Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation

Traditional 3D segmentation methods can only recognize a fixed range of classes that appear in the training set, which limits their application in real-world scenarios due to the lack of generalization ability. Large-scale visual-language pre-trained models, such as CLIP, have shown their generalization ability in the zero-shot 2D vision tasks, but are still unable to be applied to 3D semantic segmentation directly. In this work, we focus on zero-shot point cloud semantic segmentation and propose a simple yet effective baseline to transfer the visual-linguistic knowledge implied in CLIP to point cloud encoder at both feature and output levels. Both feature-level and output-level alignments are conducted between 2D and 3D encoders for effective knowledge transfer. Concretely, a Multi-granularity Cross-modal Feature Alignment (MCFA) module is proposed to align 2D and 3D features from global semantic and local position perspectives for feature-level alignment. For the output level, per-pixel pseudo labels of unseen classes are extracted using the pre-trained CLIP model as supervision for the 3D segmentation model to mimic the behavior of the CLIP image encoder. Extensive experiments are conducted on two popular benchmarks of point cloud segmentation. Our method outperforms significantly previous state-of-the-art methods under zero-shot setting (+29.2% mIoU on SemanticKITTI and 31.8% mIoU on nuScenes), and further achieves promising results in the annotation-free point cloud semantic segmentation setting, showing its great potential for label-efficient learning

Nanotube spin defects for omnidirectional magnetic field sensing

Optically addressable spin defects in three-dimensional (3D) crystals and two-dimensional (2D) van der Waals (vdW) materials are revolutionizing nanoscale quantum sensing. Spin defects in one-dimensional (1D) vdW nanotubes will provide unique opportunities due to their small sizes in two dimensions and absence of dangling bonds on side walls. However, optically detected magnetic resonance of localized spin defects in a nanotube has not been observed. Here, we report the observation of single spin color centers in boron nitride nanotubes (BNNTs) at room temperature. Our findings suggest that these BNNT spin defects possess a spin $S=1/2$ ground state without an intrinsic quantization axis, leading to orientation-independent magnetic field sensing. We harness this unique feature to observe anisotropic magnetization of a 2D magnet in magnetic fields along orthogonal directions, a challenge for conventional spin $S=1$ defects such as diamond nitrogen-vacancy centers. Additionally, we develop a method to deterministically transfer a BNNT onto a cantilever and use it to demonstrate scanning probe magnetometry. Further refinement of our approach will enable atomic scale quantum sensing of magnetic fields in any direction.Comment: 9 pages, 5 figure

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF

Detection of groundwater storage variability based on GRACE and CABLE model in the Murray-Darling Basin

Monitoring groundwater storage is in great importance for economic and social development. In this paper, the monthly GRACE data from 2003 to 2015 is combined with the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate the variations of groundwater storage (GWS) in the Murray-Darling Basin (MDB). The results show that (1) the simulations of TWS from CABLE are more accurate than GLDAS over the MDB, and there is a higher correlation coefficient of 0.94 and a lower RMSE of 15.74 between CABLE and GRACE. (3) The spatial pattern of GWS trends shows decline trends in the southwest, east and south, and increasing trends in the north and south central (3) For the whole MDB, the average GWS has strong seasonality and shows an increasing trend with a rate of 1.19 0.41 mmyyear between 2003 and 2015

Adaptation of Abies fargesii var. faxoniana (Rehder et E.H. Wilson) Tang S Liu seedlings to high altitude in a subalpine forest in southwestern China with special reference to phloem and xylem traits

International audienceContext: Maintenance of xylem and phloem transport is particularly important for the survival and growth of trees at the treeline. How plants modify the allocation to leaf, xylem, and phloem structures to adapt to the treeline environment is an important issue.Aims: The purpose of this study was to estimate how xylem and phloem anatomy and volume as well as leaf functional traits of A. fargesii seedlings vary with elevation.MethodsWe examined elevation-related differences in a variety of phloem and xylem functional areas and hydraulic conduit diameters of A. fargesii seedlings growing at elevations between 2600 and 3200 m in the subalpine conifer forest of southwest China.Results: Xylem area, last xylem ring area, and leaf:sapwood area significantly decreased, while xylem:leaf area, phloem:leaf area, and non-collapsed phloem:xylem area significantly increased with elevation. Principal components analysis showed that xylem area, non-collapsed phloem area, and xylem:phloem area were positively correlated with growth rates.Conclusion: Our results showed that A. fargesii tree seedlings at the treeline tend to facilitate growth and maintain functional water and sugar balance between stem and leaves by the enhancement in xylem:leaf area, phloem:leaf area, and phloem:xylem area, but not through differences in vessel lumen diameter

Quantum control and Berry phase of electron spins in rotating levitated diamonds in high vacuum

Abstract Levitated diamond particles in high vacuum with internal spin qubits have been proposed for exploring macroscopic quantum mechanics, quantum gravity, and precision measurements. The coupling between spins and particle rotation can be utilized to study quantum geometric phase, create gyroscopes and rotational matter-wave interferometers. However, previous efforts in levitated diamonds struggled with vacuum level or spin state readouts. To address these gaps, we fabricate an integrated surface ion trap with multiple stabilization electrodes. This facilitates on-chip levitation and, for the first time, optically detected magnetic resonance measurements of a nanodiamond levitated in high vacuum. The internal temperature of our levitated nanodiamond remains moderate at pressures below 10−5 Torr. We have driven a nanodiamond to rotate up to 20 MHz (1.2 × 109 rpm), surpassing typical nitrogen-vacancy (NV) center electron spin dephasing rates. Using these NV spins, we observe the effect of the Berry phase arising from particle rotation. In addition, we demonstrate quantum control of spins in a rotating nanodiamond. These results mark an important development in interfacing mechanical rotation with spin qubits, expanding our capacity to study quantum phenomena

The effects of Co-Ti co-doping on the magnetic, electrical, and magnetodielectric behaviors of M-type barium hexaferrites

Magnetic, electrical and magnetodielectric properties have been studied in Co-Ti co-doped M-type hexaferrite BaCoxTixFe12-2xO19 (x = 0 ∼ 4). With the incorporation of Co-Ti, both their ferromagnetic magnetization and coercivity have been greatly changed. The temperature dependent magnetization curve shows a apparent hump at around 420 K, likely in association with more complicated cycloidal spin ordering, which is closely related to ferroelectric polarization. Interestingly, a significantly enhancement in resistivity (∼3 orders in magnitude) can be obtained in co-doped samples (x > 2), which is beneficial for magnetoelectric properties. The magnetoelectric effect were examined by dielectric tunibility under external magnetic field, which shows apparent tunability up to ∼−3% for sample with x = 2 at 1T magnetic field, further supporting it is a room temperature single phase mutliferroic material