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Foiling covert channels and malicious classical post-processing units in quantum key distribution

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arXiv 2017年

作者： Curty, Marcos Lo, Hoi-Kwong Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain Center for Quantum Information and Quantum Control Department of Physics Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada

Existing security proofs of quantum key distribution (QKD) suffer from two fundamental weaknesses. First, memory attacks have emerged as an important threat to the security of even device-independent quantum key distribution (DI-QKD), whenever QKD devices are re-used. This type of attacks constitutes an example of covert channels, which have attracted a lot of attention in security research in conventional cryptographic and communication systems. Second, it is often implicitly assumed that the classical post-processing units of a QKD system are trusted. This is a rather strong assumption and is very hard to justify in practice. Here, we propose a simple solution to these two fundamental problems. Specifically, we show that by using verifiable secret sharing and multiple optical devices and classical post-processing units, one could re-establish the security of QKD. Our techniques are rather general and they apply to both DI-QKD and non-DI-QKD. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum cryptography

Characterizing multi-photon quantum interference with practical light sources and threshold single-photon detectors

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arXiv 2017年

作者： Navarrete, Álvaro Wang, Wenyuan Xu, Feihu Curty, Marcos EI Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain Center for Quantum Information and Quantum Control Department of Physics Department of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Hefei Anhui230026 China

The experimental characterization of multi-photon quantum interference effects in optical networks is essential in many applications of photonic quantum technologies, which include quantum computing and quantum communication as two prominent examples. However, such characterization often requires technologies which are beyond our current experimental capabilities, and today's methods suffer from errors due to the use of imperfect sources and photodetectors. In this paper, we introduce a simple experimental technique to characterise multi-photon quantum interference by means of practical laser sources and threshold single-photon detectors. Our technique is based on well-known methods in quantum cryptography which use decoy settings to tightly estimate the statistics provided by perfect devices. As an illustration of its practicality, we use this technique to obtain a tight estimation of both the generalized Hong-Ou-Mandel dip in a beamsplitter with six input photons, as well as the three-photon coincidence probability at the output of a tritter. Copyright © 2017, The Authors. All rights reserved.

关键词： Photons

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

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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

3D Mesostructures: Fabrication and Deformation of 3D Multilayered Kirigami Microstructures (Small 11/2018)

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Small 2018年第11期14卷

作者： Mohammad Humood Yan Shi Mengdi Han Joseph Lefebvre Zheng Yan Matt Pharr Yihui Zhang Yonggang Huang John A. Rogers Andreas A. Polycarpou Department of Mechanical Engineering College of Engineering Texas A&M University College Station TX 77843-3123 USA State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Center for Mechanics and Materials and Center for Flexible Electronics Technology AML Department of Engineering Mechanics Tsinghua University Beijing 100084 P. R. China Department of Materials Science and Engineering Fredrick Seitz Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 USA Bruker Nano Surfaces Eden Prairie MN 55344 USA Department of Chemical Engineering and Department of Mechanical and Aerospace Engineering University of Missouri Columbia MI 65211 USA Departments of Mechanical Engineering and Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA Department of Biomedical Engineering Department of Chemistry Department of Mechanical Engineering Department of Electrical Engineering and Department of Computer Science Center for Bio-Integrated Electronics Simpson Querrey Institute for Nano/Biotechnology Northwestern University Evanston IL 60208 USA

Compound control for speed and tension multivariable coupling system of reversible cold strip mill

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Journal of Central South University 2015年第2期22卷 529-538页

作者：方一鸣刘乐李建雄常茹 Key Laboratory of Industrial Computer Control Engineering of Hebei Province(Institute of Electrical Engineering Yanshan University) National Engineering Research Center for Equipment and Technology of Cold Strip Rolling(Yanshan University)

To weaken the nonlinear coupling influence among the variables in the speed and tension system of reversible cold strip mill, a compound control(CC) strategy based on invariance principle was proposed. Firstly, invariance principle was used to realize static decoupling between the speed and tension of reversible cold strip mill. Then, considering the influence caused by the time variation of steel coil radius and rotational inertia of the left and right coilers, as well as the uncertainties, a CC strategy that is composed of extended state observer(ESO) and global sliding mode control(GSMC) with backstepping adaptive was proposed,which further realized dynamic decoupling and coordination control for the speed and tension system. Theoretical analysis shows that the resulting closed-loop system is global bounded stable. Finally, the simulation was carried out on the speed and tension system of a 1422 mm reversible cold strip mill by using the actual data, and through the comparison of the other control strategies, validity of the proposed CC strategy was shown by the results.

关键词： reversible cold strip mill speed and tension system decoupling invariance principle compound control extended state observer global sliding mode control

Design of a nonlinear adaptive natural oscillator: Towards natural dynamics exploitation in cyclic tasks

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Design of a nonlinear adaptive natural oscillator: Towards n...

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Rezvan Nasiri Mahdi Khoramshahi Majid Nili Ahmadabadi Cognitive Robotics Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9781509037636

In this paper, we present the dynamical equations of a nonlinear adaptive natural oscillator (NANO) in order to exploit the natural dynamics in robotic systems. The presented oscillator tries to minimize an energy-based cost function by adapting the shape and frequency of the reference trajectory. Stability, convergence, and optimality of this oscillator are guaranteed analytically. Moreover, the performance of this oscillator is investigated by applying it to three different types of robotic models;i.e., the pendulum, the adaptive-toy, and the hopper-leg.

关键词： Trajectory Learning Energy Efficiency Cyclic Tasks Natural Dynamics

A tensor-based framework for studying eigenvector multicentrality in multilayer networks

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arXiv 2017年

作者： Wu, Mincheng He, Shibo Zhang, Yongtao Chen, Jiming Sun, Youxian Liu, Yang-Yu Zhang, Junshan Vincent Poor, H. State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang310027 China Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Center for Cancer Systems Biology Dana-Farber Cancer Institute BostonMA02115 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

Design of Process control System in Water Cooling Processing Interaction Tank using Integral control and Robust control in The Tire Industry

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IOP Conference Series: Materials Science and engineering 2018年第1期384卷

作者： D Handaya P Bakti Y P Nugraha Electronic Engineering Politeknik Gajah Tunggal Jl. Gatot Subroto KM. 7 Tangerang 15135 Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Science Gedung 417 Jl. Kawasan Puspitek Muncul Setu Tangerang Selatan 15314 Indonesia Control System and Computer Laboratory School of Electrical Engineering and Informatics Bandung Institute of Technology Jl. Ganesha no. 10 Bandung 40132 Indonesia

This laboratory based research build a process control system as water level scanner in interaction tank used for water cooling. The control valve parameter based on water level between tanks are interacted. The control method of tank valve ratio choosed in this reasearch is integral control and robust control. The desired control specification for maximum overshoot is 10%, peak time is 1 second, and error steady state is 0. The design process executed in physic system modelling step and system design criteria. Experiment executed using Matlab Script and Matlab Simulink. The simulation result will be compared to see an effective performance for processing of water cooling interaction tank control method. The design result for integral control can tracking reference accord to system design criteria and robust control can tracking reference without different experience either the system without disturbance or the system with disturbance.

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