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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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TZID:Asia/Macau
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
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DTSTART:20170101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170103T150000
DTEND;TZID=Asia/Macau:20170103T160000
DTSTAMP:20260513T131239
CREATED:20170103T070036Z
LAST-MODIFIED:20220927T045330Z
UID:6001-1483455600-1483459200@www.fst.um.edu.mo
SUMMARY:Safer Refactorings with Assertions
DESCRIPTION:Instructors/Speakers\nDr. Volker Stolz\nBergen University College and the University of Oslo\nNorway \nAbstract\nRefactorings often require that non-trivial semantic correctness conditions are met. IDEs such as Eclipse’s Java Development Tools rely on simpler\, static pre- condition checks for refactorings. This leads to the phenomenon that a seemingly innocuous refactoring can change the behavior of the program. In this thesis we demonstrate our technique of introducing runtime checks of two particular refactorings for the Java programming language: Extract And Move Method\, and Extract Local Variable. These checks can\, in combination with unit tests\, detect changed behavior and identify the refactoring step that introduced it. \nBiography\nDr. Volker Stolz is an associate professor at the Bergen University College and the University of Oslo\, Norway. He is site-leader for the European Horizon 2020 project “COEMS — Continuous Observation of Embedded Multicore Systems”\, vice chair of the EU COST Action IC1402 “ARVI — Runtime Verification Beyond Monitoring”\, and Visiting Professor at the Guizhou Academy of Sciences\, Guiyang. Before moving to Norway\, Dr. Stolz held an Assistant Research Fellow position at UNU-IIST\, Macao (now UNU-CS)\, where he was principal investigator in the MSTDF-funded project on “Applied Runtime Verification”. \nHis interests are formal methods in software engineering\, correctness and verification of software\, and programming language semantics. \n 
URL:https://www.fst.um.edu.mo/event/safer-refactorings-with-assertions/
LOCATION:E11-1035
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170111T103000
DTEND;TZID=Asia/Macau:20170111T113000
DTSTAMP:20260513T131239
CREATED:20170111T023012Z
LAST-MODIFIED:20220927T045329Z
UID:6006-1484130600-1484134200@www.fst.um.edu.mo
SUMMARY:Modeling transcription factor binding affinity landscape through machine learning
DESCRIPTION:Instructors/Speakers\nProf. Xin GAO\nAssociate Professor of Computer Science\, Computational Bioscience Research Center (CBRC)\nKing Abdullah University of Science and Technology (KAUST)\nSaudi Arabia \nAbstract\nTranscription factors (TF) are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. An accurate characterization of TF-DNA binding affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. In this talk\, I will introduce two machine learning methods that we recently developed for modeling TF-DNA binding affinity. The first method is a two-round support vector regression with weighted degree kernel\, which can accurately capture important k-mers that contribute to high and low affinity values. In contrast\, the second method aims at incorporating both position-specific information and long-range interaction. It is an end-to-end learning framework that combines the strength of graphical models\, Hilbert space embedding\, and deep learning. \nBiography\nDr. Xin Gao is an associate professor of computer science in the Computer\, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST)\, Saudi Arabia. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo\, Canada. Prior to joining KAUST\, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University\, U.S.. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University\, China\, and his Ph.D. degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo\, Canada. \nDr. Gao’s research interests are building computational models\, developing machine learning techniques\, and designing efficient and effective algorithms\, with particular focus on applications to key open problems in structural biology\, systems biology and synthetic biology. He has co-authored more than 100 research articles in the fields of bioinformatics and machine learning. \n 
URL:https://www.fst.um.edu.mo/event/modeling-transcription-factor-binding-affinity-landscape-through-machine-learning/
LOCATION:E11-1012
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170111T143000
DTEND;TZID=Asia/Macau:20170111T153000
DTSTAMP:20260513T131239
CREATED:20170111T063022Z
LAST-MODIFIED:20220927T045329Z
UID:6011-1484145000-1484148600@www.fst.um.edu.mo
SUMMARY:The rare event study and its applications in the biological jump processes
