A decades-long fast-rise-exponential-decay flare in low-luminosity AGN NGC 7213

We analysed the four-decades-long X-ray light curve of the low-luminosity active galactic nucleus (LLAGN) NGC 7213 and discovered a fast-rise-exponential-decay (FRED) pattern, i.e. the X-ray luminosity increased by a factor of $\approx 4$ within 200d, and then decreased exponentially with an $e$-folding time $\approx 8116$d ($\approx 22.2$ yr). For the theoretical understanding of the observations, we examined three variability models proposed in the literature: the thermal-viscous disc instability model, the radiation pressure instability model, and the tidal disruption event (TDE) model. We find that a delayed tidal disruption of a main-sequence star is most favourable; either the thermal-viscous disk instability model or radiation pressure instability model fails to explain some key properties observed, thus we argue them unlikely.Comment: Accepted for publication in MNRAS, updated version after proof correction

Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.Comment: Accepted by ICIP 201

Slow dynamics of Zero Range Process in the Framework of Traps Model

The relaxation dynamics of zero range process (ZRP) has always been an interesting problem. In this study, we set up the relationship between ZRP and traps model, and investigate the slow dynamics of ZRP in the framework of traps model. Through statistical quantities such as the average rest time, the particle distribution, the two-time correlation function and the average escape time, we find that the particle interaction, especially the resulted condensation, can significantly influence the dynamics. In the stationary state, both the average rest time and the average escape time caused by the attraction among particles are obtained analytically. In the transient state, a hierarchical nature of the aging dynamics is revealed by both simulations and scaling analysis. Moreover, by comparing the particle diffusion in both the transient state and the stationary state, we find that the closer ZRP systems approach the stationary state, the more slowly particles diffuse.Comment: 5 pages, 4 figure

Feature Selective Networks for Object Detection

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of RoIs by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a light-weight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets