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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p><div><p class=MsoNormal><b><span style='font-family:"Microsoft YaHei UI","sans-serif";color:red'>--ZOOM<span lang=ZH-CN>会议室信息</span>--</span></b><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>会议时间：</span><span style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>7<span lang=ZH-CN>月</span>10<span lang=ZH-CN>日</span>9:00-12:00</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>会议号：</span><span style='font-size:13.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>686 465 68231</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>会议密码：</span><span style='font-size:13.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>190132</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>参会链接：</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><a href="https://zoom.com.cn/j/68646568231"><span style='font-size:13.5pt'>https://zoom.com.cn/j/68646568231</span></a><o:p></o:p></span></p></div><p class=MsoNormal style='margin-bottom:12.0pt'><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p><div><p class=MsoNormal><b><span style='font-family:"Times","serif";color:red'>---</span></b><b><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:red'>陈露博士报告信息</span></b><b><span style='font-family:"Times","serif";color:red'>---</span></b><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Title: Universal Conversational Agents with Structured Deep Learning<o:p></o:p></span></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-line-height-alt:9.65pt'><span style='color:black'><o:p> </o:p></span></p><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Abstract<span lang=ZH-CN>：</span>A task-oriented spoken dialogue system is a system that can continuously interact with human to accomplish a predefined task through speech. Dialogue policy plays an important role in task-oriented spoken dialogue systems. It determines how to respond to users. Deep reinforcement learning (DRL) approaches have been widely used for policy optimization. However, these deep models are still challenging for two reasons: first, many DRL-based policies are not sample-efficient; and second, most models do not have the capability of policy transfer between different domains. In this talk, I will introduce our proposed universal framework, AgentGraph, to tackle these two problems. The model AgentGraph is the combination of graph neural network (GNN) based architecture and DRL-based algorithm. It can be regarded as one of the multi-agent reinforcement learning approaches. In addition to the universal dialogue policy AgentGraph, I will also briefly introduce our proposed universal dialogue state tracking models.<o:p></o:p></span></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-line-height-alt:9.65pt'><span style='color:black'><o:p> </o:p></span></p><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Bio: Lu Chen obtained his Ph.D. degree in computer science and engineering from Shanghai Jiao Tong University (SJTU) in 2020. Before coming to SJTU, he received a Bachelor's degree in computer science and engineering from Huazhong University of Science & Technology (HUST) in 2013. His research interests include dialogue systems, reinforcement learning, structured deep learning. The goal of his research is to build evolvable and universal conversational agents, which can converse with humans among many domains and improve their performance with various signals. He has authored/co-authored more than 20 journal articles (e.g. IEEE/ACM transactions) and peer-reviewed conference papers (e.g. ACL, EMNLP, AAAI, COLING, ICASSP), one of them was selected as COLING2018 Area Chair Favorites.<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-line-height-alt:9.65pt'><span style='color:black'><o:p> </o:p></span></p></div><div><div><p class=MsoNormal><b><span style='font-family:"Times","serif";color:red'>---</span></b><b><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:red'>李松泽博士报告信息</span></b><b><span style='font-family:"Times","serif";color:red'>---</span></b><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify;text-justify:inter-ideograph;mso-line-height-alt:9.65pt'><b><span style='font-family:"Times New Roman","serif";color:black'>Title:</span></b><span style='font-family:"Times New Roman","serif";color:black'> </span><span style='font-family:"Microsoft YaHei UI","sans-serif";color:black'>Nakamoto meets Shannon: Scaling blockchains using codes</span><span style='color:black'><o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify;text-justify:inter-ideograph;mso-line-height-alt:9.65pt;text-align:start'><span style='color:black'><o:p> </o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify;text-justify:inter-ideograph;mso-line-height-alt:9.65pt;text-align:start'><b><span style='font-family:"Times New Roman","serif";color:black'>Abstract:</span></b><span style='font-family:"Calibri","sans-serif";color:black;background:snow'>Current blockchain systems do not scale with the network resources. Sharding, as a promising proposal to achieve horizontal scalability, fail to maintain security of the system in the presence of adaptive adversaries. This talk will cover an emerging paradigm of designing scalable and secure blockchain protocols using error correcting codes. Specifically, we consider the following two problems 1) transaction verification, and 2) verifying data availability for light clients. For transaction verification, we propose PolyShard that creates coded shard ledgers and input transactions using Lagrange polynomial interpolation. PolyShard scales system throughput with the network size, while maintaining the security of verification results via Reed-Solomon decoding. For the problem of verifying data availability, we propose a novel cryptographic accumulator named Coded Merkle Tree (CMT) to commit a block. CMT iteratively encodes a block and the hashes of its chunks using a family of regular LDPC codes, into a constant number of hash values that are stored in the block header. CMT enables verifying the availability of a block with constant number of samples, a liner decoding complexity, and a constant size of fraud proof for incorrect coding.