电路基本理论
电路基本理论
绿水 07-31 15:02
穆永吉 05-17 10:02
Introduction to Computer Science and Programming Using Python
About this course [[https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-9#!]] [[https://github.com/nilesh-patil/MITx-Foundations-of-Computer-Science]] This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3.5. Even if you took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning. Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not "computation appreciation" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.
Eric Grimson 01-10 12:45
20160328Trace species sensing by infrared laser spectroscopy
Abstract:   Chemical sensing and analyses of gas samples by laser spectroscopic methods are attractive owing to the high sensitivity and specificity, large dynamic range, multi-component capability, and lack of pretreatment or preconcentration procedures. The availability of broadly tunable mid-infrared sources like external cavity quantum cascade lasers (EC-QCLs), interband cascade lasers (ICLs), difference frequency generation (DFG), optical parametric oscillators (OPOs), recent developments of diode-pumped lead salt semiconductor lasers, of supercontinuum sources or of frequency combs have eased the implementation of laser-based sensing devices. Sensitive techniques for molecular absorption measurements include multipass absorption, various configurations of cavity-enhanced techniques such as cavity ringdown (CRD), or of photoacoustic spectroscopy (PAS). The application requirements finally determine the optimum selection of laser source and detection scheme.   In this talk I shall discuss the basic principles, present various experimental setups and illustrate the performance of selected systems for chemical trace species sensing. Examples include the fast analysis of C1-C4 alkanes at sub-ppm concentrations, measurements on short-lived species like nitrous acid (HONO) or a true analysis of a multi-component gas mixture like surgical smoke in a medical application. Finally, results will be presented on non-invasive glucose sensing through human skin.
Markus W. Sigrist 03-28 10:03
20160327大规模社交网络上的推断与学习Inference and Learning over Large-Scale Social Networks
Georgios B. Giannakis is a professor with the University of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing. He is a Fellow of the IEEE and EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SPS. Real-world social networks are fraught with unique challenges that limit the efficacy of contemporary tools. For example, such networks are big (billions of nodes), evolve over time, and are often not directly observable. Viewed through a statistical learning lens, many network analytics problems boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting this point of view, this talk will put forth novel learning approaches for network visualization, anomaly and community detection, prediction of network processes, and dynamic network inference. Key emphasis will be placed on parsimonious models exploiting sparsity, low rank, or low-dimensional manifolds, attributes that have been shown useful for complexity reduction. The merits of the novel schemes will be demonstrated on both simulated and real-world social networks.
20160327时变优化问题的预测校正方法Prediction-Correction Methods for Time-Varying Optimization
Geert Leus is an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the area of signal processing for communications. He is a Fellow of the IEEE and a Fellow of EURASIP. We propose algorithms with a discrete-time sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate 1/h, where h is the step size. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists of either one or multiple gradient steps or Newton steps. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as quadratic in h, and in some cases even as quartic in h, which outperforms the state-of-the-art error bound for correction-only methods in the gradient-correction step, which is only linear in h. We do not only discuss centralized implementations, but also focus on distributed versions of these algorithms.
Geert Leus 03-28 10:03
20160326中微子-幽灵粒子之谜
The ghost particle neutrinos is one kind of the elementary particle that called leptons. It carries no charge and almost has no mass and no interaction with any other particles and therefore can freely penetrate the earth. Neutrinos can be created from many different ways, such as the Sun, the atmosphere, the nuclear power plant and the accelerator. Mystery of neutrinos has been fascinating scientists since it is postulated in 1930 by W. Pauli and discovered in 1956 by Reine and Cown, and will continue be so for many decades. This presentation will briefly review the history of the neutrino, the source of neutrino and explain you how scientists solve the puzzle of solar neutrino and the atmospheric neutrino, which were awarded the Nobel prize in physics of 2015.
