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PRODID:-//Faculty of Science and Technology | University of Macau - ECPv6.14.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.fst.um.edu.mo
X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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X-Robots-Tag:noindex
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BEGIN:VTIMEZONE
TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:CST
DTSTART:20190101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190528T143000
DTEND;TZID=Asia/Macau:20190528T153000
DTSTAMP:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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;VALUE=DATE:20190626
DTEND;VALUE=DATE:20190629
DTSTAMP:20260611T074803
CREATED:20190626T095239Z
LAST-MODIFIED:20220927T042635Z
UID:5832-1561507200-1561766399@www.fst.um.edu.mo
SUMMARY:Wireless Technology Study Summer Camp 2019
DESCRIPTION:
URL:
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190703T103000
DTEND;TZID=Asia/Macau:20190703T113000
DTSTAMP:20260611T074803
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;VALUE=DATE:20190704
DTEND;VALUE=DATE:20190706
DTSTAMP:20260611T074803
CREATED:20190704T095043Z
LAST-MODIFIED:20220927T042635Z
UID:5830-1562198400-1562371199@www.fst.um.edu.mo
SUMMARY:Deep Learning and Computer Vision (DeepVision) Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/deep-learning-and-computer-vision-deepvision-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190704
DTEND;VALUE=DATE:20190707
DTSTAMP:20260611T074803
CREATED:20190704T094923Z
LAST-MODIFIED:20220927T042634Z
UID:5827-1562198400-1562457599@www.fst.um.edu.mo
SUMMARY:Sustainable Energy Storage Systems Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/sustainable-energy-storage-systems-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190704T100000
DTEND;TZID=Asia/Macau:20190704T110000
DTSTAMP:20260611T074803
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;VALUE=DATE:20190708
DTEND;VALUE=DATE:20190710
DTSTAMP:20260611T074803
CREATED:20190708T094721Z
LAST-MODIFIED:20220927T042357Z
UID:5825-1562544000-1562716799@www.fst.um.edu.mo
SUMMARY:Robotics and Artificial Intelligence Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/robotics-and-artificial-intelligence-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190710T103000
DTEND;TZID=Asia/Macau:20190710T113000
DTSTAMP:20260611T074803
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;VALUE=DATE:20190713
DTEND;VALUE=DATE:20190720
DTSTAMP:20260611T074803
CREATED:20190713T094533Z
LAST-MODIFIED:20220927T042356Z
UID:5822-1562976000-1563580799@www.fst.um.edu.mo
SUMMARY:科技學院『第一屆內地優秀大學生夏令營交流活動』
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/ugsummercamp2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190715
DTEND;VALUE=DATE:20190718
DTSTAMP:20260611T074803
CREATED:20190715T094004Z
LAST-MODIFIED:20220927T042356Z
UID:5815-1563148800-1563407999@www.fst.um.edu.mo
SUMMARY:Climate Change and Civil Engineering Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/climate-change-and-civil-engineering-summer-camp-2019/#new_tab
CATEGORIES:activities,cee_events,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190716T110000
DTEND;TZID=Asia/Macau:20190716T120000
DTSTAMP:20260611T074803
CREATED:20190716T040859Z
LAST-MODIFIED:20190716T040859Z
UID:6515-1563274800-1563278400@www.fst.um.edu.mo
SUMMARY:The Stator-PM Brushless Machines and Potential Applications
DESCRIPTION:Instructors/Speakers\nProf. Wei HUA\nProfessor\nSchool of Electrical Engineering\, Southeast University\nNanjing\, China \nAbstract\nThe stator-PM brushless machines employ both permanent magnets (PMs) and armature windings in stator\, and the rotor is only made of salient iron laminations. Hence\, this type of machine has attracted considerable interests due to the advantages of brushless\, simple and robust structure\, high torque (power) density\, strong anti-demagnetization capability\, high efficiency\, and flexible topologies. In this presentation\, an overview of stator-PM brushless machines\, including basic topology\, operation principle\, performance analysis\, and control strategies\, especially for the potential applications. \nBiography\nProf. Wei HUA was born in Taizhou\, China\, in 1978. He received the B.Sc. and Ph.D. degrees in electrical engineering from Southeast University\, Nanjing\, China\, in 2001 and 2007\, respectively. From September 2004 to August 2005\, he visited the department of Electronics and Electrical Engineering\, The University of Sheffield\, UK\, as a Joint-Supervised Ph. D student. Since 2007\, he has been with Southeast University\, where he is currently a Professor with the School of Electrical Engineering. Prof. Wei HUA is an expert in the area of design\, analysis\, and control of electrical machines. He is the recipient of National Distinguished Youth Science Foundation\, Changjiang Scholars Program (Youth Scholars) of MOE and National Excellent Youth Science Foundation. He is the author or coauthor of over 150 technical papers\, and he is the holder of 50 patents. \n 
