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X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
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DTSTART:20190101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190325T110000
DTEND;TZID=Asia/Macau:20190325T120000
DTSTAMP:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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:20260523T061435
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T111500
DTEND;TZID=Asia/Macau:20190814T120000
DTSTAMP:20260523T061435
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T151500
DTEND;TZID=Asia/Macau:20190814T160000
DTSTAMP:20260523T061435
CREATED:20190814T071525Z
LAST-MODIFIED:20220927T042351Z
UID:5854-1565795700-1565798400@www.fst.um.edu.mo
SUMMARY:Integrating Learning with Game Theory for Societal Challenges
DESCRIPTION:Instructors/Speakers\nProf. Fei Fang\nInstitute for Software Research\nSchool of Computer Science\nCarnegie Mellon University\nUSA \nAbstract\nThere is a rising interest in developing artificial intelligence-based tools to help address societal challenges. Motivated by these challenges\, we have proposed game theory and machine learning/reinforcement learning-based models and algorithms for problems with strategic interactions among agents. In this talk\, I will introduce our models and algorithms that have led to two successfully deploy applications: one used by US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013\, the other used in multiple conservation areas around the world for anti-poaching effort. In addition\, I will highlight our most recent advances in integrating deep learning with game theory\, including computing equilibrium by learning from self-play and end-to-end learning of game parameters. \nBiography\nFei Fang is an Assistant Professor at the Institute for Software Research in the School of Computer Science at Carnegie Mellon University. Before joining CMU\, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University. She received her Ph.D. from the Department of Computer Science at the University of Southern California in June 2016. She received her bachelor degree from the Department of Electronic Engineering\, Tsinghua University in July 2011. Her research lies in the field of artificial intelligence and multi-agent systems\, focusing on integrating game theory and mechanism design with machine learning. Her work has been motivated by and applied to security\, sustainability\, and mobility domains\, contributing to the theme of AI for Social Good. Her work has won the Distinguished Paper at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI’18)\, Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16)\, the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award\, and is selected to be the winner of the William F. Ballhaus\, Jr. Prize for Excellence in Graduate Engineering Research as well as the Best Dissertation Award in Computer Science at the University of Southern California. Her work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work has led to the deployment of PAWS (Protection Assistant for Wildlife Security) in multiple conservation areas around the world\, which provides predictive and prescriptive analysis for anti-poaching effort. \n 
URL:https://www.fst.um.edu.mo/event/integrating-learning-with-game-theory-for-societal-challenges/
LOCATION:N1-1004\, Centre for Innovation and Entrepreneurship\, Guest House\, University of Macau
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190816T111500
DTEND;TZID=Asia/Macau:20190816T121500
DTSTAMP:20260523T061435
CREATED:20190816T031526Z
LAST-MODIFIED:20220927T040651Z
UID:5852-1565954100-1565957700@www.fst.um.edu.mo
SUMMARY:Gap probability in critical random matrix ensembles and the coupled Painleve II system
