Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.Comment: Accepted by AAAI 201

Multi-View 3D Object Detection Network for Autonomous Driving

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Mechanism of Profilin—1 in regulating eNOS/NO signaling pathway and its role in hypertensive myocardial hypertension

AbstractObjectiveTo explore the mechanism of Profilin-1 in regulating eNOS/NO pathway and its role in the development of myocardial hypertrophy.MethodsSpontaneously hypertensive rats (SHR) aged 5 weeks were injected with different adenovirus vectors to induce Profilin-1 expression knockdown (SHR-I) or over express (SHR-H) or to use as control (SHR-C). All these treatment were compared with Wistar-Kyoto rats (SKY) treated with control adenovirus vectors (WKY-C). The same injection was executed at the sixth week during the experiment of 12 weeks. After experiment, the left ventricular weight-to-heart weight ratio (LVW/HW) and left ventricular long axis (LVLA) were measured. Meanwhile, NO contents in blood and myocardium, Profilin-1, eNOS and Caveolin-3 mRNA and protein levels and phosphorylated eNOS (P-eNOS) protein level in myocardium were determined.ResultsCompared with WKY-C group, the SHR-C group was statistically higher in LVW/HW (0.79±0.03), LVLA (11.82±0.58 mm) and Profilin-1 mRNA and protein level (P<0.05), but lower in NO content [(18.63±6.23) μmol/L] in blood and [(2.71±0.17) μmol/L] in myocardium), eNOS activity and Caveolin-3 expression (P<0.05). The over expressing Profilin-1 led SHR-H group to a higher value of LVW/HW [(0.93±0.03) mm and LVLA (14.17±0.69) mm] in comparison with SHR-C group (P<0.05), and to a lower value of NO content (in myocardium), eNOS activity and Caveolin-3 expression (P<0.05); however, this phenomenon was reversed by the knockdown Profilin-1 expression (SHR-I group).ConclusionsProfilin-1 expression, being negative in regulating Caveolin-3 expression and eNOS/NO pathway activity, promotes the development of myocardial hypertrophy which can be reversed by Profilin-1 silencing