2017-01-06
Can you solve the counterfeit coin riddle? - Jennifer Lu
source: TED-Ed 2017年1月3日
View full lesson: http://ed.ted.com/lessons/can-you-sol...
You’re the realm’s greatest mathematician, but ever since you criticized the Emperor’s tax laws, you’ve been locked in the dungeon. Luckily for you, one of the Emperor’s governors has been convicted of paying his taxes with a counterfeit coin, which has made its way into the treasury. Can you earn your freedom by finding the fake? Jennifer Lu shows how.
Lesson by Jennifer Lu, animation by Artrake Studio.
Compressive Sensing and Sparse Recovery by Justin Romberg
# click the upper-left icon to select videos from the playlist
source: 谷实验室 2013年10月24日
Compressive Sensing and Sparse Recovery is a short course taught by Professor Justin Romberg during his visit to Tsinghua University from Oct. 14h to Oct. 18th, 2013. Professor Justin Romberg is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology.
More details about the course: http://gu.ee.tsinghua.edu.cn/index.ph...
All rights reserved by Professor Justin Romberg.
Lecture 1 (Oct 14th) Basis expansion fundamentals 1:01:42
This lecture talks about some fundamental mathematical background which will be quite useful in the subsequent lectures. It mainly focuses on how to represent the signal of interest as a discrete linear combination of basis signals. For orthogonal basis expansions, the generalized Parseval's theorem shows that all signal processing can be done by manipulating discrete sequences of expansion coefficients. This lecture also talks about the Discrete Cosine Transform (DCT) with its applications in image and video compression, and it ends with non-orthogonal bases and overcomplete frames.
Lecture 2 (Oct 14th) overview of sparsity theory and applications 56:38
This lecture reviews the theory of sparse representations and its applications in image processing. A detailed performance comparison between DCT and wavelets in image approximation is implemented, which reveals the superior of wavelets. With sparse representations, more efficient algorithms can be put forward for applications such as compression, denoising, inverse problems, and acquisition.
Lecture 3 (Oct 14th) sparse approx algorithms 42:54
This lecture introduces the problem of finding a sparse representation of a signal, and gives two very different frameworks of algorithms: greedy algorithms and convex programming. Greedy algorithms are based on the idea of matching pursuit, which aims to select a subset of a dictionary with which to represent a given signal. Convex programming relaxes the combinatorial problem into a closely related convex optimization program, Basis Pursuit, which can be recast as a linear program.
Lecture 4 (Oct 15th) uncertainty principles for unions of bases 1:03:43
This lecture first introduces the uncertainty principle between the identity basis and the Fourier basis, which ensures that sparse combinations of these dictionary elements can only be written one way with a small number of terms. Generally, this uncertainty principle can be characterized by the concept of coherence, which greatly affects the performance of sparse representations under the overcomplete dictionary.
Lecture 5 (Oct 15th) overview of compressive sensing 1:20:18
This lecture gives a high-level survey of compressive sensing and its applications. Compressive sensing is a new sampling theory that leverages compressibility of signals to replace front-end acquisition complexity with back-end computing. The key roles played are the new uncertainty principle and randomness. A bunch of applications are introduced to enhance the understanding of how compressive sensing works.
Lecture 6 (Oct 17th) 1 minimization duality and optimality 52:00
This is the first lecture of a trilogy about l1 minimization for sparse recovery. First, a necessary and sufficient condition for l1 recovery is derived, which is determined only by the set on which the sparse signal is supported and the signs of the elements on this set. Second, a more workable sufficient condition for l1 recovery is derived based on duality and optimality. Some examples are followed to show how to use these conditions in practice.
Lecture 7 (Oct 17th) 1 recovery with the restricted isometry property 44:33
This is the second lecture of a trilogy about l1 minimization for sparse recovery. It is proved that if the sensing matrix is an approximate isometry for all 3S-sparse vectors, l1 recovery is guaranteed to find the S-sparse vector. All these derivations are based on the fact that the cone condition is a sufficient condition for l1 recovery.
Lecture 8 (Oct 17th) stability of L1 recovery 38:22
This is the last lecture of a trilogy about l1 minimization for sparse recovery. It considers how l1 recovery performs in more realistic situations, such as the signal of interest is not sparse and the measurements are noisy. It is shown that the recovery error is bounded by how well the signal is approximated by a sparse signal and the energy of measurement noise.