</span><span style='color:black'><o:p></o:p></span></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify;text-justify:inter-ideograph;line-height:12.1pt'><span style='color:black'><o:p> </o:p></span></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify;text-justify:inter-ideograph;mso-line-height-alt:9.65pt;text-align:start'><b><span style='font-family:"Times New Roman","serif";color:black'>Bio:</span></b><span style='font-family:"Times New Roman","serif";color:black'> </span><span style='font-family:"Calibri","sans-serif";color:black'>Dr. Songze Li is a research scientist at Stanford University working on designing and developing next-generation scalable blockchain systems. Prior to that, Dr. Li spent a year as a research scientist with Applied Protocol Research, where he invented a novel cryptographic accumulator <i>Coded Merkle Tree</i>, which enables efficient verification of data availability for light clients in blockchains. Songze received his Ph.D. degree from <a href="http://www.usc.edu/" target="_blank">University of Southern Californi</a>a in 2018, and his B.Sc. degree from <a href="http://engineering.nyu.edu/">New York </a>University in 2011, both in electrical engineering. Songze’s research interests lie on the intersection of theory and system of designing efficient, scalable, and secure distributed computing solutions for machine learning and blockchains. Specifically, he first introduced leveraging techniques from information/coding theory to design distributed computing algorithms, which opened up a new research direction of code design for speeding up computations. Songze received USC Viterbi School of Engineering Doctoral Fellowship in 2011. He is among Qualcomm Innovation Fellowship Finalists in 2017.</span><span style='color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><b><span style='font-family:"Times","serif";color:red'>---</span></b><b><span lang=ZH-CN style='font-family:"Microsoft YaHei UI","sans-serif";color:red'>洪义博士报告信息</span></b><b><span style='font-family:"Times","serif";color:red'>---</span></b><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><b><span style='font-family:"Times","serif";color:red'><br><br></span></b><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Title: Parametric Regression for Medical Analysis: Beyond Euclidean Space<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Abstract:<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Uncovering time-varying trends from spatiotemporal data is important in medical analysis, for instance, in understanding brain development, aging, and disease progression. Data for these studies have different types, e.g., shapes, image scans, videos, which have complex structures and are best treated as elements of non-Euclidean spaces. In this talk, I present parametric regression models on two non-Euclidean spaces, i.e., the Grassmann manifold and the manifold of diffeomorphisms, to handle multiple types of spatiotemporal data. These models are generalized from Euclidean regression and provide efficient and straightforward solutions for analyzing non-Euclidean data. The experimental results demonstrate the resulting models<span lang=ZH-CN>’</span> effectiveness in studying Alzheimer<span lang=ZH-CN>’</span>s Disease. <o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Biography:<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>Dr. Yi Hong is a tenure-track assistant professor of CS at the University of Georgia (UGA). Before joining UGA, she received her Ph.D. from the University of North Carolina (UNC) at Chapel Hill in 2016. Dr. Hong's primary research interest lies in developing novel techniques for medical multimedia data, mainly in medical image understanding, statistical shape analysis, and population studies. Dr. Hong is a recipient of the 2014 MICCAI young scientist award, a 2015-2016 UNC dissertation completion fellowship, and two NSF grant awards.<o:p></o:p></span></p></div></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>祝好，</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span lang=ZH-CN style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'>陈哲轩</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div><div class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><hr size=1 width=263 style='width:157.5pt' noshade style='color:#B5C4DF' align=left></span></div><div><div style='margin-left:7.5pt;margin-top:7.5pt;margin-right:7.5pt;margin-bottom:7.5pt'><div><p class=MsoNormal style='margin:7.5pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";color:black'>chen-zx@cs.sjtu.edu.cn</span><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p></o:p></span></p></div></div></div><blockquote style='margin-left:6.0pt'><div><div><div><div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Microsoft YaHei UI","sans-serif";color:black'><o:p> </o:p></span></p></div></div></div></div></div></blockquote></div></body></html>