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The formation mechanism of light bridges (LBs) is strongly related to the dynamic evolution of solar active regions (ARs). To study the relationship between LB formation and AR evolution phases, we employ 109 LB samples from 69 ARs in 2014 using observational data from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (HMI/SDO). LBs are well matched with the weak field lanes (WFLs), except that aligned on the polarity inversion line of {\delta} sunspots. For penumbral intrusion (type-A) and umbral-dot emergence (type-C) LBs, the WFLs represent the splitting of magnetic flux systems. The sunspots tend to decay and split into several parts after type-A and type-C LBs formed. For sunspot/umbra merging (type-B) LBs, the WFLs declining are caused by collisions of flux systems. The sunspots merge and keep stable after type-B LBs formed. We conclude that type-B LBs are formed by collisions of flux systems, while type-A and type-C LBs are generated by splits. The time differences ({\delta}T) between LBs appearing and ARs peaking have average value of 1.06, -1.60, 1.82 for type-A, B, C LBs, with the standard deviation of 3.27, 2.17, 1.89, respectively. A positive value of {\delta}T means that the LB appear after AR peaking, whereas a minus {\delta}T before the peak. Type-A LBs trend to form in the decaying phase or around the peak time. Type-B LBs are more likely to be formed in the developing phase. Type-C LBs mostly take shape in the decaying phase of ARs.

Object picking in cluttered scenes is a widely investigated field of robot manipulation, however, ambidextrous robot picking is still an important and challenging issue. We found the fusion of different prehensile actions (grasp and suction) can expand the range of objects that can be picked by robot, and the fusion of prehensile action and nonprehensile action (push) can expand the picking space of ambidextrous robot. In this paper, we propose a Push-Grasp-Suction (PGS) tri-mode grasping learning network for ambidextrous robot picking through the fusion of different prehensile actions and the fusion of prehensile action and nonprehensile aciton. The prehensile branch of PGS takes point clouds as input, and the 6-DoF picking configuration of grasp and suction in cluttered scenes are generated by multi-task point cloud learning. The nonprehensile branch with depth image input generates instance segmentation map and push configuration, cooperating with the prehensile actions to complete the picking of objects out of single-arm space. PGS generalizes well in real scene and achieves state-of-the-art picking performance., Comment: 8 pages

Sausage waves have been frequently reported in solar magnetic structures such as sunspots, pores, and coronal loops. However, they have not been unambiguously identified in photospheric bright points (BPs). Using high-resolution TiO image sequences obtained with the Goode Solar Telescope at the Big Bear Solar Observatory, we analyzed four isolated BPs. It was found that their area and average intensity oscillate for several cycles in an in-phase fashion. The oscillation periods range from 100 to 200 seconds. We interpreted the phase relation as a signature of sausage waves, particularly slow waves, after discussing sausage-wave theory and the opacity effect., Comment: 16 pages, 8 figures

Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin., Comment: 7 pages, accepted to ICRA2021

Light bridges (LBs) are bright lanes that divide an umbra into multiple parts in some sunspots. Persistent oscillatory bright fronts at a temperature of $\sim$$10^5$ K are commonly observed above LBs in the 1400/1330 \AA~passbands of the Interface Region Imaging Spectrograph (IRIS). Based on IRIS observations, we report small-scale bright blobs from the oscillating bright front above a light bridge. Some of these blobs reveal a clear acceleration, whereas the others do not. The average speed of these blobs projected onto the plane of sky is $71.7\pm14.7$ km s$^{-1}$, with an initial acceleration of $1.9\pm1.3$ km s$^{-2}$. These blobs normally reach a projected distance of 3--7 Mm from their origin sites. From the transition region images we find an average projected area of $0.57\pm0.37$ Mm$^{2}$ for the blobs. The blobs were also detected in multi-passbands of the Solar Dynamics Observatory, but not in the H$\alpha$ images. These blobs are likely to be plasma ejections, and we investigate their kinematics and energetics. Through emission measure analyses, the typical temperature and electron density of these blobs are found to be around $10^{5.47}$ K and $10^{9.7}$ cm$^{-3}$, respectively. The estimated kinetic and thermal energies are on the order of $10^{22.8}$ erg and $10^{23.3}$ erg, respectively. These small-scale blobs appear to show three different types of formation process. They are possibly triggered by induced reconnection or release of enhanced magnetic tension due to interaction of adjacent shocks, local magnetic reconnection between emerging magnetic bipoles on the light bridge and surrounding unipolar umbral fields, and plasma acceleration or instability caused by upward shocks, respectively.

Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented., Comment: Accepted by IEEE SSCI 2020, 6 pages

Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance and night vision. Deep learning based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain, is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images, and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain. And then the off-the-shelf Domain Adaptive Faster RCNN is utilized to reduce the gap between generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method., Comment: 5 pages, 4 figures