DESCRIPTION:Instructors/Speakers\nDr. Shuai-Xia XU\nLecturer\nSun Yat-sen University\nChina \nAbstract\nWe study the gap probabilities in the critical unitary invariant random matrix ensembles\, where the Painleve II and Painleve XXXIV kernels arise. By studying the Fredholm determinants of the Painleve II and Painlev XXXIV kernels\, we obtain integral expression of the gap probabilities by using solutions to the coupled Painleve II system. Moreover\, the large gap asymptotics are derived with the constant terms given explicitly in terms of the Riemann zeta-function. This talk is based on a joint work with Dan Dai from City University of HongKong. \nBiography\nProf. Xu Shuaixia got his PhD degree from Sun Yat-sen University in 2011. From 2011 to 2013\, he worked as a postdoctoral fellow at Sun Yat-sen and is currently a lecturer at Sun Yat-sen University. His research areas include nuclear science and technology-other disciplines of nuclear science and technology. \n 
URL:https://www.fst.um.edu.mo/event/gap-probability-in-critical-random-matrix-ensembles-and-the-coupled-painleve-ii-system/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190909T103000
DTEND;TZID=Asia/Macau:20190909T233000
DTSTAMP:20260523T061435
CREATED:20190909T023011Z
LAST-MODIFIED:20220927T040651Z
UID:5849-1568025000-1568071800@www.fst.um.edu.mo
SUMMARY:History of the Generalized Inverse & Linear Least Squares
DESCRIPTION:Instructors/Speakers\nProf. Yimin WEI\nProfessor\nSchool of Mathematical Sciences\nFudan University\nChina \nAbstract\nIn this talk\, we will introduce the history of the generalized inverse and linear least squares problem. Some recent research in this area will be given. Possible future work will also be discussed. \nBiography\nProf. Yimin WEI is a professor in Fudan University\, China. He has published over 100 papers in mathematics. His recent interest includes matrix analysis\, least squares problems and study of multilinear systems. \n 
URL:https://www.fst.um.edu.mo/event/history-of-the-generalized-inverse-linear-least-squares/
LOCATION:E11-1035
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190911T143000
DTEND;TZID=Asia/Macau:20190911T153000
DTSTAMP:20260523T061435
CREATED:20190911T063058Z
LAST-MODIFIED:20220927T040650Z
UID:5823-1568212200-1568215800@www.fst.um.edu.mo
SUMMARY:Security design of IoT products
DESCRIPTION:Instructors/Speakers\nMr. Zexin JIANG\nGuangzhou Bonson Information System Co. Ltd. \nAbstract\n\n簡單自我介紹\n\n平凡的我出生在不平凡的地方\n不吃辣椒，卻孤身一路向北\n明明可以安穩舒適，卻非要折騰\n\n\n大四之後的求學\n\n沒有畢業合照的大四\n我是如何保送到清華進修的\n國家高鐵系統真的運行著我寫的代碼嗎\n深深地遺憾\n\n\n職場折騰\n\n內地壟斷國企是一種什麼樣的存在\n獨檔一面是一種什麼樣的感受\n如何避免當背鍋俠\n我在電網工作5年獲得的5個第一\n辭職之後我拿著兩份工資\n民企與國企的差異極大\n一年時間從產品經理到CTO\n技術、產品、工程有什麼區別，變現的誤區\n\n\n幾點感悟分享\n\n選擇比努力重要\n多做事少計較得失\n適當包裝自己\n風險管理與自我保護\n\n\n\nBiography\n廣東省普寧人，高級工程師职称，（ISC）2會員、全國電力運行與控制標準委員會標準工作組成員、廣東省電機工程學會成員、廣州市科技評審專家，獲得國家授權專利56項，發表論文12篇，出版專著1本，獲省級科技進步壹等獎、獲2018年廣州市“珠江科技新星”專項資助、2019年廣州市“產業急需緊缺人才”補貼。擁有國際註冊信息安全專家和國家重要信息系統保護人員等職業資格。 \n2004年-2008年，南京理工大學自動化專業本科學習（專業第1名，GPA 3.9/4.0）。參加全國大學生電子設計大賽獲國家二等獎、全國大學生智能汽車比賽獲得國家二等獎、江蘇省大學生機器人比賽獲得壹等獎、江蘇省優秀本科畢業論文壹等獎、獲得學校最高榮譽獎學金以及每年特等獎學金。 \n2008年-2011年，清華大學（免試）控制科學與工程專業碩士研究生。期間參與“十壹五”國家重大科技攻關（中國高鐵國產化裝備研發）項目以及和諧號CRH5動車組網絡控制系統的引進消化吸收創新的產品研發工作。研發了支持硬件在環的大規模分布式半實物仿真引擎，研發成果支撐應用於武廣、京津、北京地鐵等線路設備的上線前驗證。 \n2011年至2016年，廣東電網電力科學研究院（南方電網自動化重點實驗室），歷任助理專責、專責、技術專家、電力機器人攻關團隊組長。從事電力物聯網和電力監控安全防護體系領域的研究工作，主筆編制了南方電網電力監控系統最頂層安全防護技術規範和管理規範等多項企業標準，參與電力行業信息安全標準編制2項，推動商用密碼技術在電力行業的應用，相關成果於2012年獲得廣東省電力行業協會科技創新成果獎、於2013年南方電網青工創新創效成果壹等獎、2014年獲得南方電網科技進步壹等獎。2015年在南方電網全網信息安全攻防競賽獲第1名。 \n2016年至今，廣州邦訊信息系統有限公司（深交所股票代碼：300366），歷任產品經理、產品副總監、CTO。主要從事工業物聯網和物聯網安全相關產品研發管理工作。創建了公司商用密碼產品線，帶領團隊研發多款物聯網及安全產品，獲得國家密碼局型號認證和公安部檢測認證。 \n 
URL:https://www.fst.um.edu.mo/event/security-design-of-iot-products/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190911T150000
DTEND;TZID=Asia/Macau:20190911T160000
DTSTAMP:20260523T061435
CREATED:20190911T070002Z
LAST-MODIFIED:20220927T040650Z
UID:5814-1568214000-1568217600@www.fst.um.edu.mo
SUMMARY:Image Authentication and Tamper Localization
