DNA Methylation in Autophagy-associated Genes and Risk of Prostate Cancer

The study of autophagy is a growing field that is emerging as one of the most important studies in cancer due to the nature of autophagy’s significant biological functions. The complex relationship between autophagy and prostate cancer is still under debate, with studies demonstrating inconsistent results in terms of tumor growth. DNA methylation is one of the key dynamic epigenetic mechanisms in gene regulation. This prospective study aims to understand the role of DNA methylation in autophagy-related genes and prostate cancer development. Among over 740 human autophagy-related genes we examined, 10 methylation biomarkers in the promoter regions of 12 genes, including 6 novel genes and 6 well-known genes, were found to be predictive to inform risk of prostate cancer at least 4 years before cancer diagnosis. Pathways analysis revealed that these genes involved in necroptosis and calcium signaling, which play key roles in autophagy and prostate cancer development. Within 4 years pre-diagnosis, the relationships between methylation of these genes and cancer development became obscure and insignificant, which indicate an accumulation of epigenetic “noise” in advancing malignant disease that confounds the methylation biomarkers and thus may explain prior inconsistent studies. Our study suggests that methylation in autophagy-related genes may serve as novel therapeutic biomarkers for further study

An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection

Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation Systems (ITSC 2012

Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to this pape

Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online