Lecture 9 (Oct 18th) the RIP for Gaussian matrices 49:09
This lecture focuses on another mathematical fundamental of compressive sensing --- random matrices are restricted isometries. By putting together a bunch of easy results from probability theory, it is proved that an M by N iid Gaussian random matrix is a restricted isometry of size 2S with extraordinarily high probability when M is on the order of S*log(N/S).
Lecture 10 (Oct 18th) low rank recovery and bilinear problems 51:44
This lecture introduces some results on low rank recovery. Matrix completion aims to complete a matrix with observations of only some of its entries. It is shown that those blanks can be filled if the matrix is low rank and its singular vectors are incoherent. A more general problem of recovering a low rank matrix from a set of linear measurements is also considered. Finally, it is demonstrated that the problem of solving bilinear equations can be formulated to low rank recovery.
Lecture 11 (Oct 18th) dynamic l1 recovery 29:10
This lecture introduces a dynamical framework for sparse recovery, which is a recent research of Prof. Romberg and his collaborators. First, it talks about fast updating of solutions of l1 optimization programs in various scenarios, including the underlying signal changes slightly, add and remove measurements, the weights change, and have streaming measurements for an evolving signal. Second, it also introduces systems of nonlinear differential equations that solve l1 and related optimization programs, and it is implemented as continuous-time neural nets.
source: 谷实验室 2013年10月24日
Compressive Sensing and Sparse Recovery is a short course taught by Professor Justin Romberg during his visit to Tsinghua University from Oct. 14h to Oct. 18th, 2013. Professor Justin Romberg is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology.
More details about the course: http://gu.ee.tsinghua.edu.cn/index.ph...
All rights reserved by Professor Justin Romberg.
Lecture 1 (Oct 14th) Basis expansion fundamentals 1:01:42
This lecture talks about some fundamental mathematical background which will be quite useful in the subsequent lectures. It mainly focuses on how to represent the signal of interest as a discrete linear combination of basis signals. For orthogonal basis expansions, the generalized Parseval's theorem shows that all signal processing can be done by manipulating discrete sequences of expansion coefficients. This lecture also talks about the Discrete Cosine Transform (DCT) with its applications in image and video compression, and it ends with non-orthogonal bases and overcomplete frames.
Lecture 2 (Oct 14th) overview of sparsity theory and applications 56:38
This lecture reviews the theory of sparse representations and its applications in image processing. A detailed performance comparison between DCT and wavelets in image approximation is implemented, which reveals the superior of wavelets. With sparse representations, more efficient algorithms can be put forward for applications such as compression, denoising, inverse problems, and acquisition.
Lecture 3 (Oct 14th) sparse approx algorithms 42:54
This lecture introduces the problem of finding a sparse representation of a signal, and gives two very different frameworks of algorithms: greedy algorithms and convex programming. Greedy algorithms are based on the idea of matching pursuit, which aims to select a subset of a dictionary with which to represent a given signal. Convex programming relaxes the combinatorial problem into a closely related convex optimization program, Basis Pursuit, which can be recast as a linear program.
Lecture 4 (Oct 15th) uncertainty principles for unions of bases 1:03:43
This lecture first introduces the uncertainty principle between the identity basis and the Fourier basis, which ensures that sparse combinations of these dictionary elements can only be written one way with a small number of terms. Generally, this uncertainty principle can be characterized by the concept of coherence, which greatly affects the performance of sparse representations under the overcomplete dictionary.
Lecture 5 (Oct 15th) overview of compressive sensing 1:20:18
This lecture gives a high-level survey of compressive sensing and its applications. Compressive sensing is a new sampling theory that leverages compressibility of signals to replace front-end acquisition complexity with back-end computing. The key roles played are the new uncertainty principle and randomness. A bunch of applications are introduced to enhance the understanding of how compressive sensing works.
Lecture 6 (Oct 17th) 1 minimization duality and optimality 52:00
This is the first lecture of a trilogy about l1 minimization for sparse recovery. First, a necessary and sufficient condition for l1 recovery is derived, which is determined only by the set on which the sparse signal is supported and the signs of the elements on this set. Second, a more workable sufficient condition for l1 recovery is derived based on duality and optimality. Some examples are followed to show how to use these conditions in practice.
