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X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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BEGIN:VTIMEZONE
TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:CST
DTSTART:20180101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181206T110000
DTEND;TZID=Asia/Macau:20181206T120000
DTSTAMP:20260523T060130
CREATED:20181206T024425Z
LAST-MODIFIED:20220927T043400Z
UID:6490-1544094000-1544097600@www.fst.um.edu.mo
SUMMARY:Probability Distribution Modelling of Data and Its Applications in Structural Health Monitoring
DESCRIPTION:Instructors/Speakers\nProf. He-Qing MU\nAssociate Professor\nSchool of Civil Engineering and Transportation\nSouth China University of Technology\nGuangzhou\nChina \nAbstract\nProbability distribution modelling of data is an important task in science and engineering. This research attempts to conduct probability distribution modelling of univariate and multivariate data. The first part introduces the theories of the traditional Bayesian inference and the Bayesian Network for probability distribution modelling. The second part introduced the structural health monitoring system of the Xinguang Bridge\, which is a three-span half-through arch bridge with the mid span of 428 m\, two side spans of 177 m each\, and width of 37.62 m\, over the Pearl River of Guangzhou City of China. The third part is devoted to the probability distribution modelling of the traffic load effect data and modal frequency–multiple environmental factor data of the Xinguang Bridge. \nBiography\nProf. He-Qing MU is an Associate Professor of Civil Engineering Department of South China University of Technology. He serves as the corresponding member of the Engineering Practice of Risk Assessment and Management Committee of the International Society of Soil Mechanics and Geotechnical Engineering\, the Member of Youth Committee of Structural Control & Health Monitoring\, Youth Committee of Random Vibration\, of Chinese Society for Vibration Engineering. He received his Ph.D. in civil engineering from University of Macau. He was a visiting scholar of California Institute of Technology (Caltech) and University of California\, Berkeley (UC Berkeley). He has obtained funding for several research projects\, including NSFC\, Natural Science Foundation of Guangdong Province; Foundation of the State Key Laboratory of Subtropical Building Science\, etc. In 2018\, he was elected as the recipient of the Pearl River S&T Nova Program of Guangzhou (珠江科技新星). His research interests include Bayesian Inference\, Earthquake Engineering\, Robust Data Analysis\, Structural Health Monitoring. \n 
URL:https://www.fst.um.edu.mo/event/probability-distribution-modelling-of-data-and-its-applications-in-structural-health-monitoring/
LOCATION:N6-2031
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181207T110000
DTEND;TZID=Asia/Macau:20181207T120000
DTSTAMP:20260523T060130
CREATED:20181207T030005Z
LAST-MODIFIED:20220927T043400Z
UID:5958-1544180400-1544184000@www.fst.um.edu.mo
SUMMARY:Can Deep Learning Learn to Count? on cognitive deficit of the current state of deep learning
DESCRIPTION:Instructors/Speakers\nProf. Xiaolin WU\nIEEE Fellow\nMcMaster University\nCanada \nAbstract\nSubitizing\, or the sense of small natural numbers\, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills\, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense\, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly\, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number\, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the connectionist CNN machinery itself. A recurrent neural network capable of subitizing does exist\, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also\, we investigate\, using subitizing as a test bed\, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting\, pointing to both cognitive deficit of pure DL\, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful as visual numerosity represents a minimum level of human intelligence. \nBiography\nXiaolin Wu\, Ph.D. in computer science\, University of Calgary\, Canada\, 1988. Dr. Wu started his academic career in 1988\, and has since been on the faculty of University of Western Ontario\, New York Polytechnic University (NYU Poly)\, and currently McMaster University\, where he is a McMaster Distinguished Engineering Professor at the Department of Electrical & Computer Engineering and holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing\, multimedia signal coding and communication\, joint source-channel coding\, multiple description coding\, and network-aware visual communication. He has published over three hundred research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow\, an associated editor of IEEE Transactions on Image Processing\, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors. \n 
URL:https://www.fst.um.edu.mo/event/can-deep-learning-learn-to-count-on-cognitive-deficit-of-the-current-state-of-deep-learning/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181211T110000
DTEND;TZID=Asia/Macau:20181211T120000
DTSTAMP:20260523T060130
CREATED:20181211T030050Z
LAST-MODIFIED:20220927T043400Z
UID:5954-1544526000-1544529600@www.fst.um.edu.mo
SUMMARY:Is a Picture Worth a Thousand Words? A Deep Learning Approach for Assessing the Impact of Investors’ Emotions Mined from Multimodal Social Postings on Corporate Credit Ratings
DESCRIPTION:Instructors/Speakers\nProf. Raymond Yiu Keung LAU\nDepartment of Information Systems\nCity University of Hong Kong \nAbstract\nWith the rise of the Social Web\, it is increasingly more popular for investors to express their feelings about firms and products on online social media. Though previous studies have examined the relationship between investors’ sentiments captured in texts (e.g.\, online news articles) and corporate credit ratings\, there is a research gap in terms of studying the relationship between investors’ emotions captured in multimodal social postings and corporate credit ratings. By drawing upon the appraisal theory and the notion of affect-as-information\, this study aims to fill the current research gap by examining the influence of investors’ emotions (e.g.\, trust\, joy\, and anger) captured in multimodal social postings on corporate credit ratings. In particular\, we apply state-of-the-art machine learning methods (e.g.\, topic modeling and deep convolutional neural networks) to extract investors’ emotions toward corporations from both texts and images posted to online social media. Our empirical results show that investors’ emotions such as “trust” and “joy” captured in textual social postings are positively associated with corporate credit rating\, while investors’ emotion such as “anger” captured in the images of the social postings is negatively associated with corporate credit rating. Moreover\, the negative influence of the “anger” emotion extracted from images is larger than the positive influence of “trust” and “joy” extracted from texts. The managerial implication of our study is that corporations should pay attention to both texts and images posted to online social media by investors as these postings affect corporate credit ratings\, and subsequently the costs of corporate borrowings. Our findings also offer new insights for corporations\, investors\, and ratings agencies with respect to the predictive power of investors’ multimodal social posts toward corporate credit ratings. \nBiography\nRAYMOND Y.K. LAU is an Associate Professor in the Department of Information Systems at City University of Hong Kong. He has worked at the academia and the ICT industry for over twenty years. He is the author of more than 200 refereed international journals and conference papers. His research work is published in renowned journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)\, IEEE Transactions on Knowledge and Data Engineering\, IEEE Intelligent Systems\, IEEE Internet Computing\, INFORMS Journal on Computing\, MIS Quarterly\, ACM Transactions on Information Systems\, etc. His research interests include Financial Technology (FinTech)\, Social Media Analytics\, Big Data Analytics\, and Artificial Intelligence (AI) for Business. He is a senior member of the IEEE and the ACM\, respectively. \n 
