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Past Seminars and Events
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| October 28, 2019 |
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| October 24, 2019 |
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| September 27, 2019 |
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Title: What is Virtual Bank? (Co-organized with HKUGA)
Time: 05:30pm
Venue: Room MBG07, Main Building, The University of Hong Kong
Speaker(s): Mr. Lawrence Li & Dr. S.M. Yiu
Remark(s):
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Title: Using and reusing coherence to realize quantum processes
Time: 02:00pm
Venue: Rm 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Matteo Rosati, Universitat Autonoma de Barcelona
Remark(s): Abstract:
Using and reusing coherence to realize quantum processes Coherent superposition is a key feature of quantum mechanics that underlies the advantage of quantum technologies over their classical counterparts. Recently, coherence has been recast as a resource theory in an attempt to identify and quantify it in an operationally well-defined manner.Here we study how the coherence present in a state can be used to implement a quantum channel via incoherent operations and, in turn, to assess its degree of coherence. We introduce the robustness of coherence of a quantum channel-which reduces to the homonymous measure for states when computed on constant-output channels-and prove that: i) it
quantifies the minimal rank of a maximally coherent state required to implement the channel; ii) its logarithm quantifies the amortized cost of implementing the channel provided some coherence is recovered at the output; iii) its logarithm also quantifies the zero-error asymptotic cost of implementation of many independent copies of a channel. We also consider the generalized problem of imperfect implementation with arbitrary resource states. Using the robustness of coherence, we find that in general a quantum channel can be implemented without employing a maximally coherent resource state. In fact, we prove that every pure coherent state in dimension larger than 2, however weakly so, turns out to be a valuable resource to implement some coherent unitary channel. We illustrate our findings for the case of single-qubit unitary channels.
About the Speaker:
Matteo Rosati did his BSc and MSc studies in Physics (2009-2014) at Università La Sapienza, Rome, studying the modelling of disordered and complex systems under the supervision of Prof. Giorgio Parisi. He took his PhD in Theoretical Physics (2017) at Scuola Normale Superiore,
Pisa with Prof. Vittorio Giovannetti, with a thesis aimed at devising efficient and implementable decoders for classical communication on quantum guassian channels. Since then, he has been a postdoctoral fellow at the Universitat Autonoma de Barcelona, working with Profs. Andreas
Winter and John Calsamiglia on resource theories and quantum learning.
In 2019 he has been awarded a Marie Skłodowska-Curie Fellowship from the EU, starting in January 2020.
All are welcome!
For enquiries, please call 2859 2180 or email enquiry@cs.hku.hk
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| September 06, 2019 |
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Title: The Power of Data Analytics and AI Techniques in the Digital Sector
Time: 05:30pm
Venue: Lecture Theatre A, Ground Floor, Chow Yei Ching Building, Main Campus, HKU
Speaker(s): Mr Alan Chan
Remark(s): Speaker:
Mr Alan Chan
Executive Vice President Lazada (Alibaba's SE Asia Commerce Business)
Date: September 6, 2019 (Friday)
Time: 5:30 - 6:45pm (Refreshments will be served from 5:00pm)
Venue: Lecture Theatre A, Ground Floor, Chow Yei Ching Building, Main Campus, HKU
About the talk:
In this talk, Mr Alan Chan will introduce how data analytics and AI techniques are used in the digital sector, and the changes that the industry is facing now and in the future. He will also share some tips on starting a career in data and analytics.
About the speaker:
With a background in strategy and analytics, and having led several organisations through their digital transformations, Alan is the Executive Vice President in Lazada (Alibaba’s South East Asia Commerce Business) and also part of the Alibaba Management Council. Alan joined Alibaba Group in 2016 and took on management roles in marketplace policy setting, data analytics and platform governance.
Before joining Alibaba, he spent 13 years in consulting with Accenture and left in 2016 as the Managing Director and Partner of Accenture Digital team in China. Alan is passionate about leadership, digital marketplaces and data science.
