| Prof. Pingyi FanTsinghua University, ChinaDr. Pingyi Fan is a professor of the Department of Electronic Engineering of Tsinghua University. He received Ph.D. degree at the Department of Electronic Engineering of Tsinghua University in 1994. From 1997 to 1999, he visited the Hong Kong University of Science and Technology and the University of Delaware in the United States. He also visited many universities and research institutes in the United States, Europe, Japan, Hong Kong and Singapore. He has obtained many research grants, including national 973 Project, 863 Project, mobile special project and the key R&D program, national natural funds and international cooperation projects. He has published more than 190 SCI papers (more than 130 IEEE journals), and 4 academic books. He also applied for more than 30 national invention patents, 5 international patents and. He won seven best paper awards of international conferences, including IEEE ICC2020 and Globecom 2014, and received the best paper award of IEEE TAOS Technical Committee in 2020, the excellent editor award of IEEE TWC (2009), etc. He has served as the editorial board member of several Journals, including IEEE and MDPI. He is currently the editorial board member of Open Journal of Mathematical Sciences, the deputy director of China Information Theory society, the co-chair of China's 6G-ANA TG4, and the chairman of Network and Communication Technology Committee of IEEE ChinaSIP. His current research interests are in 6G wireless communication network and machine learning, semantic information theory and generalized information theory, big data processing theory, intelligent network and system detection, etc. Title:Variational Auto-Encoder Technique and New Applications Abstract: In this report, we first Introduce the inference problem with Variational auto-Encoder (VAE) technology and massive data, and then present the theory of VAE as well as its possible application scenarios. Secondly, we introduce some recent new developments by using VAE in different areas. Later on, we present an new result by jointly using VAE and GAN (generative adversial Networks) --- AEGAN, for machine fault detection by using Mechanical sounds. Some interesting observation are obtained. Finally, we give a summary and point out some promising research directions for combinations of VAE and other other Machine learning methods. |
| Prof. Wanyang DaiNanjing University, ChinaWanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum, President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Quantum Information Processing, Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications. Title:Quantum computer via neutral atom and quantum entanglement for big model with big data Abstract:We study quantum computer via neutral atom and quantum entanglement for big model with big data via federated learning. Operational rules via quantum entanglements for quantum computers are established through deriving a general spherical coordinate formula for a quantum state of n-qubit register. The associated angle-based n-qubit operational rules on a (n+1)-manifold are established, which are simple and efficient in the sense that they reduce the complicated quantum multiplication and division operations to simple addition and subtraction operations just like those used in a conventional computer. The rules for n-qubit operations are realized through neutral atom & measurement-oriented feedback controls to reach quantum entanglements. The performance models are derived for n-qubit quantum computer-based quantum storage systems. The generative AI based decision-making big model with big data via federated learning is also established and simulated. |
