CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

Simultaneous faults identification of rolling element bearings and gears by combining kurtogram and independent component analysis

A combination of kurtogram and independent component analysis (ICA) is proposed in this paper to identify the faults of rolling element bearings (REBs) and gears existing simultaneously in a gearbox. In the proposed scheme, multi-channel vibrations are picked up from the gearbox at first. Then, the fast kurtogram algorithm is employed to extract the envelopes of each vibration from different channels. Subsequently, the envelopes are separated by an ICA algorithm into independent envelope components according to different sources. Finally, the characteristic frequencies of both the faulty REB and the faulty gear can be exposed simultaneously in the envelope spectral plots. A simulation and an experimental test are introduced to show the effectiveness of the proposed method

Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction.

MRI is an advanced imaging modality with the unfortunate disadvantage of long data acquisition time. To accelerate MR image acquisition while maintaining high image quality, extensive investigations have been conducted on image reconstruction of sparsely sampled MRI. Recently, deep convolutional neural networks have achieved promising results, yet the local receptive field in convolution neural network raises concerns regarding signal synthesis and artifact compensation. In this study, we proposed a deep learning-based reconstruction framework to provide improved image fidelity for accelerated MRI. We integrated the self-attention mechanism, which captured long-range dependencies across image regions, into a volumetric hierarchical deep residual convolutional neural network. Basically, a self-attention module was integrated to every convolutional layer, where signal at a position was calculated as a weighted sum of the features at all positions. Furthermore, relatively dense shortcut connections were employed, and data consistency was enforced. The proposed network, referred to as SAT-Net, was applied on cartilage MRI acquired using an ultrashort TE sequence and retrospectively undersampled in a pseudo-random Cartesian pattern. The network was trained using 336 three dimensional images (each containing 32 slices) and tested with 24 images that yielded improved outcome. The framework is generic and can be extended to various applications

Scheduling irrigation for jujube (Ziziphus jujuba Mill.)

This study was performed to select suitable indicator for scheduling the irrigation of jujube (Ziziphus jujuba Mill.) grown in the Loess Plateau. The relationships between plant-based indicators and soil matrix potential as well as meteorological factors of jujube under deficit irrigation compared with well irrigation were determined. The results showed that maximum daily trunk shrinkage increased and maximum daily trunk diameter, gas conductance and midday leaf water potential decreased in response to higher and lower soil matrix potential, respectively. However, the maximum daily trunk shrinkage signal intensity to noise ratio was highest in response to higher and lower soil matrix potential. Besides, the maximum daily trunk shrinkage correlated well with reference evapotranspiration and vapor pressure deficit (r2= 0.702 and 0.605 respectively). When the soil water potential was greater than -25kPa or less than –40 kPa, maximum daily trunk shrinkage values showed increasing trend, suggesting that Jujube might be subject to water stress. Based on this, the suitable soil water potential values of pear-jujube in anthesis and setting periods were identified between -40 kPa and - 25 kPa and the values can conduct precise irrigation of jujube in the Loess Plateau.Keywords: Water stress, water status indicators, soil water potential, Jujube (Ziziphus jujuba Mill.), anthesis, fruit setting periodsAfrican Journal of Biotechnology Vol. 9(35), pp. 5694-5703, 30 August, 201

Synergistic effect of phosphodiesterase 4 inhibitor and serum on migration of endotoxin-stimulated macrophages.

Macrophage migration is an essential step in host defense against infection and wound healing. Elevation of cAMP by inhibiting phosphodiesterase 4 (PDE4), enzymes that specifically degrade cAMP, is known to suppress various inflammatory responses in activated macrophages, but the role of PDE4 in macrophage migration is poorly understood. Here we show that the migration of Raw 264.7 macrophages stimulated with LPS was markedly and dose-dependently induced by the PDE4 inhibitor rolipram as assessed by scratch wound healing assay. Additionally, this response required the involvement of serum in the culture medium as serum starvation abrogated the effect. Further analysis revealed that rolipram and serum exhibited synergistic effect on the migration, and the influence of serum was independent of PDE4 mRNA expression in LPS-stimulated macrophages. Moreover, the enhanced migration by rolipram was mediated by activating cAMP/exchange proteins directly activated by cAMP (Epac) signaling, presumably via interaction with LPS/TLR4 signaling with the participation of unknown serum components. These results suggest that PDE4 inhibitors, together with serum components, may serve as positive regulators of macrophage recruitment for more efficient pathogen clearance and wound repair