Can Deep Learning Learn to Count? on cognitive deficit of the current state of deep learning
2018-12-07 11:00 - 12:00 (Asia/Macau) | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Xiaolin WU
IEEE Fellow
McMaster University
Canada
Abstract
Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the connectionist CNN machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful as visual numerosity represents a minimum level of human intelligence.
Biography
Xiaolin Wu, Ph.D. in computer science, University of Calgary, Canada, 1988. Dr. Wu started his academic career in 1988, and has since been on the faculty of University of Western Ontario, New York Polytechnic University (NYU Poly), and currently McMaster University, where he is a McMaster Distinguished Engineering Professor at the Department of Electrical & Computer Engineering and holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing, multimedia signal coding and communication, joint source-channel coding, multiple description coding, and network-aware visual communication. He has published over three hundred research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow, an associated editor of IEEE Transactions on Image Processing, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors.

Is a Picture Worth a Thousand Words? A Deep Learning Approach for Assessing the Impact of Investors’ Emotions Mined from Multimodal Social Postings on Corporate Credit Ratings
2018-12-11 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Raymond Yiu Keung LAU
Department of Information Systems
City University of Hong Kong
Abstract
With the rise of the Social Web, it is increasingly more popular for investors to express their feelings about firms and products on online social media. Though previous studies have examined the relationship between investors’ sentiments captured in texts (e.g., online news articles) and corporate credit ratings, there is a research gap in terms of studying the relationship between investors’ emotions captured in multimodal social postings and corporate credit ratings. By drawing upon the appraisal theory and the notion of affect-as-information, this study aims to fill the current research gap by examining the influence of investors’ emotions (e.g., trust, joy, and anger) captured in multimodal social postings on corporate credit ratings. In particular, we apply state-of-the-art machine learning methods (e.g., topic modeling and deep convolutional neural networks) to extract investors’ emotions toward corporations from both texts and images posted to online social media. Our empirical results show that investors’ emotions such as “trust” and “joy” captured in textual social postings are positively associated with corporate credit rating, while investors’ emotion such as “anger” captured in the images of the social postings is negatively associated with corporate credit rating. Moreover, the negative influence of the “anger” emotion extracted from images is larger than the positive influence of “trust” and “joy” extracted from texts. The managerial implication of our study is that corporations should pay attention to both texts and images posted to online social media by investors as these postings affect corporate credit ratings, and subsequently the costs of corporate borrowings. Our findings also offer new insights for corporations, investors, and ratings agencies with respect to the predictive power of investors’ multimodal social posts toward corporate credit ratings.
Biography
RAYMOND Y.K. LAU is an Associate Professor in the Department of Information Systems at City University of Hong Kong. He has worked at the academia and the ICT industry for over twenty years. He is the author of more than 200 refereed international journals and conference papers. His research work is published in renowned journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Knowledge and Data Engineering, IEEE Intelligent Systems, IEEE Internet Computing, INFORMS Journal on Computing, MIS Quarterly, ACM Transactions on Information Systems, etc. His research interests include Financial Technology (FinTech), Social Media Analytics, Big Data Analytics, and Artificial Intelligence (AI) for Business. He is a senior member of the IEEE and the ACM, respectively.

Improving Information Security in Ranking Systems and Control Systems
2018-12-13 15:00 - 16:00 | E11-1006
Instructors/Speakers
Dr. Guilherme RAMOS
Ph.D. (2018)
Information Security at Instituto Superior Técnico
Lisbon, Portugal
Abstract
In this work, we propose to advance in the field of information security in the areas of ranking systems (RS) and control systems, using ideas from the area of information theory. First, we propose a RS that groups users based on their preferences, introducing similarity measures. The system presents possibly distinct rankings for the same product in different user groups. Besides presenting more personalized rankings to users, it is a system more resistant to attacks than the state-of-the-art. We then explore the effect of bribing in reputation-based RS, in the usual scenario (a ranking for each product) and in the scenario we proposed. We find the optimal bribing strategies, and we evaluate our methodology with real data, being the devised ranking system more robust to bribery. Finally, in control systems, we present methods to find the placement of the minimum number of inputs in LTI systems and switched LTI systems, in the eventual scenario where a set of controllers may fail, e.g., due to a cyberattack. In the first case, we prove that the problem is NP-complete. We design algorithms to solve the problems explicitly, and also to approximate the solution in polynomial time.
Keywords: Ranking Systems, Control Systems, Information Security, Bribing.
Biography
Dr. Guilherme RAMOS has a Ph.D. (2018) in Information Security at Instituto Superior Técnico, Lisbon, Portugal, through the doctoral programme in Physics and Mathematics of Information. He received the B.Sc. (2011) and M.Sc. (2013) degrees in Applied Mathematics from the Instituto Superior Técnico, Lisbon, Portugal. His areas of interest are: Structural Systems; Control Theory; Cryptography; Information Theory; Ranking Systems; Recommender Systems; Consensus Algorithms.