Lecture 7 (Oct 17th) 1 recovery with the restricted isometry property 44:33
This is the second lecture of a trilogy about l1 minimization for sparse recovery. It is proved that if the sensing matrix is an approximate isometry for all 3S-sparse vectors, l1 recovery is guaranteed to find the S-sparse vector. All these derivations are based on the fact that the cone condition is a sufficient condition for l1 recovery.
Lecture 8 (Oct 17th) stability of L1 recovery 38:22
This is the last lecture of a trilogy about l1 minimization for sparse recovery. It considers how l1 recovery performs in more realistic situations, such as the signal of interest is not sparse and the measurements are noisy. It is shown that the recovery error is bounded by how well the signal is approximated by a sparse signal and the energy of measurement noise.
Lecture 9 (Oct 18th) the RIP for Gaussian matrices 49:09
This lecture focuses on another mathematical fundamental of compressive sensing --- random matrices are restricted isometries. By putting together a bunch of easy results from probability theory, it is proved that an M by N iid Gaussian random matrix is a restricted isometry of size 2S with extraordinarily high probability when M is on the order of S*log(N/S).
Lecture 10 (Oct 18th) low rank recovery and bilinear problems 51:44
This lecture introduces some results on low rank recovery. Matrix completion aims to complete a matrix with observations of only some of its entries. It is shown that those blanks can be filled if the matrix is low rank and its singular vectors are incoherent. A more general problem of recovering a low rank matrix from a set of linear measurements is also considered. Finally, it is demonstrated that the problem of solving bilinear equations can be formulated to low rank recovery.
Lecture 11 (Oct 18th) dynamic l1 recovery 29:10
This lecture introduces a dynamical framework for sparse recovery, which is a recent research of Prof. Romberg and his collaborators. First, it talks about fast updating of solutions of l1 optimization programs in various scenarios, including the underlying signal changes slightly, add and remove measurements, the weights change, and have streaming measurements for an evolving signal. Second, it also introduces systems of nonlinear differential equations that solve l1 and related optimization programs, and it is implemented as continuous-time neural nets.
Caltech Computes: "CS+X" (Alumni College 2016)
# click the upper-left icon to select videos from the playlist
source: caltech 2016年12月20日
On November 12, 2016, the Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. http://alumni.caltech.edu/alumni-college
Adam Wierman - Caltech Computes - Alumni College 2016 25:57
Pietro Perona - CS+Data - Alumni College 2016 30:52
Yisong Yue - CS+Data - Alumni College 2016 21:35
George Djorgovski - CS+Astronomy - Alumni College 2016 27:19
Richard Murray - CS+Biology - Alumni College 2016 29:24
Lulu Qian - CS+Biology - Alumni College 2016 21:32
Thomas Vidick - CS+Physics - Alumni College 2016 28:29
Xie Chen - CS+Physics - Alumni College 2016 27:09
Federico Echenique - CS+Economics - Alumni College 2016 21:51
Tom Miller - CS+Chemistry - Alumni College 2016 24:10
Steven Low - CS+Energy - Alumni College 2016 24:30
Peter Schröder - CS+Visualization - Alumni College 2016 17:10
source: caltech 2016年12月20日
On November 12, 2016, the Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. http://alumni.caltech.edu/alumni-college
Adam Wierman - Caltech Computes - Alumni College 2016 25:57
Pietro Perona - CS+Data - Alumni College 2016 30:52
Yisong Yue - CS+Data - Alumni College 2016 21:35
George Djorgovski - CS+Astronomy - Alumni College 2016 27:19
Richard Murray - CS+Biology - Alumni College 2016 29:24
Lulu Qian - CS+Biology - Alumni College 2016 21:32
Thomas Vidick - CS+Physics - Alumni College 2016 28:29
Xie Chen - CS+Physics - Alumni College 2016 27:09
Federico Echenique - CS+Economics - Alumni College 2016 21:51
Tom Miller - CS+Chemistry - Alumni College 2016 24:10
Steven Low - CS+Energy - Alumni College 2016 24:30
Peter Schröder - CS+Visualization - Alumni College 2016 17:10
Approximate Dynamic Programming Lectures by Dimitri P. Bertsekas at MIT
# click the upper-left icon to select videos from the playlist
source: Shuvomoy Das Gupta 2014年10月27日
Videos for a 6-lecture short course on Approximate Dynamic Programming by Professor Dimitri P. Bertsekas (Massachusetts Institute of Technology, Cambridge, Massachusetts, United States) at Tsinghua University, Beijing, China, June 2014. The lecture slides are available at http://adpthu2014.weebly.com/slides--...
