Search Constraints
Number of results to display per page
Results for:
Search Results
-
Video
古代中國幅員遼闊，習俗各異，如何實行統一管理是一大難題。秦朝用嚴刑峻法移風易俗，激起東方社會的激烈反抗，很快歸於失敗。西漢初年郡國並行，允許東方王國在一定程度上從俗而治。但王國勢力太大，危及國家的統一，文景二帝不得不收奪諸侯王的自治權。為了避免重蹈亡秦覆轍，儒生們提出“德教”主張，其中又包含“以禮為治”和“以德化民”兩種方案。受其影響，武帝以後的朝廷政策繼續表現出大幅度搖擺，至漢末魏晉才確立了基本符合當時國情的治理模式。
日期：2022年11月22日
講者：陳蘇鎮博士
主辦：香港孔子學院, 中國文化學系
- Subjects:
- Chinese Studies
- Keywords:
- Han Dynasty (China) Politics government Jin Dynasty (China : 265-419) China Public administration Qin Dynasty (China)
- Resource Type:
- Video
-
Video
More than one hundred years ago, Albert Einstein published his Theory of General Relativity (GR). One year later, Karl Schwarzschild solved the GR equations for a non-rotating, spherical mass distribution; if this mass is sufficiently compact, even light cannot escape from within the so-called event horizon, and there is a mass singularity at the center. The theoretical concept of a 'black hole' was born, and was refined in the next decades by work of Penrose, Wheeler, Kerr, Hawking and many others. First indirect evidence for the existence of such black holes in our Universe came from observations of compact X-ray binaries and distant luminous quasars. I will discuss the forty-year journey, which my colleagues and I have been undertaking to study the mass distribution in the Center of our Milky Way from ever more precise, long-term studies of the motions of gas and stars as test particles of the space time. These studies show the existence of a four million solar mass object, which must be a single massive black hole, beyond any reasonable doubt.
Even date: 9/2/2023
Speaker: Prof. Reinhard GENZEL
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Physics and Cosmology and Astronomy
- Keywords:
- Nobel Prize winners Astrophysics Astronomy Deep space -- Milky Way Black holes (Astronomy) General relativity (Physics)
- Resource Type:
- Video
-
Video
As a recent New York Times editorial proclaimed, "The Global Order Isn't Working. It's Time for Something Different." To teach environmental history and environmental ethics is to reacquaint ourselves with the facts that we need to try to build, while there is still time, a new cooperative order that understands this: simple fact: that other people and other countries are quite literally "the air we breathe." Moreover, all who claim to be ethical persons must take seriously the notion of inter-generational equity and try to act upon it. This notion should, in theory, come more easily to countries whose traditions have built upon classical/ Confucian learning, for those traditions say that the most important marker of human behavior is working toward common ends (qun 群) while "learning what is enough" (zhi zu 知足). Put another way, many resources within the Chinese tradition may strengthen our resolve to act more constructively in less short-sighted ways.
Event Date: 14/11/2022
Speaker: Prof. Michael Nylan (University of California, Berkeley)
Hosted by: Faculty of Humanities
- Keywords:
- Environmental ethics Intergenerational relations Philosophy Confucian Confucian ethics
- Resource Type:
- Video
-
Video
The notion of expertise is integral to all forms of institutional and professional practice in many domains – in education, healthcare, social welfare, law, journalism, banking, information technology, marketing, translating and interpreting services etc. It is a concept addressed by scholars across many disciplines – cognitive science, sociology, anthropology, psychology, language/communication studies, among others. There are, however, enduring problems of definition, description and measurement of expertise. Some scholars draw attention to the ongoing ‘crisis in expertise’ and others pronounce the ‘death of expertise’ in contemporary society.
More humbly, I begin with a characterisation of professional expertise very broadly to include scientific, experiential, technological, organisational, legal, ethical and communicative knowledge. This then leads me to the notion of ‘distributed expertise’, which extends beyond the individual remit and the conventional lay-expert divide. For instance, in the healthcare domain, a significant development afforded by internet-based technology is the increased level of patients’ e-health literacy and, consequently, democratisation of expertise. This amounts not only to accessing health information digitally, but also the phenomenon of patients ‘doctoring’ themselves in ‘the now of its presence’, i.e., ‘expert patients’ becoming instrumental in self-diagnosis and even self-treatment.
Additionally, ‘distributed expertise’ is also constitutive of ‘expert systems’, e.g., diagnostic and interventionist technologies as well as decision aids mediated by algorithms and templates. This is what I refer to as the technologization of expertise. I suggest that there is overreliance on ‘expert systems’ by both experts and lay persons in everyday decision making. Access to and use of ‘expert systems’ in optimal ways inevitably necessitates a reconfiguration of the very conditions and consequences of professional expertise.
Event Date: 25/11/2022
Speaker: Prof. Srikant Sarangi (Hong Kong Polytechnic University)
Hosted by: Faculty of Humanities
- Keywords:
- Information technology -- Social aspects Democratization Expertise
- Resource Type:
- Video
-
Video
Professor Yigong Shi will reflect on the challenges in global higher education, based on his 37 years of learning, scientific research, and teaching experience in academia. He will present Westlake University's educational reforms in university operations, governance, talent recruitment, student development, research and academic evaluation, and interdisciplinary studies, which altogether provide new opportunities for future-oriented higher education.
Even date: 6/12/2022
Speaker: Prof. Yigong Shi
Moderator: Prof. Qingyan Chen (Hong Kong Polytechnic University)
Hosted by: PolyU Academy for Interdisciplinary Research
- Keywords:
- Educational change China Education Higher
- Resource Type:
- Video
-
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
-
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
-
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
-
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
-
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
- « Previous
- Next »
- 1
- 2
- 3
- 4