DESCRIPTION:Instructors/Speakers\nProf. Tiejun LI\nProfessor\nSchool of Mathematical Sciences\nPeking University\nChina \nAbstract\nThe construction of energy landscape for bio-dynamics is attracting more and more attention recent years. In this talk\, I will introduce the strategy to construct the landscape from its connection with rare events\, which relies on the large deviation theory for Gillespie-type jump dynamics. In the application to a typical genetic switching model\, the two-scale large deviation theory is developed to take into account the fast switching of DNA states. The comparison with other proposals are also discussed. We demonstrate different diffusive limits arise when considering different regimes for genetic translation and switching processes. I will also talk about its applications in understanding the S-phase checkpoint activation mechanism for budding yeast. This is a joint work with Fangting Li\, Xianggang Li\, Cheng Lv and Peijie Zhou. \nBiography\nProf. Li obtained his bachelor degree and PhD at Tsinghua University and is now a full professor at Peking University\, his basic interest is the stochastic modeling and simulations in Science such as chemical reaction kinetics\, rare events\, Anderson localization\, multiscale modeling of complex fluids\, statistical data analysis and so on. Prof. Li has published more than 40 papers on PNAS\, J. Chem. Phys.\, Comm. Math. Phys. and so on. \n 
URL:https://www.fst.um.edu.mo/event/the-rare-event-study-and-its-applications-in-the-biological-jump-processes/
LOCATION:E11-1038
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170112T100000
DTEND;TZID=Asia/Macau:20170112T110000
DTSTAMP:20260513T131239
CREATED:20170112T020012Z
LAST-MODIFIED:20220927T045328Z
UID:6018-1484215200-1484218800@www.fst.um.edu.mo
SUMMARY:Local One Dimensional Embedding for Hyperspectral Image Classification
DESCRIPTION:Instructors/Speakers\nProf. Hong LI\nProfessor\nSchool of Mathematics and Statistics\nHuazhong University of Science and Technology\nWuhan \nAbstract\nHyperspectral image (HSI) classification aims to allocate a unique label to each pixel in the HSI dataset. However\, limited number of labeled pixels and high dimensionality of the HSI dataset makes HSI classification a challenging task. To address these issues\, we propose local one dimensional embedding (L1DE)\, which makes full use of the characteristics of HSI dataset. First\, pixels have similar spectral signatures should be the same class. Second\, pixels in the same spatial area are more likely to be the same class. Advantages of L1DE can be summarized as follows. The L1DE maps the high dimensional feature vector into 1D space. And it ensures that if the coordinates of two pixels are adjacent in the 1D space\, they are more likely to be close to each other in the original spatial domain\, and have similar spectral signatures. Additionally\, the local strategy used in L1DE can reduce the computation burden significantly. By use of L1DE\, we propose two frameworks for HSI classification: multiple L1DE interpolation (ML1DEI) and multiscale spatial information fusion (MSIF). Analysis of the computational cost for these frameworks are given in this report. Experimental results on four widely used HSI datasets indicate that\, the proposed frameworks show outstanding performance when compared with other state-of-the-art methods\, even when very limited labeled pixels are available. \nBiography\nProf. Hong Li is a Professor in School of Mathematics and Statistics\, Huazhong University of Science and Technology. Her research interests include Approximation and Calculation\, Wavelet Analysis and its Application\, Machine Leaning and Artificial Intelligence\, Pattern recognition and image processing\, Time-Frequency Analysis and Signal Processing. \n 
URL:https://www.fst.um.edu.mo/event/local-one-dimensional-embedding-for-hyperspectral-image-classification/
LOCATION:E11-1028
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170112T143000
DTEND;TZID=Asia/Macau:20170112T153000
DTSTAMP:20260513T131239
CREATED:20170112T063042Z
LAST-MODIFIED:20220927T045328Z
UID:6021-1484231400-1484235000@www.fst.um.edu.mo
SUMMARY:Synergy between Software Engineering and Big Data & Artificial Intelligence
DESCRIPTION:Instructors/Speakers\nProf. Tao Xie\nAssociate Professor\nDepartment of Computer Science\nUniversity of Illinois at Urbana-Champaign\nUSA \nAbstract\nBig data analytic or artificial intelligence (AI) systems are software systems too; thus\, software engineering for such software systems plays a critical role for improving development productivity and system dependability. On the other hand\, a huge wealth of various data exists in software life cycle\, including source code\, feature specifications\, bug reports\, test cases\, execution traces/logs\, and real-world user feedback\, etc. Data plays an essential role in modern software development\, because hidden in the data is information about the quality of software and services as well as the dynamics of software development. In recent years\, software analytics has emerged to utilize data-driven approaches to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for completing various tasks around software and services. This talk presents an overview of recent achievements and future opportunities in the space of software engineering for big data & AI and big data for software engineering. \nBiography\nTao Xie is an Associate Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign\, USA. He worked as a visiting researcher at Microsoft Research. His research interests are in software engineering\, focusing on software testing\, program analysis\, software analytics\, software security\, and educational software engineering. He received a 2016 Microsoft Research Outstanding Collaborators Award\, a 2014 Google Faculty Research Award\, 2008\, 2009\, and 2010 IBM Faculty Awards. He is an ACM Distinguished Speaker and an IEEE Computer Society Distinguished Visitor. He is an ACM Distinguished Scientist. His homepage is at http://taoxie.cs.illinois.edu. \n 
