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arXiv 2017年

作者： Pryce, John D. Nedialkov, Nedialko S. Tan, Guangning Li, Xiao Cardiff University School of Mathematics Senghennydd Rd CardiffCF24 4AG United Kingdom McMaster University Department of Computing & Software HamiltonL8S 4K1 Canada Massachusetts Institute of Technology Process Systems Engineering Laboratory Boston United States McMaster University School of Computational Science and Engineering HamiltonL8S 4K1 Canada

A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of so called index reduction or regularisation, to prepare them for numerical solution. This is often done with the help of a computer algebra system. We show in two significant cases that it can be done efficiently by pure algorithmic differentiation. The first is the Dummy Derivatives method;here we give a mainly theoretical description, with tutorial examples. The second is the solution of a mechanical system directly from its Lagrangian formulation. Here we outline the theory and show several non-trivial examples of using the "Lagrangian facility" of the Nedialkov-Pryce initial-value solver DAETS, namely: a spring-mass-multipendulum system;a prescribed-trajectory control problem;and long-time integration of a model of the outer planets of the solar system, taken from the DETEST testing package for ODE solvers. Copyright © 2017, The Authors. All rights reserved.

关键词： Differential equations

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

引用

arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Data collection in open ended learning environment for learning analytics

Data collection in open ended learning environment for learn...

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24th International Conference on computers in Education - Workshop, ICCE 2016

作者： Tscholl, Michael Rajendran, Ramkumar Biswas, Gautam Goldberg, Benjamin S. Sottilare, Robert A. Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 United States U.S. Army Research Laboratory - Human Research and Engineering Simulation and Training Technology Center OrlandoFL32826 United States

ISBN: (纸本)9789869401210

As the development and use of Open-Ended Learning Environments (OELEs) continues to increase, supporting students' learning in these environments with Intelligent Tutoring is rapidly becoming an important area of research. Many existing learning environments guide students in step-by-step processes to reach their learning goal;consequently, data preprocessing is well defined. In OELEs, in contrast, students may achieve task goals through multiple pathways, and there exist multiple ways to assess performance. We present a simulation OELE designed to teach students decision-making in a complex problem solving task. To provide Intelligent Tutoring Support, we are required to track performance along several dimensions. We present our approach to extract data for performance assessments that can be leveraged to provide Intelligent Tutoring Support. We generalize our approach and present guidelines applicable for similar OELEs. Copyright 2016 Asia-Pacific Society for computers in Education. All rights reserved.

关键词： Students

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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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Corrigendum to: More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis

引用

Chemical senses 2021年第46期46卷 bjab050页

作者： Valentina Parma Kathrin Ohla Maria G Veldhuizen Masha Y Niv Christine E Kelly Alyssa J Bakke Keiland W Cooper Cédric Bouysset Nicola Pirastu Michele Dibattista Rishemjit Kaur Marco Tullio Liuzza Marta Y Pepino Veronika Schöpf Veronica Pereda-Loth Shannon B Olsson Richard C Gerkin Paloma Rohlfs Domínguez Javier Albayay Michael C Farruggia Surabhi Bhutani Alexander W Fjaeldstad Ritesh Kumar Anna Menini Moustafa Bensafi Mari Sandell Iordanis Konstantinidis Antonella Di Pizio Federica Genovese Lina Öztürk Thierry Thomas-Danguin Johannes Frasnelli Sanne Boesveldt Özlem Saatci Luis R Saraiva Cailu Lin Jérôme Golebiowski Liang-Dar Hwang Mehmet Hakan Ozdener Maria Dolors Guàrdia Christophe Laudamiel Marina Ritchie Jan Havlícek Denis Pierron Eugeni Roura Marta Navarro Alissa A Nolden Juyun Lim Katherine L Whitcroft Lauren R Colquitt Camille Ferdenzi Evelyn V Brindha Aytug Altundag Alberto Macchi Alexia Nunez-Parra Zara M Patel Sébastien Fiorucci Carl M Philpott Barry C Smith Johan N Lundström Carla Mucignat Jane K Parker Mirjam van den Brink Michael Schmuker Florian Ph S Fischmeister Thomas Heinbockel Vonnie D C Shields Farhoud Faraji Enrique Santamaría William E A Fredborg Gabriella Morini Jonas K Olofsson Maryam Jalessi Noam Karni Anna D'Errico Rafieh Alizadeh Robert Pellegrino Pablo Meyer Caroline Huart Ben Chen Graciela M Soler Mohammed K Alwashahi Antje Welge-Lüssen Jessica Freiherr Jasper H B de Groot Hadar Klein Masako Okamoto Preet Bano Singh Julien W Hsieh Danielle R Reed Thomas Hummel Steven D Munger John E Hayes Department of Psychology Temple University Philadelphia PA USA. Institute of Neuroscience and Medicine (INM-3) Research Center Jülich Wilhelm-Johnen-Straße Jülich Germany. Department of Anatomy Faculty of Medicine Mersin University Çiftlikköy Campus Yenişehir Mersin Turkey. Institute of Biochemistry Food Science and Nutrition The Hebrew University of Jerusalem Rehovot Israel. AbScent Andover Hampshire UK. Department of Food Science The Pennsylvania State University Erickson Food Science Building University Park PA USA. Center for the Neurobiology of Learning and Memory University of California and Qureshey Research Laboratory Irvine CA USA. Institut de Chimie de Nice UMR CNRS 7272 Université Côte d'Azur Avenue Valrose Nice France. Centre for Global Health Research Usher Institute The University of Edinburgh Old Medical School Teviot Place Edinburgh UK. Department of Basic Medical Sciences Neuroscience and Sensory Organs Università degli Studi di Bari A. Moro P.zza G. Cesare Bari Italy. CSIR-Central Scientific Instruments Organisation Chandigarh India. Department of Medical and Surgical Sciences "Magna Graecia" University of Catanzaro Viale Europa (Loc. Germaneto) Catanzaro Italy. Department of Food Science and Human Nutrition and Division of Nutritional Sciences University of Illinois at Urbana Champaign Urbana IL USA. Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Währinger Gürtel Vienna Austria. Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthese UMR 5288 CNRS Universitéde Toulouse Toulouse France. National Centre for Biological Sciences Tata Institute of Fundamental Research GKVK Campus Bengaluru India. School of Life Sciences Arizona State University Tempe AZ USA. Department of Psychology and Anthropology University of Extremadura Avenida de la Universidad s/n Cáceres Spain. Department of General Psychology University of Padova Via Venezia Padova Italy. Department of Psychiatry