Additional supporting material can be obtained from Prof. Bertsekas' web site
http://web.mit.edu/dimitrib/www/publ....
including a complete set of slides from http://web.mit.edu/dimitrib/www/...
Lecture 1 Part 1 52:26
Lecture 1 Part 2 41:57
Lecture 1 Part 3 28:13
Lecture 2 Part 1 39:48
Lecture 2 Part 2 45:41
Lecture 2 Part 3 31:01
Lecture 3 Part 1 1:12:45
Lecture 3 Part 2 56:01
Lecture 4 Part 1 38:14
Lecture 4 Part 2 45:31
Lecture 5 Part 1 38:26
Lecture 5 Part 2 36:28
Lecture 5 Part 3 40:46
Lecture 6 Part 1 47:44
Lecture 6 Part 2 45:19
source: Shuvomoy Das Gupta 2014年10月27日
Videos for a 6-lecture short course on Approximate Dynamic Programming by Professor Dimitri P. Bertsekas (Massachusetts Institute of Technology, Cambridge, Massachusetts, United States) at Tsinghua University, Beijing, China, June 2014. The lecture slides are available at http://adpthu2014.weebly.com/slides--...
Additional supporting material can be obtained from Prof. Bertsekas' web site
http://web.mit.edu/dimitrib/www/publ....
including a complete set of slides from http://web.mit.edu/dimitrib/www/...
Lecture 1 Part 1 52:26
Lecture 1 Part 2 41:57
Lecture 1 Part 3 28:13
Lecture 2 Part 1 39:48
Lecture 2 Part 2 45:41
Lecture 2 Part 3 31:01
Lecture 3 Part 1 1:12:45
Lecture 3 Part 2 56:01
Lecture 4 Part 1 38:14
Lecture 4 Part 2 45:31
Lecture 5 Part 1 38:26
Lecture 5 Part 2 36:28
Lecture 5 Part 3 40:46
Lecture 6 Part 1 47:44
Lecture 6 Part 2 45:19
Chubb Fellowship Lecture - João Vale de Almeida
source: Yale University 2016年11月30日
João Vale de Almeida is a senior European Union diplomat who was appointed as EU Ambassador to the United Nations October 16, 2015. Ambassador Vale de Almeida previously served as the first EU Ambassador to the United States of America, from 2010 to 2014
History and Human Rights: A Panel Discussion
source: Harvard University 2016年12月1日
Opening Remarks
Jacqueline Bhabha
Professor of the Practice of Health and Human Rights, Harvard T. H. Chan School of Public Health
Discussants
Bernard Harcourt
Isidor and Seville Sulzbacher Professor of Law and Professor of Political Science, Columbia University
Samuel Moyn
Jeremiah Smith, Jr. Professor of Law and Professor of History, Harvard University
Kathryn Sikkink
Ryan Family Professor of Human Rights Policy, Harvard Kennedy School
Carol K. Pforzheimer Professor, Radcliffe Institute for Advanced Study
Carr Center for Human Rights Policy
Nine unique stories from members of the Harvard Graduate School of Education community
source: HarvardEducation 2016年11月4日
Nine unique stories from members of the Harvard Graduate School of Education community.
http://www.hgsevoice.org/double-take/
Human Embryo Research: Opening the “Black Box”
source: Cambridge University 2016年12月22日
Cambridge research that will enable scientists to grow and study embryos in the lab for almost two weeks has been named as the People’s Choice for Science magazine’s ‘Breakthrough of the Year 2016’. Professor Magdalena Zernicka-Goetz at the Department of Physiology, Development and Neuroscience has developed a new technique that allows embryos to develop in vitro, in the absence of maternal tissue, beyond the implantation stage (when the embryo would normally implant into the womb). This will allow researchers to analyse for the first time key stages of human embryo development up to 13 days after fertilisation.
