Vanishing of the anomalous Hall effect and enhanced carrier mobility in the spin-gapless ferromagnetic Mn2CoGa1-xAlx alloys

Spin gapless semiconductor (SGS) has attracted long attention since its theoretical prediction, while concrete experimental hints are still lack in the relevant Heusler alloys. Here in this work, by preparing the series alloys of Mn2CoGa1-xAlx (x=0, 0.25, 0.5, 0.75 and 1), we identified the vanishing of anomalous Hall effect in the ferromagnetic Mn2CoGa (or x=0.25) alloy in a wide temperature interval, accompanying with growing contribution from the ordinary Hall effect. As a result, comparatively low carrier density (1020 cm-3) and high carrier mobility (150 cm2/Vs) are obtained in Mn2CoGa (or x=0.25) alloy in the temperature range of 10-200K. These also lead to a large dip in the related magnetoresistance at low fields. While in high Al content, despite the magnetization behavior is not altered significantly, the Hall resistivity is instead dominated by the anomalous one, just analogous to that widely reported in Mn2CoAl. The distinct electrical transport behavior of x=0 and x=0.75 (or 1) is presently understood by their possible different scattering mechanism of the anomalous Hall effect due to the differences in atomic order and conductivity. Our work can expand the existing understanding of the SGS properties and offer a better SGS candidate with higher carrier mobility that can facilitate the application in the spin-injected related devices

Robustness-Guided Image Synthesis for Data-Free Quantization

Quantization has emerged as a promising direction for model compression. Recently, data-free quantization has been widely studied as a promising method to avoid privacy concerns, which synthesizes images as an alternative to real training data. Existing methods use classification loss to ensure the reliability of the synthesized images. Unfortunately, even if these images are well-classified by the pre-trained model, they still suffer from low semantics and homogenization issues. Intuitively, these low-semantic images are sensitive to perturbations, and the pre-trained model tends to have inconsistent output when the generator synthesizes an image with poor semantics. To this end, we propose Robustness-Guided Image Synthesis (RIS), a simple but effective method to enrich the semantics of synthetic images and improve image diversity, further boosting the performance of downstream data-free compression tasks. Concretely, we first introduce perturbations on input and model weight, then define the inconsistency metrics at feature and prediction levels before and after perturbations. On the basis of inconsistency on two levels, we design a robustness optimization objective to enhance the semantics of synthetic images. Moreover, we also make our approach diversity-aware by forcing the generator to synthesize images with small correlations in the label space. With RIS, we achieve state-of-the-art performance for various settings on data-free quantization and can be extended to other data-free compression tasks.Comment: Accepted at AAAI 202

The linkages between stomatal physiological traits and rapid expansion of exotic mangrove species (Laguncularia racemosa) in new territories

The fast-growing exotic mangrove species (Laguncularia racemosa) has been widely introduced in new territories such as China to restore mangrove ecosystems. However, the invasiveness, as well as the mechanisms for the rapid expansion after the introduction are still not well studied. Here, we try to reveal possible micro-mechanisms for the fast expansion of L. racemosa, using the data on leaf stomata straits, gas-exchange parameters, stable isotope ratios, carbon-nitrogen allocation from L. racemosa and the adjacent native mangroves (Avicennia marina, Aegiceras corniculatum, Bruguiera gymnorhiza, Kandelia obovata) in Hainan Island, China. We found that the higher density but smaller size stoma of L. racemosa enhanced stomatal conductance and shorten the diffusion path of carbon dioxide, thereby increasing the photosynthetic rate. Moreover, the higher stomatal density of L. racemosa exerts a significant positive effect on transpiration, which thus accelerated the water transport and nutrient uptake to meet the advanced need for nutrients and water for fast-growing. The evidence from leaf δ13C and carbon-nitrogen allocation further proved that L. racemosa has a lower intrinsic water use efficiency but a higher rate of photosynthesis than native mangrove species. Our results suggest that stomatal morphological and physiological traits could strongly influence the growth of L. racemosa compared to the adjacent native mangroves, which provides a new perspective for the fast expansion of exotic mangrove species in China. These findings also suggest that L. racemosa has an invasive potential in native mangrove habitats, thereby the mangrove reforestation projects by introducing L. racemosa should be treated with caution

Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning

Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the history, surveys, ethics, and future directions of AD and IV technologies. The second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors" delves into the development of control, computing system, communication, HD map, testing, and human behaviors in IVs. This part, the third part, reviews perception and planning in the context of IVs. Aiming to provide a comprehensive overview of the latest advancements in AD and IVs, this work caters to both newcomers and seasoned researchers. By integrating the SoS and Part I, we offer unique insights and strive to serve as a bridge between past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and Cybernetics: System

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their implementation is still restricted to small-scale validation due to various issues, among which precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs. This paper aims to review state-of-the-art planning methods, including pipeline planning and end-to-end planning methods. In terms of pipeline methods, a survey of selecting algorithms is provided along with a discussion of the expansion and optimization mechanisms, whereas in end-to-end methods, the training approaches and verification scenarios of driving tasks are points of concern. Experimental platforms are reviewed to facilitate readers in selecting suitable training and validation methods. Finally, the current challenges and future directions are discussed. The side-by-side comparison presented in this survey not only helps to gain insights into the strengths and limitations of the reviewed methods but also assists with system-level design choices.Comment: 20 pages, 14 figures and 5 table

Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes.

Epigenetics contributes to the pathogenesis of immune-mediated diseases like rheumatoid arthritis (RA). Here we show the first comprehensive epigenomic characterization of RA fibroblast-like synoviocytes (FLS), including histone modifications (H3K27ac, H3K4me1, H3K4me3, H3K36me3, H3K27me3, and H3K9me3), open chromatin, RNA expression and whole-genome DNA methylation. To address complex multidimensional relationship and reveal epigenetic regulation of RA, we perform integrative analyses using a novel unbiased method to identify genomic regions with similar profiles. Epigenomically similar regions exist in RA cells and are associated with active enhancers and promoters and specific transcription factor binding motifs. Differentially marked genes are enriched for immunological and unexpected pathways, with "Huntington's Disease Signaling" identified as particularly prominent. We validate the relevance of this pathway to RA by showing that Huntingtin-interacting protein-1 regulates FLS invasion into matrix. This work establishes a high-resolution epigenomic landscape of RA and demonstrates the potential for integrative analyses to identify unanticipated therapeutic targets

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the comprehensive information of them benefits more robust perceptions. In this paper, we present a unified multi-modal LiDAR segmentation network, termed UniSeg, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously. Specifically, we first design the Learnable cross-Modal Association (LMA) module to automatically fuse voxel-view and range-view features with image features, which fully utilize the rich semantic information of images and are robust to calibration errors. Then, the enhanced voxel-view and range-view features are transformed to the point space,where three views of point cloud features are further fused adaptively by the Learnable cross-View Association module (LVA). Notably, UniSeg achieves promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes, and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks, including the LiDAR semantic segmentation challenge of nuScenes and panoptic segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase. It contains most of the popular outdoor LiDAR segmentation algorithms and provides reproducible implementations. The OpenPCSeg codebase will be made publicly available at https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at https://github.com/PJLab-ADG/PCSe