Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released

PreConfig: A Pretrained Model for Automating Network Configuration

Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design, limiting their adaptability to diverse scenarios and complex application needs. This paper introduces PreConfig, an innovative NCA tool that leverages a pretrained language model for automating network configuration tasks. PreConfig is designed to address the complexity and variety of NCA tasks by framing them as text-to-text transformation problems, thus unifying the tasks of configuration generation, translation, and analysis under a single, versatile model. Our approach overcomes existing tools' limitations by utilizing advances in natural language processing to automatically comprehend and generate network configurations without extensive manual re-engineering. We confront the challenges of integrating domain-specific knowledge into pretrained models and the scarcity of supervision data in the network configuration field. Our solution involves constructing a specialized corpus and further pretraining on network configuration data, coupled with a novel data mining technique for generating task supervision data. The proposed model demonstrates robustness in configuration generation, translation, and analysis, outperforming conventional tools in handling complex networking environments. The experimental results validate the effectiveness of PreConfig, establishing a new direction for automating network configuration tasks with pretrained language models

Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://github.com/Pamphlett/Outram.Comment: 8 pages, 5 figure

Cluster aggregates surrounding Pismis 5 in the Vela Molecular Ridge

Context. In the Gaia era, the precision of astrometric data is unprecedented. High-quality data make it easier to find more cluster aggregates and support further confirmation of these open clusters. Aims. We use Gaia DR3 to redetermine the open clusters surrounding Pismis 5 in the Vela Molecular Ridge. We also investigate the basic properties of these clusters. Methods. We apply two clustering algorithms (StarGO and pyUPMASK) to identify the open cluster members in a five-dimensional space with Gaia DR3. Results. We identify eight open clusters surrounding Pismis 5 in the Vela Molecular Ridge. The open cluster QZ 1 is newly discovered. Through investigating the comprehensive properties of the clusters, one open binary cluster candidate (Alessi 43 and Collinder 197) and one triple open cluster candidate (Pismis 5, Pismis 5A, and Pismis 5B) are discussed. Conclusions. Binary and triple open cluster candidates have been identified as potential primordial aggregates based on their similar age, position, and motion. According to kinematic speculations, the two aggregate candidates will gradually separate, and their interiors will slowly disintegrate.Comment: 10 pages, 7 figure

Acquired reactive perforating collagenosis triggered by trauma with eosinophilia: a case report and literature review

Acquired reactive perforating collagenosis (ARPC) is a rare dermatological disorder condition defined by the perforation of altered collagen fibers through the epidermis. The presence of underlying conditions such as diabetes or renal disease is helpful in the ARPC diagnosis. Although skin rashes related to ARPC have been reported, the exact causative factors and mechanisms remain unclear. Here, we present a unique case of ARPC triggered by trauma in a 67-year-old male without concurrent systemic alterations. The diagnosis of ARPC with eosinophilia was made following comprehensive diagnostic testing, including clinical presentation, histological results, and blood tests, ruling out other possible diseases. Intriguingly, the histopathological examination revealed collagen penetration into the epidermis at different tissue sections. In addition, we reviewed existing literature on ARPC, which documented the causation. To help confirm the diagnosis, clinicians have to pay attention to traumatic triggers for ARPC and its rare manifestation with eosinophilia