HSBC/HKU Asia Pacific Business Case Competition 2016
# click the up-left corner to select videos from the playlist
source: ACRC HKU 2016年10月26日
2016 Round 1 A1 The University of British Columbia--Gravity Payments: Minimum Wage is a Heavy Load 39:04
2016 Round 1 A2 36:19
2016 Round 1 A3 28:10
2016 Round 1 B1 32:55
2016 Round 1 B2 35:44
2016 Round 1 B3 23:40
2016 Round 1 C1 34:34
2016 Round 1 C2 34:10
2016 Round 1 C3 34:45
2016 Round 1 D1 30:12
2016 Round 1 D2 30:21
2016 Round 2 D2 32:52
2016 Round 1 D3 30:39
2016 Round 1 E1 34:48
2016 Round 1 E2 35:27
2016 Round 1 E3 26:11
2016 Round 1 F1 35:31
2016 Round 1 F2 35:11
2016 Round 1 F3 32:09
2016 Round 1 G1 18:14
2016 Round 1 G2 33:01
2016 Round 1 G3 29:17
2016 Round 1 H1 37:25
2016 Round 1 H2 36:19
2016 Round 1 H3 35:32
2016 Round 2 A1 36:14
2016 Round 2 A2 26:03
2016 Round 2 B1 32:27
2016 Round 2 B2 35:24
2016 Round 2 C1 32:49
2016 Round 2 C2 32:13
2016 Round 2 D1 32:17
2016 Round 2 E1 34:21
2016 Round 2 E2 34:18
2016 Round 2 F1 35:46
2016 Round 2 F2 35:52
2016 Round 2 G1 37:58
2016 Round 2 G2 36:33
2016 Round 2 H1 35:00
2016 Round 2 H2 29:12
2016 Round 3 A1 32:48
2016 Round 3 A2 30:37
2016 Round 3 B1 36:56
2016 Round 3 B2 32:38
2016 Round 3 C1 34:16
2016 Round 3 C2 29:55
2016 Round 3 D1 37:20
2016 Round 3 D2 35:37
2016 Round 4 Team 1 40:04
[私人影片]
2016 Round 4 Team 3 30:08
2016 Round 4 Team 4 27:22
Cocktail-cum-award Ceremony 38:21
source: ACRC HKU 2016年10月26日
2016 Round 1 A1 The University of British Columbia--Gravity Payments: Minimum Wage is a Heavy Load 39:04
2016 Round 1 A2 36:19
2016 Round 1 A3 28:10
2016 Round 1 B1 32:55
2016 Round 1 B2 35:44
2016 Round 1 B3 23:40
2016 Round 1 C1 34:34
2016 Round 1 C2 34:10
2016 Round 1 C3 34:45
2016 Round 1 D1 30:12
2016 Round 1 D2 30:21
2016 Round 2 D2 32:52
2016 Round 1 D3 30:39
2016 Round 1 E1 34:48
2016 Round 1 E2 35:27
2016 Round 1 E3 26:11
2016 Round 1 F1 35:31
2016 Round 1 F2 35:11
2016 Round 1 F3 32:09
2016 Round 1 G1 18:14
2016 Round 1 G2 33:01
2016 Round 1 G3 29:17
2016 Round 1 H1 37:25
2016 Round 1 H2 36:19
2016 Round 1 H3 35:32
2016 Round 2 A1 36:14
2016 Round 2 A2 26:03
2016 Round 2 B1 32:27
2016 Round 2 B2 35:24
2016 Round 2 C1 32:49
2016 Round 2 C2 32:13
2016 Round 2 D1 32:17
2016 Round 2 E1 34:21
2016 Round 2 E2 34:18
2016 Round 2 F1 35:46
2016 Round 2 F2 35:52
2016 Round 2 G1 37:58
2016 Round 2 G2 36:33
2016 Round 2 H1 35:00
2016 Round 2 H2 29:12
2016 Round 3 A1 32:48
2016 Round 3 A2 30:37
2016 Round 3 B1 36:56
2016 Round 3 B2 32:38
2016 Round 3 C1 34:16
2016 Round 3 C2 29:55
2016 Round 3 D1 37:20
2016 Round 3 D2 35:37
2016 Round 4 Team 1 40:04
[私人影片]
2016 Round 4 Team 3 30:08
2016 Round 4 Team 4 27:22
Cocktail-cum-award Ceremony 38:21
HSBC/HKU Asia Pacific Business Case Competition 2015
# click the up-left corner to select videos from the playlist
source: ACRC HKU 2015年8月3日
3D1 The Hong Kong University of Science and Technology 30:51
3D2 Universiti Brunei Darussalam 31:02
4B2 The Hong Kong Polytechnic University 31:52
4B1 Universiti Brunei Darussalam 34:13
4A2 University of Auckland 43:05
4A1 University of the Philippines Diliman 37:48
3C2 University of Auckland 33:39
3C1 The University of Sydney 38:44
3B2 The Hong Kong Polytechnic University 34:45
3B1 The University of Nottingham Malaysia Campus 32:18
3A2 Nanyang Technological University 30:33
3A1 University of the Philippines Diliman 36:39
2H2 Hong Kong Shue Yan University 39:28
2H1 The University of Sydney 39:28
2G2 The Hong Kong University of Science and Technology 35:33
