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arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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Special Issue on Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷

作者： Jorge Munoz-Gama Niels Martin Carlos Fernandez-Llatas Owen Johnson Marcos Sepúlveda Human & Process Research Lab School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Department of Clinical Sciences Intervention and Technology (CLINTEC) Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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Inland port location planning based on user equilibrium 17

Inland port location planning based on user equilibrium

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Wei, Jingyi Tian, Daxin Rong, Hui Guo, Peng Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Automotive Engineering Research Institute China Automotive Technology and Research Center Tianjin300300 China Quartermaster Equipment Institute of the General Logistics Dept. Of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

With stable development of container transportation in shipping industry, the transit shipment from road to railway of inland ports plays a significant role in multimodal transport. This paper presents the method of inland port location planning based on user equilibrium. In the study, the virtual connection simulating the process of transfer in inland ports is conducted to measure the fitness of inland port location through the cargo volume on virtual connection. At last, the methodology was applied into practical experiment with the results showing that the method can locate the suit inland port location. © ASCE.

关键词： Location

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data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

arXiv

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arXiv 2022年

作者： Nan, Yang Del Ser, Javier Walsh, Simon Schönlieb, Carola Roberts, Michael Selby, Ian Howard, Kit Owen, John Neville, Jon Guiot, Julien Ernst, Benoit Pastor, Ana Alberich-Bayarri, Angel Menzel, Marion I. Walsh, Sean Vos, Wim Flerin, Nina Charbonnier, Jean-Paul van Rikxoort, Eva Chatterjee, Avishek Woodruff, Henry Lambin, Philippe Cerdá-Alberich, Leonor Martí-Bonmatí, Luis Herrera, Francisco Yang, Guang Xing, Xiaodan Li, Ming Wagers, Scott Baker, Rebecca Nardi, Cosimo van Eeckhout, Brice Skipp, Paul Powell, Pippa Carroll, Miles Ruggiero, Alessandro Thillai, Muhunthan Babar, Judith Sala, Evis Murch, William Hiscox, Julian Baralle, Diana Sverzellati, Nicola Blanco, Ana Miguel Bataller, Fuensanta Bellvís Aznar, Mario Suarez, Amelia Figueiras, Sergio Krischak, Katharina Hierath, Monika Mirsky, Yisroel Elovici, Yuval Beregi, Jean Paul Fournier, Laure Sardanelli, Francesco Penzkofer, Tobias Seymour, Karine Blanquer, Nacho Neri, Emanuele Laghi, Andrea França, Manuela Martinez, Ricard National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao48013 Spain Derio48160 Spain Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Data Interchange Standards Consortium AustinTX United States Respiratory medicine department Liège Belgium University of Liege Department of Clinical Sciences Pneumology-Allergology Liège Belgium QUIBIM Valencia Spain Technische Hochschule Ingolstadt Ingolstadt Germany GE Healthcare GmbH Munich Germany Liège Belgium Thirona Nijmegen Netherlands Department of Precision Medicine Maastricht University Maastricht Netherlands Medical Imaging Department Hospital Universitari i Politècnic La Fe Valencia Spain University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia National Heart and Lung Institute Imperial College London London United Kingdom BioSci Consulting Maasmechelen Belgium Clinical Data Interchange Standards Consortium AustinTX United States University of Florence Firenze Italy Medical Cloud Company Liège Belgium TopMD Southampton United Kingdom European Lung Foundation Sheffield United Kingdom Department of Health Public Health England London United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Owlstone Medical Cambridge United Kingdom University of Liverpool Liverpool United Kingdom University of Southampton Southampton United Kingdom University of Parma Parma Italy Medical Imaging Department Hospital Universi

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research. © 2022, CC BY-NC-ND.

关键词： Health care

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τ-FPL: Tolerance-constrained learning in linear time

arXiv

引用

arXiv 2018年

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research - China Shanghai China College of Computing Georgia Institute of Technology AtlantaGA United States

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, τ -False Positive Learning (τ - FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ -FPL over existing approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Mathematical transformations

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First principles calculations of superconducting critical temperature of ThCr2Si2-type structure

arXiv

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arXiv 2019年

作者： Sinaga, Gewinner Senderanto Utimula, Keishu Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

High critical temperature (Tc) superconductor has a great potential in many industrial applications. However, discovering a compound having high Tc is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (Tc) of several compounds of ThCr2Si2-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (Tc) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from NIMS database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu2Si2 and ThAu2Si2 which were most likely to exhibit a superconductivity around 3.88 K and 4.27. Since, these compounds have not been studied experimentally, then we futher investigated their electronic properties showing non-negative phonons which indicate the dynamical stability of the compounds at ambient condition. Band structure and DOS analysis showed metallic nature where the highest contributor come from d-orbital of rare-earth element and p-orbital of Si emerging a mixture of covalent, ionic and metallic bonds exists in both the compounds. Finally, we calculated the electron phonon coupling coefficient showing a quite large different (27%) leading to the increment of Tc, however, both of compounds possess three dimensional electron-like and hole-like multi-band fermi surface which is related to the phonon mediated which is still under electron-phonon mediated superconductivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