URL:https://www.fst.um.edu.mo/event/is-a-picture-worth-a-thousand-words-a-deep-learning-approach-for-assessing-the-impact-of-investors-emotions-mined-from-multimodal-social-postings-on-corporate-credit-ratings/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181214T110000
DTEND;TZID=Asia/Macau:20181214T120000
DTSTAMP:20260523T060130
CREATED:20181214T030052Z
LAST-MODIFIED:20220927T043400Z
UID:5951-1544785200-1544788800@www.fst.um.edu.mo
SUMMARY:On Text Analysis and Its Applications
DESCRIPTION:Instructors/Speakers\nProf. Raymond WONG\nSchool of Computer Science & Engineering\nUniversity of New South Wales\, Sydney\nAustralia \nAbstract\nDue to the increasing computation power and availability of big text data\, text analysis research\, such as text mining\, natural language processing\, and sentiment analysis\, has drawn significant attention from both research and industry communities. This seminar presents and summarizes our recent research findings in the area of text analysis\, and shares our experience in several related applications. \nBiography\nRaymond Wong is an Associate Professor at the School of Computer Science & Engineering\, University of New South Wales\, Sydney\, Australia. From 2005-2011\, he founded and led the Data Management Program at NICTA (the largest ICT organization in Australia)\, and was a Visiting Professor at Tsinghua University\, Beijing in 2011-13. Raymond co-founded several companies including Cohesive Data Inc that have successfully exited. He has also been involved in numerous technology fund raising\, M&A\, and patent valuation activities. His technical expertise lies in database systems\, big data\, machine learning and NLP. He has published more than 200 research publications and 2 US patents in these areas. He has also supervised 20 PhD students to completion. He received his BSc from Australian National University; MPhil and PhD from Hong Kong University of Science & Technology; and did his Postdoc at UCLA and Stanford University. \n 
URL:https://www.fst.um.edu.mo/event/on-text-analysis-and-its-applications/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181218T143000
DTEND;TZID=Asia/Macau:20181218T143000
DTSTAMP:20260523T060130
CREATED:20181218T063043Z
LAST-MODIFIED:20220927T043359Z
UID:5946-1545143400-1545143400@www.fst.um.edu.mo
SUMMARY:Smart City and Sustainability Forum
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/smart-city-and-sustainability-forum/
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190104T110000
DTEND;TZID=Asia/Macau:20190104T120000
DTSTAMP:20260523T060130
CREATED:20190104T030035Z
LAST-MODIFIED:20220927T043359Z
UID:5944-1546599600-1546603200@www.fst.um.edu.mo
SUMMARY:Recent Progress on Deep Learning Based Face Recognition\, Analysis and Translation
DESCRIPTION:Instructors/Speakers\nProf. Linlin SHEN\nShenzhen University\nShenzhen\nChina \nAbstract\nIn this talk\, I will mainly introduce deep learning and its applications in face recognition\, analysis and translation. The history of face recognition using hand-crafted features and deep neural networks will be firstly briefed. Publicly available face datasets for both training and testing will be introduced. The most recent progress about the performance of facial recognition algorithms and their real applications will be followed. The methodologies to address face spoofing and classification of facial attributes including age\, gender and expressions will be explored. Finally\, the progress about face translation using GAN\, pix2pix\, starGAN and our proposed GAN will be presented. \nBiography\nProf. Linlin Shen is currently a professor at Computer Science and Software Engineering\, Shenzhen University\, Shenzhen\, China. He is also an Honorary professor at School of Computer Science\, University of Nottingham\, UK. He serves as the director of Computer Vision Institute and China-UK joint research lab for visual information processing. He received the BSc and MEng degrees from Shanghai Jiaotong University\, Shanghai\, China\, and the Ph.D. degree from the University of Nottingham\, Nottingham\, U.K. He was a Research Fellow with the University of Nottingham\, working on MRI brain image processing. His research interests include deep learning\, facial recognition\, analysis/synthesis and medical image processing. Prof. Shen is listed as the Most Cited Chinese Researcher by Elsevier. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016. \n 
URL:https://www.fst.um.edu.mo/event/recent-progress-on-deep-learning-based-face-recognition-analysis-and-translation/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190124T100000
DTEND;TZID=Asia/Macau:20190124T110000
DTSTAMP:20260523T060130
CREATED:20190124T020021Z
LAST-MODIFIED:20220927T043358Z
UID:5941-1548324000-1548327600@www.fst.um.edu.mo
SUMMARY:Infiltration and excess pore water pressure in front of a tunnel boring machine (TBM): Experiments\, Mechanisms and Computational models
DESCRIPTION:Instructors/Speakers\nDr. Tao XU\nPost-Doctoral Fellow\nFaculty of Engineering and Architecture\nGhent University\nBelgium \nAbstract\nThe Tunnel Boring Machine (TBM) tunneling technique has been developed to construct tunnels that require strict settlement control\, for example\, in urban areas with a large amount of buildings\, historic areas etc. When tunneling with a TBM in saturated sand\, groundwater flow into the excavation face needs to be impeded. Both stability of the tunnel face and limitation of the groundwater flow are achieved by pressurizing the drilling fluid at the tunnel face. Due to the pressure difference between the excavation chamber of the TBM and the ground\, drilling fluid (slurry or foam) will infiltrate into the ground and thus there will be a flow in front of the tunnel face. In such a situation\, part of support pressure applied through the drilling fluid at the tunnel face will be transferred into excess pore water pressure in the ground. As a result\, the effective support pressure at the tunnel face and thus the stability of tunnel face will be reduced. The reduction of the effective support pressure depends on the infiltration distance\, the infiltration velocity and the drilling speed. However\, the infiltration distance of the foam or the slurry\, influence of this infiltration on the permeability of the infiltrated ground\, and consequently on the excess pore water pressure\, are still only partly understood. Finding answers to these questions\, therefore\, is significantly important for the safety of tunnel. \nThe purpose of this study is to improve our understanding of the mechanism of drilling fluid infiltration in front of the tunnel face through laboratory experiments\, and develop computational models considering such an infiltration process to predict the excess pore water pressures induced by TBM tunneling. The research concerns both slurry and Earth Pressure Balance shields (EPB shields). With the results of the experiments\, a sounder theoretical basis for the description of infiltration of drilling fluid (slurry or foam)\, and the computation of excess pore water pressure in front of tunnel face have been established. \nBiography\nCurrently\, Dr. Tao Xu is a Post-Doctoral Fellow at Ghent University. In 2018 he gained his PhD from Ghent University. His research has been published in top geotechnical journals Géotechnique and Tunnelling and Underground Space Technology. Prior to his doctoral research\, he carried out research on hydro-mechanical behavior of unsaturated soils and stability of unsaturated soil nailed slopes subjected to rainfall at Harbin Institute of Technology from 2012 to 2014. In 2014\, he started his PhD at Ghent University. His doctoral research focused on slurry and foam infiltration in front of a TBM and excess pore water pressures caused by TBM drilling. In the meantime\, as an invited guest researcher\, he participated in German Collaborative Project SFB 837 ‘Interaction modelling in mechanized tunneling’. He will join University of Macau on March 2019 as a research fellow responsible for assisting on establishing TBM tunneling research. \n 
