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Video
An online lecture on the topic of "What is Microgravity? Discovering Interesting Phenomena in Microgravity".This lecture of “Science World: Exploring Space to Benefit Mankind” Education Programme in the 2021/22 school year for secondary students, which aims to cultivate the interest of local youth in space science and elevate their enthusiasm for participating in the development of space technology.
- Subjects:
- Physics and Aeronautical and Aviation Engineering
- Keywords:
- Gravity Reduced gravity environments
- Resource Type:
- Video
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Video
What is your dream? And how to make it come true? Ms Guo Jingjing, the Olympic Gold Medalist, will share with us her story of chasing dreams.
你的夢想是什麼?你有好好努力使它實現嗎?奧運金牌得主郭晶晶女士將與大家分享她追逐夢想的故事。
Event Date: 22.11.2022
- Keywords:
- China Athletes -- Training of Diving
- Resource Type:
- Video
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MOOC
This award-winning course aims to sharpen your competitive edge in work and life. It empowers you with positive values and practical problem-solving skills, including creative strategies for addressing challenges from COVID-19. Enriched with interesting animations, a new success story and breakthrough pedagogies, this updated version (2.5) effectively helps you master knowledge and skills requisite for a successful life.
- Keywords:
- Creative thinking Learning ability Critical thinking
- Resource Type:
- MOOC
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Video
An online lecture on the topic of "Space Station’s Contribution to Space Resources and Human Developments".This lecture of “Science World: Exploring Space to Benefit Mankind” Education Programme in the 2021/22 school year for secondary students, which aims to cultivate the interest of local youth in space science and elevate their enthusiasm for participating in the development of space technology.
- Subjects:
- Industrial and Systems Engineering and Aeronautical and Aviation Engineering
- Keywords:
- Space stations Exploration of outer space
- Resource Type:
- Video
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Video
An online lecture on the topic of "Solar Vehicles for Space".This lecture of “Science World: Exploring Space to Benefit Mankind” Education Programme in the 2021/22 school year for secondary students, which aims to cultivate the interest of local youth in space science and elevate their enthusiasm for participating in the development of space technology.
- Subjects:
- Electrical Engineering
- Keywords:
- Solar energy Space vehicles -- Solar engines
- Resource Type:
- Video
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MOOC
The real-life stroke scenario presented in ANA101x Human Anatomy has invited vigorous discussions on whether fully recovery from a severe stroke is possible and how it could happen. The knowledge of anatomy has arisen a series of queries on body functioning that are commonly implicated in stroke. An extension of human anatomy fundamentals towards functional anatomy has formed the basis of intervention approaches for functional recovery undertaken by different healthcare professionals, which is guiding the ultimate goals of post-stroke rehabilitation program for regaining independence and quality-of-life of the individuals. Therefore, this course is particularly designed to delineate the stroke recovery process and its underlying scientific rationales.
Continuing using the same clinical case of Mr Law, this course walks you through the recovery journey, known as stroke care pathway involving multiple healthcare professionals to compose module ONE. In module TWO, intervention approaches practiced in key healthcare disciplines underpinned by the functional anatomy will be explored. Finally, the course knowledge will be assessed using an experiential approach using a set of mini case studies derived from the mainstream scenario of Mr Law.
- Subjects:
- Health Sciences
- Keywords:
- Cerebrovascular disease -- Patients -- Rehabilitation
- Resource Type:
- MOOC
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Video
Before the advent of computers around 1950, optimization centered either on small-dimensional problems solved by looking at zeroes of first derivatives and signs of second derivatives, or on infinite-dimensional problems about curves and surfaces. In both cases, "variations" were employed to understand how a local solution might be characterized. Computers changed the picture by opening the possibility of solving large-scale problems involving inequalities, instead of only equations. Inequalities had to be recognized as important because the decisions to be optimized were constrained by the need to respect many upper or lower bounds on their feasibility. A new kind of mathematical analysis, beyond traditional calculus, had to be developed to address these needs. It built first on appealing to the convexity of sets and functions, but went on to amazingly broad and successful concepts of variational geometry, subgradients, subderivatives, and variational convergence beyond just that. This talk will explain these revolutionary developments and why there were essential.
Event date: 1/11/2022
Speaker: Prof. Terry Rockafellar (University of Washington)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Convex functions Convex sets Mathematical optimization Computer science -- Mathematics
- Resource Type:
- Video
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Video
Adaptive computation is of great importance in numerical simulations. The ideas for adaptive computations can be dated back to adaptive finite element methods in 1970s. In this talk, we shall first review some recent development for adaptive methods with some application. Then, we will propose a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions. In particular, we propose the failure informed PINNs (FI-PINNs), which can adaptively refine the training set with the goal of reducing the failure probability. Compared with the neural network approximation obtained with uniformly distributed collocation points, the proposed algorithms can significantly improve the accuracy, especially for low regularity and high-dimensional problems.
Event date: 18/10/2022
Speaker: Prof. Tao Tang (Beijing Normal University-Hong Kong Baptist University United International College)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Sampling (Statistics) Differential equations Partial -- Numerical solutions Mathematical models Adaptive computing systems
- Resource Type:
- Video
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Video
We introduce a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only Hessian-vector products in two directions. Moreover; the computational overhead remains comparable to the first-order such as the gradient descent method. We show that the method has a local super-linear convergence and a global convergence rate of 0(∈-3/2) to satisfy the first-order and second-order conditions under a commonly used approximated Hessian assumption. We further show that this assumption can be removed if we perform one step of the Krylov subspace method at the end of the algorithm, which makes DRSOM the first first-order-type algorithm to achieve this complexity bound. The applicability and performance of DRSOM are exhibited by various computational experiments in logistic regression, L2-Lp minimization, sensor network localization, neural network training, and policy optimization in reinforcement learning. For neural networks, our preliminary implementation seems to gain computational advantages in terms of training accuracy and iteration complexity over state-of-the-art first-order methods including SGD and ADAM. For policy optimization, our experiments show that DRSOM compares favorably with popular policy gradient methods in terms of the effectiveness and robustness.
Event date: 19/09/2022
Speaker: Prof. Yinyu Ye (Stanford University)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Convex programming Nonconvex programming Mathematical optimization
- Resource Type:
- Video
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Video
Convex Matrix Optimization (MOP) arises in a wide variety of applications. The last three decades have seen dramatic advances in the theory and practice of matrix optimization because of its extremely powerful modeling capability. In particular, semidefinite programming (SP) and its generalizations have been widely used to model problems in applications such as combinatorial and polynomial optimization, covariance matrix estimation, matrix completion and sensor network localization. The first part of the talk will describe the primal-dual interior-point methods (IPMs) implemented in SDPT3 for solving medium scale SP, followed by inexact IPMs (with linear systems solved by iterative solvers) for large scale SDP and discussions on their inherent limitations. The second part will present algorithmic advances for solving large scale SDP based on the proximal-point or augmented Lagrangian framework In particular, we describe the design and implementation of an augmented Lagrangian based method (called SDPNAL+) for solving SDP problems with large number of linear constraints. The last part of the talk will focus on recent advances on using a combination of local search methods and convex lifting to solve low-rank factorization models of SP problems.
Event date: 11/10/2022
Speaker: Prof. Kim-Chuan Toh (National University of Singapore)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Convex programming Semidefinite programming
- Resource Type:
- Video