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Preface

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application 2021年 iii页

作者： Ning, Xiansheng Feng, Yongxin Zhang, Wenbo Yue, Mingkai Du, Ke-Lin Huang, Shutao Zhou, Fang Ba, Shuhong Fu, Chong Shui, Penglang Xu, Shuwen Wu, Sheng Wang, Shushan Du, Zhonghua Gao, Zhijun Fan, Chunlong Li, Yufeng Wang, Yupeng Liu, Qingli Yu, Jingang Yin, Zhenyu Zhang, Deyu Gao, Hongwei Wen, Feng Guo, Cean Hao, Yongping Chan, Yung-Kuan Pavlovich, Vinogradov Gennady Kirpichnikova, Irina Qian, Zhihong Ivanov, Dmitriy V. Zhou, Xueguan Nezamabadi-Pour, Hossein Yang, Qing Zhang, Yongtang Liao, Haibin Huang, Ying Srinivasan, Kathiravan Xu, Zhenghua Pachori, Ram Bilas Zhang, Jialu AlShawabkeh, Mahmoud Guo, Chengjun Chaugule, Archana Ajit Lima, Alberto Sampaio Liu, Huayong Vidal, Jorge Maestre Alrshah, Mohamed A. Ghafoor, Kayhan Zrar Kubicek, Jan Chaugule, Archana Ajit Rodríguez-Pérez, Miguel Kumar, Gulshan Nasri, Mehdi Borrego, Carlos Persico, Valerio Wagh, Sanjeev Yan, Wenjun Yang, Li Yang, Dawei Xing, Yongkang Yang, Huadong Shang, Chengguo Chang, Xingong Shi, Hongbo Li, Aijun Feng, Liping Lv, Yali Liang, Minfu Wang, Kaixuan Bai, Zengliang Liang, Anhui Nan, Zhihong Ji, Suqin Chang, Liwei Zhou, Hongbin Yao, Chunli Cao, Guodong Zhang, Juling School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Concordia University Canada School of Automotive and Transportation Shenyang Ligong University China School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Northeastern University China Xidian University China Beijing University of Posts and Telecommunications China Beijing Institute of Technology China Nanjing University of Science & Technology China Shenyang Jianzhu University China Shenyang Aerospace University China Dalian University China Shenyang Institute of Computing Technology Chinese Academy of Sciences China Shenyang Ligong University China National Chun Hsing University Taiwan Department of Informatics and Applied Mathematics Russia South Ural State University Russia Jilin University China Samara State University of Transport Russia Naval Universit of Engineering China Shahid Bahonar University of Kermanto Iran Central China Normal University China Guangdong Neusoft Institute China Hubei University of Science and Technology China Liuzhou Railway Vocational Technical College China Vellore Institute of Technology India Hebei University of Technology China Indian Institute of Technology Indore India Xiangnan University China Guangxi Normal University for Nationalities China UESTC China Pimpri-Chinchwad College of Engineering & Research India Federal University of Ceará Brazil Complutense University of Madrid Spain Universiti Putra Malaysia Malaysia Shanghai Jiao Tong University China VŠB - Technical University of Ostrava Cuw Pimpri-Chinchwad College of Engineering & Research Ravet Pune India University of Vigo Spain Division of Research and Development India Islamic Azad University Iran Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Opportunistic Networks United States Univers

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Forecasting transportation network speed using deep capsule networks with nested LSTM models

arXiv

引用

arXiv 2018年

作者： Ma, Xiaolei Li, Yi Cui, Zhiyong Wang, Yinhai School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Civil and Environmental Engineering University of Washington Seattle98195 United States

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Comparison of UAV-based multispectral sensors for detection of Solenopsis invicta Nests

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IOP Conference Series: Earth and Environmental Science 2020年第1期569卷

作者： Yuejun Song Feng Chen Kaitao Liao Jiangxi Institute of Soil and Water Conservation Nanchang 330029 China Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang 330029 China College of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen 361005 China

The invasive red imported fire ant (Solenopsis invicta Buren) (Hymenoptera: Formicidae) has been continuing to expand its range in China, resulting in adverse ecological impacts to where it has invaded. As such, the reliable detection of Solenopsis invicta Buren nests is needed to effectively manage the species. In this paper, the spectral features of the Solenopsis invicta Buren nests were demonstrated using spectra samples obtained by ASD FieldSpec 4 during field surveys in Shanggao County, Jiangxi Province. Furthermore, channel reflectance estimations for three unmanned aerial vehicle (UAV)-based multispectral sensors were simulated correspondingly. Based on the channel reflectance from individual sensors, the detection of the Solenopsis invicta Buren nests was investigated. Generally, between-class differences in reflectance were relatively significant in both Green channel and Red channel, which suggests the importance of these two channels for the detection of Solenopsis invicta Buren nest. Minor between-sensor differences observed in both Green channel and Red channel suggest that detection results obtained from UAV-based multispectral observations could be comparable among the three sensors, considering only the spectral features. There are significant differences in reflectance between the soil of Solenopsis invicta Buren nests and ordinary soil over the longwave region of near infrared (NIR). It suggests the spectral measurement over the longwave region of NIR could be more useful in distinguishing the soil of Solenopsis invicta Buren nests from ordinary soil. Nevertheless, the multispectral sensors provided with channels covering visible and the shortwave region of NIR, possibly do not meet requirements completely for application. An effective method for detecting Solenopsis invicta Buren nests using UAV-based multispectral sensors is currently being evaluated.

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Chemically intuited,large-scale screening of MOFs by machine learning techniques

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npj Computational Materials 2017年第1期3卷 123-129页

作者： Giorgos Borboudakis Taxiarchis Stergiannakos Maria Frysali Emmanuel Klontzas Ioannis Tsamardinos George E.Froudakis Department of Computer Science University of CreteVoutes CampusGR-70013 HeraklionCreteGreece Gnosis Data Analysis PC Palaiokapa 6571305 HeraklionGreece Department of Chemistry University of CreteVoutes CampusGR-70013 HeraklionCreteGreece School of Computing and Engineering University of HuddersfieldQueensgateHuddersfieldHD13DHUK

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning *** approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches,strategically combined with chemical *** results demonstrate that the chemical properties of MOFs are indeed predictable(stochastically,not deterministically)using machine learning methods and automated analysis protocols,with the accuracy of predictions increasing with sample *** initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

关键词： apply stochastic learning

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