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Taylor-Zhang Discretization Formula Extended to Time-Varying Four Fundamental Operations with Numerical Experiments

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Taylor-Zhang Discretization Formula Extended to Time-Varying...

Annual Conference of the IEEE Industrial Electronics Society

作者： Yunong Zhang Huihui Gong Jian Li Binbin Qiu Huanchang Huang SYSU-CMU Shunde International Joint Reseach Institute Shunde China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing (SYSU) China School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China

Discrete time-varying problems are frequently encountered in mathematics and engineering fields, such as numerical analysis, signal processing and computer computing. However, conventional algorithms mainly solve time-invariant problems. Employed for discrete time-varying problems solving, conventional algorithms may generate quite large and unacceptable lagging errors. In this paper, discrete time-varying four fundamental operations (DTVFFOs) are studied. In order to eliminate the lagging errors, based on the zeroing dynamics (ZD) and Taylor-Zhang discretization formula, discrete computing models, which are termed T-Z-K and T-Z-U models, are proposed and investigated. Note that the aforementioned models have an error pattern of O(g~3), where g denotes the sampling gap. For comparison, Euler-type discrete models and Newton iteration (NI) models are also presented. Eventually, illustrative numerical experiments are displayed to testify the great performances of the proposed Taylor-Zhang discrete ZD models.

关键词： Numerical models Computational modeling Digital TV Nickel Analytical models Time-varying systems Signal processing algorithms

The medical segmentation decathlon

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal 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 Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification

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arXiv 2017年

作者： Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China School of Electronics and Information Technology Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing NUDT China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised REID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures. Copyright © 2017, The Authors. All rights reserved.

关键词： machine learning

Latent embeddings for collective activity recognition

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arXiv 2017年

作者： Tang, Yongyi Zhang, Peizhen Hu, Jian-Fang Zheng, Wei-Shi School of Electronics and Information Technology Sun Yat-Sen University China School of Data and Computer Science Sun Yat-Sen University China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previous state-of-The-Art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural dependencies among individuals involved in a collective scenario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interactions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble attention mechanism during embedding for achieving more compact representations. We evaluate our method on three collective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-Theart methods in our experiments. Copyright © 2017, The Authors. All rights reserved.

关键词： Embeddings

Structure-preserving image super-resolution via contextualized multi-task learning

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arXiv 2017年

作者： Shi, Yukai Wang, Keze Chen, Chongyu Xu, Li Lin, Liang School of Data and Computer Science Sun Yat-sen University Guangzhou China Dept. of Computing Hong Kong Polytechnic University Hong Kong Sense-Time Group Limited China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China

Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials. Despite the breakthroughs of recently proposed SR methods using convolutional neural networks (CNNs), their generated results usually lack of preserving structural (high-frequency) details. In this paper, regarding global boundary context and residual context as complimentary information for enhancing structural details in image restoration, we develop a contextualized multi-task learning framework to address the SR problem. Specifically, our method first extracts convolutional features from the input LR image and applies one deconvolutional module to interpolate the LR feature maps in a content-adaptive way. Then, the resulting feature maps are fed into two branched sub-networks. During the neural network training, one sub-network outputs salient image boundaries and the HR image, and the other sub-network outputs the local residual map, i.e., the residual difference between the generated HR image and ground-truth image. On several standard benchmarks (i.e., Set5, Set14 and BSD200), our extensive evaluations demonstrate the effectiveness of our SR method on achieving both higher restoration quality and computational efficiency compared with several state-of-the-art SR approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Optical resolving power

Correction to: Emotion recognition based on physiological signals using brain asymmetry index and echo state network

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Neural computing and Applications 2018年第10期32卷 6415-6415页

作者： Ren, Fuji Dong, Yindong Wang, Wei School of Computer and Information Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

The authors quite agree that the AsI model is first developed by Panagiotis Petrantonakis from references [11] and [18] in their thesis.

关键词：

Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad Hoc Networks 12

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Software Defined Networking Based On-Demand Routing Protocol...

12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Dong, Baihong Wu, Weigang Yang, Zhiwei Li, Junjie School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Lab of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509056965

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others. © 2016 IEEE.

关键词： Routing protocols

Point of interest recommendation with social and geographical influence

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Point of interest recommendation with social and geographica...

IEEE International Conference on Big Data

作者： Da-Chuan Zhang Mei Li Chang-Dong Wang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781467390064

Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly.

关键词： Collaboration Social network services History Computational modeling Big data Mobile communication Internet

Test of Lepton Flavor Universality with B+→K+π+π−ℓ+ℓ− Decays

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Physical Review Letters 2025年第18期134卷 181803-181803页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides R. Ataíde Da Silva M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura A. Balboni W. Baldini L. Balzani H. Bao J. Baptista de Souza Leite C. Barbero Pretel M. Barbetti I. R. Barbosa R. J. Barlow M. Barnyakov S. Barsuk W. Barter J. Bartz J. M. Basels S. Bashir G. Bassi B. Batsukh P. B. Battista A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga N. A. Behling S. Belin K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella R. B. Bonacci N. Bondar A. Bordelius F. Borgato S. Borghi M. Borsato J. T. Borsuk E. Bottalico S. A. Bouchiba M. Bovill T. J. V. Bowcock A. Boyer C. Bozzi J. D. Brandenburg A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan L. Buonincontri M. Burgos Marcos A. T. Burke C. Burr J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai A. C. Caillet R. Calabrese S. Calderon Ramirez L. Calefice S. Cali M. Calvi M. Calvo Gomez P. Camargo Magalhaes J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse M Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany Also at Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany. European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Authors affiliated with an institute formerly covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy Also at Università degli Studi di Milano-Bicocca Milano Italy. University of Chinese Academy o

The first test of lepton flavor universality between muons and electrons using B+→K+π+π−ℓ+ℓ− (ℓ=e, μ) decays is presented. The measurement is performed with data from proton-proton collisions collected by the LHCb experiment at center-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 9 fb−1. The ratio of branching fractions between B+→K+π+π−e+e− and B+→K+π+π−μ+μ− decays is measured in the dilepton invariant-mass-squared range 1.1

关键词： Charged particles