Ordering-sensitive and Semantic-aware Topic Modeling

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.Comment: To appear in proceedings of AAAI 201

Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.Comment: Accepted by AAAI 1

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

On the zeros of solutions of any order of derivative of second order linear differential equations taking small functions

In this paper, we investigate the hyper-exponent of convergence of zeros of $f^{(j)}(z)-\varphi(z) (j\in N)$, where $f$ is a solution of second or $k(\geq2)$ order linear differential equation, $\varphi(z)\not\equiv0$ is an entire function satisfying $\sigma(\varphi)<\sigma(f)$ or $\sigma_{2}(\varphi)<\sigma_{2}(f)$. We obtain some precise results which improve the previous results in [3, 5] and revise the previous results in [11, 13]. More importantly, these results also provide us a method to investigate the hyper-exponent of convergence of zeros of $f^{(j)}(z)-\varphi(z)(j\in N)$

Pure Partial Awareness or Interaction between the Mask and the Masked Stimuli?

Many studies using masking paradigm have shown that stimuli can be processed unconsciously. However, different researchers put forward different ideas about the mechanisms of the unconscious information processing. For example, one idea is that the unconscious information might be derived from a partial awareness of the masked stimulus. Another idea is that it is derived from a perceptual interaction between a masked stimulus and the masking stimulus. We used a masking paradigm (with a briefly displayed target followed by a mask) and a subjective rating and an objective forced-choice test (with a word and picture version) given after the display to study the nature of partial awareness. The question we attempted to answer was whether people did perceive fragmentary features of a masked object picture correctly when they rated it as partially perceivable. The results showed that even when the masked stimuli only had simple features and when the subjects subjectively reported that they could perceive something of the masked stimuli, the objective forced-choice test performance was at chance level. The results were discussed in the context of interaction hypothesis and level of processing hypothesis