BEGIN:VCALENDAR
VERSION:2.0
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
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:CST
DTSTART:20180101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180104T103000
DTEND;TZID=Asia/Macau:20180104T113000
DTSTAMP:20260610T051012
CREATED:20180104T023035Z
LAST-MODIFIED:20220927T043745Z
UID:6178-1515061800-1515065400@www.fst.um.edu.mo
SUMMARY:Learning to see: from Big-data driven to X-Data driven
DESCRIPTION:Instructors/Speakers\nProf. Shiguang Shan\nInstitute of Computing Technology (ICT)\nChinese Academy of Sciences (CAS) \nAbstract\nAI needs to see the world as human beings in order to complete its missions smoothly. In the last 5 years\, Computer Vision has made remarkable progress by leveraging ABC engines: Algorithms\, Big-data\, and Computing. In this talk\, I will firstly introduce the recent progress in computer vision\, which are mainly achieved by deep learning from big-data on GPU. Then\, I will analyze that the ABC methodology is not enough to develop a seeing AI. We need learning algorithms that can learn from more complex data conditions\, which is called by me X-data driven methods with X-data standing for small data\, noisy data\, weakly supervised data\, semi-supervised data\, and self-created or adaptively collected data. Some of the efforts to this direction in my lab will be introduced for discussion. \nBiography\nShiguang Shan is now a Professor with the Institute of Computing Technology (ICT)\, Chinese Academy of Sciences (CAS)\, where he received the Ph.D. in computer science in 2004. He is now the deputy director of the Key Lab of Intelligent Information Processing of CAS. He is also the founder/Chairman/CTO of Seeta Tech. Inc. He published more than 200 academic papers in refereed journals and proceedings in the areas of Computer Vision and Pattern Recognition\, and his work has been cited more than 12\,000 times in Google scholar. He especially focuses on face recognition and deep learning related researches. He has served as Area Chair for a number of international conferences including ICCV’11\, ICPR’12\, ACCV’12\, FG’13\, ICPR’14\, ICASSP’14\, ACCV’16\, and FG’18. He is Associate Editor of the IEEE Trans. on Image Processing\, the Neurocomputing\, the Journal of Computer Vision and Image Understanding\, and Pattern Recognition Letters. He is the winner of the China’s State Natural Science Progress Awards in 2015 for his work on non-linear visual modeling and learning\, the China’s State Scientific and Technological Progress Awards in 2005 for his work on face recognition technologies\, and the Best Student Poster Award Runner-up in CVPR’08. He also won the NSFC excellent young scientific fund in 2012 and the CCF young scientist award in 2015. \n 
URL:https://www.fst.um.edu.mo/event/learning-to-see-from-big-data-driven-to-x-data-driven/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180111T100000
DTEND;TZID=Asia/Macau:20180111T110000
DTSTAMP:20260610T051012
CREATED:20180111T020045Z
LAST-MODIFIED:20220927T043705Z
UID:6175-1515664800-1515668400@www.fst.um.edu.mo
SUMMARY:A Conceptual Framework for Multi-sourced Event Management and Multi-dimensional Analysis
DESCRIPTION:Instructors/Speakers\nProf. Qing LI\nCity University of Hong Kong \nAbstract\nThe publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities\, personal views and expressions\, which points to the importance of understanding and discovering the knowledge patterns underlying the big data\, and the need of developing methodologies and techniques to discover real-world events from such big data\, to manage and to analyze the discovered events in an efficient and elegant way. In this talk we introduce techniques of discovering events from the multi-modal big data and building an event cube model to support event queries and analysis\, by addressing the tasks of data cleansing\, data fusion\, event detection and modeling. Preliminary experimental results on some of the tasks will be reported. We further explore and connect the important events discovered in a multimodal collection of inputs from various public sources\, uncover their co-occurrence and track down the spatial and temporal dependency to answer the challenging questions of “how” and “why”. A novel event cube (EC) model is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. More specifically\, based on essential event elements of 5W1H\, the EC model is developed to organize the discovered events from multiple dimensions\, and to operate on the events at various levels of granularity\, which facilitates analyzing and mining hidden/inherent relationships among the events effectively. \nBiography\nQing Li is a Professor at the Department of Computer Science\, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong\, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha)\, and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles)\, all in computer science. His research interests include multi-modal data management\, conceptual data modeling\, social media and Web services\, and e-learning systems. He has authored/co-authored over 400 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE)\, ACM Transactions on Internet Technology (TOIT)\, Data and Knowledge Engineering (DKE)\, World Wide Web (WWW)\, and Journal of Web Engineering\, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA\, ER\, ACM RecSys\, IEEE U-MEDIA\, and ICWL. Prof. Li is a Fellow of IET (UK)\, a senior member of IEEE (US) and a distinguished member of CCF (China). \n 