URL:https://www.fst.um.edu.mo/event/infiltration-and-excess-pore-water-pressure-in-front-of-a-tunnel-boring-machine-tbm-experiments-mechanisms-and-computational-models/
LOCATION:E11-3033
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190213T143000
DTEND;TZID=Asia/Macau:20190213T153000
DTSTAMP:20260523T060130
CREATED:20190213T063031Z
LAST-MODIFIED:20220927T042640Z
UID:5939-1550068200-1550071800@www.fst.um.edu.mo
SUMMARY:AI in Bloomberg
DESCRIPTION:Instructors/Speakers\nMr. Iat Chong CHAN\nMachine Learning Team\, London\, United Kingdom \nAbstract\nThe Bloomberg Terminal brings together real-time data on every market\, breaking news\, in-depth research\, powerful analytics in one fully integrated solution. In the News product\, we provide\, our award-winning news coverage ensures our clients could get the information they need. While at the same time\, putting a lot of effort into trying to avoid overloading them with excessive information. \nIn this talk\, we will have a general overview of how AI (Artificial intelligence) techniques are being utilised in Bloomberg to allow our clients obtaining the information needed efficiently. Then\, we will focus on a particular application\, which was designed to refine information from a massive amount of news stories. We will also discuss the AI algorithms that underpin it. \nThere is no pre-requisite for most part of this talk. 30% of the talk requires basic understanding of statistics and probabilities. \n  \nBiography\nIat Chong Chan (https://www.linkedin.com/in/iatchongchan) is a research scientist/software developer in Bloomberg Machine Learning Team. His interests mostly lie in the intersection of Computational Linguistics\, Machine Learning\, and High Performance Computing. He has been working on a scalable infrastructure to infer topics of social contents ingested to Bloomberg by statistical models\, and a multi-documents summarisation system to extract the most important information from a text collection. Iat Chong also leads the NLP guild inside Bloomberg\, to advocate the use of ML/NLP techniques for new business problems. Before he joined the company\, he was a MSc student in Dept. of Computer Science at University of Oxford\, supervised by Prof. Stephen Pulman\, and worked on building a better input method on small hand-held devices by a novel Bayesian Network with Variational Inference. \n 
URL:https://www.fst.um.edu.mo/event/ai-in-bloomberg/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190325T110000
DTEND;TZID=Asia/Macau:20190325T120000
DTSTAMP:20260523T060130
CREATED:20190325T030020Z
LAST-MODIFIED:20220927T042639Z
UID:5937-1553511600-1553515200@www.fst.um.edu.mo
SUMMARY:AI Driven Smart City Development
DESCRIPTION:Instructors/Speakers\nProf. Jane YOU\nDepartment of Computing at the Hong Kong Polytechnic University\nHong Kong \nAbstract\nThe fast advances in AI (artificial intelligence) impose significant impacts on the world on all aspects. Smart sensing and big data analytics are emerging as the driving force to revolutionize our living in the era of information. This seminar presents the study of artificial intelligence for smart city development\, in particular the integration of new portable/wearable devices/sensors with computing technologies for the applications to smart transportation and healthcare. A vision-based approach to anonymous vehicle re-identification (VRI) overcomes the limitations of the existing systems by the fusion of multiple features with learning capacity. The automated truck loading monitoring system is equipped with a portable vehicle-mounted multi-function tool kit to provide a wide range of services for real-time logistic management\, environment protection and road safety control. The smart fetal monitoring belt is the first of its kind which integrates soft sensors\, textile and information technologies to detect fetal movement and uterine contraction for personalized monitoring of fetal wellbeing with quantitative assessment safely\, reliably\, conveniently and cost effectively. \nThese projects are supported by the Hong Kong Government under the schemes of General Research Fund and Innovation Technology Fund together with industry sponsorship. The impact of the work is evidenced by the outputs including research publications\, US patent\, international awards\, technology transfer and entrepreneurship. \nBiography\nJane You is currently a professor in the Department of Computing at the Hong Kong Polytechnic University. She is also the Associate Head of the department. Prof. You obtained her BEng. in Electronic Engineering from Xi’an Jiaotong University in 1986 and Ph.D in Computer Science from La Trobe University\, Australia in 1992. She was a lecturer at the University of South Australia and senior lecturer (tenured) at Griffith University from 1993 till 2002. Prof. You was awarded French Foreign Ministry International Postdoctoral Fellowship in 1993 and worked on the project on real-time object recognition and tracking at Universite Paris XI. She also obtained the Academic Certificate issued by French Education Ministry in 1994. \nProf. Jane You has worked extensively in the fields of image processing\, medical imaging\, computer-aided detection\, pattern recognition. So far\, she has more than 280 research papers published. She has been a principal investigator for several ITF (Innovation Technology Fund)\, and GRF (General Research Fund) projects supported by Hong Kong Government. Prof. You is a team member for three successful US patents and three awards including Hong Kong Government Industrial Awards. Her work on retinal imaging led to a US patent (2015)\, a technology transfer agreement (2011) and three international awards – Innovation Award of Excellence (Hong Kong\, 2015)\, a Special Prize and Gold Medal with Jury’s Commendation at the 39th International Exhibition of Inventions of Geneva (April 2011) and the second place of SPIE Medical Imaging’2009 Retinopathy Online Challenge (ROC’2009)). Her recent work on smart fetal monitoring was awarded the special prize and Silver Medal at the 44th International Exhibition of Inventions of Geneva (April 2016) and led to a technology transfer agreement with a company for production (2018). Prof. You is also an associate editor of Pattern Recognition and other journals. \n 
URL:https://www.fst.um.edu.mo/event/ai-driven-smart-city-development/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190402T160000
DTEND;TZID=Asia/Macau:20190402T170000
DTSTAMP:20260523T060130
CREATED:20190402T080059Z
LAST-MODIFIED:20220927T042639Z
UID:5935-1554220800-1554224400@www.fst.um.edu.mo
SUMMARY:Security Challenges in the Internet of Things