赵振国 03-28 10:03
20160325A tutorial on Bayesian Non-Parametric methods and its application in relational models
Abstract: Bayesian Non-Parametric methods (BNP) has been one of the important research topics in the machine learning community in the recent times. In this tutorial, I will present some of the theory and applications in this area which include Dirichlet Process, Hierarchical Dirichlet Process, HDP-HMM and Indian Buffet Process. I will also present our latest findings to apply BNP to Relational Learning Models, particularly, in its time-sequence setting. Bio: Dr Richard Xu is an academic working at Global Big Data Technologies Centre (GBDTC) and School of Computing and Communications at University of Technology Sydney (UTS). He received PhD in Computer Science and B.E of Computer Engineering from UTS and UNSW respectively. He has been research active in machine learning, probabilistic data analytics and computer vision since 2002. He has published many high impact factor journals in these areas and has spent six months visiting Department of Statistics, Oxford University to collaborate with world’s top statisticians. He published a series of machine learning lecture notes and videos and received 40,000+ views since late 2015. Apart from theoretical machine learning research, he has also been an industry focused data scientist and engineer, whom is collaborating with several government agencies and private sector industries.More details of his machine learning research can be found at: http://www-staff.it.uts.edu.au/~ydxu/index.htm Dr. Yi Da (Richard) Xu Core member of Global Big Data Technology Centre Faculty of Engineering and IT, University of Technology, Sydney (UTS), Australia Room: B11.8.113 P: +61 2 9514 4587 F: +61 2 9514 4535
Richard Xu 03-28 10:03
20160325Compressive Sensing: Structured Sensing Matrices and Applications in Tomography
Abstract: The compressive/compressed sensing (CS) theory, which was proposed in around 2006, is a framework for estimating sparse signals based on incomplete set of noiseless or noisy measurements. In CS, there are two fundamental problems: compression and reconstruction. In this talk, after reviewing the basic concepts of CS, we focus on the compression part, in particular structured sensing matrices. After that, the applications of CS in tomography are introduced and analyzed briefly. This talk is suitable for senior bachelor, graduate /PhD students and researchers who are interested in signal processing and compressed sensing. 压缩感知理论诞生于2006年前后,是基于不完整测量数据从而估计稀疏信号的有效手段 。在压缩感知中有两个基本问题:压缩和恢复。在本讲座中,在回顾压缩感知的基本理 论后,着重阐述压缩部分,特别是结构化压缩矩阵的性质和特点。在此基础上,介绍压 缩感知在图像处理中的一些应用。此讲座适合高年级本科生、研究生、博士生以及其他 对压缩感知理论感兴趣的学者。 Bio: Dr. Kezhi Li is currently a research scientist at Imperial College London, United Kingdom. He graduated from University of Science and Technology of China (USTC) and Imperial College London with Bachelor and PhD degree, respectively. After that he was a postdoctoral fellow at Royal Institute of Technology (KTH), USTC and then a researcher at University of Cambridge. His research interests include statistical signal processing and machine learning, particularly compressive sensing and its applications in data/image processing. 李克之博士本科毕业于中国科学技术大学电子工程专业,博士毕业于英国帝国理工学院 ,现为英国帝国理工学院研究科学家。他曾在瑞典皇家理工学院、中国科大和英国剑桥 大学从事博士后研究工作。他的研究领域包括:统计信号处理、机器学习、压缩感知及 其在数字和图像处理方面的应用。
李克之 03-28 10:03
20160325Neutrino Oscillation - The 2015 Nobel Prize
交叉中心本周五报告如下: 题 目:Neutrino Oscillation - The 2015 Nobel Prize 报告人:何小刚教授,台湾大学物理系/上海交大物理与天文系 时 间:3月25日(周五)下午 16:00 地 点:科大东区5306教室
何小刚 03-28 10:03
Mechanics of Compaction and Strain Localization in Porous Rock
个人简介: Teng-fong Wong (黃庭芳) is Professor and Director of the Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong. Before joining CUHK in January 2013, he has taught for thirty years in the State University of New York at Stony Brook, where he served as Chair of the Department of Geosciences and Associate Dean of the Graduate School. Wong's undergraduate and postgraduate degrees were from Brown, Harvard and MIT. He served on the San Andreas Fault Observatory at Depth (SAFOD) Advisory Board, and is currently on the physical sciences panel of the Hong Kong Research Grants Council. He has two US patents for an ultrasonic seepage meter for hydrogeological field measurement. He has also co-authored two monographs: Experimental Rock Deformation, The Brittle Field (with Professor Mervyn Paterson, Australian National University), and 岩石物理学 (with CAS academician 陈颙, and 刘恩儒, ExxonMobil), a volume in the 中国科学技术大学校友文库. Wong is also a recipient of the Basic Research Award of the U.S. National Committee for Rock Mechanics, SUNY Chancellor's Award for Excellence in Scholarship, as well as the Louis Néel Medal of the European Geosciences Union.
