检索结果-内蒙古大学图书馆

MeDaS: An open-source platform as service to help break the walls between medicine and informatics

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhang, Liang Li, Johann Li, Ping Lu, Xiaoyuan Shen, Peiyi Zhu, Guangming Shah, Syed Afaq Bennarmoun, Mohammed Qian, Kun Schuller, Björn W. Embedded Technology & Vision Processing Research Center School of Computer and Technology Xidian University Xian China Shanghai BNC Shanghai China College of Science Health Enginering and Education Murdoch University Australia School of Computer Science and Software Engineering University of Western Australia Australia Educational Physiology Laboratory University of Tokyo Japan GLAM - Group on Language Audio & Music Imperial College London United Kingdom Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing development in applications for advanced medical image analysis in terms of analysis, segmentation, classification, and furthermore. On the one hand, tremendous needs that leverage the power of DL for medical image analysis are arising from the research community of a medical, clinical, and informatics background to jointly share their expertise, knowledge, skills, and experience. On the other hand, barriers between disciplines are on the road for them often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i. e., MEDAS–the MeDical open-source platform as Service. To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side. Based on a series of toolkits and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed MEDAS platform can implement preprocessing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks including the subjects of lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realisable by using MEDAS. Copyright © 2020, The Authors. All rights reserved.

关键词： Clinical research

Constraint monotonicity, epistemic splitting and foundedness are too strong in answer set programming

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Shen, Yi-Dong Eiter, Thomas State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Vienna Institut für Logic and Computation Technische Universität Wien Favoritenstrasse 9-11 ViennaA-1040 Austria

Recently, some researchers [9, 1, 2] introduced the notions of subjective constraint monotonicity, epistemic splitting, and foundedness for epistemic logic programs, aiming to use them as main criteria/intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions. In this note we demonstrate that these three properties are too strong and may exclude some desired answer sets/world views. Therefore, such properties should not be used as necessary conditions for answer set semantics. Copyright © 2020, The Authors. All rights reserved.

关键词： Logic programming

Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

Spatial sparse subspace clustering for compressive spectral imaging

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

Multiobjective optimization differential evolution enhanced with principle component analysis for constrained optimization

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Huang, Wei Xu, Tao He, Jun Li, Kangshun Tianjin Key Laboratory of Intelligent and Novel Software Technology School of Computer Science and Engineering Tianjin University of Technology China Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Multiobjective optimization evolutionary algorithms have been successfully applied to solving constrained optimization problems. This paper proposes a new multiobjective optimization differential evolution algorithm for constrained optimization. Through a study of fitness landscapes using principle component analysis, we discover a statistic method of identifying the valley direction in a valley landscape. Based on this discovery, a new search operator called PCA-projection is constructed which projects an individual to a position along the valley direction. Then multiobjective optimization differential evolution using this projection operator is designed for constrained optimization. A comparative experiment has been implemented between the proposed algorithm and a state-of-the-art multiobjective differential evolution algorithm on a standard set of 24 benchmarks. Experimental results show that the new algorithm makes a significant improvement in terms of solution accuracy. The proposed algorithm is also competitive with ten evolutionary algorithms participated in an IEEE CEC 2006 competition and is ranked third in terms of the final rank. Copyright © 2018, The Authors. All rights reserved.

关键词： Multiobjective optimization

VFlash: Virtualized Flash for Optimizing the I/O Performance in Mobile Devices

学校读者我要写书评

暂无评论

VFlash: Virtualized Flash for Optimizing the I/O Performance...

作者： Chen, Renhai Wang, Yi Hu, Jingtong Liu, Duo Shao, Zili Guan, Yong Beijing Advanced Innovation Center for Imaging Technology Beijing100048 China College of Information Engineering Capital Normal University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74074 United States College of Computer Science Chongqing University Chongqing400044 China Embedded Systems and CPS Laboratory Department of Computing Hong Kong Polytechnic University Hong Kong

I/O is becoming one of major performance bottlenecks in NAND-flash-based mobile devices. Novel nonvolatile memories (NVMs), such as phase change memory and spin-transfer torque random access memory, can provide fast read/write operations. In this paper, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, virtualized flash (vFlash), is also proposed to manage the unified architecture. Within vFlash, interapp and intra-app techniques are proposed to optimize the application performance by exploiting the historical locality and I/O access patterns of applications. Since vFlash is on the bottom of the I/O stack, the application features will be lost. Therefore, we also propose a cross-layer technique to transfer the application information from the application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. © 1982-2012 IEEE.