DESCRIPTION:Instructors/Speakers\nProf. Kui REN\nSchool of Computer Science and Technology\nZhejiang University\nChina \nAbstract\nThe vision of Internet of things (IoT) is the interconnected physical devices of various forms\, embedded with electronics\, software\, sensors\, actuators\, jointly perform sophisticated tasks ranging from data collection\, exchange\, and aggregation to task scheduling and system operation. IoT is expected to support abundant unprecedented services for the world and referred as “the infrastructure of the information society.” Penetrating into almost every critical aspect of the modern society\, IoT\, however\, also poses critical security challenges. In this talk\, I will discuss the uniqueness of these security challenges. Particularly\, three topics will be covered in depth; that is\, 1) The challenge of the device interfaces; 2) IoT hub security; and 3) Data privacy. \nBiography\nKui Ren is currently a Distinguished Professor in the School of Computer Science and Technology at Zhejiang University\, where he also directs the Institute of Cyber Space Research. His research interests include Cloud and data security\, AI and IoT security\, and Privacy-enhancing technologies. He is a Fellow of IEEE and a Distinguished Member of ACM. \n 
URL:https://www.fst.um.edu.mo/event/security-challenges-in-the-internet-of-things/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190412T110000
DTEND;TZID=Asia/Macau:20190412T120000
DTSTAMP:20260523T060130
CREATED:20190412T030046Z
LAST-MODIFIED:20220927T042638Z
UID:5933-1555066800-1555070400@www.fst.um.edu.mo
SUMMARY:Spatial and Spatio-Temporal Data Analytics and their applications in urban cities
DESCRIPTION:Instructors/Speakers\nProf. Cheng LONG\nThe School of Computer Science and Engineering (SCSE)\nNanyang Technological University (NTU)\nSingapore \nAbstract\nIt is expected that by 2050\, more than 2.5 billion people would reside in cities. While the urbanization has modernized many peoples’ lives\, it causes big challenges such as traffic congestion\, air pollution\, energy consumption\, etc. As part of the effort for solving these problems\, people have been collecting and analyzing data that is being generated in the urban space\, e.g.\, traffic data\, mobility data\, POI data\, etc.\, for finding insights into the problems and/or serving citizens better decision making. Since the majority of the data involves the spatial and/or temporal dimensions\, techniques for spatial and spatio-temporal data analytics are playing critical roles. In this talk\, we will overview this data-driven process\, introduce some of its interesting applications\, and also present some of our recent work on spatial and spatio-temporal data analytics\, including dynamic spatial matching\, co-location pattern mining\, traffic anomaly detection\, and interesting region discovery\, etc. \nBiography\nLONG Cheng is currently an Assistant Professor at the School of Computer Science and Engineering (SCSE)\, Nanyang Technological University (NTU). From 2016 to 2018\, he worked as a lecturer (Asst Professor) at Queen’s University Belfast\, UK. He got the PhD degree from the Department of Computer Science and Engineering\, The Hong Kong University of Science and Technology (HKUST) in 2015. His research interests are broadly in data management and data mining with his vision to achieve scalable spatial computing\, to make sense of urban related data for smarter cities\, and to manage and analyze emerging big data such as IoT data for richer knowledge. His research has been recognized with one “Best Research Award” provided by ACM-Hong Kong\, one “Fulbright-RGC Research Award” provided by Research Grant Council (Hong Kong)\, two “PG Paper Contest Awards” provided by IEEE-HK\, and one “Overseas Research Award” provided by HKUST. He has served as a Program Committee member/referee for several top data management and data mining conferences/journals (TODS\, VLDBJ\, TKDE\, ICDM\, CIKM\, etc.). He is member of ACM and IEEE. \n 
URL:https://www.fst.um.edu.mo/event/spatial-and-spatio-temporal-data-analytics-and-their-applications-in-urban-cities/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190416T150000
DTEND;TZID=Asia/Macau:20190416T150000
DTSTAMP:20260523T060130
CREATED:20190416T070028Z
LAST-MODIFIED:20220927T042638Z
UID:5929-1555426800-1555426800@www.fst.um.edu.mo
SUMMARY:Comprehensive strategies for eco-environment remediation on the Chinese Loess Plateau
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/comprehensive-strategies-for-eco-environment-remediation-on-the-chinese-loess-plateau/
LOCATION:E12-G004
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190506T110000
DTEND;TZID=Asia/Macau:20190506T120000
DTSTAMP:20260523T060130
CREATED:20190506T030009Z
LAST-MODIFIED:20220927T042637Z
UID:5927-1557140400-1557144000@www.fst.um.edu.mo
SUMMARY:Efficient and accurate structure preserving schemes for a class of complex nonlinear systems
DESCRIPTION:Instructors/Speakers\nProf. Jie SHEN\nProfessor of Department of Mathematics and Director of Center for Computational and Applied Mathematics\nPurdue University\nU.S.A. \nAbstract\nWe present in this talk the scalar auxiliary variable (SAV) approach to deal with nonlinear terms in a large class of complex dissipative/conservative systems. In particular\, for gradient flows driven by a free energy\, it leads to linear and unconditionally energy stable second-order (extendable to higher-orders) schemes which only require solving decoupled linear equations with constant coefficients. Hence\, these schemes are extremely efficient as well as accurate\, which are also validated by ample numerical results. \nWe shall present a convergence and error analysis under mild assumptions on the nonlinear free energy\, and discuss applications of the SAV approach to various complex dissipative/conservative systems. \nBiography\nProf. Jie SHEN will present a talk on “Efficient and accurate structure preserving schemes for a class of complex nonlinear systems”. Prof. Shen is currently Professor of Department of Mathematics and Director of Center for Computational and Applied Mathematics in Purdue University. He got his PhD degree from Universite de Paris-Sud. His research areas include numerical analysis\, spectral methods\, scientific computing\, computational fluid dynamics and computational materials science\, etc. \n 
URL:https://www.fst.um.edu.mo/event/efficient-and-accurate-structure-preserving-schemes-for-a-class-of-complex-nonlinear-systems/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190509T100000
DTEND;TZID=Asia/Macau:20190509T110000
DTSTAMP:20260523T060130
CREATED:20190509T020029Z
LAST-MODIFIED:20220927T042637Z
UID:5924-1557396000-1557399600@www.fst.um.edu.mo
SUMMARY:Wearable and Transparent Bioelectronics for Wireless Remote Sensing
DESCRIPTION:Instructors/Speakers\nProf. Mark Ming-Cheng CHENG\nAssociate Professor of Department of Electrical and Computer Engineering\nBiomedical Engineering and Director of Nanofabrication Core (nFab)\nWayne State University\nU.S.A \nAbstract\nIn this talk\, we will discuss two telemetric sensing principles for the wireless monitoring of physiological parameters with potential advantages of improved accuracy and sensitivity. We propose self-powered wireless biosensors based on graphene radio-frequency (RF) components\, which have advantages of transparent\, flexible\, and monolithically integrated on biocompatible soft substrate. All-graphene wireless sensors is envisioned to consist of optically transparent graphene antenna and biosensor\, which receives the fundamental tone and retransmits the sensed signal at its second harmonic\, thus allowing low-noise sensing in a severe interference/clutter background. Pressure is also an important part of our human body. In the current paradigm\, implantable wireless pressure sensors relies on magnetically coupling for signal readout\, where the hardware is bulky and inadequate for wearable use. We investigate parity-time (PT) telemetric sensing to address this challenge. Low-cost sensing wearable devices for real-time reporting of physical data is critical in personalized alerts and managed care services. \nBiography\nProf. Mark Ming-Cheng CHENG will present a talk on ” Wearable and Transparent Bioelectronics for Wireless Remote Sensing”. Prof. Cheng is currently Associate Professor of Department of Electrical and Computer Engineering\, Biomedical Engineering\, and Director Nanofabrication Core (nFab)\, Wayne State University. He received his BS and PhD degrees in Electrical Engineering from National Tsing-Hua University\, Hsinchu\, Taiwan. His research interests include biomedical devices\, wireless sensing\, cyber physical systems and machine learning. At WSU\, his research has been involved in design\, and characterization of sensors for the analysis of biological signals and environmental monitoring. Prof. Cheng received National Science Foundation (NSF) CAREER award in 2011 and 2013 ONR Faculty Summer Fellowship. He served as symposium chair of 2011 Annual Spring Symposium of American Vacuum Society (AVS) -Michigan Chapter\, and have served in numerous committees in the international conferences\, such as IEEE Sensors\, BMES\, IFCS\, and etc. \n 
