Interior Schauder estimates for Stokes systems in non-divergence form

The global Schauder estimates for Stokes systems are established by Solonnikov [15] and [16] while the interior ones may fail generally from Serrin's counterexample (cf. [14]). Nevertheless, this paper obtains interior $C^{2,\alpha}$ estimates for velocity and interior $C^{1,\alpha}$ estimates for pressure in spatial direction. Furthermore, the $C^{\alpha, \frac{\alpha}2}$ estimate is attained for derivatives of curl of velocity. The estimates for velocity can be achieved pointwisely. The results are sharp and surprising since no continuity in time variable is assumed for the coefficients and the righthand side terms.Comment: arXiv admin note: text overlap with arXiv:2304.0352

Obj-NeRF: Extract Object NeRFs from Multi-view Images

Neural Radiance Fields (NeRFs) have demonstrated remarkable effectiveness in novel view synthesis within 3D environments. However, extracting a radiance field of one specific object from multi-view images encounters substantial challenges due to occlusion and background complexity, thereby presenting difficulties in downstream applications such as NeRF editing and 3D mesh extraction. To solve this problem, in this paper, we propose Obj-NeRF, a comprehensive pipeline that recovers the 3D geometry of a specific object from multi-view images using a single prompt. This method combines the 2D segmentation capabilities of the Segment Anything Model (SAM) in conjunction with the 3D reconstruction ability of NeRF. Specifically, we first obtain multi-view segmentation for the indicated object using SAM with a single prompt. Then, we use the segmentation images to supervise NeRF construction, integrating several effective techniques. Additionally, we construct a large object-level NeRF dataset containing diverse objects, which can be useful in various downstream tasks. To demonstrate the practicality of our method, we also apply Obj-NeRF to various applications, including object removal, rotation, replacement, and recoloring

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in previous bottom-up approaches. Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module to directly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific features of each 3D proposal. Our model achieves state-of-the-art 3D detection performance with remarkable gains of +\textit{3.6\%} on ScanNet V2 and +\textit{2.6}\% on SUN RGB-D in term of [email protected]. Code will be available at https://github.com/Haiyang-W/CAGroup3D.Comment: Accept by NeurIPS202

Freeze-thaw damage assessment of engineered cementitious composites using the electrochemical impedance spectroscopy method

The mechanical properties of engineered cementitious composites (ECC) in service in cold regions can be significantly degraded by periodic freezing and thawing. In this work, the damage degree of freeze–thaw of ECC was systematically assessed by using the electrochemical impedance spectroscopy (EIS) technique. In addition, Nuclear Magnetic Resonance (NMR) Relaxometry measurements were also performed to obtain pore structure parameters, and the uniaxial tensile tests were also carried out to analyse the tensile performance after freeze–thaw cycles. From the acquired results, it was demonstrated that the EIS behaviour of ECC varied with the freeze–thaw cycles. The diameter of the Nyquist curve in high-frequency was gradually reduced by increasing the freeze–thaw cycles. Furthermore, the volume resistance of ECC after freeze–thaw gradually decreased with the increase in the number of freeze–thaw cycles. The simplified microstructure and conductive paths were used to describe the freeze–thaw damage mechanism of ECC. An equivalent circuit model of ECC exposed to freeze–thaw cycles was proposed, and the parameters of the equivalent circuit model were thoroughly analysed. The experimental findings clearly indicate that the EIS method is an appropriate technique for evaluating the damage degree of freeze–thaw of ECC

FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection

3D object detection with multi-sensors is essential for an accurate and reliable perception system of autonomous driving and robotics. Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement. Though impressive, the sparsity of point clouds, especially for the points far away, making it difficult for the LiDAR-only refinement module to accurately recognize and locate objects.To address this problem, we propose a novel multi-modality two-stage approach named FusionRCNN, which effectively and efficiently fuses point clouds and camera images in the Regions of Interest(RoI). FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from camera in a unified attention mechanism. Specifically, it first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step; then leverages an intra-modality self-attention to enhance the domain-specific features, following by a well-designed cross-attention to fuse the information from two modalities.FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors.Remarkably, FusionRCNN significantly improves the strong SECOND baseline by 6.14% mAP on Waymo, and outperforms competing two-stage approaches. Code will be released soon at https://github.com/xxlbigbrother/Fusion-RCNN.Comment: 7 pages, 3 figure

