Instructors/Speakers Prof. Yimin WEI Professor School of Mathematical Sciences Fudan University China Abstract In this talk, we will introduce the history of the generalized inverse and linear least squares problem. Some recent research in this area will be given. Possible future work will also be discussed. Biography Prof. Yimin WEI is a professor in Fudan University, China. He has published over 100 papers in mathematics. His recent interest includes matrix analysis, least squares problems and study of multilinear systems.  

Instructors/Speakers Mr. Zexin JIANG Guangzhou Bonson Information System Co. Ltd. Abstract 簡單自我介紹 平凡的我出生在不平凡的地方 不吃辣椒，卻孤身一路向北 明明可以安穩舒適，卻非要折騰 大四之後的求學 沒有畢業合照的大四 我是如何保送到清華進修的 國家高鐵系統真的運行著我寫的代碼嗎 深深地遺憾 職場折騰 內地壟斷國企是一種什麼樣的存在 獨檔一面是一種什麼樣的感受 如何避免當背鍋俠 我在電網工作5年獲得的5個第一 辭職之後我拿著兩份工資 民企與國企的差異極大 一年時間從產品經理到CTO 技術、產品、工程有什麼區別，變現的誤區 幾點感悟分享 選擇比努力重要 多做事少計較得失 適當包裝自己 風險管理與自我保護 Biography 廣東省普寧人，高級工程師职称，（ISC）2會員、全國電力運行與控制標準委員會標準工作組成員、廣東省電機工程學會成員、廣州市科技評審專家，獲得國家授權專利56項，發表論文12篇，出版專著1本，獲省級科技進步壹等獎、獲2018年廣州市“珠江科技新星”專項資助、2019年廣州市“產業急需緊缺人才”補貼。擁有國際註冊信息安全專家和國家重要信息系統保護人員等職業資格。 2004年-2008年，南京理工大學自動化專業本科學習（專業第1名，GPA 3.9/4.0）。參加全國大學生電子設計大賽獲國家二等獎、全國大學生智能汽車比賽獲得國家二等獎、江蘇省大學生機器人比賽獲得壹等獎、江蘇省優秀本科畢業論文壹等獎、獲得學校最高榮譽獎學金以及每年特等獎學金。 2008年-2011年，清華大學（免試）控制科學與工程專業碩士研究生。期間參與“十壹五”國家重大科技攻關（中國高鐵國產化裝備研發）項目以及和諧號CRH5動車組網絡控制系統的引進消化吸收創新的產品研發工作。研發了支持硬件在環的大規模分布式半實物仿真引擎，研發成果支撐應用於武廣、京津、北京地鐵等線路設備的上線前驗證。 2011年至2016年，廣東電網電力科學研究院（南方電網自動化重點實驗室），歷任助理專責、專責、技術專家、電力機器人攻關團隊組長。從事電力物聯網和電力監控安全防護體系領域的研究工作，主筆編制了南方電網電力監控系統最頂層安全防護技術規範和管理規範等多項企業標準，參與電力行業信息安全標準編制2項，推動商用密碼技術在電力行業的應用，相關成果於2012年獲得廣東省電力行業協會科技創新成果獎、於2013年南方電網青工創新創效成果壹等獎、2014年獲得南方電網科技進步壹等獎。2015年在南方電網全網信息安全攻防競賽獲第1名。 2016年至今，廣州邦訊信息系統有限公司（深交所股票代碼：300366），歷任產品經理、產品副總監、CTO。主要從事工業物聯網和物聯網安全相關產品研發管理工作。創建了公司商用密碼產品線，帶領團隊研發多款物聯網及安全產品，獲得國家密碼局型號認證和公安部檢測認證。  

Instructors/Speakers Prof. Yulin WANG Full Professor of Wuhan University China Abstract Image authentication can be used in many fields, including e-government, e-commerce, national security, news pictures, court evidence, medical image, engineering design, and so on. Since some content-preserving manipulations, such as JPEG compression, contrast enhancement, and brightness adjustment, are often acceptable—or even desired—in practical application, an authentication method needs to be able to distinguish them from malicious tampering, such as removal, addition, and modification of objects. Therefore, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk, we propose a hybrid authentication technique based on ...

Instructors/Speakers Prof. Bingding HUANG College of Big Data and Internet Shenzhen University Abstract We have developed a computational framework called Sinotools to analyze cancer genomic sequencing data for precision oncology. First of all, Sinotools can identify very low-frequency variants in ctDNA samples from cancer patients using duplex barcode sequencing technology. Sinotools also includes a novel computational algorithm to identify actionable gene fusion events from targeted sequencing data. Sinotools is available at https://github.com/SinOncology. Biography Prof. Bingding Huang received his BSc in Cell Biology from University of Science and Technology of China in 2002. Then he received his Master degree in Computer Science from Saarland University and Max Planck Institute for Informatics under Scholarship from International Max Planck Research School in 2004. ...

Instructors/Speakers Prof. Qin SHENG Professor Department of Mathematics Baylor University USA Abstract Neural network based deep learnings have been used for solving high dimensional (number of dimensions may reach more than several hundreds) partial differential equations. But what is a neural network and how to use it for approximating, or predicting, natural phenomena? In this exploration, we tend to consider approximations of multivariate data through an explicit feedforward neural network (FNN) which is the basic type of neural network. The easy-to-follow formula provides a piecewise constant function for the data. Our FNN architecture has two hidden layers, where the weights and thresholds are explicitly defined and no numerical optimization is required for training. The explicit algorithm does not rely on ...