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arXiv 2020年

作者： Dobratulin, Konstantin Gaidel, Andrey Aupova, Irina Ivleva, Anna Kapishnikov, Aleksandr Zelter, Pavel Faculty of Informatics Samara National Research University Samara Russia Video Mining Laboratory IPSI RAS Brach of the FSRC"Crystallography and Photonics" RAS Samara Russia Department of Pediatric Dentistry FSBEI HE SamSMU MOH Russia Samara Russia Laboratory of analysis and modeling of complex systems Institute for the Control of Complex Systems of Russian Academy of Sciences Samara Russia Department of Radiation Diagnostics and Radiation Therapy FSBEI HE SamSMU MOH Russia Samara Russia X-ray Department FSBEI HE SamSMU MOH Russia Samara Russia

In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Multimodal transformer with multi-view visual representation for image captioning

arXiv

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arXiv 2019年

作者： Yu, Jun Li, Jing Yu, Zhou Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

Image captioning aims to automatically generate a natural language description of a given image, and most state-ofthe-art models have adopted an encoder-decoder framework. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN)-based caption decoder that generates the output caption words based on the visual features with the attention mechanism. Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intramodal interactions. Inspired by the success of the Transformer model in machine translation, here we extend it to a Multimodal Transformer (MT) model for image captioning. Compared to existing image captioning approaches, the MT model simultaneously captures intra- and inter-modal interactions in a unified attention block. Due to the in-depth modular composition of such attention blocks, the MT model can perform complex multimodal reasoning and output accurate captions. Moreover, to further improve the image captioning performance, multi-view visual features are seamlessly introduced into the MT model. We quantitatively and qualitatively evaluate our approach using the benchmark MSCOCO image captioning dataset and conduct extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method significantly outperforms the previous state-of-the-art methods. With an ensemble of seven models, our solution ranks the 1st place on the real-time leaderboard of the MSCOCO image captioning challenge at the time of the writing of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Theoretical analysis and simulation of phase separation in a driven bidirectional two-lane system

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Physical Review E 2019年第3期100卷 032133-032133页

作者： Qing-Yi Hao Rui Jiang Mao-Bin Hu Yunxin Zhang Chao-Yun Wu Ning Guo Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes School of Mathematics and Computational Science Anqing Normal University Anqing 246133 China School of Mathematical Sciences Fudan University Shanghai 200433 China MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong University Beijing 100044 China School of Engineering Science University of Science and Technology of China Hefei 230026 China School of Automotive and Transportation Engineering Hefei University of Technology Hefei 230009 China

The two-lane driven system is a type of important model to research some transport systems, and also a powerful tool to investigate properties of nonequilibrium state systems. This paper presents a driven bidirectional two-lane model. The dynamic characteristics of the model with periodic boundary are investigated by Monte Carlo simulation, simple mean field, and cluster mean field methods, respectively. By simulations, phase separations are observed in the system with some values of model parameters. When the phase separation does not occur, cluster mean field results are in good agreement with simulation results. According to the cluster mean field analysis and simulations, a conjecture about the condition that the phase separation happens is proposed. Based on the conjecture, the phase boundary distinguishing phase separation state and homogeneous state is determined, and a corresponding phase diagram is drawn. The conjecture is validated through observing directly the spatiotemporal diagram and investigating the coarsening process of the system by simulation, and a possible mechanism causing the phase separation is also discussed. These outcomes maybe contribute to understand deeply transport systems including the congestion and efficiency of the transport, and enrich explorations of nonequilibrium state systems.

关键词： Dynamical phase transitions Phase diagrams Phase separation Phase transitions Transport phenomena

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Reliability Analysis of Manufacturing Machine with Degradation and Low-quality Feedstocks

Reliability Analysis of Manufacturing Machine with Degradati...

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Advanced Reliability and Maintenance modeling (APARM), Asia-Pacific International Symposium on

作者： Zhenggeng Ye Zhiqiang Cai Hui Yang Department of Industrial Engineering Northwestern Polytechnical University Xi’an China School of Mechanical Engineering Northwestern Polytechnical University Xi’an P.R. China Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing Northwestern Polytechnical University Xi’an P.R. China Complex Systems Monitoring Modeling and Control Laboratory in the Harold Pennsylvania State University (University Park) State College PA USA The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering The Pennsylvania State University (University Park) State College USA Department of Industrial and Manufacturing Engineering Pennsylvania State University (University Park) State College PA USA

