United International College

Zhuhai, China

United International College

Zhuhai, China
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News Article | May 5, 2017
Site: www.prnewswire.com

"Worldwide, video is helping instructors share information and expertise, while helping students customise their learning experiences," said Tom Davy, Managing Director, Panopto Global. "With CEREDU, Panopto is delighted to partner with such a well-known organization to offer the best possible performance and service to academic and education institutions in China." "CEREDU wants to bring the best technologies and opportunities the students in China, and Panopto is the best video platform for our students," said Yidong Zhu, President, CERNET Education. "Panopto is the best video platform for our partner institutions." Well established in the United States and Europe, Panopto uptake is growing in China. Customers in the region include NYU Shanghai, Schwarzman Scholars at Tsinghua University, United International College, Guizhou Department of Education, and Tianjin Municipal Education Commission. About Panopto Panopto helps universities and businesses create searchable video libraries of their institutional knowledge. Since 2007, the company has been a pioneer in video content management systems, video capture software, and inside-video search technology. Today, Panopto's video platform is the largest repository of expert learning videos in the world. Headquartered in Seattle, with offices in Pittsburgh, Sydney, Hong Kong, Beijing, and London. Panopto has received industry recognition of its innovation, rapid growth, and company culture. For more information, visit www.panopto.com. About CERNET Education CERNET Education (CEREDU), established in January 2006, is the largest subsidiary of CERNET Corporation, which is the only enterprise that is directly managed by the Ministry of Education. CEREDU focuses on the development of education technology and international education. In China, CEREDU provides international courses for schools, and education and training institutions to provide a complete curriculum. The education platform and management of CEREDU leads the future of the one-stop service and consulting solutions. CEREDU has hired Chinese nationals and American experts to develop materials for Chinese students according to the international curriculum system, to create connectivity in China with the United States educational resources, and for blended learning platforms. CEREDU offers a Total-Care service system located in Boston, Wisconsin, and Chicago for Chinese students to study and live, which provides an immersive international cultural experience and understanding. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/panopto-and-cernet-education-announce-video-platform-partnership-300452148.html


Zhang X.,University of Hong Kong | Yeung P.K.K.,University of Hong Kong | McAlonan G.M.,King's College London | Chung S.S.M.,United International College | Chung S.K.,University of Hong Kong
Neurobiology of Learning and Memory | Year: 2013

Increased level of endothelin-1 (ET-1), a potent vasoconstrictor, has been found in the cerebral spinal fluid (CSF) of patients with multi-infarction dementia, suggesting a possible rolre of ET-1 in cognitive deficit associated with stroke. Previously, we have reported that synthesis of ET-1 is induced in endothelial cells in hypoxic/ischemic conditions. Transgenic mice over-expressing endothelin-1 in endothelial cells (TET-1) developed systemic hypertension and showed more severe brain damage after transient ischemia. To further understand the significance of endothelial ET-1 in cognitive deficit, we subjected adult TET-1 mice to 30. min middle cerebral artery occlusion (MCAO) with 7. days reperfusion. At baseline, TET-1 mice showed similar locomotor activity, emotion and cognitive function compared to non-transgenic (NTg) mice. However, after 30. min MCAO and 7. days reperfusion, although the sensorimotor function measured by neurological scores was recovered in both genotypes, TET-1 mice showed increased anxiety-like behavior in the open field test and impaired spatial learning and reference memory in the Morris water maze. Parallel with these behavioral changes, TET-1 mice showed more severe brain damage with blood-brain-barrier breakdown (BBB), reactive astrogliosis, increased caspase-3, and increased peroxiredoxin 6 (Prx6) expressions around blood vessels in the ipsilateral hippocampus, compared to that of NTg mice, suggesting that ET-1 over-expression in the endothelial cells leads to more severe BBB breakdown and increased oxidative stress which may resulted in neuronal apoptosis and glial reactivity, which might contribute to the emotional changes and cognitive deficits after short-term ischemia with long-term reperfusion. © 2013 Elsevier Inc.


Yeung P.K.K.,University of Hong Kong | Shen J.,Chinese University of Hong Kong | Chung S.S.M.,United International College | Chung S.K.,University of Hong Kong
BMC Neuroscience | Year: 2013

Background: Endothelin-1 (ET-1) is a potent vasoconstrictor, and astrocytic ET-1 is reported to play a role in the pathogenesis of cerebral ischemic injury and cytotoxic edema. However, it is still unknown whether astrocytic ET-1 also contributes to vasogenic edema and vasospasm during subarachnoid hemorrhage (SAH). In the present study, transgenic mice with astrocytic endothelin-1 over-expression (GET-1 mice) were used to investigate the pathophysiological role of ET-1 in SAH pathogenesis. Results: The GET-1 mice experienced a higher mortality rate and significantly more severe neurological deficits, blood-brain barrier breakdown and vasogenic edema compared to the non-transgenic (Ntg) mice following SAH. Oral administration of vasopressin V1a receptor antagonist, SR 49059, significantly reduced the cerebral water content in the GET-1 mice. Furthermore, the GET-1 mice showed significantly more pronounced middle cerebral arterial (MCA) constriction after SAH. Immunocytochemical analysis showed that the calcium-activated potassium channels and the phospho-eNOS were significantly downregulated, whereas PKC-α expression was significantly upregulated in the MCA of the GET-1 mice when compared to Ntg mice after SAH. Administration of ABT-627 (ETA receptor antagonist) significantly down-regulated PKC-α expression in the MCA of the GET-1 mice following SAH. Conclusions: The present study suggests that astrocytic ET-1 involves in SAH-induced cerebral injury, edema and vasospasm, through ETA receptor and PKC-mediated potassium channel dysfunction. Administration of ABT-627 (ETA receptor antagonist) and SR 49059 (vasopressin V1a receptor antagonist) resulted in amelioration of edema and vasospasm in mice following SAH. These data provide a strong rationale to investigate SR 49059 and ABT-627 as therapeutic drugs for the treatment of SAH patients. © 2013 Yeung et al.; licensee BioMed Central Ltd.


