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In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A(More)
MicroRNAs (miRNAs) are small, non-coding RNAs that negatively regulate gene expression. It has been reported that miRNAs are involved in host-virus interaction, but evidence that cellular miRNAs promote virus replication has been limited. Here, we found that miR-23a promoted the replication of human herpes simplex virus type 1 (HSV-1) in HeLa cells, as(More)
MicroRNAs are short regulatory RNAs that negatively modulate gene expression at the post-transcriptional level, and are deeply involved in the pathogenesis of several types of cancers. To investigate whether specific miRNAs and their target genes participate in the molecular pathogenesis of laryngeal carcinoma, oligonucleotide microarrays were used to(More)
MicroRNAs are a group of endogenously expressed, single-stranded, 18-24 nt RNAs that regulate diverse cellular pathways. Although documented evidence indicates that some microRNAs can function as oncogenes or tumor-suppressors, the role of miR-214 in regulating human cervical cancer cells remains unexplored. We determined the expression level of miR-214 and(More)
Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and(More)
Recent successes in training large, deep neural networks (DNNs) have prompted active investigation into the underlying representations learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by millions of learned parameters. However, despite the difficulty, such research is(More)
Machine learning is increasingly used in high impact applications such as prediction of hospital re-admission, cancer screening or bio-medical research applications. As predictions become increasingly accurate, practitioners may be interested in identifying actionable changes to inputs in order to alter their class membership. For example, a doctor might(More)
Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life — they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However , research community still knows little about the formation and evolution of groups in the(More)
In this paper, we investigate the multicast capacity for static ad hoc networks with heterogeneous clusters. We study the effect of heterogeneous cluster traffic (HCT) on the achievable capacity. HCT means cluster clients are more likely to appear near the cluster head instead of being uniformly distributed across the network. Such a property is commonly(More)
Based on the definition of local spectral subspace, we propose a novel approach called LOSP for local overlapping community detection. Using the power method for a few steps, LOSP finds an approximate invariant subspace, which depicts the embedding of the local neighborhood structure around the seeds of interest. LOSP then identifies the local community(More)