On Text Analysis and Its Applications
2018-12-14 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Raymond WONG
School of Computer Science & Engineering
University of New South Wales, Sydney
Australia
Abstract
Due to the increasing computation power and availability of big text data, text analysis research, such as text mining, natural language processing, and sentiment analysis, has drawn significant attention from both research and industry communities. This seminar presents and summarizes our recent research findings in the area of text analysis, and shares our experience in several related applications.
Biography
Raymond Wong is an Associate Professor at the School of Computer Science & Engineering, University of New South Wales, Sydney, Australia. From 2005-2011, he founded and led the Data Management Program at NICTA (the largest ICT organization in Australia), and was a Visiting Professor at Tsinghua University, Beijing in 2011-13. Raymond co-founded several companies including Cohesive Data Inc that have successfully exited. He has also been involved in numerous technology fund raising, M&A, and patent valuation activities. His technical expertise lies in database systems, big data, machine learning and NLP. He has published more than 200 research publications and 2 US patents in these areas. He has also supervised 20 PhD students to completion. He received his BSc from Australian National University; MPhil and PhD from Hong Kong University of Science & Technology; and did his Postdoc at UCLA and Stanford University.

Smart City and Sustainability Forum
2018-12-18 14:30

Future Electricity Supply for Smart City
2019-01-03 14:30 | N1-1005

Recent Progress on Deep Learning Based Face Recognition, Analysis and Translation
2019-01-04 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Linlin SHEN
Shenzhen University
Shenzhen
China
Abstract
In this talk, I will mainly introduce deep learning and its applications in face recognition, analysis and translation. The history of face recognition using hand-crafted features and deep neural networks will be firstly briefed. Publicly available face datasets for both training and testing will be introduced. The most recent progress about the performance of facial recognition algorithms and their real applications will be followed. The methodologies to address face spoofing and classification of facial attributes including age, gender and expressions will be explored. Finally, the progress about face translation using GAN, pix2pix, starGAN and our proposed GAN will be presented.
Biography
Prof. Linlin Shen is currently a professor at Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. He is also an Honorary professor at School of Computer Science, University of Nottingham, UK. He serves as the director of Computer Vision Institute and China-UK joint research lab for visual information processing. He received the BSc and MEng degrees from Shanghai Jiaotong University, Shanghai, China, and the Ph.D. degree from the University of Nottingham, Nottingham, U.K. He was a Research Fellow with the University of Nottingham, working on MRI brain image processing. His research interests include deep learning, facial recognition, analysis/synthesis and medical image processing. Prof. Shen is listed as the Most Cited Chinese Researcher by Elsevier. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016.

