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1st International Conference on Medical engineering, Health Informatics and Technology, MediTec 2016

作者： Islam, Md. Ariful Alvi, Hassan Nomani Mamun, Khondaker Abdullah Al Advanced Intelligent Multidisciplinary Systems Lab Department of Computer Science and Engineering United International University Dhaka Bangladesh

ISBN: (纸本)9781509054213

Worldwide the number of diabetes patient is increasing and the rate is alarming in countries with low and middle income. Medication of a diabetes patient still costly which leads to high mortality in developing and low income countries. Considering the fact that diabetes is a non-curable disease, prevention and management is a big deal. However, proper lifestyle management requires awareness, knowledge and care. Information and Communication technology has made life easier as information can be found more easily. Smartphone based applications are also becoming more popular as prices are lowering every day. Sharing knowledge, creating awareness, disease management and even diagnosis is possible using smartphone applications. In this article we discussed possible solution of diabetes management and prevention through mobile application and also presented our intelligent application, DiaHealth for diabetes management and prevention. To do this, we added recent trends of diabetes based mobile application and compared DiaHealth with other popular applications. Then detailed feature is given in a structured manner. With the technological advancement, we believe DiaHealth will shed light to diabetes patients to manage their health and life style, consequently it will prevent health consequence and reduce healthcare cost. © 2016 IEEE.

关键词： Smartphones

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Kramers nodal lines in intercalated TaS2 superconductors

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Nature communications 2025年第1期16卷 4984页

作者： Yichen Zhang Yuxiang Gao Aki Pulkkinen Xingyao Guo Jianwei Huang Yucheng Guo Ziqin Yue Ji Seop Oh Alex Moon Mohamed Oudah Xue-Jian Gao Alberto Marmodoro Alexei Fedorov Sung-Kwan Mo Makoto Hashimoto Donghui Lu Anil Rajapitamahuni Elio Vescovo Junichiro Kono Alannah M Hallas Robert J Birgeneau Luis Balicas Ján Minár Pavan Hosur Kam Tuen Law Emilia Morosan Ming Yi Department of Physics and Astronomy Rice University Houston TX 77005 USA. New Technologies Research Center University of West Bohemia Plzen 301 00 Czech Republic. Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong China. Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX 77005 USA. Department of Physics University of California Berkeley Berkeley CA 94720 USA. National High Magnetic Field Laboratory Tallahassee Tallahassee FL 32310 USA. Physics Department Florida State University Tallahassee FL 32306 USA. Stewart Blusson Quantum Matter Institute University of British Columbia Vancouver Vancouver BC V6T 1Z4 Canada. Advanced Light Source Lawrence Berkeley National Laboratory Berkeley Berkeley CA 94720 USA. Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA. National Synchrotron Light Source II Brookhaven National Lab Upton NY 11973 USA. Rice Center for Quantum Materials Rice University Houston TX 77005 USA. Department of Electrical and Computer Engineering Rice University Houston TX 77005 USA. Department of Materials Science and NanoEngineering Rice University Houston TX 77005 USA. Smalley-Curl Institute Rice University Houston TX 77005 USA. Department of Physics & Astronomy University of British Columbia Vancouver Vancouver BC V6T 1Z1 Canada. Canadian Institute for Advanced Research Toronto Toronto ON M5G 1M1 Canada. Materials Science Division Lawrence Berkeley National Laboratory Berkeley Berkeley CA 94720 USA. Department of Physics and Texas Center for Superconductivity University of Houston Houston TX 77204 USA. Department of Physics and Astronomy Rice University Houston TX 77005 USA. emorosan@rice.edu. Rice Center for Quantum Materials Rice University Houston TX 77005 USA. emorosan@rice.edu. Smalley-Curl Institute Rice University Houston TX 77005 USA.

Kramers degeneracy is one fundamental embodiment of the quantum mechanical nature of particles with half-integer spin under time reversal symmetry. Under the chiral and noncentrosymmetric achiral crystalline symmetries, Kramers degeneracy emerges respectively as topological quasiparticles of Weyl fermions and Kramers nodal lines (KNLs), anchoring the Berry phase-related physics of electrons. However, an experimental demonstration for ideal KNLs well isolated at the Fermi level is lacking. Here, we establish a class of noncentrosymmetric achiral intercalated transition metal dichalcogenide superconductors with large Ising-type spin-orbit coupling, represented by InTaS, to host an ideal KNL phase. We provide evidence from angle-resolved photoemission spectroscopy with spin resolution, angle-dependent quantum oscillation measurements, and ab-initio calculations. Our work not only provides a realistic platform for realizing and tuning KNLs in layered materials, but also paves the way for exploring the interplay between KNLs and superconductivity, as well as applications pertaining to spintronics, valleytronics, and nonlinear transport.