URL:https://www.fst.um.edu.mo/event/wearable-and-transparent-bioelectronics-for-wireless-remote-sensing/
LOCATION:E11-1036
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190528T143000
DTEND;TZID=Asia/Macau:20190528T153000
DTSTAMP:20260523T060130
CREATED:20190528T063054Z
LAST-MODIFIED:20220927T042637Z
UID:5920-1559053800-1559057400@www.fst.um.edu.mo
SUMMARY:Parallel algorithms for the simulation of blood flows in human artery
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/parallel-algorithms-for-the-simulation-of-blood-flows-in-human-artery/
LOCATION:E11-G015\, Taipa\, Macau
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190531T110000
DTEND;TZID=Asia/Macau:20190531T120000
DTSTAMP:20260523T060130
CREATED:20190531T030006Z
LAST-MODIFIED:20220927T042636Z
UID:5916-1559300400-1559304000@www.fst.um.edu.mo
SUMMARY:Application of Structure Descriptor for Rational Design of Transition Metal Catalysts
DESCRIPTION:Instructors/Speakers\nProf. Daojian CHENG\nProfessor of Department of Chemical Engineering\nBeijing University of Chemical and Technology\nChina \nAbstract\nIn this talk\, Prof. Cheng will present an overview of some exciting results from our recently proposed structure descriptor\, mapping the quantitative relationship between intrinsic structural feature and catalytic performance for transition metal catalysis\, as well as its application in the high-throughput screening on catalyst and rational construction of catalytic sites. The central concept of our structure descriptor contains following points: (1) The features parameters inside structure descriptor have to be unique in representing electronic and geometric structures of a catalytic site. (2) The features parameters inside structure descriptor must be easily computed\, experimentally quantified or readily available physical properties from databases\, which is conveniently used for rapid screening. (3) Most importantly\, structure descriptor should be physically intuitive to ensure model robustness and direct inference of chemical insights\, the variation of which is unambiguously linked to changes in adsorption energies or catalytic activity. With the constructed structure descriptor for each transition metal catalyst system\, such as single-atom catalyst\, nanocluster\, alloy and so on\, it is helpful for fundamental understanding of structure–activity relationships between catalytic activity and the physical properties of transition metal catalysts\, which is validated by available experimental data. \nBiography\nProf. Daojian Cheng is currently a professor at Department of Chemical Engineering\, Beijing University of Chemical Technology\, China. He obtained his Ph.D. Degree in Chemical Engineering from Beijing University of Chemical Technology in 2008. During 2008-2010\, he worked as a Postdoctoral Research Fellow at Université Libre de Bruxelles\, Belgium. Currently he has interests in theoretical study\, computational design and experimental synthesis of metal clusters and nanoalloys as catalysts for renewable clean energy and environmental protection applications. He is author of roughly 120 journal articles. He has been named a Fellow of the Royal Society of Chemistry in 2016 and obtained National Natural Science Foundation of China–Outstanding Youth Foundation in 2018. \n 
URL:https://www.fst.um.edu.mo/event/application-of-structure-descriptor-for-rational-design-of-transition-metal-catalysts/
LOCATION:E11-1028
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190613T110000
DTEND;TZID=Asia/Macau:20190613T120000
DTSTAMP:20260523T060130
CREATED:20190613T030014Z
LAST-MODIFIED:20220927T042636Z
UID:5911-1560423600-1560427200@www.fst.um.edu.mo
SUMMARY:Bug Detection and Execution Replay for Concurrent Software Systems
DESCRIPTION:Instructors/Speakers\nProf. Zijiang YANG\nWestern Michigan University\nUnited States \nAbstract\nBugs in concurrent software systems are very difficult to detect and replay. This is due to the complexity of the software itself and the non-determinism of concurrency. To detect data races\, the major source of concurrent bugs\, we present a new approach to sample memory accesses across two threads and executions as a data race involves two threads and a program under testing is repeatedly executed. To detect deadlocks\, we interestingly observe that every two events of a deadlock usually occur within a short range called bug radius. Based on bug radius we present an approach to select priority change points within the bug radius that guarantees larger probabilities to trigger deadlocks. Finally\, we present a processor-based record-and-replay solution that does not require detecting and logging shared-memory dependencies to enable multi-processor execution replay. Shared-memory dependencies between threads are reconstructed offline\, during replay\, using an algorithm based on an SMT solver. \nBiography\nZijiang James Yang is the founder of GuardStrike Inc\, a company that focuses on providing tools and services for the quality and security of emerging software systems. Yang is also a professor at Western Michigan University. His research is in the broad areas of software engineering and formal methods. He has published over eighty conference and journal papers. He is also an inventor of ten United States patents. Yang received his Ph.D. from the University of Pennsylvania\, M.S. from Rice University\, and B.S. from the University of Science and Technology of China\, all in computer science. He was a recipient of the award and the 2008 CEAS outstanding new researcher award. He was a visi2018 ACM SIGSOFT Distinguished Paper Award\, 2015 CEAS outstanding researcher award\, 2010 PADTAD best paper award\, 2008 ACM TODAES best paper ting professor at EECS\, University of Michigan from 2009 to 2013. He is the general chair of the 12th IEEE Conference on Software Testing\, Validation and Verification (ICST). \n 
URL:https://www.fst.um.edu.mo/event/bug-detection-and-execution-replay-for-concurrent-software-systems/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190621T110000
DTEND;TZID=Asia/Macau:20190621T120000
DTSTAMP:20260523T060130
CREATED:20190621T030006Z
LAST-MODIFIED:20220927T042636Z
UID:5908-1561114800-1561118400@www.fst.um.edu.mo
SUMMARY:Learning from Blockchain
DESCRIPTION:Instructors/Speakers\nProf. Jeff SANDERS\nAcademic Director of the African Institute for Mathematical Sciences (AIM)\nProfessor of Mathematics of Stellenbosch University\nSouth Africa \nAbstract\nThe remarkable decade-old history of blockchain is summarised and the (usual?) case is made for its non-financial applications. The properties of blockchain are compared with those of a distributed database and a parameterization considered for a variety of instantiations. This talk is planned to be midway between a research seminar and a tutorial. \nBiography\nJeff Sanders is Academic Director of AIMS\, the African Institute for Mathematical Sciences\, South Africa and a Professor of Mathematics at Stellenbosch University. He is Australian: BSc (Hons)\, Pure Mathematics\, Monash University and PhD (Abstract Harmonic Analysis)\, Australian National University. He worked for 5 years in Macao at the United Nations University’s International Institute for Software Technology. His interests lie in Theoretical Computer Science\, and the topics on which he has worked have in common that they use pure mathematics to elucidate and design information systems. Currently he is working on privacy in distributed systems\, using epistemic logic; and on blockchain. \n 