Infiltration and excess pore water pressure in front of a tunnel boring machine (TBM): Experiments, Mechanisms and Computational models
2019-01-24 10:00 - 11:00 | E11-3033
Instructors/Speakers
Dr. Tao XU
Post-Doctoral Fellow
Faculty of Engineering and Architecture
Ghent University
Belgium
Abstract
The Tunnel Boring Machine (TBM) tunneling technique has been developed to construct tunnels that require strict settlement control, for example, in urban areas with a large amount of buildings, historic areas etc. When tunneling with a TBM in saturated sand, groundwater flow into the excavation face needs to be impeded. Both stability of the tunnel face and limitation of the groundwater flow are achieved by pressurizing the drilling fluid at the tunnel face. Due to the pressure difference between the excavation chamber of the TBM and the ground, drilling fluid (slurry or foam) will infiltrate into the ground and thus there will be a flow in front of the tunnel face. In such a situation, part of support pressure applied through the drilling fluid at the tunnel face will be transferred into excess pore water pressure in the ground. As a result, the effective support pressure at the tunnel face and thus the stability of tunnel face will be reduced. The reduction of the effective support pressure depends on the infiltration distance, the infiltration velocity and the drilling speed. However, the infiltration distance of the foam or the slurry, influence of this infiltration on the permeability of the infiltrated ground, and consequently on the excess pore water pressure, are still only partly understood. Finding answers to these questions, therefore, is significantly important for the safety of tunnel.
The purpose of this study is to improve our understanding of the mechanism of drilling fluid infiltration in front of the tunnel face through laboratory experiments, and develop computational models considering such an infiltration process to predict the excess pore water pressures induced by TBM tunneling. The research concerns both slurry and Earth Pressure Balance shields (EPB shields). With the results of the experiments, a sounder theoretical basis for the description of infiltration of drilling fluid (slurry or foam), and the computation of excess pore water pressure in front of tunnel face have been established.
Biography
Currently, Dr. Tao Xu is a Post-Doctoral Fellow at Ghent University. In 2018 he gained his PhD from Ghent University. His research has been published in top geotechnical journals Géotechnique and Tunnelling and Underground Space Technology. Prior to his doctoral research, he carried out research on hydro-mechanical behavior of unsaturated soils and stability of unsaturated soil nailed slopes subjected to rainfall at Harbin Institute of Technology from 2012 to 2014. In 2014, he started his PhD at Ghent University. His doctoral research focused on slurry and foam infiltration in front of a TBM and excess pore water pressures caused by TBM drilling. In the meantime, as an invited guest researcher, he participated in German Collaborative Project SFB 837 ‘Interaction modelling in mechanized tunneling’. He will join University of Macau on March 2019 as a research fellow responsible for assisting on establishing TBM tunneling research.

AI in Bloomberg
2019-02-13 14:30 - 15:30 | E11-G015 (University of Macau)
Instructors/Speakers
Mr. Iat Chong CHAN
Machine Learning Team, London, United Kingdom
Abstract
The Bloomberg Terminal brings together real-time data on every market, breaking news, in-depth research, powerful analytics in one fully integrated solution. In the News product, we provide, our award-winning news coverage ensures our clients could get the information they need. While at the same time, putting a lot of effort into trying to avoid overloading them with excessive information.
In this talk, we will have a general overview of how AI (Artificial intelligence) techniques are being utilised in Bloomberg to allow our clients obtaining the information needed efficiently. Then, we will focus on a particular application, which was designed to refine information from a massive amount of news stories. We will also discuss the AI algorithms that underpin it.
There is no pre-requisite for most part of this talk. 30% of the talk requires basic understanding of statistics and probabilities.

Biography
Iat Chong Chan (https://www.linkedin.com/in/iatchongchan) is a research scientist/software developer in Bloomberg Machine Learning Team. His interests mostly lie in the intersection of Computational Linguistics, Machine Learning, and High Performance Computing. He has been working on a scalable infrastructure to infer topics of social contents ingested to Bloomberg by statistical models, and a multi-documents summarisation system to extract the most important information from a text collection. Iat Chong also leads the NLP guild inside Bloomberg, to advocate the use of ML/NLP techniques for new business problems. Before he joined the company, he was a MSc student in Dept. of Computer Science at University of Oxford, supervised by Prof. Stephen Pulman, and worked on building a better input method on small hand-held devices by a novel Bayesian Network with Variational Inference.