ISBN: (数字)9781728171029

ISBN: (纸本)9781728171036

Machine reliability is one major concern in manufacturing industries, which is affected by interior degradation and outside shocks simultaneously. Low-quality feedstocks, as one typical kind of shocks, may arrive randomly during the operation of a machine. However, due to the instability of environment and manufacturing factors, low-quality feedstocks may arrive in clusters in some specific batches. In this paper, we first proposed a reliability evaluation model for the repairable machine, accounting for machine degradation and shocks caused by low-quality feedstocks. Then, the cluster arrival of low-quality feedstocks is modeled by the Hawkes point process with the properties of self-exciting and history dependent. Moreover, considering the degradation and shocks, the mixture failure rate of a machine is modeled. Further, the expectation of remaining lifetime is derived. Finally, the simulation experiment is implemented to compare the performance between the machine when the arrival intervals of low-quality feedstocks follow the Poisson point process and the Hawkes point process. Experimental results show the effectiveness of the proposed model for machine reliability analysis.

关键词： Electric shock Degradation Maintenance engineering Mathematical model Manufacturing Reliability engineering

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The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study

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The Lancet Planetary Health 2022年第4期6卷 e350-e358页

作者： Wang, Zengmiao Liu, Yonghong Li, Yapin Wang, Guangze Lourenço, José Kraemer, Moritz He, Qixin Cazelles, Bernard Li, Yidan Wang, Ruixue Gao, Dongqi Li, Yuchun Song, Wenjing Sun, Dingwei Dong, Lu Pybus, Oliver G Stenseth, Nils Chr Tian, Huaiyu State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering College of Life Sciences Beijing Normal University Beijing China School of National Security and Emergency Management Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Hainan Center for Disease Control and Prevention Haikou China Department of Zoology University of Oxford UK Oxford United Kingdom Harvard Medical School Harvard University Boston MA United States Boston Children's Hospital Boston MA United States Department of Ecology and Evolution University of Chicago Chicago IL United States Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche 8197 Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale 209 Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Centre for Ecological and Evolutionary Synthesis Department of Biosciences University of Oslo Oslo Norway Biosystems and Integrative Sciences Institute University of Lisbon Lisbon Portugal Department of Pathobiology and Population Science The Royal Veterinary College UK London United Kingdom

Background: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. Methods: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984–2010 and the WorldClim dataset. A temperature-dependent R₀ model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. Findings: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from –5·6% (95% CI –4·5 to –6·6) to –9·2% (95% CI –7·6 to –10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. Interpretation: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. Funding: S

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Author Correction: Malaria elimination on Hainan Island despite climate change

Communications Medicine

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Communications Medicine 2022年第1期2卷 1页

作者： Huaiyu Tian Naizhe Li Yonghong Liu Yidan Li Ben Wang Peiyi Wu Yapin Li Dongqi Gao Wenjing Song Moritz U. G. Kraemer José Lourenço Oliver G. Pybus Christopher Dye Hua Tan Bernard Cazelles Dingwei Sun Yuchun Li Guangze Wang State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Department of Zoology University of Oxford Oxford UK Department of Pathobiology and Population Science The Royal Veterinary College London UK Oxford Martin School University of Oxford Oxford UK School of Biomedical Informatics the University of Texas Health Science Center at Houston Houston TX USA Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Hainan Center for Disease Control and Prevention Haikou China

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Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

arXiv

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arXiv 2017年

作者： Yu, Jun Shi, Shengjie Gao, Fei Tao, Dacheng Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China UBTech Sydney Artificial Intelligence Institute The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney J12 Cleveland St DarlingtonNSW2008 Australia School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Fréchet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: Https://***/ca-gan/. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Multistability in simplest models of the population dynamics

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AIP Conference Proceedings 2016年第1期1738卷

作者： Oksana L. Zhdanova Efim Ya. Frisman Laboratory of Mathematical Modeling of Ecological Systems Institute for Automation and Control Processes FEB RAS Radio Str. 5 690041 Vladivostok Russia Far Eastern Federal University Suhanova Str. 8 6909450 Vladivostok Russia Institute of Complex Analysis of Regional Systems FEB RAS Sholom Alejkhema Str. 4 679016 Birobidzhan Russia

The investigation of dynamics behavior of population number and genetic structure has been conducted for a homogeneous limited population influenced by density-dependent selection in single di-allelic genetic locus. The detailed investigation of the mechanisms of the loss of stability in the considered model is carried out. It is shown that coexistence of several different asymptotic dynamic regimes (with own attraction basins) is possible in numerous enough parametric regions which are meaningful biologically.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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