Outside of work, Alan engages actively in university collaborations and serves on the ex-officio board of a few start-ups in Asia. He received his Honors Degree in Economics and Statistics from the National University of Singapore, and is currently residing in Singapore.
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| August 28, 2019 |
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Title: Learning Neural Character Controllers from Motion Capture Data
Time: 03:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Taku Komura
Remark(s): Prof. Taku Komura
Institute of Perception, Action and Behaviour
School of Informatics
University of Edinburgh
Date: August 28, 2019 Wednesday
Time: 3:00pm
Venue: Room 328 Chow Yei Ching Building The University of Hong Kong
Abstract:
I will cover our recent development of neural network-based character controllers. Using neural networks for character controllers significantly increases the scalability of the system - the controller can be trained with a large amount of motion capture data while the run-time memory can be kept low. As a result, such controllers are suitable for real-time applications such as computer games and virtual reality systems. The main challenge is in designing an architecture that can produce movements in production-quality and also manage a wide variation of motion classes. Our development covers lowlevel locomotion controllers for bipeds and quadrupeds, which allow the characters to walk, run, sidestep and climb over uneven terrain, as well as a high level character controller for humanoid characters to interact with objects and the environment, which allows the character to sit on chairs, open doors and carry objects. In the end of the talk, I will discuss about the open problems and future directions of character animation.
About the speaker:
Taku Komura is a Professor at the Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh. As the leader of the Computer Graphics and Visualization Unit his research has focused on data-driven character animation, physically-based character animation, crowd simulation, cloth animation, anatomy-based modelling, and robotics. Recently, his main research interests have been the application of machine learning techniques for animation synthesis. He received the Royal Society Industry Fellowship (2014) and the Google AR/VR Research Award (2017).
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| August 21, 2019 |
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Title: Deep Composer: Music Generation Using Deep Neural Hashing
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Kien A. Hua
Remark(s): Prof. Kien A. Hua
Pegasus Professor and Director of the Data Systems Lab
University of Central Florida
Date: August 21, 2019 Wednesday
Time: 2:00pm
Venue: Room 328 Chow Yei Ching Building The University of Hong Kong
Abstract:
Recurrent neural networks have successfully generated pleasing melodies; however, they have struggled to create a full piece that has structure, theme, and originality. To overcome this limitation, we discuss a music retrieval approach for music generation. Composability, instead of the usual similarity, is used as the metric for retrieval. The musical segments (tiny building blocks each with only 16 music notes) in the database are encoded using a deep hashing method to facilitate the composability retrieval. Music composition is performed by using the current segment as a query to retrieve the next composable segment from the database until the song is complete. This encoding scheme incorporates both theme and structure so that musical segments can be joined to generate a piece that is both unique and pleasing to listen to. Each music segment is assigned four hash codes learned by a multi-LSTM system, each defining the given segment's compatibility with other segments for a distinct structural location (beginning, middle, or ending section) in the piece. Additionally, a two-phase music segmentation technique captures structural information while minimizing the segment length. We compare this scheme to multiple recent music generation methods using both objective and subjective evaluation metrics to demonstrate that the pieces generated by our Deep Composer system are not only unique and musically pleasing but also contain more structure and theme features like that of a professionally composed piece. A secondary goal of this research is to bring back the great composers (e.g., Mozart, Chopin, Beethoven, …) to compose their new original music for us today by using their music segments as the building blocks. In fact, the best composers of different times would be able to collaborate today through the Deep Composer. Deep Composer can also generate world fusion music beyond the capacity of any human composers.
