Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset) and Video Question and Answering (TVQA dataset). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods.Comment: 8 pages, 3 figures, Association for the Advancement of Artificial Intelligence (AAAI2020). arXiv admin note: substantial text overlap with arXiv:1907.0170

Renormalization group theory for percolation in time-varying networks

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memory-less Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.Comment: 8 pages, 3 figure

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells

AbstractPiwi-interacting RNAs (piRNAs) are 26–31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies

Caloric restriction of db/db mice reverts hepatic steatosis and body weight with divergent hepatic metabolism

Non-alcoholic fatty liver disease (NAFLD) is one of the most frequent causes of liver disease and its prevalence is a serious and growing clinical problem. Caloric restriction (CR) is commonly recommended for improvement of obesity-related diseases such as NAFLD. However, the effects of CR on hepatic metabolism remain unknown. We investigated the effects of CR on metabolic dysfunction in the liver of obese diabetic db/db mice. We found that CR of db/db mice reverted insulin resistance, hepatic steatosis, body weight and adiposity to those of db/m mice. H-NMR- and UPLC-QTOF-MS-based metabolite profiling data showed significant metabolic alterations related to lipogenesis, ketogenesis, and inflammation in db/db mice. Moreover, western blot analysis showed that lipogenesis pathway enzymes in the liver of db/db mice were reduced by CR. In addition, CR reversed ketogenesis pathway enzymes and the enhanced autophagy, mitochondrial biogenesis, collagen deposition and endoplasmic reticulum stress in db/db mice. In particular, hepatic inflammation-related proteins including lipocalin-2 in db/db mice were attenuated by CR. Hepatic metabolomic studies yielded multiple pathological mechanisms of NAFLD. Also, these findings showed that CR has a therapeutic effect by attenuating the deleterious effects of obesity and diabetes-induced multiple complications