URL:https://www.fst.um.edu.mo/event/learning-from-blockchain/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190703T103000
DTEND;TZID=Asia/Macau:20190703T113000
DTSTAMP:20260523T060130
CREATED:20190703T023021Z
LAST-MODIFIED:20220927T042635Z
UID:5906-1562149800-1562153400@www.fst.um.edu.mo
SUMMARY:Extrapolation multigrid methods for solving large linear system arising from the discretizations of PDEs
DESCRIPTION:Instructors/Speakers\nProf. Kejia PAN\nProfessor\nSchool of Mathematics and Statistics\nCentral South University\nChina \nAbstract\nThe multigrid method is an efficient iterative method for solving discrete partial differential equations. The geometric multigrid method has the nested mesh required for Richardson extrapolation. We propose two extrapolation multigrid methods: extrapolation cascadic multigrid method (EXCMG)\, extrapolation full multigrid method (EXFMG)\, and show the superoptimality of the EXCMG method for solving second-order elliptic problems. Finally\, some numerical experiments include second-order and fourth-order elliptic problems and fractional diffusion equations are given to show the efficiency of the EXCMG method. \nBiography\nProf. Pan is a professor and the vice dean of School of Mathematics and Statistics\, Central South University\, China. He got his PhD degree in Fudan University in 2009. His research area include multigrid method\, finite difference method for solving nonlinear PDEs\, and finite element methods. \n 
URL:https://www.fst.um.edu.mo/event/extrapolation-multigrid-methods-for-solving-large-linear-system-arising-from-the-discretizations-of-pdes/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190704T100000
DTEND;TZID=Asia/Macau:20190704T110000
DTSTAMP:20260523T060130
CREATED:20190704T025839Z
LAST-MODIFIED:20220927T042634Z
UID:5904-1562234400-1562238000@www.fst.um.edu.mo
SUMMARY:Extremely Low Order Explosive Models from Combustion Process
DESCRIPTION:Instructors/Speakers\nProf. Yufeng XU\nAssociate Professor\nSchool of Mathematics and Statistics\nCentral South University\nChina \nAbstract\nIn this talk\, we will review some explosive models from combustion theory. These models do not satisfy global Lipschitz condition in general\, therefore nonexistence or multiplicity of classic solution are of extensive interests. We shall study a class of generalized explosive model with extremely low order temporal fractional derivative. Blowup phenomenon is theoretically analyzed and numerical simulation is carried out via a mixed numerical method based on adaptive finite difference and discontinuous Galerkin method. It is shown that the size of spatial domain cannot be too small\, for guaranteeing the appearance of explosion of solution\, which coincides with the physical observation. \nBiography\nProf. Xu is an associate professor of School of Mathematics and Statistics\, Central South University\, China. He got his PhD degree in Central South University in 2014. His research area include finite difference method and finite element methods for solving nonlinear fractional PDEs. \n 
URL:https://www.fst.um.edu.mo/event/extremely-low-order-explosive-models-from-combustion-process/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190710T103000
DTEND;TZID=Asia/Macau:20190710T113000
DTSTAMP:20260523T060130
CREATED:20190710T023045Z
LAST-MODIFIED:20220927T042357Z
UID:5900-1562754600-1562758200@www.fst.um.edu.mo
SUMMARY:CN-WSGD schemes and CN-EWSGD schemes for space-fractional advection-diffusion equations
DESCRIPTION:Instructors/Speakers\nProf. Furong LIN\nProfessor\nDepartment of Mathematics\nShantou University\nChina \nAbstract\nWe consider high order finite difference schemes for one-dimensional space-fractional advection-diffusion equation (SFADE). The temporal derivative is approximated by the Crank-Nicolson (CN) scheme and the space fractional derivatives are approximated by the weighted and shifted Gr\”{u}nwald difference (WSGD) scheme. In general WSGD schemes have second order accuracy\, and by selecting a special parameter\, we get the third order accuracy scheme. However\, the third order scheme may not be stable. In this talk\, some results on the accuracy and the stability of CN-WSGD schemes are reported. \nBiography\nProf. Lin is a professor of Department of Mathematics\, Shantou University\, China. He got his PhD degree in Hong Kong University in 1995. His research area include numerical linear algebra\, fast algorithms for Toeplitz matrix\, numerical methods for PDEs. \n 
URL:https://www.fst.um.edu.mo/event/cn-wsgd-schemes-and-cn-ewsgd-schemes-for-space-fractional-advection-diffusion-equations/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190726T150000
DTEND;TZID=Asia/Macau:20190726T160000
DTSTAMP:20260523T060130
CREATED:20190726T070004Z
LAST-MODIFIED:20220927T042355Z
UID:5896-1564153200-1564156800@www.fst.um.edu.mo
SUMMARY:Computational protein design by accommodating flexibility and binding free energy in improving the affinity
DESCRIPTION:Instructors/Speakers\nProf. Vannajan Sanghiran Lee\nUniversity of Malaya\nMalaysia \nAbstract\nComputational structure-based protein design programs are becoming an increasingly important tool in molecular biology. The talk will review recent developments in algorithms for protein design\, emphasizing how novel algorithms enable the use of more accurate biophysical models. The focus is on the relationship between protein flexibility and binding free energy and some useful hints for understanding when\, and to what extent\, flexibility. Lessons learned using molecular dynamics simulations and gaussian network model in designing DARPins (designed ankyrin repeat proteins)\, a genetically engineered antibody mimetic proteins\, in HIV\, dengue\, and cancer targets will be discussed and concluded with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins. \nBiography\nAssoc. Prof. Dr. Vannajan Sanghiran Lee received her BSc (1994) in Chemistry from Chiang Mai University\, Thailand and PhD (2001) in Pharmaceutical Sciences and Physical Chemistry from University of Missouri-Kansas City\, USA under the scholarship from the Institute of Promotion and Development Science and Technology Project\, Thailand. After that she received the Post Doctoral Scholarship (2002) from the Thailand Research Fund and worked at the Computational Chemistry Unit Cell (CCUC)\, Chulalongkorn University\, Thailand. She worked as a lecturer and researcher in Computational Simulation and Modeling Laboratory (CSML)\, Department of Chemistry and Center for Innovation in Chemistry\, Chiang Mai University\, Chiang Mai\, Thailand from 2001-2011. In 2010\, she joined the school of pharmaceutical sciences\, University Sains Malaysia as a visiting researcher. She presently works as a Assoc. Prof. at Department of chemistry\, University of Malaya and as deputy head of Center of Theoretical and Computational Physics (TCP). Her present research interest includes computer-aided molecular modeling and computational chemistry using Molecular Dynamics (MD)\, Monte Carlo Simulations (MC)\, Quantum Mechanics (QM)\, Data Analytics and Machine Learning in diverse research and development fields such as biomolecular/material design. \n 
URL:https://www.fst.um.edu.mo/event/computational-protein-design-by-accommodating-flexibility-and-binding-free-energy-in-improving-the-affinity/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190809T163000
DTEND;TZID=Asia/Macau:20190809T173000
DTSTAMP:20260523T060130
CREATED:20190809T083024Z
