Schiller-University Jena D-07743 Jena Germany. Laboratory of Neurobiology Scuola Normale Superiore University of Pisa I-56100 Pisa Italy. Molecular Biology Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Faculty of Biology and Pharmacy Friedrich-Schiller-University Jena D-07743 Jena Germany. Biocomputing Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Department of Computer Science and Center for Bioinformatics and Computational Biology University of Maryland College Park M

The original version of this Article contained an error in the spelling of the author Jule Müller, which was incorrectly given as Julia Müller. Additionally, in Fig. 4a, the blue-red colour scale for fold change in ageing/disease regulation included a blue stripe in place of a red stripe at the right-hand end of the scale. These errors have been corrected in both the PDF and HTML versions of the Article.

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TERO-Based Detection of Hardware Trojans on FPGA Implementation of the AES Algorithm

TERO-Based Detection of Hardware Trojans on FPGA Implementat...

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Euromicro Symposium on Digital System Design

作者： Paris Kitsos Kyriakos Stefanidis Artemios G. Voyiatzis Computer and Informatics Engineering Department DICES Lab Greece Industrial Systems Institute “Athena” Research and Innovation Center in ICT and Knowledge Technologies Patras Greece SBA Research Vienna Austria

A Transient Effect Ring Oscillator (TERO) is a special case of a Ring Oscillator (RO) design that exhibits increased sensitivity to intrinsic noise. It can serve as a basis for implementing a True Random Number Generator (TRNG) or a Physically Uncloneable Function (PUF). Also, as a digital sensor for detecting insertion of malicious hardware logic (Trojans) in digital circuits. Here, we explore the application of TERO for detecting hardware Trojans injected in FPGA implementations of the AES cryptographic algorithm. Experiments and comparisons are reported in terms of the frequency as a function of the TERO length. Our findings indicate that TERO-based digital sensors can be used to efficiently detect the presence of the Trojan.

关键词： Trojan horses Hardware Field programmable gate arrays Ring oscillators Logic gates Sensitivity

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A fast dynamic unipolar switching control scheme for single phase inverters in DC microgrids

A fast dynamic unipolar switching control scheme for single ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Nicolai Hildebrandt Mandip Pokharel Carl Ngai-Man Ho Yuanbin He Department of Power Electronics Fraunhofer Institute for Solar Energy Systems ISE Freiburg Germany Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada Centre for Smart Energy Conversion and Utilization Research City University of Hong Kong Hong Kong

ISBN: (纸本)9781509007387

This paper presents the digital implementation of a boundary controller with unipolar switching characteristic for single phase voltage source full bridge inverters, which will be used as an interface between a low voltage DC microgrid and AC loads. This paper expands the second order switching surface control method for single phase inverters by unipolar switching with the use of a finite-state machine. Compared to bipolar switching, unipolar switching has the advantage of lower switching losses and filter size. Besides, the switching losses can be equally distributed among semiconductor switches compared to conventional three-level inverter switching scheme. The operating principles of proposed control scheme and mathematical derivation of the switching functions will be given. A 550 VA, 120 V, 60 Hz voltage source inverter (VSI) with a digital signal processor has been implemented to verify the theoretical predictions. Experimental results for steady state operation at different loading conditions show a tightly regulated output voltage, for large-signal disturbances the control is fast and precise.

关键词： Switches Trajectory Inverters Voltage control Microgrids Bridge circuits

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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