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781509046331

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： Mobile robots

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OpenMP and POSIX threads implementation of Jerasure 2.0

OpenMP and POSIX threads implementation of Jerasure 2.0

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Suayb S. Arslan Hoa Le Joseph Landman Turguy Goker Faculty of Computer Engineering MEF University Maslak Istanbul Turkey Advanced Development Lab. Quantum Corporation Irvine CA USA Joyent Inc. San Francisco CA USA

ISBN: (纸本)9781509050505;9781509050499

In shared memory multiprocessor architectures, threads can be used to implement parallelism. POSIX threads (pthreads) is a low-level bare-bones programming interface for working with threads. Therefore, we have extremely fine-grained control over thread management (create/join/etc), mutexes, and so on. On the other hand, openMP, as a shared-memory standard, is much higher level and portable interface which makes it easier to use multi-threading capability and obtain satisfactory performance improvements. Since pthreads is more flexible, it helps programmers gain more control on performance optimizations. Jerasure 2.0 erasure coding library has encoding/decoding engines which comprise independent “for” loop iterations and hence possess huge potential for multi-threaded processing. In this short paper, we investigate multi-threaded implementations of encoder/decoder pair of Jerasure 2.0 using two different technologies: OpenMP and pthreads. We constrain our changes to a minimum possible and compare the pure encoding/decoding performance with respect to each other as well as against that of the original single-threaded version by running them on two different server systems.

关键词： Encoding Instruction sets Decoding Strips Libraries Mathematical model Parallel processing

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Evolving boxes for fast vehicle detection

arXiv

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arXiv 2017年

作者： Wang, Li Lu, Yao Wang, Hong Zheng, Yingbin Ye, Hao Xue, Xiangyang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China School of Computer Science and Engineering University of Washington Seattle United States

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions;a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Multifunctional Optoelectronic Devices: Multifunctional Optoelectronic Device Based on an Asymmetric Active Layer Structure (Adv. Funct. Mater. 17/2019)

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advanced Functional Materials 2019年第17期29卷

作者： Beitao Ren Gancheong Yuen Sunbin Deng Le Jiang Dingjian Zhou Leilei Gu Ping Xu Meng Zhang Zhiyong Fan Fion Sze Yan Yueng Rongsheng Chen Hoi‐Sing Kwok Guijun Li College of Electronic Science and Technology Shenzhen University Shenzhen 518060 P. R. China State Key Lab of Advanced Displays and Optoelectronics Technologies Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Electronic and Information Engineering South China University of Technology Guangzhou 510640 P. R. China

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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

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Ultra-thin high-dichroic-ratio polarizer generated by photoalignment 54

Ultra-thin high-dichroic-ratio polarizer generated by photoa...

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54th Annual SID Symposium, Seminar, and Exhibition 2016, Display Week 2016

作者： Pan, Su Ho, Jacob Y. Chigrinov, Vladimir G. Kwok, Hoi-Sing State Key Lab on Advanced Displays and Optoelectronics Department of Electronic and Computer Engineering Hong Kong University of Science & Technology Hong Kong

ISBN: (纸本)9781510832909

A dichroic polarizer with a high dichroic ratio of up to 60 is achieved by photoalignment induced anisotropic azo dye ordering. The film thickness is well controlled in 200 nm, and it is 500 times thinner than a conventional iodine Polaroid polarizer, which is desirable for ultra-thin high performance liquid crystal display applications. © 2016, Blackwell Publishing Ltd. All rights reserved.

关键词： Azo dyes

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TensorFlow quantum: A software framework for quantum machine learning

arXiv

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arXiv 2020年

作者： Broughton, Michael Verdon, Guillaume McCourt, Trevor Martinez, Antonio J. Yoo, Jae Hyeon Isakov, Sergei V. Massey, Philip Halavati, Ramin Niu, Murphy Yuezhen Zlokapa, Alexander Peters, Evan Lockwood, Owen Skolik, Andrea Jerbi, Sofiene Dunjko, Vedran Leib, Martin Streif, Michael Von Dollen, David Chen, Hongxiang Cao, Shuxiang Wiersema, Roeland Huang, Hsin-Yuan McClean, Jarrod R. Babbush, Ryan Boixo, Sergio Bacon, Dave Ho, Alan K. Neven, Hartmut Mohseni, Masoud Google Quantum Ai Mountain ViewCA United States Sandbox Alphabet Mountain ViewCA United States Google Mountain ViewCA United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Division of Physics Mathematics and Astronomy Caltech PasadenaCA91125 United States Fermi National Accelerator Laboratory P.O. Box 500 BataviaIL605010 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Data:Lab Volkswagen Group Ungererstr. 69 München80805 Germany Leiden University Niels Bohrweg 1 Leiden2333 CA Netherlands Institute for Theoretical Physics University of Innsbruck Technikerstr. 21a InnsbruckA-6020 Austria Institute of Theoretical Physics Staudtstr. 7 Erlangen91058 Germany Volkswagen Group Advanced Technologies San FranciscoCA94108 United States Rahko Ltd. Finsbury Park LondonN4 3JP United Kingdom Department of Computer Science University College London WC1E 6BT United Kingdom Clarendon Laboratory Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Vector Institute MaRS Centre TorontoONM5G 1M1 Canada Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada Institute for Quantum Information and Matter Caltech PasadenaCA United States Department of Computing and Mathematical Sciences Caltech PasadenaCA United States

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

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Evolving boxes for fast vehicle detection

Evolving boxes for fast vehicle detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li Wang Yao Lu Hong Wang Yingbin Zheng Hao Ye Xiangyang Xue Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai China School of Computer Science and Engineering University of Washington Seattle USA

ISBN: (纸本)9781509060689

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.

关键词： Proposals Vehicle detection Machine learning Training Detectors Object detection Surveillance

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