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OpenMP is Not as Easy as It Appears

OpenMP is Not as Easy as It Appears

引用

Hawaii International Conference on System sciences

作者： Rogerio Goncalves Marcos Amaris Thiago Okada Pedro Bruel Alfredo Goldman Department of Computer Science - DACOM Federal Technological University of Parana - UTFPR Software Systems Laboratory - LSS Institute of Mathematics and Statistics - IME University of Sao Paulo - USP

ISBN: (纸本)9781509019816

This paper aims to show that knowing the core concepts related to a given parallel architecture is necessary to write correct code, regardless of the parallel programming paradigm used. Programmers unaware of architecture concepts, such as beginners and students, often write parallel code that is slower than their sequential versions. It is also easy to write code that produces incorrect answers under specific conditions, which are hard to detect and correct. The increasing popularization of multi-core architectures motivates the implementation of parallel programming frameworks and tools, such as OpenMP, that aim to lower the difficulty of parallel programming. OpenMP uses compilation directives, or pragmas, to reduce the number of lines that the programmer needs to write. However, the programmer still has to know when and how to use each of these directives. The documentation and available tutorials for OpenMP give the idea that using compilation directives for parallel programming is easy. In this paper we show that this is not always the case by analysing a set of corrections of OpenMP programs made by students of a graduate course in Parallel and Distributed Computing, at University of Sao Paulo. Several incorrect examples of OpenMP pragmas were found in tutorials and official documents available in the Internet. The idea that OpenMP is easy to use can lead to superficial efforts in teaching fundamental parallel programming concepts. This can in its turn lead to code that does not develop the full potential of OpenMP, and could also crash inexplicably due to very specific and hard-to-detect conditions. Our main contribution is showing how important it is to teach core architecture and parallel programming concepts properly, even when you have powerful tools such as OpenMP available.

关键词： Parallel programming core construction computer personnel Tutorials Standing Orders Collected papers Codes Distributed computing Device Programmer Component

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Ultra Wideband Slot-loaded, Dielectric-filled Discone Antenna for WLAN Applications

Ultra Wideband Slot-loaded, Dielectric-filled Discone Antenn...

引用

European Conference on Antennas and Propagation

作者： D. Tran M. Wang A. Yarovoy Microwave Sensing Signals and Systems Laboratory Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 The Netherlands

ISBN: (纸本)9781509022342

Novel antenna design for WLAN applications comply IEEE 802.11 a/b/g/n protocol is proposed. The design is based on a dielectric filled discone antenna. The antenna is characterized by omnidirectional radiation patterns in the azimuthal plane and similar to each other radiation patterns at both WLAN operational bands at 2.4GHz and 5.5GHz in the elevation plane in the angular sector from 40° to 60°.

关键词： UWB WLAN dielectric filled discone antenna omni-directional slot-loadding wireless local area network (WLAN) Ultra wideband technologies Radiation patterns ANTENNA antennae antenna design

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Variable selection in identification of a high dimensional nonlinear non-parametric system

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Control Theory and Technology 2015年第1期13卷 1-16页

作者： Er-Wei BAI Wenxiao ZHAO Weixing ZHENG Department of Electrical and Computer Engineering University of Iowa School of Electronics Electrical Engineering and Computer Science Queen's UniversityBelfast U.K. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences School of Computing Engineering and Mathematics University of Western Sydney

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

关键词： System identification variable selection nonlinear non-parametric system curse of dimensionality

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Quantum state and process tomography via adaptive measurements

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science China(Physics,Mechanics & Astronomy) 2016年第10期59卷 25-32页

作者： HengYan Wang WenQiang Zheng NengKun Yu KeRen Li DaWei Lu Tao Xin Carson Li ZhengFeng Ji David Kribs Bei Zeng XinHua Peng JiangFeng Du Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Department of Applied Physics Zhejiang University of TechnologyHangzhou 310023China Institute for Quantum Computing University of WaterlooWaterloo N2L 3G1Canada 4Department of Mathematics&Statistics University of GuelphGuelph N1G 2W1Canada Centre for Quantum Computation&Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney NSW 2007Australia State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Department of Physics University of GuelphGuelph N1G 2W1Canada State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Canadian Institute for Advanced Research Toronto M5G 1Z8Canada

We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.

关键词： quantum tomography state reconstruction nuclear magnetic resonance

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A separability-entanglement classifier via machine learning

arXiv

引用

arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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