DESCRIPTION:Instructors/Speakers\nProf. Yulin WANG\nFull Professor of Wuhan University\nChina \nAbstract\nImage authentication can be used in many fields\, including e-government\, e-commerce\, national security\, news pictures\, court evidence\, medical image\, engineering design\, and so on. Since some content-preserving manipulations\, such as JPEG compression\, contrast enhancement\, and brightness adjustment\, are often acceptable—or even desired—in practical application\, an authentication method needs to be able to distinguish them from malicious tampering\, such as removal\, addition\, and modification of objects. Therefore\, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique\, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk\, we propose a hybrid authentication technique based on what we call fragile hash value. The technique can blindly detect and localize malicious tampering\, while maintaining reasonable tolerance to conventional content-preserving manipulations. The hash value is derived from the relative difference between each pair of the selected DCT coefficient in a central block and its counterpart which is estimated by the DC values of the center block and its adjacent blocks. In order to maintain the relative difference relationship when the image undergoes legitimate processing\, we make a pre-compensation for the coefficients. Finally\, we point out the direction using deep leaning technique for image authentication. \nBiography\nProf. Yulin Wang is a full professor and PhD supervisor in the School of Computer Science\, Wuhan University\, China. He got PhD degree in 2005 from University of London\, UK. Before that\, he had worked in National Institute of Research and Huawei@ for more than ten years. He has involved more than 20 national and international research projects\, and hold 8 patents. He got his master and bachelor degree in 1990 and 1987 respectively from Xi-Dian University\, and Huazhong University of Science and Technology\, both in China. His research interests include image and video processing\, software engineering\, information security and artificial intelligence. In recently 10 years\, Prof. Wang has published as first author 3 books\, 40 conference papers and 45 journal papers\, including in IEEE Transactions and ACM Transactions journals. Prof. Wang served as editor-in-chief for International Journal of Advances in Multimedia in 2010. He served as reviewer for many journals\, including IEEE Transactions on Image Processing\, IEEE Transactions on CSVP and IEEE Transactions on Multimedia. He served as reviewer for many research funds\, including National High Technology Research and Development Program of China (‘863’ project). Prof. Wang was the external PhD adviser of Dublin City University\, Ireland during 2013-2016. He has served as chairman and keynote speaker at more than 30 international conferences since 2008. He bas been listed in Marcus “who’s who in the world” since 2008. He was selected as scientific and technological innovative talents in Hubei Province in 2014. Since 2012\, he has served as Deputy Director of Hubei Science and Technology Commission of China Association for the Promotion of Democracy (CAPD). \n 
URL:https://www.fst.um.edu.mo/event/image-authentication-and-tamper-localization/
LOCATION:E11-4045
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190919T143000
DTEND;TZID=Asia/Macau:20190919T153000
DTSTAMP:20260523T061435
CREATED:20190919T063006Z
LAST-MODIFIED:20220927T040650Z
UID:5801-1568903400-1568907000@www.fst.um.edu.mo
SUMMARY:Sinotools: a computational framework to analyze cancer genomic sequencing data for precision oncology
DESCRIPTION:Instructors/Speakers\nProf. Bingding HUANG\nCollege of Big Data and Internet\nShenzhen University \nAbstract\nWe have developed a computational framework called Sinotools to analyze cancer genomic sequencing data for precision oncology. First of all\, Sinotools can identify very low-frequency variants in ctDNA samples from cancer patients using duplex barcode sequencing technology. Sinotools also includes a novel computational algorithm to identify actionable gene fusion events from targeted sequencing data. Sinotools is available at https://github.com/SinOncology. \nBiography\nProf. Bingding Huang received his BSc in Cell Biology from University of Science and Technology of China in 2002. Then he received his Master degree in Computer Science from Saarland University and Max Planck Institute for Informatics under Scholarship from International Max Planck Research School in 2004. Afterwards\, he joined the Bioinformatics group in Technical University of Dresden where he received his PhD (Dr.ret.nat) in Computer Science from the Computer Science department in 2007. Then he joined the Molecular and Cellular Modeling group in Heidelberg Institute