Drone-assisted Mobile Edge Computing
2019-03-20 15:30 - 16:30 | E11-1006
Instructors/Speakers
Prof. Nirwan ANSARI
Distinguished Professor
Electrical and Computer Engineering, Newark College of Engineering
New Jersey Institute of Technology
New Jersey, USA
Abstract
In mobile access networks, different types of Internet of Things (IoT) devices (e.g., sensor nodes and smartphones) will generate vast traffic demands, thus dramatically increasing the traffic loads of their connected access nodes, especially in the 5G era. Mobile edge computing enables data collected by IoT devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users, drone base station (DBS), which can be flexibly deployed over hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) DBS should be deployed in suitable areas with heavy traffic demands to serve more users; 2) traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a TrAffic Load baLancing (TALL) scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two sub-problems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of TALL.
Biography
Nirwan Ansari is Distinguished Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). He has also been a visiting (chair) professor at several universities. Professor Ansari authored Green Mobile Networks: A Networking Perspective (IEEE-Wiley, 2017) with T. Han, and co-authored two other books. He has also (co-)authored more than 550 technical publications, over 250 published in widely cited journals/magazines. He has guest-edited a number of special issues covering various emerging topics in communications and networking. He has been serving on the editorial/advisory board of more than ten journals. His current research focuses on green communications and networking, cloud computing, drone-assisted networking, and various aspects of broadband networks.
Professor Ansari was elected to serve in the IEEE Communications Society (ComSoc) Board of Governors as a member-at-large, has chaired some ComSoc technical and steering committees, has been serving in many committees such as the IEEE Fellow Committee, and has been actively organizing numerous IEEE International Conferences/Symposia/Workshops. He has frequently been delivering keynote addresses, distinguished lectures, tutorials, and invited talks. Some of his recognitions include IEEE Fellow, several Excellence in Teaching Awards, a few best paper awards, the NCE Excellence in Research Award, the ComSoc TC-CSR Distinguished Technical Achievement Award, the ComSoc AHSN TC Technical Recognition Award, the IEEE TCGCC Distinguished Technical Achievement Recognition Award, the NJ Inventors Hall of Fame Inventor of the Year Award, the Thomas Alva Edison Patent Award, Purdue University Outstanding Electrical and Computer Engineering Award, NCE 100 Medal, and designation as a COMSOC Distinguished Lecturer. He has also been granted 38 U.S. patents.
He received a Ph.D. from Purdue University—West Lafayette, IN, an MSEE from the University of Michigan—Ann Arbor, MI, and a BSEE (summa cum laude with a perfect GPA) from NJIT—Newark, NJ.

AI Driven Smart City Development
2019-03-25 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Jane YOU
Department of Computing at the Hong Kong Polytechnic University
Hong Kong
Abstract
The 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.
These 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.
Biography
Jane 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.
Prof. 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.

Security Challenges in the Internet of Things
2019-04-02 16:00 - 17:00 | E11-1006 (University of Macau)
Instructors/Speakers
Prof. Kui REN
School of Computer Science and Technology
Zhejiang University
China
Abstract
The 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.
Biography
Kui 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.

35th IEEE International Conference on Data Engineering 2019 (ICDE 2019)
2019-04-08 - 2019-04-12

Spatial and Spatio-Temporal Data Analytics and their applications in urban cities
2019-04-12 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Cheng LONG
The School of Computer Science and Engineering (SCSE)
Nanyang Technological University (NTU)
Singapore
Abstract
It 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.
Biography
LONG 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.

The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019)
2019-04-14 - 2019-04-17

Comprehensive strategies for eco-environment remediation on the Chinese Loess Plateau
2019-04-16 15:00 | E12-G004

Efficient and accurate structure preserving schemes for a class of complex nonlinear systems
2019-05-06 11:00 - 12:00 | E11-2027
Instructors/Speakers
Prof. Jie SHEN
Professor of Department of Mathematics and Director of Center for Computational and Applied Mathematics
Purdue University
U.S.A.
Abstract
We 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.
We 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.
Biography
Prof. 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.

Wearable and Transparent Bioelectronics for Wireless Remote Sensing
2019-05-09 10:00 - 11:00 | E11-1036
Instructors/Speakers
Prof. Mark Ming-Cheng CHENG
Associate Professor of Department of Electrical and Computer Engineering
Biomedical Engineering and Director of Nanofabrication Core (nFab)
Wayne State University
U.S.A
Abstract
In 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.
Biography
Prof. 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.