LAST-MODIFIED:20220927T042355Z
UID:5889-1565368200-1565371800@www.fst.um.edu.mo
SUMMARY:Computer Vision++: The Next Step Towards Big AI
DESCRIPTION:Instructors/Speakers\nProf. Jiebo LUO\nUniversity of Rochester\, the USA \nAbstract\nWith the huge successes of deep learning in computer vision\, many computer vision problems are seemingly being solved. Where do we go from here? We will discuss a few directions where computer vision can be either further pushed to deal with data scarcity and data noise\, or synergistically integrated with other disciplines such as NLP and data mining\, to continue to advance the frontiers of artificial intelligence. \nBiography\n\n\n\nProfessor Jiebo Luo joined the University of Rochester in 2011 after a prolific career of fifteen years at Kodak Research Laboratories. He has been involved in numerous technical conferences\, including serving as the program co-chair of ACM Multimedia 2010\, IEEE CVPR 2012\, ACM ICMR 2016\, and IEEE ICIP 2017. He has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)\, IEEE Transactions on Multimedia (TMM)\, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)\, IEEE Transactions on Big Data (TBD)\, ACM Transactions on Intelligent Systems and Technology (TIST)\, Pattern Recognition\, Knowledge and Information Systems (KAIS)\, Machine Vision and Applications\, and Journal of Electronic Imaging. He is a Fellow of the ACM\, AAAI\, IEEE\, SPIE and IAPR. He is a Data Science Distinguished Researcher with the New York State CoE Goergen Institute for Data Science. \n\n\n\n 
URL:https://www.fst.um.edu.mo/event/computer-vision-the-next-step-towards-big-ai/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190812T103000
DTEND;TZID=Asia/Macau:20190812T113000
DTSTAMP:20260523T060130
CREATED:20190812T023002Z
LAST-MODIFIED:20220927T042354Z
UID:5885-1565605800-1565609400@www.fst.um.edu.mo
SUMMARY:On the Normalized Similarity and Distance Metrics
DESCRIPTION:Instructors/Speakers\nProf. Kaizhong ZHANG\nUniversity of Western Ontario\, London\, Ontario\, Canada \nAbstract\nSimilarity and distance metrics are widely used in many research areas and applications. In some applications\, similarity or distance metrics normalized with the “size” of the objects being measured are required. In this talk\, we will first present a formal definition of similarity metric and then show general solutions to normalize a given similarity or distance metric. Examples and applications of the general solutions will also be presented. \nBiography\nK. Zhang received the M.S. degree in mathematics from Peking University\, Beijing\, China\, in 1981\, and the M.S. and Ph.D. degrees in computer science from the Courant Institute of Mathematical Sciences\, New York University\, New York\, USA\, in 1986 and 1989\, respectively. \nHe is currently a professor in the Department of Computer Science\, University of Western Ontario\, London\, Ontario\, Canada. His research interests include bioinformatics\, algorithms\, image processing and databases. \n  \n 
URL:https://www.fst.um.edu.mo/event/on-the-normalized-similarity-and-distance-metrics/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190812T110000
DTEND;TZID=Asia/Macau:20190812T120000
DTSTAMP:20260523T060130
CREATED:20190812T030053Z
LAST-MODIFIED:20220927T042354Z
UID:5872-1565607600-1565611200@www.fst.um.edu.mo
SUMMARY:Aspect-level Sentiment Analysis: Techniques and Datasets
DESCRIPTION:Instructors/Speakers\nProf. Min YANG\nShenzhen Institutes of Advanced Technology\nChinese Academy of Sciences\nChina \nAbstract\nMin Yang is currently an assistant professor at Shenzhen Institutes of Advanced Technology\, Chinese Academy of Sciences. She received her Ph.D. degree from the department of computer science\, the University of Hong Kong in 2017. Her current research interests include natural language processing\, data mining\, recommendation systems. Dr. Yang has more than 70 international\, peer-reviewed publications on top-tier conferences or journals\, such as ACL\, SIGIR\, WWW\, KDD\, AAAI\, IJCAI\, TKDE\, TMM\, etc. \nBiography\na \n 
URL:https://www.fst.um.edu.mo/event/aspect-level-sentiment-analysis-techniques-and-datasets/
LOCATION:E11-1009 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T090000
DTEND;TZID=Asia/Macau:20190814T094500
DTSTAMP:20260523T060130
CREATED:20190814T010017Z
LAST-MODIFIED:20220927T042353Z
UID:5868-1565773200-1565775900@www.fst.um.edu.mo
SUMMARY:Neural Graph Matching and Beyond
DESCRIPTION:Instructors/Speakers\nProf. Junchi YAN\nShanghai Jiao Tong University\nChina \nAbstract\nIn this talk\, I will first give a brief introduction on graph matching\, which is a combinatorial problem in nature. Then we will show two deep network based pipelines for addressing the graph matching problem via deep learning. The models involve learning of the association based graph node embedding\, cross-graph affinity learning\, and a Sinkhorn layer for solving the linear assignment task\, etc. We will also discuss some works on joint matching and link prediction among two or multiple graphs. In the end\, some discussion will be given on the future work and outlook for connecting graph matching with machine learning. \nBiography\nDr. Junchi Yan is currently an Independent Research Professor (PhD Advisor) with the Department of Computer Science and Engineering\, Shanghai Jiao Tong University. He is also affiliated with The Artificial Intelligence Institute of SJTU and an adjunct professor with the School of Data Science\, Fudan University. Before that\, he was a Research Staff Member with IBM Research – China where he started his career since April 2011. He obtained a Ph.D. at the Department of Electronic Engineering from Shanghai Jiao Tong University\, China. His work on graph matching received the ACM China Doctoral Dissertation Nomination Award and China Computer Federation Doctoral Dissertation Award. His research interests are machine learning\, data mining and computer vision. He serves as an Associate Editor for IEEE ACCESS\, (Managing) Guest Editor for IEEE Transactions on Neural Network and Learning Systems\, Pattern Recognition Letters\, Pattern Recognition\, Vice Secretary of China CSIG-BVD Technical Committee\, and on the executive board of ACM China Multimedia Chapter. He has published 50+ peer reviewed papers in top venues in AI and has filed 20+ US patents. He has won the Distinguished Young Scientist of Scientific Chinese for year 2018. \n 
URL:https://www.fst.um.edu.mo/event/neural-graph-matching-and-beyond/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T094500
DTEND;TZID=Asia/Macau:20190814T103000
DTSTAMP:20260523T060130
CREATED:20190814T014539Z
LAST-MODIFIED:20220927T042353Z
UID:5866-1565775900-1565778600@www.fst.um.edu.mo
SUMMARY:Learning to Build a New Reality
DESCRIPTION:Instructors/Speakers\nProf. Jingyi YU\nShanghaiTech University\nChina \nAbstract\nThere have been tremendous advances on applying deep learning techniques for 2d image understanding. In contrast\, very little work has focused on employing deep learning for modeling datasets beyond 2D such as 3D geometry and 4D light fields. In this talk\, I present several latest works from our group on in this exciting new arena\, with a focus on their applications to virtual and augmented reality and computational photography. I first present a novel deep surface light field (DSLF) technique. A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. Our DSLF works on sparse data and automatically filling in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network’s prediction capability. For real data\, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Next\, I present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress 3d dynamic human bodies. Our approach uses sparse set of “panoramic” depth maps or PDMs\, each emulating an inward-viewing concentric mosaics (CM). We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network to achieve ultra-high compression ratio. \nBiography\nJingyi Yu is currently a Full Professor and Associate Dean of the School of Information Science and Technology at ShanghaiTech University. He is also affiliated with the Department of Computer and Information Sciences at University of Delaware. He received B.S. from Caltech in 2000 and Ph.D. from MIT in 2005. He has published over 120 papers at highly refereed conferences and journals\, and holds over 10 international patents on computational imaging. His research interests span a range of topics in computer vision and computer graphics\, especially on computational photography and non-conventional optics and camera designs. He is a recipient of the NSF CAREER Award and the AFOSR YIP Award\, and has served as an area chair of many international conferences including CVPR\, ICCV\, ECCV\, ICCP and NIPS. He is currently an Associate Editor of IEEE TPAMI\, IEEE TIP\, and Elsevier CVIU\, and will be program chair of ICPR 2020 and IEEE CVPR 2021. \n 
