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Android library for recognition of activities of daily living: Implementation considerations, challenges, and solutions

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Open Bioinformatics Journal 2018年第1期11卷 61-88页

作者： Pires, Ivan Miguel Teixeira, Maria Canavarro Pombo, Nuno Garcia, Nuno M. Flórez-Revuelta, Francisco Spinsante, Susanna Goleva, Rossitza Zdravevski, Eftim Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal Altran portugal Lisbon Portugal ALLab - Assisted Living Computing and Telecommunications Laboratory Computing Science Department Universidade da Beira Interior Covilhã Portugal UTC de Recursos Naturais e Desenvolvimento Sustentável Polytechnique Institute of Castelo Branco Castelo Branco Portugal CERNAS - Research Centre for Natural Resources Environment and Society Polytechnique Institute of Castelo Branco Castelo Branco Portugal Department of Computer Technology Universidad de Alicante Alicante Spain Department of Information Engineering Marche Polytechnic University Ancona Italy Department of Informatics New Bulgarian University Sofia Bulgaria Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje North Macedonia

Background: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: Thi

关键词： Activities of daily living Android library Artificial neural networks Data fusion Mobile devices Pattern recognition Recognition Sensors

The effect of non-Markovian recovery on reversible failure propagation on networks

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arXiv 2019年

作者： Lin, Zhaohua Feng, Mi Tang, Ming Liu, Zonghua Hui, Pak Ming Lai, Ying-Cheng Department of Physics East China Normal University Shanghai200062 China School of Information Science Technology East China Normal University Shanghai200241 China Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Physics Chinese University of Hong Kong Shatin Hong Kong School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Compared with the classical irreversible failure propagation, the spontaneous recovery model can better depict the failure propagation on network systems such as financial and transportation networks. In this paper, we study how the non-Markovian recovery process influences cascading failure dynamics in the spontaneous recovery systems. To this end, we compare two kinds of failure-recovery models: the non-Markovian recovery (NMR) model and the corresponding Markovian recovery (MR) model. We first develop a Pairwise approximation theory for the MR and NMR models, which can predict the time evolution, steady state, and hysteresis behaviour of failure-recovery systems more accurately than the classical mean-field (MF) method. We find that although the non-Markovian recovery mechanism does not essentially affect the steady state and hysteresis behaviour of failure-recovery systems, the two models exhibit distinct evolution processes. When investigating the effect of initial conditions on the phase transition and hysteresis behavior, we find in the hysteresis region, the NMR model exhibits a non-monotonic growth characteristic: with the increase of initial failed nodes, the steady state staying in relative low-failure phase first turns into relative high-failure phase, and then into relative low-failure phase again. We then use the MF theory to explain the phenomenon qualitatively and point out when considering non-Markovian dynamics, we should study the steady state not only from the perspective of the variation of parameters, but also from the initial conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Hysteresis

An Algorithm for Describing the Convex and Concave Shape of Protein Surface

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An Algorithm for Describing the Convex and Concave Shape of ...

2018国际计算机前沿大会（原国际青年计算机大会）

作者： Wei Wang Keliang Li Hehe Lv Lin Sun Hongjun Zhang Jinling Shi Shiguang Zhang Yun Zhou Yuan Zhao Jingjing Xv Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining School of Aviation Engineering Anyang University School of International Education Xuchang University

Protein surface plays a key role in many biological *** proteins participate in the life activities of cells via binding to other proteins or ligand molecules. It is an important work to study protein structure and function by analyzing the protein surface shape. Based on the CX algorithm and the 2 D fngerprint-base method, we proposed a FCX method to identify the morphology of bulges and depressions on the protein surface. The experimental results show that the FCX algorithm has a more desirable outcome than CX algorithm. The FCX algorithm has a higher correlation with the convex and concave features than CX values with solvent accessibility, solvent accessibility, and Bfactor's Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm.