URL:https://www.fst.um.edu.mo/event/synergy-between-software-engineering-and-big-data-artificial-intelligence/
LOCATION:E4-G051\, Macau
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170116T113000
DTEND;TZID=Asia/Macau:20170116T123000
DTSTAMP:20260513T131239
CREATED:20170116T033028Z
LAST-MODIFIED:20220927T045328Z
UID:6027-1484566200-1484569800@www.fst.um.edu.mo
SUMMARY:Phase Retrieval
DESCRIPTION:Instructors/Speakers\nProf. Peter G. CASAZZA\nDirector of The Frame Research Center and Curators’ Professor\nDepartment of Mathematics\nUniversity of Missouri\nColumbia \nAbstract\nOver the 100 year history of phase retrieval\, it has had broad application to x-ray crystallography\, electron microscopy\, diffractive imaging\, DNA\, x-ray tomography and much more. Phase retrieval will even be needed to align the mirrors of the new James Webb Space Telescope scheduled for launch in 2018. We will start with the history and fundamentals of phase retrieval and its applications which have garnered a dozen Nobel Prizes over the years. Only recently have mathematicians entered this area to give a solid mathematical foundation to phase retrieval. In the second half of this talk we will look at recent advances in the mathematics of phase retrieval. \nBiography\nProf. Casazza is currently the Director of The Frame Research Center abd  Curators’ Professor of Department of Mathematics of University of Missouri. Prof. Casazza worked for 25 years in functional analysis (Banach space theory) and then switched into applied math. He does research in functional analysis\, (applied) harmonic analysis\, operator theory\, but his main research interest is in applications of Hilbert space frames to problems in mathematics\, applied mathematics and engineering. Go to the Frame Research Center to see papers and information on this subject. (http://www.framerc.org/) \n 
URL:https://www.fst.um.edu.mo/event/phase-retrieval/
LOCATION:E11-G015\, Taipa\, Macau
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170116T143000
DTEND;TZID=Asia/Macau:20170116T160000
DTSTAMP:20260513T131239
CREATED:20170116T063004Z
LAST-MODIFIED:20220927T045327Z
UID:6031-1484577000-1484582400@www.fst.um.edu.mo
SUMMARY:Fundamentals\, Properties\, and Applications of Polymer Nanocomposites
DESCRIPTION:Instructors/Speakers\nDr. Joseph H. Koo\nSenior Research Scientist\nPolymer Nanocomposites Technology Laboratory\nDepartment of Mechanical Engineering\nThe University of Texas at Austin \nAbstract\nThe introduction of inorganic nanomaterials as additives into polymers has resulted in polymer nanocomposites exhibiting a multiplicity of high-performance characteristics beyond what traditional polymeric composites possess. These “multifunctional” features attributable to polymer nanocomposites consist of improved properties\, such as thermal\, flame\, ablation\, electrical\, moisture\, chemical\, permeability\, and others. Through control/alteration of the additive at the nanoscale level\, one is able to maximize property enhancement of selected polymer systems to meet or exceed the requirements of current commercial\, military\, and aerospace applications. This seminar includes: an overview of different nanomaterials\, processing techniques\, and selective examples to examine the behavior of polymer nanocomposites for applications\, such as re-entry vehicles\, rocket engines\, additive manufacturing\, and fire protection. \nBiography\nDr. Koo has over 40 years of industrial and academic experience in program and engineering management. Currently\, he is Senior Research Scientist/Research Professor/Director of Polymer Nanocomposites Technology Lab in the Department of Mechanical Engineering at The University of Texas at Austin\, Austin\, TX. Dr. Koo is the founder of KAI\, LLC and currently serves as Vice President and CTO. He is a SAMPE Fellow and Chairman of the SAMPE Nanotechnology Committee. Dr. Koo is an Associate Fellow of AIAA and Past-Chair of the AIAA Materials Technical Committee. He specializes in polymer nanocomposites: processing\, characterization\, and applications\, such as ablatives for thermal protection systems\, flame retardant polymers\, fire resistant fabrics & textiles\, additive manufacturing\, thermally conductive polymer matrix composites\, sensor to measure in-situ ablation recession and thermal properties\, sensors to measure char strength\, thermophysical properties characterization\, ablation modeling\, modeling of polymer degradation\, and insensitive munitions technology of solid rocket motors. Dr. Koo’s publications include two books\, Polymer Nanocomposites: Processing\, Characterization\, and Applications\, McGraw-Hill\, New York (2006)\, and Fundamentals\, Properties\, and Applications of Polymer Nanocomposites\, Cambridge University Press\, Cambridge\, UK (2016)\, and over 500 papers/presentations in materials\, thermal and optical science disciplines. \n 
