Experimental Investigation on Ultimate Capacity of Eccentrically-Compressed Cold-Formed Beam-Columns with Lipped Channel Sections

This paper is mainly concerned with the in-plane behavior of eccentrically-compressed cold-formed steel beam-columns with lipped channel sections. The tested members are classified into three series by loading types including: axial compression and major axis bending (X), axial compression and minor axis bending (lips in tension, Y1), and axial compression and minor axis bending (lips in compression, Y2). A numerical model is developed and verified by the experimental results. Then the elastic local buckling loads are discussed based on test results, numerical analysis, and design methods. The comparison between test strength and nominal strength obtained by AISI specification indicates that the interaction equation can provide conservative prediction for beam-columns’ strength

Infinite-dimensional Lie bialgebras via affinization of Novikov bialgebras and Koszul duality

Balinsky and Novikov showed that the affinization of a Novikov algebra naturally defines a Lie algebra, a property that in fact characterizes the Novikov algebra. It is also an instance of the operadic Koszul duality. In this paper, we develop a bialgebra theory for the Novikov algebra, namely the Novikov bialgebra, which is characterized by the fact that its affinization (by a quadratic right Novikov algebra) gives an infinite-dimensional Lie bialgebra, suggesting a Koszul duality for properads. A Novikov bialgebra is also characterized as a Manin triple of Novikov algebras. The notion of Novikov Yang-Baxter equation is introduced, whose skewsymmetric solutions can be used to produce Novikov bialgebras and hence Lie bialgebras. Moreover, these solutions also give rise to skewsymmetric solutions of the classical Yang-Baxter equation in the infinite-dimensional Lie algebras from the Novikov algebras.Comment: 34 page

Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data

Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined values to impute missing data, then perform feature selection on the complete dataset. Separating imputation and feature selection processes fails to capitalize on the potential synergy where local structural information gleaned from feature selection could guide the imputation, thereby improving the feature selection performance in turn. Additionally, previous methods only focus on leveraging samples' local structure information, while ignoring the intrinsic locality of the feature space. To tackle these problems, a novel MUFS method, called UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces. Then, UNIFIER dynamically recovers the missing views, guided by the sample and feature similarity graphs during the feature selection procedure. Furthermore, the half-quadratic minimization technique is used to automatically weight different instances, alleviating the impact of outliers and unreliable restored data. Comprehensive experimental results demonstrate that UNIFIER outperforms other state-of-the-art methods

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.Comment: 7pages, 3 figure

Local Polynomial Regression Solution for Differential Equations with Initial and Boundary Values

Numerical solutions of the linear differential boundary issues are obtained by using a local polynomial estimator method with kernel smoothing. To achieve this, a combination of a local polynomial-based method and its differential form has been used. The computed results with the use of this technique have been compared with the exact solution and other existing methods to show the required accuracy of it. The effectiveness of this method is verified by three illustrative examples. The presented method is seen to be a very reliable alternative method to some existing techniques for such realistic problems. Numerical results show that the solution of this method is more accurate than that of other methods

Heterosis-related genes under different planting densities in maize

Heterosis and increasing planting density have contributed to improving maize grain yield (GY) for several decades. As planting densities increase, the GY per plot also increases whereas the contribution of heterosis to GY decreases. There are trade-offs between heterosis and planting density, and the transcriptional characterization of heterosis may explain the mechanism involved. In this study, 48 transcriptome libraries were sequenced from four inbred Chinese maize lines and their F1 hybrids. They were planted at densities of 45,000 plants/ha and 67,500 plants/ha. Maternal-effect differentially expressed genes (DEGs) played important roles in processes related to photosynthesis and carbohydrate biosynthesis and metabolism. Paternal-effect DEGs participated in abiotic/biotic stress response and plant hormone production under high planting density. Weighted gene co-expression network analysis revealed that high planting-density induced heterosis-related genes regulating abiotic/biotic stress response, plant hormone biosynthesis, and ubiquitin-mediated proteolysis but repressed other genes regulating energy formation. Under high planting density, maternal genes were mainly enriched in the photosynthesis reaction center, while paternal genes were mostly concentrated in the peripheral antenna system. Four important genes were identified in maize heterosis and high planting density, all with functions in photosynthesis, starch biosynthesis, auxin metabolism, gene silencing, and RNA interference

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges such as imprecise feature extraction, unreliable cross-modality associations, and high scene-specific requirements. To address this, we introduce an edge-based approach for automatic online calibration of LiDAR and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters. Specifically, stable and robust image edge features are extracted using a SAM-based method and the edge features extracted from the point cloud are weighted through a multi-frame weighting strategy for feature filtering. Finally, accurate extrinsic parameters are optimized based on edge correspondence constraints. We conducted evaluations on both the KITTI dataset and our dataset. The results show a state-of-the-art rotation accuracy of 0.086{\deg} and a translation accuracy of 0.977 cm, outperforming existing edge-based calibration methods in both precision and robustness