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Thresholded Quantum LIDAR: Exploiting Photon-Number-Resolving Detection

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Physical Review Letters 2019年第20期123卷 203601-203601页

作者： Lior Cohen Elisha S. Matekole Yoni Sher Daniel Istrati Hagai S. Eisenberg Jonathan P. Dowling Hearne Institute for Theoretical Physics and Department of Physics and Astronomy Louisiana State University Baton Rouge Louisiana 70803 USA School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem 91904 Israel Racah Institue for Physics Hebrew University of Jerusalem Jerusalem 91904 Israel NYU-ECNU Institute of Physics at NYU Shanghai 3663 Zhongshan Road North Shanghai 200062 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo 184-8795 Japan

We present a technique that improves the signal-to-noise-ratio (SNR) of range-finding, sensing, and other light-detection applications. The technique filters out low photon numbers using photon-number-resolving detectors. This technique has no classical analog and cannot be done with classical detectors. We investigate the properties of our technique and show under what conditions the scheme surpasses the classical SNR. Finally, we simulate the operation of a rangefinder, showing improvement with a low number of signal samplings and confirming the theory with a high number of signal samplings.

关键词： Photon statistics Quantum measurements Quantum sensing

A novel data clustering method based on smooth non-negative matrix factorization 1st

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A novel data clustering method based on smooth non-negative ...

1st International Conference on Smart Multimedia, ICSM 2018

作者： Leng, Chengcai Zhang, Hai Cai, Guorong School of Mathematics Northwest University Xi’an710127 China Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada School of Mathematics and Information Sciences Nanchang Hangkong University Nanchang330063 China Faculty of Information Technology State Key Laboratory of Quality Research in Chinese Medicines Macau University of Science and Technology Macau China College of Computer Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9783030043742

Non-negative matrix factorization (NMF) is a very popular dimensionality reduction method that has been widely used in computer vision and data clustering. However, NMF does not consider the intrinsic geometric information of a data set and also does not produce smooth and stable solutions. To resolve these problems, we propose a Graph regularized Lp Smooth Non-negative Matrix Factorization (GSNMF) method by incorporating graph regularization with Lp smooth constraint. The graph regularization can discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. The Lp smooth constraint can combine the merits of isotropic (L2-norm) and anisotropic (L1-norm) diffusion smoothing, and produce a smooth and more accurate solution to the optimization problem. Experimental results on some data sets demonstrate that the proposed method outperforms related state-of-the-art NMF methods. © 2018, Springer Nature Switzerland AG.

关键词： Non-negative matrix factorization

Roadmap on quantum nanotechnologies

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arXiv 2021年

作者： Laucht, Arne Hohls, Frank Ubbelohde, Niels Fernando Gonzalez-Zalba, M. Reilly, David J. Stobbe, Søren Schröder, Tim Scarlino, Pasquale Koski, Jonne V. Dzurak, Andrew Yang, Chih-Hwan Yoneda, Jun Kuemmeth, Ferdinand Bluhm, Hendrik Pla, Jarryd Hill, Charles Salfi, Joe Oiwa, Akira Muhonen, Juha T. Verhagen, Ewold LaHaye, M.D. Kim, Hyun Ho Tsen, Adam W. Culcer, Dimitrie Geresdi, Attila Mol, Jan A. Mohan, Varun Jain, Prashant K. Baugh, Jonathan Centre for Quantum Computation and Communication Technology School of Electrical Engineering and Telecommunications UNSW SydneyNSW2052 Australia Physikalisch-Technische Bundesanstalt Braunschweig38116 Germany Quantum Motion Technologies Nexus Discovery Way LeedsLS2 3AA United Kingdom School of Physics University of Sydney SydneyNSW2006 Australia Microsoft Corporation Station Q Sydney University of Sydney SydneyNSW2006 Australia Department of Photonics Engineering DTU Fotonik Technical University of Denmark Building 343 Kgs. LyngbyDK-2800 Denmark Department of Physics Humboldt-Universität zu Berlin Berlin12489 Germany Department of Physics ETH Zürich ZürichCH-8093 Switzerland Niels Bohr Institute University of Copenhagen Copenhagen2100 Denmark JARA-FIT Institute for Quantum Information RWTH Aachen University and Forschungszentrum Jülich Aachen52074 Germany School of Physics University of Melbourne Melbourne Australia Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka567-0047 Japan Center for Quantum Information and Quantum Biology Institute for open and Transdisciplinary Research Initiative Osaka University Osaka560-8531 Japan Center for Nanophotonics AMOLF Amsterdam1098 XG Netherlands School of Physics The University of New South Wales Sydney2052 Australia Australian Research Council Centre of Excellence in Future Low-Energy Electronics Technologies UNSW Node The University of New South Wales Sydney2052 Australia QuTech and Kavli Institute of Nanoscience Delft University of Technology Delft2600 GA Netherlands School of Physics and Astronomy Queen Mary University of London E1 4NS United Kingdom Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemistry University of Illinois at Urbana-Champaign Urbana