URL:https://www.fst.um.edu.mo/event/the-stator-pm-brushless-machines-and-potential-applications/
LOCATION:N21-2006
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190724
DTEND;VALUE=DATE:20190726
DTSTAMP:20260611T074803
CREATED:20190724T092853Z
LAST-MODIFIED:20220927T042356Z
UID:5804-1563926400-1564099199@www.fst.um.edu.mo
SUMMARY:3D Reconstruction with Quadcopters Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/3d-reconstruction-with-quadcopters-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190724T163000
DTEND;TZID=Asia/Macau:20190724T173000
DTSTAMP:20260611T074803
CREATED:20190724T040553Z
LAST-MODIFIED:20190724T040553Z
UID:6513-1563985800-1563989400@www.fst.um.edu.mo
SUMMARY:Cognitive Radios: A New Solution to a Not So New Problem
DESCRIPTION:Instructors/Speakers\nProf. Kai-Kit WONG\nChair Professor\nDepartment of Electronic and Electrical Engineering\nUniversity College London\nUK \nAbstract\nFor mobile communications networks\, there is a need for spectrum regulators to find ways to more intelligently and flexibly utilise the precious spectrum. Indeed\, the concept of cognitive radio which was first introduced by Joseph Mitola III in 1998\, aimed to do exactly that. Over the past decade since his work\, scientists and engineers attempted to adopt a game-theoretic approach to tackle the problem of autonomous spectrum sharing by cognitive radios. The results are unfortunately mostly negative reconfirming the fact that competition among selfish individuals is bound to result in the tragedy of the commons\, with “cognitive” radios still excessively interfering with each other without a proper reconciliation mechanism. In this talk\, we consider the autonomous resource allocation problem for the orthogonal frequency-division multiple-access (OFDMA) interference channel where each user (i.e.\, cognitive mobile radio) can freely occupy any of the subcarriers in the network\, and is required to decide its own subcarrier allocation\, with only local channel state information. We use this network as an example\, to show that collective intelligence for a group of self-organising cognitive radios is possible\, and that cognitive radios can be empowered with forward-looking ability to negotiate with each other for the benefits of not only individuals but all. Simulation results demonstrate that in the example of autonomous OFDMA\, the proposed approach can achieve the network sum-rate that is extremely close to the optimal centralised solution. The talk is concluded by a discussion of extending the use of such approach to a hierarchical primary-secondary spectrum sharing network. \nBiography\nKai-Kit Wong received the BEng\, the MPhil\, and the PhD degrees\, all in Electrical and Electronic Engineering\, from the Hong Kong University of Science and Technology\, Hong Kong\, in 1996\, 1998\, and 2001\, respectively. After graduation\, he took up academic and research positions at the University of Hong Kong\, Lucent Technologies\, Bell-Labs\, Holmdel\, the Smart Antennas Research Group of Stanford University\, and the University of Hull\, UK. He is Chair in Wireless Communications at the Department of Electronic and Electrical Engineering\, University College London\, UK. \nHis current research centers around 5G and beyond mobile communications\, including topics such as massive MIMO\, full-duplex communications\, millimetre-wave communications\, edge caching and fog networking\, physical layer security\, wireless power transfer and mobile computing\, V2X communications\, and of course cognitive radios. There are also a few other unconventional research topics that he has set his heart on\, including for example\, fluid antenna communications systems\, remote ECG detection and etc. He is a co-recipient of the 2013 IEEE Signal Processing Letters Best Paper Award and the 2000 IEEE VTS Japan Chapter Award at the IEEE Vehicular Technology Conference in Japan in 2000\, and a few other international best paper awards. \nHe is Fellow of IEEE and IET and is also on the editorial board of several international journals. He has served as Senior Editor for IEEE Communications Letters since 2012 and also for IEEE Wireless Communications Letters since 2016. He had also previously served as Associate Editor for IEEE Signal Processing Letters from 2009 to 2012 and Editor for IEEE Transactions on Wireless Communications from 2005 to 2011. He was also Guest Editor for IEEE JSAC SI on virtual MIMO in 2013 and currently the Guest Editor for IEEE JSAC SI on physical layer security for 5G. \n 
