DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://github.com/hust-jy/DDS3DComment: Accepted for publication in 2023 IEEE International Conference on Robotics and Automation (ICRA

Ferroelectricity controlled chiral spin textures and anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure

Realizing effective manipulation and explicit identification of topological spin textures are two crucial ingredients to make them as information carrier in spintronic devices with high storage density, high data handling speed and low energy consumption. Electric-field manipulation of magnetism has been achieved as a dissipationless method compared with traditional regulations. However, the magnetization is normally insensitive to the electric field since it does not break time-reversal symmetry directly, and distribution of topological magnetic quasiparticles is difficult to maintain due to the drift arising from external fluctuation, which could result in ambiguous recognition between quasiparticles and uniform magnetic background. Here, we demonstrate that electric polarization-driven skyrmionic and uniform ferromagnetic states can be easily and explicitly distinguished by transverse voltage arising from anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure LaClBr/In2Se3. Our work provides an alternative approach for data encoding, in which data are encoded by combing topological spin textures with detectable electronic transport.Comment: published in 2D materials, 9, 045030 (2022

Dzyaloshinskii-Moriya interaction and magnetic skyrmions induced by curvature

Realizing sizeable Dzyaloshinskii-Moriya interaction (DMI) in intrinsic two-dimensional (2D) magnets without any manipulation will greatly enrich potential application of spintronics devices. The simplest and most desirable situation should be 2D magnets with intrinsic DMI and intrinsic chiral spin textures. Here, we propose to realize DMI by designing periodic ripple structures with different curvatures in low-dimensional magnets and demonstrate the concept in both one-dimensional (1D) CrBr2 and two-dimensional (2D) MnSe2 magnets by using first-principles calculations. We find that DMIs in curved CrBr2 and MnSe2 can be efficiently controlled by varying the size of curvature c, where c is defined as the ratio between the height h and the length l of curved structure. Moreover, we unveil that the dependence of first-principles calculated DMI on size of curvature c can be well described by the three-site Fert-L\'evy model. At last, we uncover that field-free magnetic skyrmions can be realized in curved MnSe2 by using atomistic spin model simulations based on first-principles calculated magnetic parameters. The work will open a new avenue for inducing DMI and chiral spin textures in simple 2D magnets via curvature.Comment: Published on Physical Review B 106, 05442

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

Biological motion perception (BMP) is a vivid perception of the moving form of a human figure from a few light points on the joints of the body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between the BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants. For each participant, the intrinsic brain network was constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with the BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located at the Default Mode Network (DMN). Additionally, the discrimination analysis showed that the local efficiency over regions including thalamus could be used to classify BMP performance across participants. Notably, the association pattern between the network nodal efficiency and the BMP was different from the association pattern that of the static directional/gender information perception. Overall, these findings showed that intrinsic brain network efficiency may be considered as a neural factor that explains BMP inter-individual variability. Keywords: Biological motion; Resting-state network; Network efficiency; Multiple linear regression model; Brain-behavior analysi