Quantum phenomena are typically observable at length and time scales smaller than those of our everyday experience, often involving individual particles or excitations. The past few decades have seen a revolution in the ability to structure matter at the nanoscale, and experiments at the single particle level have become commonplace. This has opened wide new avenues for exploring and harnessing quantum mechanical effects in condensed matter. These quantum phenomena, in turn, have the potential to revolutionize the way we communicate, compute and probe the nanoscale world. Here, we review developments in key areas of quantum research in light of the nanotechnologies that enable them, with a view to what the future holds. Materials and devices with nanoscale features are used for quantum metrology and sensing, as building blocks for quantum computing, and as sources and detectors for quantum communication. They enable explorations of quantum behaviour and unconventional states in nano- and opto-mechanical systems, low-dimensional systems, molecular devices, nano-plasmonics, quantum electrodynamics, scanning tunnelling microscopy, and more. This rapidly expanding intersection of nanotechnology and quantum science/technology is mutually beneficial to both fields, laying claim to some of the most exciting scientific leaps of the last decade, with more on the horizon. © 2021, CC BY.

关键词： Quantum computers

Variational quantum algorithms

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arXiv 2020年

作者： Cerezo, Marco Arrasmith, Andrew Babbush, Ryan Benjamin, Simon C. Endo, Suguru Fujii, Keisuke McClean, Jarrod R. Mitarai, Kosuke Yuan, Xiao Cincio, Lukasz Coles, Patrick J. Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM United States Quantum Science Center Oak RidgeTN37931 United States Google Quantum AI Team VeniceCA90291 United States Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom NTT Secure Platform Laboratories NTT Corporation Musashino Tokyo180-8585 Japan Graduate School of Engineering Science Osaka University Osaka560-8531 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka University Osaka560-8531 Japan Center for Emergent Matter Science RIKEN Saitama351-0198 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka560-8531 Japan JST PRESTO Saitama332-0012 Japan Center on Frontiers of Computing Studies Department of Computer Science Peking University Beijing100871 China Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States

Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

Eavesdropper's ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

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Physical Review A 2019年第6期99卷 062315-062315页

作者： Poompong Chaiwongkhot Katanya B. Kuntz Yanbao Zhang Anqi Huang Jean-Philippe Bourgoin Shihan Sajeed Norbert Lütkenhaus Thomas Jennewein Vadim Makarov Institute for Quantum Computing University of Waterloo Waterloo ON Canada N2L 3G1 Department of Physics and Astronomy University of Waterloo Waterloo ON Canada N2L 3G1 NTT Basic Research Laboratories NTT Corporation Atsugi Kanagawa 243-0198 Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation Atsugi Kanagawa 243-0198 Japan Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada N2L 3G1 Aegis Quantum Waterloo ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada M5S 3G4 Quantum Information Science Program Canadian Institute for Advanced Research Toronto ON Canada M5G 1Z8 Russian Quantum Center Skolkovo Moscow 143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People's Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum-key-distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air's varying index of refraction. We experimentally investigate the impact turbulence has on Eve's attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve's attack.

关键词： Quantum cryptography