About the speaker:
Dr. Kien A. Hua is a Pegasus Professor and Director of the Data Systems Lab at the University of Central Florida. He was the Associate Dean for Research of the College of Engineering and Computer Science at UCF. Prior to joining the university, he was a Lead Architect at IBM Mid-Hudson Laboratory, where he led a team of senior engineers to develop a highly parallel computer system, the precursor to the highly successful commercial parallel computer known as SP2. More recently, Prof. Hua was serving as a domain expert on spaceport technology at NASA, and a data analytics expert to advise the U.S. Air Force on the Air Force Strategy 2030 Initiative. Prof. Hua received his B.S. in Computer Science, and M.S. and Ph.D. in Electrical Engineering, all from the University of Illinois at Urbana-Champaign, USA. His current research interest includes music generation,deep learning, multimedia database and analytics, network and wireless communications, and the Internet of Things. He has published widely, with 15 papers recognized as best/top papers at a conference and one as the best paper of the year for a journal. Dr. Hua introduced peer-to-peer communications and data sharing in 1997, that has inspired many impactful applications including the Blockchain technology today. He introduced graph-based data mining at the 1999 International Conference on Data Warehousing and Knowledge Discovery; and the paper was recognized as a best paper at this conference. He is also a pioneer in the Internet of Things. with the WISE (Web-based Intelligent Sensor Explorer) prototype introduced in 2004, probably the first IoT platform implemented. It enables publishing, searching, and sharing of connected sensing devices. More recently, he developed a novelrouter in 2015, that transform network congestion into advantage. His other research works such as Skyscraper Broadcasting, Patching, and Zigzag all have been heavily cited and have inspired many commercial systems in use today. Prof. Hua has served as a Conference Chair, an Associate Chair, and a Technical Program Committee Member of numerous international conferences, and on the editorial boards of several professional journals. More recently, he served as a General Co-Chair for the 2014 ACM Multimedia conference; and he is currently organizing the 2018 IEEE International Conference on Cloud Engineering (IC2E) and serving as a General Co-Chair. Prof. Hua is a Fellow of IEEE.
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| August 09, 2019 |
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Title: The curious capacities of quantum channels
Time: 02:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Debbie Leung
Remark(s): Prof. Debbie Leung
Institute for Quantum Computing & Department of Combinatorics and Optimization
University of Waterloo, Canada
Date: August 9, 2019 Friday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
The best asymptotic rate of a communication channel to process information (such as transmitting data or creating correlation) is called the capacity of the channel for the task involved. This talk focuses on the capacities of a quantum channel to communicate quantum or private classical data. We first summarize well known surprising results, followed by sharing a few recent developments in the subject.
About the speaker:
Debbie Leung joined the Institute for Quantum Computing (IQC) and the Department of Combinatorics and Optimization at the University of Waterloo in 2005. She has been an associate member of the Perimeter Institute since 2019. She was a Tolman postdoctoral fellowship at the Institute for Quantum Information, Caltech, after spending four months at the Workshop on Quantum Computation, September-December 2002, at the Mathematical Sciences Research Institute, Berkeley, and a twoyear stay at the Physics of Information group at the IBM TJ Watson Research Center, 2000-2002. After a BSc in Phys/Math from Caltech in 1995, she did a PhD in Physics at Stanford under the supervision of Professor Yoshihisa Yamamoto and Professor Isaac Chuang. Event website: https://qift.weebly.com/events.html
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| August 08, 2019 |
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Title: Building Systems for Machine Learning
Time: 02:00pm
Venue: Room 313, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr Hong Xu
Remark(s): Dr Hong Xu
Department of Computer Science
City University of Hong Kong
Date: August 9, 2019 Thursday
Time: 2:00pm
Venue: Room 313, Chow Yei Ching Building, The University of Hong Kong
Abstract:
Systems research is critical for machine learning because the recent success of AI and big data is in large part enabled by datacenter-scale computing infrastructures, which employ an army of machines to harness massive datasets in a continuous fashion. In this talk, I will present my research that focuses on systems for machine learning. First, we build a new distributed training system called Stanza that improves the training throughput of parameter server systems by 1.25x to 10.12x. Second, we build a serving system called Saec for recommendation models that reduces the memory footprint of embedding based recommendation models by 27x without performance loss.