黄庭芳 03-28 10:03
高质量氧化物薄膜规模化制备中的一些科学问题探讨
  氧化物功能薄膜不仅是基础物理研究的重要对象,也对未来的功能器件展现了光明的应用前景。报告人以晶圆级高质量外延的VO2薄膜以及集成于镍基底上的BaTiO3薄膜为例,针对拓展氧化物薄膜生长窗口和控制金属/氧化物界面两个方面探讨“控氧”的机理和方法,使用易于产业化的化学溶液生长技术,基于薄膜生长热力学模型,对生长中氧化反应动力学行为进行调控,实现了晶圆级高质量外延的VO2薄膜和界面可控的BaTiO3/Ni集成薄膜。报告中也提出了规模化制备高质量氧化物薄膜所面临的一些其它问题。
林媛 03-28 10:03
学术报告Towards Big Graph Processing及新南威尔士大学计算机学院招生简况交流会
学术报告及新南威尔士大学计算机学院招生简况交流会 交流会时间:2016年3月23日下午15:50-17:30 地点:西区电三楼312-314会议室具体安排: 1.学术报告:Towards Big Graph Processing 报告人:Xuemin Lin(林学民) 2. 新南威尔士大学(UNSW)计算机学院招生简况介绍及座谈交流介绍人:Prof. Xuemin Lin, Prof. Lijun Chang, and Prof. Zengfeng Huang
林学民 03-28 10:03
向量丛模空间的Frobenius分层
吴文俊数学重点实验室代数学系列报告之八十三【李灵光】 报告题目:向量丛模空间的Frobenius分层报 告 人:李灵光 博士 同济大学数学学院时间:3月24号上午,10:00-11:00 地点:管理科研楼1518
李灵光 03-28 10:03
晚晴科举制度改革和中国的知识转变
应科技史与科技考古系邀请,德国法兰克福东亚研究中心教授、国际著名科技史专家阿梅龙(IMO Amelung)来我校做《晚晴科举制度改革和中国的知识转变》的报告,欢迎感兴趣的广大师生积极参加。  报告时间:3月21日(周一)14:30;  报告地点:东区人文学院南平105会议室。  特此通知。
阿梅龙 03-28 10:03
CNKI总库平台,助力学术科研
培训名称:CNKI总库平台,助力学术科研培训时间:3月23日(周三)下午2:30-4:00 培训人:CNKI高级讲师 奚亚萍培训地点:人文与社会科学学院 南平105(东区羽毛球馆旁)
奚亚萍 03-28 10:03
The differential structure of metric measure space
摘要:In the past ten years, the metric measure spaces with Ricci curvature bound which was proposed by Lott-Sturm-Villani, was studied by researcher from many different areas. In this talk I will introduce some recent results on the differential structure of metric measure spaces, including the non-smooth Sobolev space, Barky- Emery theory, and the notion of tangent/cotangent modules in non-smooth framework. On the metric measure spaces with curvature-dimension condition RCD (K;N), we obtainan improved Bochner inequality and propose a de nition of N-dimensional Ricci tensor.
Bang-Xian Han 03-28 10:03
晚晴科举制度改革和中国的知识转变
应科技史与科技考古系邀请,德国法兰克福东亚研究中心教授、国际著名科技史专家阿梅龙(IMO Amelung)来我校做《晚晴科举制度改革和中国的知识转变》的报告,欢迎感兴趣的广大师生积极参加。  报告时间:3月21日(周一)14:30;  报告地点:东区人文学院南平105会议室。  特此通知。
阿梅龙 03-28 10:03
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