URL:https://www.fst.um.edu.mo/event/cognitive-radios-a-new-solution-to-a-not-so-new-problem/
LOCATION:E11-G015
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190725
DTEND;VALUE=DATE:20190727
DTSTAMP:20260611T074803
CREATED:20190725T092107Z
LAST-MODIFIED:20220927T042355Z
UID:5795-1564012800-1564185599@www.fst.um.edu.mo
SUMMARY:Smart transportation summer camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/smart-transportation-summer-camp-2019/#new_tab
CATEGORIES:activities,eme_events,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190726T150000
DTEND;TZID=Asia/Macau:20190726T160000
DTSTAMP:20260611T074803
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;VALUE=DATE:20190728
DTEND;VALUE=DATE:20190802
DTSTAMP:20260611T074803
CREATED:20190728T090127Z
LAST-MODIFIED:20220927T042355Z
UID:5785-1564272000-1564703999@www.fst.um.edu.mo
SUMMARY:科技學院『第一屆內地優秀大學生夏令營交流活動』
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/ugsummercamp2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190809T163000
DTEND;TZID=Asia/Macau:20190809T173000
DTSTAMP:20260611T074803
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;VALUE=DATE:20190810
DTEND;VALUE=DATE:20190817
DTSTAMP:20260611T074803
CREATED:20190810T072151Z
LAST-MODIFIED:20220927T042354Z
UID:5723-1565395200-1565999999@www.fst.um.edu.mo
SUMMARY:The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
DESCRIPTION:
URL:https://www.ijcai19.org/#new_tab
CATEGORIES:conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190812T103000
DTEND;TZID=Asia/Macau:20190812T113000
DTSTAMP:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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:20260611T074803
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:20190814T163000
DTEND;TZID=Asia/Macau:20190814T173000
DTSTAMP:20260611T074803
CREATED:20190814T040035Z
LAST-MODIFIED:20250813T043827Z
UID:6508-1565800200-1565803800@www.fst.um.edu.mo
SUMMARY:AI: A Networking and Communication Perspective
DESCRIPTION:Instructors/Speakers\nProf. Quee Seng QUEK\nTenured Associate Professor\nSingapore University of Technology and Design (SUTD)\nSingapore \nAbstract\nRecent breakthroughs in artificial intelligence and machine learning\, including deep neural networks\, the availability of powerful computing platforms and big data are providing us with technologies to perform tasks that once seemed impossible. In the near future\, autonomous vehicles and drones\, intelligent mobile networks\, and intelligent internet-of-things (IoT) will become a norm. At the heart of this technological revolution\, it is clear that we will need to have artificial intelligence over a massively scalable\, ultra-high capacity\, ultra-low latency\, and dynamic new network infrastructure. In this talk\, we will provide a simple overview of AI for the perspective of networking and communications and share some interesting applications. In addition\, we will also share some of our preliminary works in this area. \nBiography\nTony Quek is IEEE Fellow\, IEEE ComSoc Distinguished Lecturer. He is also the Acting Head of Information System Technology and Design Pillar\, Sector Lead for SUTD AI Program\, and the Deputy Director of SUTD-ZJU IDEA. Singapore University of Technology and Design. His current research topics include wireless communications and networking\, big data processing\, network intelligence\, URLLC\, and IoT. \nTony Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology\, Tokyo\, Japan\, respectively. At Massachusetts Institute of Technology (MIT)\, Cambridge\, MA\, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently\, he is a tenured Associate Professor with the Singapore University of Technology and Design (SUTD). \nDr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is serving as the General Chair for IEEE ICCC 2020. He is currently serving as the Chair of IEEE VTS Technical Committee on Deep Learning for Wireless Communications as well as an elected member of the IEEE Signal Processing Society SPCOM Technical Committee. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications\, an Editor of the IEEE Transactions on Communications\, and an Editor of the IEEE Wireless Communications Letters. He is a co-author of a few books published by Cambridge University Press. \nDr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research\, the 2012 IEEE William R. Bennett Prize\, the 2016 IEEE Signal Processing Society Young Author Best Paper Award\, 2017 CTTC Early Achievement Award\, 2017 IEEE ComSoc AP Outstanding Paper Award\, and 2016-2018 Clarivate Analytics Highly Cited Researcher. \n 
URL:https://www.fst.um.edu.mo/event/ai-a-networking-and-communication-perspective/
LOCATION:E11-1006
CATEGORIES:ece_events
END:VEVENT
END:VCALENDAR