2G1 Thammasat University 34:30
2F2 University of Dhaka 33:25
[私人影片]
2E2 Nanyang Technological University 34:48
2E1 Sun Yat‐sen University 32:37
[私人影片]
2D1 National University of Singapore 34:14
2C2 Universiti Brunei Darussalam 31:18
2C1 Peking University 35:27
2B2 University of Auckland 31:21
2B1 BINUS University 35:27
[私人影片]
2A1 Keio University 30:33
1E1 University of Mauritius 28:27
1H3 Sun Yat‐sen University 30:20
1H2 The Hong Kong University of Science and Technology 35:50
1H1 Fudan University 36:07
1G3 Universiti Brunei Darussalam 28:40
1G2 University of Auckland 31:37
1G1 Yonsei University 36:13
1F3 IIT Delhi 34:52
1F2 National University of Singapore 21:49
1F1 Nanyang Technological University 33:04
1E3 Peking University 33:13
1E2 University of Dhaka 29:15
RMIT University Vietnam 26:10
1D2 The Hong Kong Polytechnic University 31:57
1D1 The University of Sydney 31:51
1C3 University of the Philippines Diliman 27:27
1C2 Thammasat University 27:29
1C1 National Chengchi University 28:52
1B3 Hong Kong Shue Yan University 33:14
1B2 Macao Polytechnic Institute 34:44
1B1 BINUS University 28:53
1A3 The University of Nottingham Malaysia 25:45
1A2 University of Colombo 27:10
1A1 Keio University 34:28
source: ACRC HKU 2015年8月3日
3D1 The Hong Kong University of Science and Technology 30:51
3D2 Universiti Brunei Darussalam 31:02
4B2 The Hong Kong Polytechnic University 31:52
4B1 Universiti Brunei Darussalam 34:13
4A2 University of Auckland 43:05
4A1 University of the Philippines Diliman 37:48
3C2 University of Auckland 33:39
3C1 The University of Sydney 38:44
3B2 The Hong Kong Polytechnic University 34:45
3B1 The University of Nottingham Malaysia Campus 32:18
3A2 Nanyang Technological University 30:33
3A1 University of the Philippines Diliman 36:39
2H2 Hong Kong Shue Yan University 39:28
2H1 The University of Sydney 39:28
2G2 The Hong Kong University of Science and Technology 35:33
2G1 Thammasat University 34:30
2F2 University of Dhaka 33:25
[私人影片]
2E2 Nanyang Technological University 34:48
2E1 Sun Yat‐sen University 32:37
[私人影片]
2D1 National University of Singapore 34:14
2C2 Universiti Brunei Darussalam 31:18
2C1 Peking University 35:27
2B2 University of Auckland 31:21
2B1 BINUS University 35:27
[私人影片]
2A1 Keio University 30:33
1E1 University of Mauritius 28:27
1H3 Sun Yat‐sen University 30:20
1H2 The Hong Kong University of Science and Technology 35:50
1H1 Fudan University 36:07
1G3 Universiti Brunei Darussalam 28:40
1G2 University of Auckland 31:37
1G1 Yonsei University 36:13
1F3 IIT Delhi 34:52
1F2 National University of Singapore 21:49
1F1 Nanyang Technological University 33:04
1E3 Peking University 33:13
1E2 University of Dhaka 29:15
RMIT University Vietnam 26:10
1D2 The Hong Kong Polytechnic University 31:57
1D1 The University of Sydney 31:51
1C3 University of the Philippines Diliman 27:27
1C2 Thammasat University 27:29
1C1 National Chengchi University 28:52
1B3 Hong Kong Shue Yan University 33:14
1B2 Macao Polytechnic Institute 34:44
1B1 BINUS University 28:53
1A3 The University of Nottingham Malaysia 25:45
1A2 University of Colombo 27:10
1A1 Keio University 34:28
HSBC/HKU Asia Pacific Business Case Competition 2014
# click the up-left corner to select videos from the playlist
source: ACRC HKU 2014年9月23日
Round 1 A1 University Of Colombo, Sri Lanka 32:09
Round 1 A2 National Chengchi University 35:07
Round 1 A3 Universiti Brunei Darussalam 34:05
Round 1 A4 Sun Yat Sen University 32:33
Round 1 B1 University Of Auckland 31:11
