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Hong Kong Polytechnic University
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Video
An online lecture on the topic of "Fast Fashion, Slow Fashion, Green Fashion".The Faculty of Applied Science and Textiles (FAST) and the Institute of Textiles & Clothing (ITC) organized the mini-lecture series for more than three years. The lectures aim to enrich students' knowledge in creative perspectives and arouse their interest in Sciences, Fashion and Textiles. In view of the unpredictable development of the COVID-19 pandemic, the upcoming mini-lecture Series will be switched from face-to-face mode to online mode.
- Subjects:
- Fashion retailing and merchandising
- Keywords:
- Clothing trade -- Environmental aspects Fashion merchising
- Resource Type:
- Video
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Video
An online lecture on the topic of "10 Things about Fashion Branding".The Faculty of Applied Science and Textiles (FAST) and the Institute of Textiles & Clothing (ITC) organized the mini-lecture series for more than three years. The lectures aim to enrich students' knowledge in creative perspectives and arouse their interest in Sciences, Fashion and Textiles. In view of the unpredictable development of the COVID-19 pandemic, the upcoming mini-lecture Series will be switched from face-to-face mode to online mode.
- Subjects:
- Fashion retailing and merchandising
- Keywords:
- Bring (Marketing) Br name products
- Resource Type:
- Video
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Video
PolyU AAE students guide you through the flight procedures of an A320 Flight Stimulator.
- Subjects:
- Aeronautical and Aviation Engineering
- Keywords:
- Flight training -- Simulation methods Flight simulators
- Resource Type:
- Video
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Others
A portfolio for ITC3089E showing how to present the theme and fabric
- Course related:
- ITC3089E Woven Fabric Design Project
- Subjects:
- Textiles
- Keywords:
- Weaving Textile fabrics
- Resource Type:
- Others
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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
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MOOC
The course describes the theoretical underpinnings of USR. It showcases some effective practices and activities carried out in institutions around the world. The course also promotes understanding of the USR concept, its implications, impacts, evaluation, possible implementation methods, and replicability. In doing so, it contributes to the expansion and consolidation of the USR movement. It provides a knowledge base for designing, planning, implementing, and evaluating programs from the fundamentals of USR and some existing good practices.
No prior knowledge is required to join the course. This course is for you especially if you are an administrator, a professor, or a professional staff member interested in learning new creative ways from the experts' at the universities to address the needs of society including SDGs (Sustainable Development Goals). If you are a government official or a foundation representative seeking ways to strengthen higher education, this course will help you explore future opportunities and directions.
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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
Geospatial information science is a discipline that focuses on using geospatial information technology to understand people, places, nature and processes of the earth. IoT refers to Internet of things, the combination of sensors, software and other technologies to connect and exchange data with other devices and systems over the Internet. The era of IoT brings us opportunities and challenges for geospatial information science. In the keynote, five characteristics and three scientific issues of geo-spatial information science in the era of IoT are summarised.
Even date: 6/9/2022
Speaker: Prof. Daren Li
Moderator: Prof. Christopher Chao (Hong Kong Polytechnic University)
Panel members: Prof. Qingyan Chen, Prof. Qinhao Chen (Hong Kong Polytechnic University)
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Land Surveying and Geo-Informatics
- Keywords:
- Internet of things Geospatial data Spatial data mining
- Resource Type:
- Video
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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