Diverse Cotraining Makes Strong Semi-Supervised Segmentor

Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple compatible and conditionally independent views. By theoretically deriving the generalization upper bound, we prove the prediction similarity between two models negatively impacts the model's generalization ability. However, most current co-training models are tightly coupled together and violate this assumption. Such coupling leads to the homogenization of networks and confirmation bias which consequently limits the performance. To this end, we explore different dimensions of co-training and systematically increase the diversity from the aspects of input domains, different augmentations and model architectures to counteract homogenization. Our Diverse Co-training outperforms the state-of-the-art (SOTA) methods by a large margin across different evaluation protocols on the Pascal and Cityscapes. For example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only 92, 183 and 366 labeled images, surpassing the previous best results by more than 5%.Comment: ICCV2023, Camera Ready Version, Code: \url{https://github.com/williamium3000/diverse-cotraining

Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions

Robust 3D perception under corruption has become an essential task for the realm of 3D vision. While current data augmentation techniques usually perform random transformations on all point cloud objects in an offline way and ignore the structure of the samples, resulting in over-or-under enhancement. In this work, we propose an alternative to make sample-adaptive transformations based on the structure of the sample to cope with potential corruption via an auto-augmentation framework, named as AdaptPoint. Specially, we leverage a imitator, consisting of a Deformation Controller and a Mask Controller, respectively in charge of predicting deformation parameters and producing a per-point mask, based on the intrinsic structural information of the input point cloud, and then conduct corruption simulations on top. Then a discriminator is utilized to prevent the generation of excessive corruption that deviates from the original data distribution. In addition, a perception-guidance feedback mechanism is incorporated to guide the generation of samples with appropriate difficulty level. Furthermore, to address the paucity of real-world corrupted point cloud, we also introduce a new dataset ScanObjectNN-C, that exhibits greater similarity to actual data in real-world environments, especially when contrasted with preceding CAD datasets. Experiments show that our method achieves state-of-the-art results on multiple corruption benchmarks, including ModelNet-C, our ScanObjectNN-C, and ShapeNet-C.Comment: Accepted by ICCV2023; code: https://github.com/Roywangj/AdaptPoin

TNF-Like Weak Inducer of Apoptosis (TWEAK) Promotes Beta Cell Neogenesis from Pancreatic Ductal Epithelium in Adult Mice

Aim/Hypothesis The adult mammalian pancreas has limited ability to regenerate in order to restore adequate insulin production from multipotent progenitors, the identity and function of which remain poorly understood. Here we test whether the TNF family member TWEAK (TNF-like weak inducer of apoptosis) promotes β-cell neogenesis from proliferating pancreatic ductal epithelium in adult mice. Methods: C57Bl/6J mice were treated with Fc-TWEAK and pancreas harvested at different time points for analysis by histology and immunohistochemistry. For lineage tracing, 4 week old double transgenic mice CAII-CreERTM: R26R-eYFP were implanted with tamoxifen pellet, injected with Fc-TWEAK or control Ig twice weekly and analyzed at day 18 for TWEAK-induced duct cell progeny by costaining for insulin and YFP. The effect of TWEAK on pancreatic regeneration was determined by pancytokeratin immunostaining of paraffin embedded sections from wildtype and TWEAK receptor (Fn14) deficient mice after Px. Results: TWEAK stimulates proliferation of ductal epithelial cells through its receptor Fn14, while it has no mitogenic effect on pancreatic α- or β-cells or acinar cells. Importantly, TWEAK induces transient expression of endogenous Ngn3, a master regulator of endocrine cell development, and induces focal ductal structures with characteristics of regeneration foci. In addition, we identify by lineage tracing TWEAK-induced pancreatic β-cells derived from pancreatic duct epithelial cells. Conversely, we show that Fn14 deficiency delays formation of regenerating foci after Px and limits their expansion. Conclusions/Interpretation We conclude that TWEAK is a novel factor mediating pancreatic β-cell neogenesis from ductal epithelium in normal adult mice

Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability