The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

The metabolic syndrome in children and adolescents - an IDF consensus report.

Abstract: Zimmet P, Alberti K George MM, Kaufman F, Tajima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S; IDF Consensus Group. The metabolic syndrome in children and adolescents – an IDF consensus report. Pediatric Diabetes 2007: 8: 299–306. Paul Zimmet, K George MM Alberti, Francine Kaufman, Naoko Tajima, Martin Silink, Silva Arslanian, Gary Wong, Peter Bennett, Jonathan Shaw and Sonia Caprio; IDF Consensus Group International Diabetes Institute, Melbourne, Victoria, Australia; Department of Endocrinology and Metabolic Medicine, St Mary’s Hospital, London, UK; Center for Diabetes, Endocrinology and Metabolism, Children’s Hospital, Los Angeles, CA, USA; Division of Diabetes, Metabolism and Endocrinology, Jikei University School of Medicine, Tokyo, Japan; Institute of Endocrinology and Diabetes, The Children’s Hospital at Westmead, Sydney, New South Wales, Australia; Division of Endocrinology, Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Paediatrics, The Chinese University of Hong Kong, Hong Kong; Phoenix Epidemiology and Clinical Research Branch, NIDDK, National Institutes of Health, Phoenix, AZ, USA; and Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA

Deformable DETR: Deformable Transformers for End-to-End Object Detection

Abstract: DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at this https URL.

Alectinib versus Crizotinib in Untreated ALK-Positive Non–Small-Cell Lung Cancer

Abstract: BackgroundAlectinib, a highly selective inhibitor of anaplastic lymphoma kinase (ALK), has shown systemic and central nervous system (CNS) efficacy in the treatment of ALK-positive non–small-cell lung cancer (NSCLC). We investigated alectinib as compared with crizotinib in patients with previously untreated, advanced ALK-positive NSCLC, including those with asymptomatic CNS disease. MethodsIn a randomized, open-label, phase 3 trial, we randomly assigned 303 patients with previously untreated, advanced ALK-positive NSCLC to receive either alectinib (600 mg twice daily) or crizotinib (250 mg twice daily). The primary end point was investigator-assessed progression-free survival. Secondary end points were independent review committee–assessed progression-free survival, time to CNS progression, objective response rate, and overall survival. ResultsDuring a median follow-up of 17.6 months (crizotinib) and 18.6 months (alectinib), an event of disease progression or death occurred in 62 of 152 patients (41%) in ...

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

Abstract: Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.