关键词： Phase change memory

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Price-Whelan, Adrian M. Lim, Pey Lian Earl, Nicholas Starkman, Nathaniel Bradley, Larry Shupe, David L. Patil, Aarya A. Corrales, Lia Brasseur, C.E. Nöthe, Maximilian Donath, Axel Tollerud, Erik Morris, Brett M. Ginsburg, Adam Vaher, Eero Weaver, Benjamin A. Tocknell, James Jamieson, William Van Kerkwijk, Marten H. Robitaille, Thomas P. Merry, Bruce Bachetti, Matteo Aldcroft, Thomas L. Alvarado-Montes, Jaime A. Archibald, Anne M. Bódi, Attila Bapat, Shreyas Barentsen, Geert Bazán, Juanjo Biswas, Manish Boquien, Médéric Burke, D.J. Cara, Daria Cara, Mihai Conroy, Kyle E. Conseil, Simon Craig, Matthew W. Cross, Robert M. Cruz, Kelle L. D'Eugenio, Francesco Dencheva, Nadia Devillepoix, Hadrien A. R. Dietrich, Jörg P. Eigenbrot, Arthur Davis Erben, Thomas Ferreira, Leonardo Foreman-Mackey, Daniel Fox, Ryan Freij, Nabil Garg, Suyog Geda, Robel Glattly, Lauren Gondhalekar, Yash Gordon, Karl D. Grant, David Greenfield, Perry Groener, Austen M. Guest, Steve Gurovich, Sebastian Handberg, Rasmus Hart, Akeem Hatfield-Dodds, Zac Homeier, Derek Hosseinzadeh, Griffin Jenness, Tim Jones, Craig K. Joseph, Prajwel Kalmbach, J. Bryce Karamehmetoglu, Emir Kaluszynski, Mikolaj Kelley, Michael S. P. Kern, Nicholas Kerzendorf, Wolfgang E. Koch, Eric W. Kulumani, Shankar Lee, Antony Ly, Chun Ma, Zhiyuan MacBride, Conor Maljaars, Jakob M. Muna, Demitri Murphy, N.A. Norman, Henrik O'Steen, Richard Oman, Kyle A. Pacifici, Camilla Pascual, Sergio Pascual-Granado, J. Patil, Rohit R. Perren, Gabriel I. Pickering, Timothy E. Rastogi, Tanuj Roulston, Benjamin R. Ryan, Daniel F. Rykoff, Eli S. Sabater, Jose Sakurikar, Parikshit Salgado, Jesús Sanghi, Aniket Saunders, Nicholas Savchenko, Volodymyr Schwardt, Ludwig Seifert-Eckert, Michael Shih, Albert Y. Jain, Anany Shrey Shukla, Gyanendra Sick, Jonathan Simpson, Chris Singanamalla, Sudheesh Singer, Leo P. Singhal, Jaladh Sinha, Manodeep Sipocz, Brigitta M. Spitler, Lee R. Stansby, David Streicher, Ole Šumak, Jani Swinbank, John D. Taranu, Dan S. Tewary, Nikita Tremblay, Grant R. De Val-Borro, Mi Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green St. UrbanaIL61801 United States David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada IPAC MC 100-22 Caltech 1200E. California Blvd. PasadenaCA91125 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada University of Michigan Astronomy 1085 S. University Ann ArborMI48109 United States School of Physics and Astronomy University of St Andrews North Haugh St AndrewsKY16 9SS United Kingdom Astroparticle Physics TU Dortmund Otto-Hahn-Str. 4a Dortmund44227 Germany Center for Astrophysics-Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Center for Space and Habitability University of Bern Gesellschaftsstrasse 6 Bern3012 Switzerland Department of Astronomy University of Florida P.O. Box 112055 GainesvilleFL United States Lund Observatory Department of Astronomy and Theoretical Physics Lund University Box 43 Lund22100 Sweden NSF's National Optical-Infrared Astronomy Research Laboratory 950 North Cherry Avenue TucsonAZ85719 United States School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astronomy Astrophysics and Astrophotonics Research Centre Macquarie University SydneyNSW2109 Australia Australian Astronomical Optics Macquarie University SydneyNSW2109 Australia Aperio Software Ltd. Insight House Riverside Business Park Stoney Common Road Essex StanstedCM24 8PL United Kingdom South African Radio Astronomy Observatory 2 Fir Street Black River Park Observatory7925 South Africa I-09047 Italy San Diego Supercomputer Center University of California San Diego 95

The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project. © 2022, CC BY-SA.

关键词： Python

Multi-modality stacked deep polynomial network based feature learning for Alzheimer's disease diagnosis

学校读者我要写书评

暂无评论

Multi-modality stacked deep polynomial network based feature...

IEEE International Symposium on Biomedical Imaging

作者： Xiao Zheng Jun Shi Yan Li Xiao Liu Qi Zhang School of Communication and Information Engineering Shanghai University Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781479923519

Feature representation is the critical factor for the computer-aided Alzheimer's disease (AD) diagnosis. Deep polynomial network (DPN) is a novel deep learning algorithm, which can effectively learn feature representation from small samples. In this work, a stacked DPN (S-DPN) algorithm is proposed to further improve feature representation. We then propose a multi-modality S-DPN (MM-S-DPN) algorithm to fuse multi-modality neuroimaging data and learn more discriminative and robust feature representation for AD classification. Experiments are performed on ADNI dataset with MRI and PET images as multi-modality data. The results indicate that S-DPN is superior to DPN and stacked auto-encoder algorithms. Moreover, MM-S-DPN achieves best performance compared with single-modality S-DPN and other multi-modality feature learning based algorithms.

关键词： Classification algorithms Magnetic resonance imaging Positron emission tomography Neuroimaging Diseases Training Machine learning

Semantic theories of programs with nested interrupts

学校读者我要写书评

暂无评论

Frontiers of computer science 2015年第3期9卷 331-345页

作者： Yanhong HUANG Jifeng HE Huibiao ZHU Yongxin ZHAO Jianqi SHI Shengchao QIN National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai 200062 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai 200062 China School of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

In the design of dependable software for embed- ded and real-time operating systems, time analysis is a cru- cial but extremely difficult issue, the challenge of which is exacerbated due to the randomness and nondeterminism of interrupt handling behaviors. Thus research into a theory that integrates interrupt behaviors and time analysis seems to be important and challenging. In this paper, we present a pro- gramming language to describe programs with interrupts that is comprised of two essential parts： main program and inter- rupt handling programs. We also explore a timed operational semantics and a denotational semantics to specify the mean- ings of our language. Furthermore, a strategy of deriving de- notational semantics from the timed operational semantics is provided to demonstrate the soundness of our operational se- mantics by showing the consistency between the derived de- notational semantics and the original denotational semantics.

关键词： embedded and real-time operating systems in-terrupts operational semantics denotational semantics semantics linking