for Theoretical Studies for one year postdoc training. From 2009 to 2012 he was a Adjunct Associate Professor in Division of Systems Biology\, Zhejiang-California International NanoSystems Institute\, Zhejiang University China. From 2012 to 2015 Dr. Huang worked as a Bioinformatics Scientist in German Cancer Research Center Heidelberg. Then from 2015 to 2017 he worked as a Senior Computational Biologist in Neo New Oncology AG (Part of Siemens Healthineers). Afterwards\, he joined Sinotech Genomics Inc. Shanghai as Vice President of Research and Development and Head of Bioinformatics. Recently Dr. Huang joined the College of Big Data and Internet\, Shenzhen University as a full Professor in Bioinformatics and Big data. \n 
URL:https://www.fst.um.edu.mo/event/sinotools-a-computational-framework-to-analyze-cancer-genomic-sequencing-data-for-precision-oncology/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190923T150000
DTEND;TZID=Asia/Macau:20190923T160000
DTSTAMP:20260523T061435
CREATED:20190923T070040Z
LAST-MODIFIED:20220927T040649Z
UID:5796-1569250800-1569254400@www.fst.um.edu.mo
SUMMARY:Exploring Deep Learning from Feedforward Neural Network Experiments
DESCRIPTION:Instructors/Speakers\nProf. Qin SHENG\nProfessor\nDepartment of Mathematics\nBaylor University\nUSA \nAbstract\nNeural network based deep learnings have been used for solving high dimensional (number of dimensions may reach more than several hundreds) partial differential equations. But what is a neural network and how to use it for approximating\, or predicting\, natural phenomena? In this exploration\, we tend to consider approximations of multivariate data through an explicit feedforward neural network (FNN) which is the basic type of neural network. The easy-to-follow formula provides a piecewise constant function for the data. Our FNN architecture has two hidden layers\, where the weights and thresholds are explicitly defined and no numerical optimization is required for training. The explicit algorithm does not rely on any tensor structure in multiple dimensions. Instead\, it automatically creates Voronoi tessellation of the domain based on the given data\, and generates a reliable piecewise constant approximation of the targeted function. These make the construction more practical for multiple applications. \nBiography\nProf. Sheng is a professor of Department of Mathematics\, Baylor University\, USA. He got his PhD degree from the University of Cambridge under the supervision of Professor Arieh Iserles in 1990. His research area include splitting and adaptive numerical methods for solving linear and nonlinear partial differential equations. \n 
URL:https://www.fst.um.edu.mo/event/exploring-deep-learning-from-feedforward-neural-network-experiments/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190925T100000
DTEND;TZID=Asia/Macau:20190925T110000
DTSTAMP:20260523T061435
CREATED:20190925T091150Z
LAST-MODIFIED:20220927T040649Z
UID:5792-1569405600-1569409200@www.fst.um.edu.mo
SUMMARY:Summability and Lebesgue points of two-dimensional Fourier transforms
DESCRIPTION:Instructors/Speakers\nProf. Weisz FERENC\nProfessor\nDepartment of Numerical Analysis\nEötvös L. University\nHungary \nAbstract\n \nBiography\nProfessor Ferenc Weisz works for Eötvös Loránd University\, Department of Numerical Analysis\, Budapest\, Hungary\, Europe. His special field is harmonic analysis\, one and higher dimensional Fourier analysis\, summability theory\, martingale theory\, Hardy spaces\, spaces with variable exponents\, Gabor- and wavelet analysis. He is author of four books published by Springer and Birkhäuser\, two course books and of about 200 papers published by international journals. He has got several prizes and awards\, amongst other in the last two years the Gabor prize and teh Prize of the Hungarian Academy of Sciences. He has got the Doctor of Science (DSc) degrree of the Hungarian Academy of Sciences in 2001 and the Habilitation of the Eötvös Loránd University\, Budapest in 2002. \n 
URL:https://www.fst.um.edu.mo/event/summability-and-lebesgue-points-of-two-dimensional-fourier-transforms/
LOCATION:E11-1015
CATEGORIES:event_list,math_events,seminarslectures
END:VEVENT
END:VCALENDAR