Round 1 B2 Shanghai Jiao Tong University 34:48
Round 1 B3 Tsinghua University 32:19
Round 1 B4 Chulalongkorn University 26:08
Round 1 C1 University Of Delhi 30:55
Round 1 C2 The University of Sydney 32:09
Round 1 C3 Lingnan University 30:39
Round 1 C4 Taylor's University 30:40
Round 1 D1 University Of the Philippines 34:18
Round 1 D2 The University Of Hong Kong 34:09
Round 1 D3 University Of Dhaka 31:55
Round 1 D4 Universitas Indonesia 35:18
Round 1 E1 South China University of Technology 35:24
Round 1 E2 Macao Polytechnic Insitute 33:53
Round 1 E3 National University of Singapore 34:56
Round 1 E4 Chung Ang University 27:49
Round 1 F1 Fudan University 31:25
Round 1 F2 City University of Hong Kong 35:10
Round 1 F3 National Taiwan Normal University 23:01
Round 1 F4 Singapore Management University 34:38
Round 2 A1 Singapore Management University 35:47
Round 2 A2 The University of Sydney 35:24
Round 2 B1 Chulalongkorn University 35:19
Round 2 B2 City University of Hong Kong 34:55
Round 2 C1 Chulalongkorn University 35:39
Round 2 C2 Lingnan University 36:32
Round 2 D1 University of Auckland 35:22
Round 2 D2 Sun Yat sen University 34:52
Round 2 E1 National University of Singapore 34:50
Round 2 E2 National Chengchi University 34:46
Round 2 F1 University Of the Philippines 34:44
Round 2 F2 The University of Hong Kong 35:22
Round 3 The University of Hong Kong 35:27
Round 3 Chulalongkorn University 35:13
Round 3 City University of Hong Kong 34:20
Round 4 National University of Singapore 44:24
Round 3 University of Auckland 34:29
Round 3 The University of Sydney 33:52
Round 4 The University of Hong Kong 37:33
Round 4 University of Auckland 37:02
Round 4 National University of Singapore 44:24
source: ACRC HKU 2014年9月23日
Round 1 A1 University Of Colombo, Sri Lanka 32:09
Round 1 A2 National Chengchi University 35:07
Round 1 A3 Universiti Brunei Darussalam 34:05
Round 1 A4 Sun Yat Sen University 32:33
Round 1 B1 University Of Auckland 31:11
Round 1 B2 Shanghai Jiao Tong University 34:48
Round 1 B3 Tsinghua University 32:19
Round 1 B4 Chulalongkorn University 26:08
Round 1 C1 University Of Delhi 30:55
Round 1 C2 The University of Sydney 32:09
Round 1 C3 Lingnan University 30:39
Round 1 C4 Taylor's University 30:40
Round 1 D1 University Of the Philippines 34:18
Round 1 D2 The University Of Hong Kong 34:09
Round 1 D3 University Of Dhaka 31:55
Round 1 D4 Universitas Indonesia 35:18
Round 1 E1 South China University of Technology 35:24
Round 1 E2 Macao Polytechnic Insitute 33:53
Round 1 E3 National University of Singapore 34:56
Round 1 E4 Chung Ang University 27:49
Round 1 F1 Fudan University 31:25
Round 1 F2 City University of Hong Kong 35:10
Round 1 F3 National Taiwan Normal University 23:01
Round 1 F4 Singapore Management University 34:38
Round 2 A1 Singapore Management University 35:47
Round 2 A2 The University of Sydney 35:24
Round 2 B1 Chulalongkorn University 35:19
Round 2 B2 City University of Hong Kong 34:55
Round 2 C1 Chulalongkorn University 35:39
Round 2 C2 Lingnan University 36:32
Round 2 D1 University of Auckland 35:22
Round 2 D2 Sun Yat sen University 34:52
Round 2 E1 National University of Singapore 34:50
Round 2 E2 National Chengchi University 34:46
Round 2 F1 University Of the Philippines 34:44
Round 2 F2 The University of Hong Kong 35:22
Round 3 The University of Hong Kong 35:27
Round 3 Chulalongkorn University 35:13
Round 3 City University of Hong Kong 34:20
Round 4 National University of Singapore 44:24
Round 3 University of Auckland 34:29