URL:https://www.fst.um.edu.mo/event/a-conceptual-framework-for-multi-sourced-event-management-and-multi-dimensional-analysis/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180111T110000
DTEND;TZID=Asia/Macau:20180111T120000
DTSTAMP:20260610T051012
CREATED:20180111T030001Z
LAST-MODIFIED:20220927T043705Z
UID:6172-1515668400-1515672000@www.fst.um.edu.mo
SUMMARY:Smart Medicine: Medical Data Mining and Innovative Applications with Patient Monitoring and Whole-life Cycle Management
DESCRIPTION:Instructors/Speakers\nProf. Yanchun ZHANG\nVictoria University\, Austria\nFudan University\, China \nAbstract\nDue to the recent development or maturation of database\, data storage\, data capturing\, and sensor technologies\, huge medical and health data have been generated at hospitals and medical organizations at unprecedented speed. Those data are a very valuable resource for improving health delivery\, health care and decision making and better risk analysis and diagnosis. Health care and medical service is now becoming more data-intensive and evidence-based since electronic health records are used to track individuals’ and communities’ health information (particularly changes). These substantially motivate and advance the emergence and the progress of data-centric health data and knowledge management research and practice. \nIn this talk\, we will introduce several innovative data mining techniques and case studies to address the challenges encountered in e-health and medical big data. This includes techniques and development on medical data streams\, correlation analysis\, abnormally detection and risk predictions with patient monitoring and aging care applications. \nBiography\nYanchun Zhang is a Professor and Director of Centre for Applied Informatics at Victoria University since 2004. Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases\, data mining\, web services and e-health. He has published over 300 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI)\, IEEE Transactions on Knowledge and Data Engineering (TKDE)\, VLDBJ\, SIGMOD and ICDE conferences\, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer)\, and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). He was a member of Australian Research Council’s College of Experts (2008-2010)\, and also serves as expert panel member at various international funding agencies such as National Natural Science Fund of China (NSFC)\, “National 1000 Talents Program” of China\, the Royal Society of New Zealand Marsden Fund and National Natural Science Fund of China (NSFC). He is one of the National “Thousand Talents Program” Experts in China since 2010 (currently with Fudan University). \n 
URL:https://www.fst.um.edu.mo/event/smart-medicine-medical-data-mining-and-innovative-applications-with-patient-monitoring-and-whole-life-cycle-management/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180122T150000
DTEND;TZID=Asia/Macau:20180122T160000
DTSTAMP:20260610T051012
CREATED:20180122T070047Z
LAST-MODIFIED:20220927T043705Z
UID:6165-1516633200-1516636800@www.fst.um.edu.mo
SUMMARY:Hybrid Methods Guide Structure Based Vaccine Design for Picornaviruses
DESCRIPTION:Instructors/Speakers\nDr. Abhay KOTECHA\nDivision of Structural Biology\nUniversity of Oxford \nAbstract\nThe physical properties of viral capsids are major determinants of vaccine efficacy for picornaviruses which impact on human and animal health. Current vaccines are produced from inactivated virus. Inactivation often reduces the stability of the virus capsid\, causing a problem for Foot and Mouth Disease Virus (FMDV) where certain serotypes fall apart into pentameric assemblies below pH 6.5 or at temperatures above 37°C\, destroying their effectiveness in eliciting a protective immune response. As a result\, vaccines require a cold chain for storage and animals need to be frequently immunised. Globally there are seven FMDV serotypes: O\, A\, Asia1\, C and SAT-1\, -2 and -3\, contributing to a dynamic pool of antigenic variation. We sought to rationally engineer FMDV capsids either as infectious copy virus or recombinant empty capsids with improved thermo-stability for improved vaccines. Here we used in-silico MD simulations\, molecular modelling\, free energy calculations\, X-ray crystallography\, Cryo-electron microscopy (CryoEM) and various biochemical/biophysical techniques to design and help characterise the improved capsids. For