About the speaker:
Hong Xu is an associate professor in Department of Computer Science, City University of Hong Kong. His research area is computer networking and systems, particularly data center networks and big data systems. He received the B.Eng. degree from The Chinese University of Hong Kong in 2007, and the M.A.Sc. and Ph.D. degrees from University of Toronto in 2009 and 2013, respectively. He was the recipient of an Early Career Scheme Grant from the Hong Kong Research Grants Council in 2014. He received several best paper awards, including the IEEE ICNP 2015 best paper award. He is a senior member of IEEE and member of ACM.
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| August 06, 2019 |
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Title: Volumetric Representations: the Geometric Modeling of the Next Generation
Time: 03:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Gershon Elber
Remark(s): Professor Gershon Elber
Department of Computer Science
Technion
Date: August 6, 2019 Tuesday
Time: 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
The needs of modern (additive) manufacturing (AM) technologies can be satisfied no longer by boundary representations (B-reps), as AM requires the representation and manipulation of interior fields and materials as well. Further, while the need for a tight coupling between design and analysis has been recognized as crucial almost since geometric modeling (GM) has been conceived, contemporary GM systems only offer a loose link between the two, if at all. For about half a century, (trimmed) Non Uniform Rational B-spline (NURBs) surfaces has been the B-rep of choice for virtually all the GM industry. Fundamentally, B-rep GM has evolved little during this period. In this talk, we seek to examine an extended (trimmed) NURBs volumetric representation (V-rep) that successfully confronts the existing and anticipated design, analysis, and manufacturing foreseen challenges. We extend all fundamental B-rep GM operations, such as primitive and surface constructors and Boolean operations, to trimmed trivariate V-reps. This enables the much needed tight link to (Isogeometric) analysis on one hand and the full support of (heterogeneous and anisotropic) additive manufacturing on the other. Special capabilities toward the support of modern AM and the support of Isogeometric analysis will also be presented, that enable robust queries over the V-reps, including volumetric covering by curves, precise contact analysis, maximal penetration depth, and accurate integration over trimmed domains. Examples and other applications of V-rep GM, including AM and lattice- and micro- structure synthesis (with heterogeneous materials) will also be demonstrated. In collaboration with many others, including Ben Ezair, Fady Massarwi, Boris van Sosin, Jinesh Machchhar, Annalisa Buffa, Giancarlo Sangalli, Pablo Antolin, Massimiliano Martinelli, Stefanie Elgeti, and Robert Haimes.
About the speaker:
Gershon Elber is a professor in the Computer Science Department, Technion, Israel. His research interests span computer aided geometric designs and computer graphics. Prof. Elber received a BSc in computer engineering and an MSc in computer science from the Technion, Israel in 1986 and 1987, respectively, and a PhD in computer science from the University of Utah, USA, in 1992. He is a member of SIAM and the ACM. Prof. Elber has served on the editorial board of the Computer Aided Design, Computer Graphics Forum, The Visual Computer, Graphical Models, and the International Journal of Computational Geometry & Applications and has served in many conference program committees including Solid Modeling, Shape Modeling, Geometric Modeling and Processing, Pacific Graphics, Computer Graphics International, and Siggraph. Prof. Elber was one of the paper chairs of Solid Modeling 2003 and Solid Modeling 2004, one of the conference chairs of Solid and Physical Modeling 2010, the chair of GDM 2014, the conference co-chair of SIAM GD/SPM 2015, and the conference co-chair of SPM 2018. He has published over 200 papers in international conferences and journals and is one of the authors of a book titled "Geometric Modeling with Splines - An Introduction". Prof. Elber received the John Gregory Memorial Award, 2011, in "Appreciation for Outstanding Contributions in Geometric Modeling", the Solid Modeling Association pioneers award in 2016, and the Bezier award in 2019. Elber can be reached at the Technion, Israel Institute of Technology, Department of Computer Science, Haifa 32000, ISRAEL. Email: gershon@cs.technion.ac.il, Fax: 972-4-829-5538.
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