Round 3 The University of Sydney 33:52
Round 4 The University of Hong Kong 37:33
Round 4 University of Auckland 37:02
Round 4 National University of Singapore 44:24
Cancer evolution through space and time - Prof Charles Swanton - UCL LHL
source: UCL Lunch Hour Lectures 2016年12月1日
Speaker: Professor Charles Swanton, UCL Cancer Institute - Tuesday 22nd November 2016 #ucllhl
While it has long been appreciated that no two tumours are identical, recent work suggests that even two regions of the same tumour can be remarkably distinct, which likely contributes to treatment failure and drug resistance. Professor Charles Swanton will explore the extent of intra-tumour heterogeneity in different types of cancer and its clinical implications.
Bulletproof Custards: Fluids That Stop Flowing When You Push Them Too Hard
source: Perimeter Institute for Theoretical Physics 2016年11月3日
In his public lecture at Perimeter Institute on Nov. 3, 2016, Michael Cates (Lucasian Professor of Mathematics, University of Cambridge) explains this peculiar form of “bulletproof custard” with a few equations, plenty of diagrams, and even some hands-on demonstrations.
Samantha Ettus: "The Pie Life" | Talks at Google
source: Talks at Google 2016年12月12日
Professionals are caught in a tug of war between their work and personal lives and have been saddled with impossible frameworks for how to successfully manage both. Until now. Using her proven and groundbreaking framework, Harvard MBA, radio host and bestselling author Samantha Ettus has worked with thousands of professionals and companies to discover how to effectively and enjoyably manage the life you have – in slices. As Samantha explains, “When things go wrong in our personal lives, we tend to unfairly blame work.” With tools and techniques to manage the daily rhythm of our lives, everything becomes more manageable, less stressful and even enjoyable. Samantha will give you the inspiration and tools for you and your employees to thrive professionally and personally at the same time.
Get the book here: https://goo.gl/EF6Nb4
A World of Pure Experience - William James' Radical Empiricism
source: Philosophical Overdose 2016年12月12日
Another chapter of William James's "Essays in Radical Empiricism" from LibriVox, read by Carl Manchester.
Jon Beasley-Murray: Roberto Arlt, Jorge Luis Borges, and Felisberto Hernández, Selected Stories (14/03/2013)
source: Arts One Open 2013年3月14日
Roberto Arlt, "The Cooked Cat." Jorge Luis Borges, "Hakim, the Masked Dyer of Merv," "Man on Pink Corner," "Pierre Menard, Author of the Quixote," "The Circular Ruins," "The Lottery in Babylon," "The Library of Babel," "The Theme of the Traitor and the Hero," "The South," "Emma Zunz," "The Other Death," "Deutsches Requiem," "The Two Kings and the Two Labyrinths," "The Interloper," "The Story from Rosendo Juárez," and "Guayaquil". Felisberto Hernández, "The Daisy Dolls" ("Las hortensias").
Lecture by Jon Beasley-Murray for the "Monster in the Mirror" theme. For more, see http://artsone-digital.arts.ubc.ca/20....
For a version of this video with slides, go to http://mediasitemob1.mediagroup.ubc.c....
Jon Beasley-Murray: J M Coetzee, Foe (22/03/2013)
source: Arts One Open 2013年3月22日
Lecture by Jon Beasley-Murray for the "Monster in the Mirror" theme. For more, see http://artsone-digital.arts.ubc.ca/20....
For a version of this video with slides, go to http://mediasitemob1.mediagroup.ubc.c....
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