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arXiv 2021年

作者： Xu, Tianzhuang Li, Jing Li, Zhihui Liao, Shijun Center of Advanced Computing School of Naval Architecture Ocean and Civil Engineering Shanghai Jiaotong University Shanghai200240 China School of Physics and Astronomy Shanghai Jiaotong University Shanghai200240 China National Laboratory for Computational Fluid Dynamics Beijing100191 China China Aerodynamics Research and Development Center Mianyang621000 China

It is important to know the accurate trajectory of a free fall object in fluid (such as a spacecraft), whose motion might be chaotic in many cases. However, it is impossible to accurately predict its chaotic trajectory in a long enough duration by traditional numerical algorithms in double precision. In this paper, we give the accurate predictions of the same problem by a new strategy, namely the Clean Numerical Simulation (CNS). Without loss of generality, a free fall disk in water is considered, whose motion is governed by the Andersen-Pesavento-Wang model. We illustrate that convergent and reliable trajectories of a chaotic free fall disk in a long enough interval of time can be obtained by means of the CNS, but different traditional algorithms in double precision give disparate trajectories. Besides, unlike the traditional algorithms in double precision, the CNS can predict the accurate posture of the free fall disk near the vicinity of the bifurcation point of some physical parameters in a long duration. Therefore, the CNS can provide reliable prediction of chaotic systems in a long enough interval of time. © 2021, CC0.

关键词： Trajectories

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Learning dual context aware POI representations for geographic mapping

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International Journal of Applied Earth Observation and Geoinformation 2025年 142卷

作者： Quan Qin Tinghua Ai Shishuo Xu Yan Zhang Weiming Huang Mingyi Du Songnian Li School of Resource and Environmental Sciences Wuhan University Wuhan China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong China College of Computing and Data Science Nanyang Technological University Singapore Department of Civil Engineering Toronto Metropolitan University Toronto Canada

Driven by artificial intelligence technologies, geospatial representation learning has become a new trend to better understand urban systems. Points of Interest (POI), as the current mainstream data in urban studies, plays an important role in these methods to discover urban characteristics. Existing studies on POI representation learning focus on spatial and type information, but overlook heterogeneous semantic interaction between POIs as well as hierarchical associations among types. To tackle these two problems, we propose a novel approach, called POI Dual Context Aware Neural Network (DCA) for learning POI representations by jointly embedding both spatial context and type context. For the spatial context of POIs, we introduce a distance decay effect constrained graph attention network as an encoder of DCA, which takes the heterogeneous semantic interaction and spatial proximity of POIs into account. For the type context of POIs, we propose a type hierarchical aggregation neural network architecture for DCA, and design a type infomax optimization objective following contrastive learning mechanism. The superiority of DCA is demonstrated in three geographic mapping tasks, including urban function mapping, region popularity mapping, and housing price mapping. This study provides a new insight to mine deep information from POIs, contributing to a better understanding of urban systems. The source code is released at http://***/quan-qin/DCA .

关键词： Geospatial representation learning Graph neural network Geographic mapping Point of interest POI embedding

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Teaching Building Sciences in Immersive Environments: A Prototype Design, Implementation, and Assessment

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International Journal of Construction Education and Research 2020年第3期16卷 180-196页

作者： Vassigh, Shahin Davis, Debra Behzadan, Amir H. Mostafavi, Ali Rashid, Khandakar Alhaffar, Hadi Elias, Albert Gallardo, Giovanna Department of Architecture Florida International University Miami FL United States School of Computing & Information Sciences Florida International University Miami FL United States Department of Construction Science Texas AM University College Station TX United States Department of Civil Engineering Texas AM University College Station TX United States Department of Civil & Construction Engineering Oregon State University Corvallis OR United States

Immersive technologies are transforming all aspects of daily life. In educational context, augmented reality (AR) and virtual reality (VR) offer the promise of experiential learning, where the real world is enhanced with information and graphics or is completely simulated. The project described in this paper, builds on advances of these technologies to develop, implement, and test “Skope,” a prototype immersive learning environment. Skope is designed to facilitate interdisciplinary education of architecture, engineering, and construction (AEC) students for design and construction of environmentally sustainable buildings. The Skope learning environment is comprised of an AR and a VR visualization tool as well as learning modules that contain interdisciplinary content. Skope is implemented and tested in three interdisciplinary AEC classes. Quantitative and qualitative analyses of student performance in these classes suggest that Skope has a positive impact and increased students’ motivation and understanding of building science principles, ultimately leading to an improvement of learning across domains. In addition, findings of this research indicate that using Skope improves students’ attitude toward collaborative learning. © 2018 Associated schools of Construction.

关键词： Augmented reality learning assessment pedagogy student learning virtual reality

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Indicators for assessing the sustainable contribution of infrastructure to the mountainous townships 7

Indicators for assessing the sustainable contribution of inf...

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7th International Symposium on Project Management, ISPM 2019

作者： She, Yujuan Zuo, Jian Vivian, Tam W.Y. School of Civil Engineering and Architecture Chongqing University of Science and Technology Chongqing China Chongqing Key Laboratory of Energy Engineering Mechanics and Disaster Prevention and Mitigation Chongqing China School of Architecture and Built Environment University of Adelaide Adelaide Australia School of Computing Engineering and Mathematics Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia

ISBN: (纸本)9781921712692

Infrastructure has significant impacts on economy, society and environment, which is called "sustainable contribution" in this study. However, the infrastructure is poor in Chinese townships especially in mountainous regions as the result of limited funds. There are still many types of infrastructure in townships while impossible to invest at the same time. The degree and angle of contribution are different according to different infrastructure. So, in this study, the most important infrastructures were extracted and a general index system for assessing their sustainable contribution was set up, which will benefit to make more appropriate decision on infrastructure investment with limited financial capacity. © 2019 by Aussino Academic Publishing House.

关键词： Investments

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Generative adversarial networks (GANs) in networking: A comprehensive survey & Evaluation

arXiv

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arXiv 2021年

作者： Navidan, Hojjat Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shah-Mansouri, Vahid Windridge, David School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395-515 Iran Cognitive Telecommunication Research Group Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering Faculty of Technology and Engineering Standard Research Institute Alborz31745-139 Iran School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom Department of Computer Science School of Science and Technology Middlesex University LondonNW4 4BT United Kingdom

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

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Comparison between sound absorption coefficients of resonant membrane panels laminated bamboo tested in reverberant chamber and virtual simulation 23

Comparison between sound absorption coefficients of resonant...

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Barbosa De Sá, Brunno Guilherme Gonçalves De Almeida, Jaime de Ulhôa Carvalho, Maria Luiza Cardoso Boaventura, Rafael Caetano Architecture and Urbanism Course Paulista University Brazil Faculty of Architecture and Urbanism University of Brasília Brazil School of Computing Science and Engineering University of Salford United Kingdom Clave de Sá Architectural Acoustics Brazil

ISBN: (纸本)9783939296157

This work reviews the master's research that evaluated the possibilities of using laminated and glued bamboo panels (abbreviated to BaLC) from the species Dendrocalamus giganteus in the construction of a resonant membrane material for low frequency sound absorption. The devices were acoustically tested with internal air cavities of 100mm, 75mm, and 50mm thickness with and without glass wool filling. The procedure for obtaining the sound absorption coefficient followed the ISO 354: 2007 international standard with a sample of 12m2 of BaLC in a reverberant chamber of 207m3. The results showed the resonance frequency at 100Hz and, consequently, the maximum sound absorption coefficient at the same frequency for all sampling configurations. In the analysis of the sound-absorption coefficients, a variation at 100Hz was observed from 0,387s for empty cavity with 50mm thickness to 0,778s for 50mm cavity filled with glass wool. Furthermore, results were compared with the theoretical prediction in computational modelling to verify if the coefficients obtained in the laboratory coincide with those simulated virtually. This research represents an evidence-based contribution to reveal the benefits of bamboo technology for the built environment given that it can be an accessible resource in tropical countries due to its fast growth and easy adaptation to local soil. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Architectural acoustics Dendrocalamus giganteus Design technologies Resonant membranes Sound absorption coefficient

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

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Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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Crosstalk-aware ga-based wavelength allocation method for ring-based optical network-on-chip 19

Crosstalk-aware ga-based wavelength allocation method for ri...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Chang, Ziyi Li, Hui Chu, Zhuqin Gu, Huaxi Ye, Xiaochun School of Telecommunication Engineering XIDIAN University Xi'an Shaanxi China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450371582

Optical Network-on-Chip (ONoC) is a promising communication architecture which supports multiple communication tasks communicating at the same time at different wavelengths, i.e., Wavelength Division Multiplexing (WDM) technology. However, crosstalk is generated when several signals are transmitting concurrently in a waveguide, which has a negative impact on the Optical Signal-to-Noise Ratio (OSNR) of the optical signal, and cause power fluctuations at the receiver side. In this paper, we propose a wavelength allocation method based on genetic algorithm (GA) to assign wavelength for each communication pair. This method is able to reduce the crosstalk during the process of wavelength allocation. The simulation results show that the OSNR can be significantly improved by using our proposed method. In addition, the method is universal and can be applied to different scale of ring-based ONoC and also other kinds of ONoCs under some proper adaptation. © 2019 Association for computing Machinery.

关键词： Crosstalk

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CASIA-Face-Africa: A large-scale African face image database

arXiv

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arXiv 2021年

作者： Muhammad, Jawad Wang, Yunlong Wang, Caiyong Zhang, Kunbo Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without retraining are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website* Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer’s Disease Diagnosis

arXiv

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arXiv 2023年

作者： Huang, Yunyou Guan, Xianglong Lu, Xiangjiang Liang, Xiaoshuang Miao, Xiuxia Xie, Jiyue Liu, Wenjing Ma, Li Tang, Suqin Zhang, Zhifei Zhan, Jianfeng The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University China The School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin530015 China The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China The State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100086 China The Department of Physiology and Pathophysiology Capital Medical University Beijing100069 China The Guilin Medical University Guilin541001 China The Guangxi Key Lab of Multi-Source Information Mining & Security China

Alzheimer’s disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients’ medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior—a closed-set classification problem, which contrasts with the open-set recognition problem. This assumption hinders the application of the model in natural clinical settings. Although many open-set recognition technologies have been proposed in other fields, they are challenging to use for AD diagnosis directly since 1) AD is a degenerative disease of the nervous system with similar symptoms at each stage, and it is difficult to distinguish from its pre-state, and 2) diversified strategies for AD diagnosis are challenging to model uniformly. In this work, inspired by the concerns of clinicians during diagnosis, we propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first obtains the abnormal pattern of each patient relative to each known category through statistics or a literature search, clusters the patients’ abnormal pattern, and finally, uses extreme value theory (EVT) to model the distance between each patient’s abnormal pattern and the center of their category and modify the classification probability. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results. Copyright © 2023, The Authors. All rights reserved.

关键词： Patient treatment

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