Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local feature to obtain its weight score, which ignores interactions among local features. To address this, we propose an effective interaction-aware self-attention model inspired by PCA to learn attention maps. Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps. Moreover, our spatial pyramid attention is unrestricted to the number of its input feature maps so it is easily extended to a spatio-temporal version. Finally, our model is embedded in general CNNs to form end-to-end attention networks for action classification. Experimental results show that our method achieves the state-of-the-art results on the UCF101, HMDB51 and untrimmed Charades.Comment: Accepted by ECCV201

Resource-efficient quantum key distribution with integrated silicon photonics

Integrated photonics provides a promising platform for quantum key distribution (QKD) system in terms of miniaturization, robustness and scalability. Tremendous QKD works based on integrated photonics have been reported. Nonetheless, most current chip-based QKD implementations require additional off-chip hardware to demodulate quantum states or perform auxiliary tasks such as time synchronization and polarization basis tracking. Here, we report a demonstration of resource-efficient chip-based BB84 QKD with a silicon-based encoder and decoder. In our scheme, the time synchronization and polarization compensation are implemented relying on the preparation and measurement of the quantum states generated by on-chip devices, thus no need additional hardware. The experimental tests show that our scheme is highly stable with a low intrinsic QBER of $0.50\pm 0.02\%$ in a 6-h continuous run. Furthermore, over a commercial fiber channel up to 150 km, the system enables realizing secure key distribution at a rate of 866 bps. Our demonstration paves the way for low-cost, wafer-scale manufactured QKD system.Comment: comments are welcome

Unique allosteric effect driven rapid adsorption of carbon dioxide on a new ionogel [P4444][2-Op]@MCM-41 with excellent cyclic stability and loading-dependent capacity

Allosteric effect-driven rapid stepwise CO2 adsorption of pyridine-containing anion functionalized ionic liquid [P4444][2-Op] confined into mesoporous silica MCM-41.</p

Spatio-temporal self-organizing map deep network for dynamic object detection from videos

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. This paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. The proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. Then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios

Identification and Characterization of Abiotic Stress&ndash;Responsive NF-YB Family Genes in Medicago

Nuclear factor YB (NF-YB) are plant-specific transcription factors that play a critical regulatory role in plant growth and development as well as in plant resistance against various stresses. In this study, a total of 49 NF-YB genes were identified from the genomes of Medicago truncatula and Medicago sativa. Multiple sequence alignment analysis showed that all of these NF-YB members contain DNA binding domain, NF-YA interaction domain and NF-YC interaction domain. Phylogenetic analysis suggested that these NF-YB proteins could be classified into five distinct clusters. We also analyzed the exon&ndash;intron organizations and conserved motifs of these NF-YB genes and their deduced proteins. We also found many stress-related cis-acting elements in their promoter region. In addition, analyses on genechip for M. truncatula and transcriptome data for M. sativa indicated that these NF-YB genes exhibited a distinct expression pattern in various tissues; many of these could be induced by drought and/or salt treatments. In particular, RT-qPCR analysis revealed that the expression levels of gene pairs MsNF-YB27/MtNF-YB15 and MsNF-YB28/MtNF-YB16 were significantly up-regulated under NaCl and mannitol treatments, indicating that they are most likely involved in salt and drought stress response. Taken together, our study on NF-YB family genes in Medicago is valuable for their functional characterization, as well as for the application of NF-YB genes in genetic breeding for high-yield and high-resistance alfalfa