Ram J.,University of Adelaide | Corkindale D.,University of Western Australia | Wu M.-L.,United International College
Journal of Computer Information Systems | Year: 2013

This study identifies the key antecedent factors for accomplishing the adoption stage of enterprise resource planning (ERP) systems. Five potential antecedent factors of adoption were derived from the literature, including that on innovation theories, and data were obtained from a sample of 217 organizations across Australia. A structural equation modeling (SEM) technique was used to examine the complex relationships between antecedents and the adoption decision. We found that there were three positive drivers of a successful outcome of the ERP adoption stage. Prior findings have shown that system quality is a key enabler for innovation adoption by individuals, and we found that system quality is also an important driver for organizational adoption of ERP. It was also indicated that organizations consider adopting ERP when the market and customer patterns are relatively stable rather than in turbulent environments.


Shao L.,University of Sheffield | Gao R.,Leiden University | Liu Y.,Hong Kong Polytechnic University | Zhang H.,United International College | Zhang H.,PKU HKUST Shenzhen Hong Kong Institution
Neurocomputing | Year: 2011

Classic transformation methods have been widely and efficiently used in image processing areas, such as image de-noising, image segmentation, feature detection, and compression. Based on their compact signal and image representation ability, we apply the transform based techniques on the video recognition area to extract discriminative information from each given video sequence, and use the transformed coefficients as descriptors for representing and recognizing human actions in video sequences. We validate our proposed methods on the KTH and the Hollywood datasets, which have been extensively studied by a lot of researchers. The proposed descriptors, especially the wavelet transform based descriptor, yield promising results on action recognition. © 2010 Elsevier B.V.


Xia Y.,Tsinghua University | Su W.,United International College | Lau R.Y.K.,City University of Hong Kong | Liu Y.,Tsinghua University
Enterprise Information Systems | Year: 2013

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news. © 2013 Copyright Taylor and Francis Group, LLC.


Li X.,Hong Kong Baptist University | Mo L.,United International College | Yuan X.,Hong Kong Baptist University | Zhang J.,United International College
Computational Statistics and Data Analysis | Year: 2014

The least absolute shrinkage and selection operator (LASSO) has been playing an important role in variable selection and dimensionality reduction for linear regression. In this paper we focus on two general LASSO models: Sparse Group LASSO and Fused LASSO, and apply the linearized alternating direction method of multipliers (LADMM for short) to solve them. The LADMM approach is shown to be a very simple and efficient approach to numerically solve these general LASSO models. We compare it with some benchmark approaches on both synthetic and real datasets. © 2014 Elsevier B.V. All rights reserved.


Ma A.J.,Hong Kong Baptist University | Yuen P.C.,Hong Kong Baptist University | Yuen P.C.,United International College | Li J.,Hong Kong Baptist University
Proceedings of the IEEE International Conference on Computer Vision | Year: 2013

This paper addresses a new person re-identification problem without the label information of persons under non-overlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) image pairs which can be easily generated from target domain cameras, we propose a Domain Transfer Ranked Support Vector Machines (DTRSVM) method for re-identification under target domain cameras. To overcome the problems introduced due to the absence of matched (positive) image pairs in target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in target domain. By estimating the target positive mean using source and target domain data, a new discriminative model with high confidence in target positive mean and low confidence in target negative image pairs is developed. Since the necessary condition may not truly preserve the discriminability, multi-task support vector ranking is proposed to incorporate the training data from source domain with label information. Experimental results show that the proposed DTRSVM outperforms existing methods without using label information in target cameras. And the top 30 rank accuracy can be improved by the proposed method upto 9.40% on publicly available person re-identification datasets. © 2013 IEEE.


Jia H.,Hong Kong Baptist University | Cheung Y.-M.,Hong Kong Baptist University | Cheung Y.-M.,United International College | Liu J.,Hong Kong Baptist University
Pattern Recognition | Year: 2014

Competitive learning approaches with individual penalization or cooperation mechanisms have the attractive ability of automatic cluster number selection in unsupervised data clustering. In this paper, we further study these two mechanisms and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to locate the cluster centers more quickly and be insensitive to the number of seed points and their initial positions. Additionally, to handle nonlinearly separable clusters, we further introduce the proposed competition mechanism into kernel clustering framework. Correspondingly, a new kernel-based competitive learning algorithm which can conduct nonlinear partition without knowing the true cluster number is presented. The promising experimental results on real data sets demonstrate the superiority of the proposed methods. © 2014 Elsevier Ltd.


Feng X.,United International College | Parnas D.L.,University of Limerick | Tse T.H.,University of Hong Kong
IEEE Transactions on Software Engineering | Year: 2011

Tabular expressions have been proposed as a notation to document mathematically precise but readable software specifications. One of the many roles of such documentation is to guide testers. This paper 1) explores the application of four testing strategies (the partition strategy, decision table-based testing, the basic meaningful impact strategy, and fault-based testing) to tabular expression-based specifications, and 2) compares the strategies on a mathematical basis through formal and precise definitions of the subsumption relationship. We also compare these strategies through experimental studies. These results will help researchers improve current methods and will enable testers to select appropriate testing strategies for tabular expression-based specifications. © 2011 IEEE.

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