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.

UntrimmedNets for Weakly Supervised Action Recognition and Detection

Abstract: Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. These two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.

An Epstein-Barr virus–encoded microRNA targets PUMA to promote host cell survival

Abstract: Epstein-Barr virus (EBV) is a herpesvirus associated with nasopharyngeal carcinoma (NPC), gastric carcinoma (GC), and other malignancies. EBV is the first human virus found to express microRNAs (miRNAs), the functions of which remain largely unknown. We report on the regulation of a cellular protein named p53 up-regulated modulator of apoptosis (PUMA) by an EBV miRNA known as miR-BART5, which is abundantly expressed in NPC and EBV-GC cells. Modulation of PUMA expression by miR-BART5 and anti–miR-BART5 oligonucleotide was demonstrated in EBV-positive cells. In addition, PUMA was found to be significantly underexpressed in ∼60% of human NPC tissues. Although expression of miR-BART5 rendered NPC and EBV-GC cells less sensitive to proapoptotic agents, apoptosis can be triggered by depleting miR-BART5 or inducing the expression of PUMA. Collectively, our findings suggest that EBV encodes an miRNA to facilitate the establishment of latent infection by promoting host cell survival.

The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions.

Abstract: Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920-1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).

HotFlip: White-Box Adversarial Examples for Text Classification

Abstract: We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.

On the Electron-Transfer Mechanism in the Contact-Electrification Effect.

Abstract: A long debate on the charge identity and the associated mechanisms occurring in contact-electrification (CE) (or triboelectrification) has persisted for many decades, while a conclusive model has not yet been reached for explaining this phenomenon known for more than 2600 years! Here, a new method is reported to quantitatively investigate real-time charge transfer in CE via triboelectric nanogenerator as a function of temperature, which reveals that electron transfer is the dominant process for CE between two inorganic solids. A study on the surface charge density evolution with time at various high temperatures is consistent with the electron thermionic emission theory for triboelectric pairs composed of Ti-SiO2 and Ti-Al2 O3 . Moreover, it is found that a potential barrier exists at the surface that prevents the charges generated by CE from flowing back to the solid where they are escaping from the surface after the contacting. This pinpoints the main reason why the charges generated in CE are readily retained by the material as electrostatic charges for hours at room temperature. Furthermore, an electron-cloud-potential-well model is proposed based on the electron-emission-dominatedcharge-transfer mechanism, which can be generally applied to explain all types of CE in conventional materials.