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Protein phosphorylation and dephosphorylation have been reported to play important roles in plant cold responses. In addition, phospho-regulatory feedback is a conserved mechanism for biological processes and stress responses in animals and plants. However, it is less well known that a regulatory feedback loop is formed by the protein kinase and the protein phosphatase in plant responses to cold stress. Here, we report that OPEN STOMATA 1 (OST1) and PROTEIN PHOSPHA-TASE 2C G GROUP 1 (PP2CG1) reciprocally regulate the activity during the cold stress response. The interaction of PP2CG1 and OST1 is inhibited by cold stress, which results in the release of OST1 at the cytoplasm and nucleus from suppression by PP2CG1. Interestingly, cold-activated OST1 phosphorylates PP2CG1 to suppress its phosphatase activity, thereby amplifying cold signaling in plants. Mutations of PP2CG1 and its homolog PP2CG2 enhance freezing tolerance, whereas overexpression of PP2CG1 decreases freezing tolerance. Moreover, PP2CG1 negatively regulates protein levels of C-REPEAT BINDING FACTORs (CBFs) under cold stress. Our results uncover a phosphor/dephosphor-regulatory feedback loop mediated by PP2CG1 phosphatase and OST1 protein kinase in plant cold responses.

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We propose a novel end-to-end RGB-D SLAM, iDF-SLAM, which adopts a feature-based deep neural tracker as the front-end and a NeRF-style neural implicit mapper as the back-end. The neural implicit mapper is trained on-the-fly, while though the neural tracker is pretrained on the ScanNet dataset, it is also finetuned along with the training of the neural implicit mapper. Under such a design, our iDF-SLAM is capable of learning to use scene-specific features for camera tracking, thus enabling lifelong learning of the SLAM system. Both the training for the tracker and the mapper are selfsupervised without introducing ground truth poses. We test the performance of our iDF-SLAM on the Replica and ScanNet datasets and compare the results to the two recent NeRF-based neural SLAM systems. The proposed iDF-SLAM demonstrates state-of-the-art results in terms of scene reconstruction and competitive performance in camera tracking.

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We present a novel panoptic visual odometry framework, termed PVO, to achieve a more comprehensive modeling of the scene's motion, geometry, and panoptic segmentation information. PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, enabling the two tasks to facilitate each other. Specifically, we introduce a panoptic update module into the VO module, which operates on the image panoptic segmentation. This Panoptic-Enhanced VO module can trim the interference of dynamic objects in the camera pose estimation by adjusting the weights of optimized camera poses. On the other hand, the VO-Enhanced VPS module improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO module. These two modules contribute to each other through a recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks. Code and data are available on the project webpage: https://zju3dv.github.io/pvo/.

We present a novel dual-flow representation of scene motion that decomposes the optical flow into a static flow field caused by the camera motion and another dynamic flow field caused by the objects' movements in the scene. Based on this representation, we present a dynamic SLAM, dubbed DeFlowSLAM, that exploits both static and dynamic pixels in the images to solve the camera poses, rather than simply using static background pixels as other dynamic SLAM systems do. We propose a dynamic update module to train our DeFlowSLAM in a self-supervised manner, where a dense bundle adjustment layer takes in estimated static flow fields and the weights controlled by the dynamic mask and outputs the residual of the optimized static flow fields, camera poses, and inverse depths. The static and dynamic flow fields are estimated by warping the current image to the neighboring images, and the optical flow can be obtained by summing the two fields. Extensive experiments demonstrate that DeFlowSLAM generalizes well to both static and dynamic scenes as it exhibits comparable performance to the state-ofthe-art DROID-SLAM in static and less dynamic scenes while significantly outperforming DROID-SLAM in highly dynamic environments. Code and data are available on the project webpage: https://zju3dv.github.io/deflowslam/.

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