Joint Video and Text Parsing for Understanding Events and Answering Queries

We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results

Associations between Aquaglyceroporin Gene Polymorphisms and Risk of Stroke among Patients with Hypertension

Background: Dysregulations ofAQP7andAQP9were found to be related to lipid metabolism abnormality, which had been provento be one of the mechanisms of stroke. However, limited epidemiological studies explore the associations betweenAQP7andAQP9and the risk of stroke among patients with hypertension in China. Aims: We aimed to investigate the associations between genetic variants in AQP7andAQP9and the risk of stroke among patients with hypertension, as well as to explore gene-gene andgene-environment interactions. Methods: Baseline blood samples were drawn from 211 cases with stroke and 633 matched controls. Genomic DNA was extracted by a commercially available kit. Genotyping of 5 single nucleotide polymorphisms (SNPs) in AQP7 (rs2989924, rs3758269, and rs2542743) and AQP9 (rs57139208, rs16939881) was performed by the polymerase chain reaction assay with TaqMan probes. Results: Participants with the rs2989924 GG genotype were found to be with a 1.74-fold increased risk of stroke compared to those with the AA+AG genotype, and this association remained significant after adjustment for potential confounders (odds ratio (OR): 1.74, 95% confidence interval (CI): 1.23-2.46). The SNP rs3758269 CC+TT genotype was found to be with a 33% decreased risk of stroke after multivariate adjustment (OR: 0.67, 95% CI: 0.45-0.99) compared to the rs3758269 CC genotype. The significantly increased risk of stroke was prominent among males, patients aged 60 or above, and participants who were overweight and with a harbored genetic variant in SNP rs2989924. After adjusting potential confounders, the SNP rs3758269 CT+TT genotype was found to be significantly associated with a decreased risk of stroke compared to the CC genotype among participants younger than 60 years old or overweight. No statistically significant associations were observed between genotypes of rs2542743, rs57139208, or rs16939881 with the risk of stroke. Neither interactions nor linkage disequilibrium had been observed in this study. Conclusions: This study suggests that SNPs rs2989924 and rs3758269 are associated with the risk of stroke among patients with hypertension, while there were no statistically significant associations between rs2542743, rs57139208, and rs16939881 and the risk of stroke being observed

A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment

[[abstract]]Recognizing Textual Entailment (RTE) is a PASCAL/TAC task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. In this paper, we proposed a textual entailment system using a statistical approach that integrates syntactic and semantic techniques for Recognizing Inference in Text (RITE) using the NTCIR-9 RITE task and make a comparison between semantic and syntactic features based on their differences. We thoroughly evaluate our approach using subtasks of the NTCIR-9 RITE. As a result, our system achieved 73.28% accuracy on the Chinese Binary-Class (BC) subtask with NTCIR-9 RITE. Thorough experiments with the text fragments provided by the NTCIR-9 RITE task show that the proposed approach can significantly improve system accuracy.[[sponsorship]]IEEE[[incitationindex]]EI[[cooperationtype]]國外[[conferencetype]]國際[[conferencedate]]20120808~20120810[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Vegas, Nevada, US