the most unstable FMDV serotypes (O and SAT2)\, panels of stabilising mutants were characterised. Promising candidates were then engineered and shown to confer increased thermo- and pH-stability. Thus\, in-silico predictions translate into marked stabilisation of both infectious and recombinant empty capsids. \nAn in-situ diffraction method was used to determine crystal structures to verify that no unanticipated structural changes have occurred as a consequence of the modifications made. Where it was difficult to obtain crystals/diffraction\, structures were determined by high-resolution CryoEM (with the best electron density maps reaching 2.7Å resolution). The structures of the wildtype and two of the stabilised mutants for three different serotypes of FMDV showed the mutations made predicted interactions and the antigenic surfaces remained unchanged. \nAnimal trials showed stabilised particles can generate improved neutralising response compared to the traditional vaccines. Similar approach applied to the polio virus successfully produced antigenic VLPs using the plant based expression system. CryoEM reconstruction of polio VLPs produced 3.6Å resolution maps and the structure analysis suggested the plant based polio particles are identical to the native virus. We have successfully used a structure based rational engineering approach to increase the stability of viral capsids without affecting the antigenicity and demonstrated the direct application of structural biology and structure based design that has the potential to lead directly to a new generation of efficacious vaccines that can provide hope that the disease can be brought under control. \nIn addition\, using CryoEM\, CryoET and Focus Ion Beam milling of the infected cells\, we are working towards understanding the picronavirus life cycle in molecular details. To this end\, using localised reconstruction\, we have determined the interaction between αvβ6 and two FMDV strains at high resolution. In the preferred mode of engagement the fully open form of the integrin\, hitherto unseen at high-resolution\, attaches to an extended GH loop via interactions with the RGD motif plus downstream hydrophobic residues. In addition\, an N-linked sugar of the integrin attaches to the previously identified HS binding site\, suggesting a functional role. Finally using Ion Beam milling\, we have begun to visualise and understand the formation of virus production factories inside the infected cells. In-situ high resolution structures will allow us to see the virus life cycle in its native state and also lead to potential new targets for next generation vaccine design. \nBiography\nAbhay Kotecha is a senior research associate at the Division of Structural Biology\, Nuffield Department of Clinical Medicine\, University of Oxford\, UK. Dr. Kotecha obtained his Bachelor’s degree with Honours in Cell and Molecular Biology from Oxford Brookes University\, Oxford in June 2008 and his DPhil in Clinical medicine from University of Oxford in March 2013. During his studies\, Dr. Kotecha worked in several world class laboratories and received many fellowships\, including EMBO summer research studentship to learn advanced electron microscopy techniques at the Electron Microscopy Core Facility\, EMBL\, Heidelberg\, Germany. BBSRC/STFC research fellowship to spend a year at Science and Technology Facilities Council\, Synchrotron light source\, Daresbury\, UK where he worked on membrane protein X-ray crystallography of light harvesting complexes. Undergraduate research studentship\, Imperial College\, London. Dr. Kotecha was awarded the prestigious Wellcome Trust DPhil studentship in structural biology at the University of Oxford and the Clarendon Fund Scholarship from Oxford University Press to study viruses in atomic details. His research interests are on structure based vaccine design for infectious viruses using X-ray crystallography and Cryo-Electron Microscopy. His work funded by The Wellcome Trust Translational Award and Gates Foundation aimed at developing stable synthetic vaccines. He has successfully developed novel empty particle vaccines for FMDV which is now being taken up by commercial partners for industrial production. This work has a very broad national and international impact and may also be applicable across a range of human and animal pathogens. Dr. Kotecha has published more than 20 papers in high impact journals most of which has received national and international media coverage. \nHe is now investigating the stabilised virus particles in cellular context to understand the virus receptor interaction\, disassembly of stabilised viruses and the genome release as well as the assembly of newly synthesised particles to generate new targets for next generation of vaccines. \n 
URL:https://www.fst.um.edu.mo/event/hybrid-methods-guide-structure-based-vaccine-design-for-picornaviruses/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
END:VCALENDAR