URL:https://www.fst.um.edu.mo/event/learning-to-build-a-new-reality/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T103000
DTEND;TZID=Asia/Macau:20190814T111500
DTSTAMP:20260523T060130
CREATED:20190814T023011Z
LAST-MODIFIED:20220927T042352Z
UID:5862-1565778600-1565781300@www.fst.um.edu.mo
SUMMARY:Towards Intelligent Perception of Facial Images from End to End
DESCRIPTION:Instructors/Speakers\nProf. Junliang XING\nInstitute of Automation\, Chinese Academy of Sciences\nChina \nAbstract\nFace is perhaps the most important visual object in computer vision\, with extensive studies in the past decades. In the deep learning era\, the performances of computer vision problems related to faces have been significantly boosted\, many of which have already met the requirements in real-world applications. In the talk\, I will first make some basic introductions on the face vision problems\, including face detection\, face alignment\, face tracking\, face attribute analyses and face recognition. Then I will introduce some of our previous works related to this topic. At last\, I will point out some future trends in this direction. The main objective of this talk is to help the audiences get a comprehensive understanding of this relatively mature yet still hot research direction in computer vision. \nBiography\nDr. Junliang XING received his dual B.E. degrees in Computer Science and Applied Mathematics from Xi’an Jiaotong University\, 2007\, and his Ph.D. degree in Computer Science and Technology from Tsinghua University\, 2012. After that\, he became an assistant professor within the National Laboratory of Pattern Recognition\, Institute of Automation\, Chinese Academy of Sciences\, where he is now a Professor and master student supervisor. Dr. Xing has published over 100 papers in peer-reviewed international conferences like ICCV\, CVPR\, ECCV\, ACM Multimedia\, AAAI\, IJCAI\, and journals like TPAMI\, IJCV\, TIP\, PR. He has translated two books in computer vision and wrote one book on deep learning. Dr. Xing was the recipient of Google PhD Fellowship in 2011\, the Best Paper Award of ACM International Conference on Multimedia in 2013\, and the champions of many international AI technical competitions in face recognition\, pose estimation\, etc. His main research areas lie in pattern recognition\, computer vision\, and machine learning\, with a main focus on vision problems related to human faces and bodies. \n 
URL:https://www.fst.um.edu.mo/event/towards-intelligent-perception-of-facial-images-from-end-to-end/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T103000
DTEND;TZID=Asia/Macau:20190814T113000
DTSTAMP:20260523T060130
CREATED:20190814T023049Z
LAST-MODIFIED:20220927T042352Z
UID:5864-1565778600-1565782200@www.fst.um.edu.mo
SUMMARY:Error convolution structure in discrete Caputo derivatives and global consistency analysis
DESCRIPTION:Instructors/Speakers\nProf. Jiwei ZHANG\nSchool of Mathematics and Statistics\nWuhan University\nChina \nAbstract\nNonuniform time-stepping methods are promising for Caputo reaction sub-diffusion problems because they would be simple and effectiveness in resolving the initial singularity and other nonlinear behaviors occurred away from the initial time. Compared with traditional local methods for the first-order derivative\, the numerical analysis for nonlocal time-stepping schemes on non-uniform time meshes are challenging due to the convolution integral (nonlocal) form of fractional derivative. We develop a general framework for the stability and convergence analysis with three tools: a family of complementary discrete convolution kernels\, a discrete fractional Gronwall inequality (DFGI) and a global (convolutional) consistency analysis\, which is not limited to a specific time mesh by building a convolution structure of local truncation error. It seems that the present techniques are extendable to the variable-order\, distributed-order diffusion equations and other nonlocal-in-time diffusion problems. \nBiography\nProf Zhang is a Professor of School of Mathematics and Statistics\, Wuhan University. He got his PhD degree in Hong Kong Baptist University in 2009. His research area include fast algorithms for fractional PEDs and numerical methods for PDEs. \n 
URL:https://www.fst.um.edu.mo/event/error-convolution-structure-in-discrete-caputo-derivatives-and-global-consistency-analysis/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T111500
DTEND;TZID=Asia/Macau:20190814T120000
DTSTAMP:20260523T060130
CREATED:20190814T031550Z
LAST-MODIFIED:20220927T042352Z
UID:5859-1565781300-1565784000@www.fst.um.edu.mo
SUMMARY:Multi-View Fusion and Representation for Image Analysis
DESCRIPTION:Instructors/Speakers\nProf. Zhe XUE\nBeijing University of Posts and Telecommunications\nChina \nAbstract\nReal-world data can be described from multiple views. For instance\, an image can be described by color histogram\, SIFT\, HOG and other features. The content of a Web page can be described by texts\, images and videos. Describing an object from multiple perspectives constitutes multi-view data. Multi-view learning is to use the complementary nature of different views to improve the learning performance than using a single view. In this talk\, I will first briefly introduce some basic concepts and issues in multi-view learning. Then I will introduce some of our recent multi-view learning works for image analysis including multi-view dimensionality reduction\, multi-view clustering and incomplete multi-view classification. This report aims to help the audience understand the basic tasks and latest developments of multi-view learning. \nBiography\nDr. Zhe Xue received his Ph\,D. degree in Computer Science from school of computer and control engineering\, University of Chinese Academy of Sciences (UCAS) in 2017. After that\, he became an assistant professor at school of computer science\, Beijing University of Posts and Telecommunications. His research interest is generally in machine learning and data mining\, and particularly in multi-view learning and image analysis. Dr. Xue has published over 20 papers in international conferences and journals such as AAAI\, IJCAI\, IEEE TCSVT\, CVIU\, Information Sciences. He has undertook and participated in many projects\, including National Key R&D Program of China\, 973 Program\, National Natural Science Foundation of China and so on. He is the reviewer of ACM MM\, DASFAA\, IEEE TIFS\, Multimedia Tools and Applications and other international journals and conferences. He is also a member of the Intelligent Service Committee of the Chinese Association for Artificial Intelligence. \n 
URL:https://www.fst.um.edu.mo/event/multi-view-fusion-and-representation-for-image-analysis/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
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END:VCALENDAR