Research on Cross Polarization Discrimination (XPD) for Base Station Antennas
2019-05-16 15:00 - 16:30 | E11-4045
Instructors/Speakers
Prof. Yu LUO
Professor
the School of Microelectronics
Tianjin University
China
Abstract
Cross Polarization Discrimination is a key factor to evaluate the performance of base station antennas. In this talk, two novel methods to design ±45° dual-polarized base-station antennas with enhanced cross polarization ratio (XPD) are presented. The first method to improve XPD is by addition of four horizontal parasitic elements around a simple ±45° dual-polarized base-station antenna. The second method is by addition of four vertical parasitic elements between a simple ±45° dual-polarized base-station antenna and a reflective ground. Two ±45° dual-polarized base-station antennas were designed, designed, fabricated and measured to verify the two proposed methods. In addition, the proposed methods can also be employed to wide axial ratio beamwidth circularly polarized antenna design.
Biography
Prof. Yu LUO received his B.S. and the Ph.D. degree in electronic engineering from South China University of Technology, Guangzhou, Guangdong, China, in 2010 and 2015, respectively. He worked as a research assistant at the University of Macau, Macau SAR, during Apr. 2014-Sep. 2014, worked as a post-doctoral fellow in the University of Victoria, BC, Canada during Sep. 2015-Aug. 2016 and worked as a research fellow at the National University of Singapore during Sep. 2016-Sep. 2018. Currently, he works as a full professor in the School of Microelectronics, Tianjin University. His research interest focuses on antennas in new generation mobile communications and synthetic aperture radar (SAR) systems, such as SIW antennas, base-station antennas, circularly polarized antennas, MIMO antennas, and Yagi-Uda antennas. He is a Member of IEEE, reviewer of many International Journals, and member of TPC of many International conferences.

Parallel algorithms for the simulation of blood flows in human artery
2019-05-28 14:30 - 15:30 | E11-G015

Application of Structure Descriptor for Rational Design of Transition Metal Catalysts
2019-05-31 11:00 - 12:00 | E11-1028
Instructors/Speakers
Prof. Daojian CHENG
Professor of Department of Chemical Engineering
Beijing University of Chemical and Technology
China
Abstract
In 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.
Biography
Prof. 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.

Bug Detection and Execution Replay for Concurrent Software Systems
2019-06-13 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Zijiang YANG
Western Michigan University
United States
Abstract
Bugs 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.
Biography
Zijiang 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).

Learning from Blockchain
2019-06-21 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Jeff SANDERS
Academic Director of the African Institute for Mathematical Sciences (AIM)
Professor of Mathematics of Stellenbosch University
South Africa
Abstract
The 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.
Biography
Jeff 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.

Wireless Technology Study Summer Camp 2019
2019-06-26 - 2019-06-28

Extrapolation multigrid methods for solving large linear system arising from the discretizations of PDEs
2019-07-03 10:30 - 11:30 | E11-2027
Instructors/Speakers
Prof. Kejia PAN
Professor
School of Mathematics and Statistics
Central South University
China
Abstract
The 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.
Biography
Prof. 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.

Deep Learning and Computer Vision (DeepVision) Summer Camp 2019
2019-07-04 - 2019-07-05

Sustainable Energy Storage Systems Summer Camp 2019
2019-07-04 - 2019-07-06

Extremely Low Order Explosive Models from Combustion Process
2019-07-04 10:00 - 11:00 | E11-2027
Instructors/Speakers
Prof. Yufeng XU
Associate Professor
School of Mathematics and Statistics
Central South University
China
Abstract
In 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.
Biography
Prof. 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.

Robotics and Artificial Intelligence Summer Camp 2019
2019-07-08 - 2019-07-09

CN-WSGD schemes and CN-EWSGD schemes for space-fractional advection-diffusion equations
2019-07-10 10:30 - 11:30 | E11-2027
Instructors/Speakers
Prof. Furong LIN
Professor
Department of Mathematics
Shantou University
China
Abstract
We 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.
Biography
Prof. 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.