URL:https://www.fst.um.edu.mo/event/fundamentals-properties-and-applications-of-polymer-nanocomposites/
LOCATION:E11-4045
CATEGORIES:eme_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170116T150000
DTEND;TZID=Asia/Macau:20170116T160000
DTSTAMP:20260513T131239
CREATED:20170116T070007Z
LAST-MODIFIED:20220927T045327Z
UID:6037-1484578800-1484582400@www.fst.um.edu.mo
SUMMARY:Analysis of two-grid methods for miscible displacement problem by mixed finite element methods
DESCRIPTION:Instructors/Speakers\nProf. Yanping CHEN\nProfessor of School of Mathematical Sciences\nSouth China Normal University \nAbstract\nThe miscible displacement of one incompressible fluid by another in a porous medium is governed by a system of two equations. One is elliptic form equation for the pressure and the other is parabolic form equation for the concentration of one of the fluids. Since only the velocity and not the pressure appears explicitly in the concentration equation\, we use a mixed finite element method for the approximation of the pressure equation. In order to find a stable finite element discretization method\, we use different discretization method for the concentration equation\, such as finite element method with characteristic; mixed finite element method with characteristic; expanded mixed finite element method with characteristic etc. To linearize the discretized equations\, we use one (two) Newton iterations on the fine grid in our methods. Firstly\, we solve an original non-linear coupling problem. Then\, solve a linear system on the fine grid and while in second method we make a correction on the coarse grid between one (two) Newton iterations on the fine grid. We obtain the error estimates of two-grid method\, it is shown that coarse space can be extremely coarse and we achieve asymptotically optimal approximation. Finally\, numerical experiment indicates that two-grid algorithm is very effective. \nBiography\nProf. Chen Yanping currently is a professor from School of Mathematical Sciences at South China Normal University. She is also a Guangdong Provincial “Zhujiang Scholar”. Prof. Chen obtained her PhD degree at Shandong University\, and continued her postdoc research at Nanjing University. After that\, Prof. Chen worked at Xiangtan University\, and served as associate director of Hunan Key Laboratory for Computation and Simulation in Science and Engineering from 2002 to 2008. Since 2008\, Prof. Chen moved to South China Normal University. \n 
URL:https://www.fst.um.edu.mo/event/analysis-of-two-grid-methods-for-miscible-displacement-problem-by-mixed-finite-element-methods/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170123T150000
DTEND;TZID=Asia/Macau:20170123T160000
DTSTAMP:20260513T131239
CREATED:20170123T070041Z
LAST-MODIFIED:20220927T045326Z
UID:6042-1485183600-1485187200@www.fst.um.edu.mo
SUMMARY:Selected Research Highlights in Computing and AI
DESCRIPTION:Instructors/Speakers\nProf. James Kwok\nHong Kong University of Science and Technology \nAbstract\nWe will present our work in software engineering\, AI and mobile computing. In software engineering\, it is common to find user reviews in Android play store complaining the performance and energy issues in various Android applications. This talk introduces some recent studies made by the CASTLE research group at HKUST on these issues. We will discuss several interesting empirical findings made from the bug reports and code fixes of popular Android applications. Challenges and opportunities are identified. We will discuss some preliminary approaches and their results. \nIn AI\, big data is everywhere. Besides the huge data scale\, big data problems are also characterized by their high complexities. Often\, there are a lots of input features and involve a lot of learning tasks related in some complicated manner. In this talk\, I will describe several recent approaches in tackling these problems. These algorithms are flexible\, computationally efficient\, and have better empirical performance than existing approaches. \nIn mobile computing\, Mobile Augmented Reality (MAR) is widely regarded as one of the most promising technologies in the next ten years. With MAR\, we are able to blend information from our senses and mobile devices in myriad ways that were not possible before. The way to supplement the real world other than to replace real world with an artificial environment makes it especially preferable for applications such as tourism\, navigation\, entertainment\, advertisement\, and education. In this talk\, I will introduce some latest MAR research activities in our HKUST-DT Systems and Media Lab. \nBiography\nProf. Kwok is a Professor in the Department of Computer Science and Engineering\, Hong Kong University of Science and Technology. He received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. Prof. Kwok served/is serving as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems and the Neurocomputing journal\, and as Program Chair for a number of international conferences. He is an IEEE Fellow. \n 
URL:https://www.fst.um.edu.mo/event/selected-research-highlights-in-computing-and-ai/
LOCATION:E11-1041 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
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