372 research outputs found
Infrared perfect absorber based on nanowire metamaterial cavities
An infrared perfect absorber based on gold nanowire metamaterial cavities
array on a gold ground plane is designed. The metamaterial made of gold
nanowires embedded in alumina host exhibits an effective permittivity with
strong anisotropy, which supports cavity resonant modes of both electric dipole
and magnetic dipole. The impedance of the cavity modes matches the incident
plane wave in free space, leading to nearly perfect light absorption. The
incident optical energy is efficiently converted into heat so that the local
temperature of the absorber will increase. Simulation results show that the
designed metamaterial absorber is polarization-insensitive and nearly
omnidirectional for the incident angle.Comment: 3 pages, 4 figure
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning
Contrastive Language-Image Pre-training (CLIP) has shown promising open-world
performance on 2D image tasks, while its transferred capacity on 3D point
clouds, i.e., PointCLIP, is still far from satisfactory. In this work, we
propose PointCLIP V2, a powerful 3D open-world learner, to fully unleash the
potential of CLIP on 3D point cloud data. First, we introduce a realistic shape
projection module to generate more realistic depth maps for CLIP's visual
encoder, which is quite efficient and narrows the domain gap between projected
point clouds with natural images. Second, we leverage large-scale language
models to automatically design a more descriptive 3D-semantic prompt for CLIP's
textual encoder, instead of the previous hand-crafted one. Without introducing
any training in 3D domains, our approach significantly surpasses PointCLIP by
+42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D
classification. Furthermore, PointCLIP V2 can be extended to few-shot
classification, zero-shot part segmentation, and zero-shot 3D object detection
in a simple manner, demonstrating our superior generalization ability for 3D
open-world learning. Code will be available at
https://github.com/yangyangyang127/PointCLIP_V2
A dimeric zinc(II) complex: bis[μ-1,2-bis(1,2,4-triazol-4-yl)ethane-κ2 N 1:N 1′]bis[dinitritozinc(II)]
The coordination geometry of the ZnII atom in the title complex, [Zn2(NO2)4(C6H8N6)2], is distorted octahedral, in which the ZnII atom is coordinated by two N atoms from the triazole rings of two symmetry-related 1,2-bis(1,2,4-triazol-4-yl)ethane ligands and four O atoms from two nitrite ligands. Two ZnII atoms are bridged by two organic ligands, forming a centrosymmetric dimer. Weak C—H⋯N and C—H⋯O hydrogen bonds play an important role in the intermolecular packing
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
To reduce the reliance on large-scale datasets, recent works in 3D
segmentation resort to few-shot learning. Current 3D few-shot semantic
segmentation methods first pre-train the models on `seen' classes, and then
evaluate their generalization performance on `unseen' classes. However, the
prior pre-training stage not only introduces excessive time overhead, but also
incurs a significant domain gap on `unseen' classes. To tackle these issues, we
propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and
a further training-based variant, TFS3D-T. Without any learnable parameters,
TFS3D extracts dense representations by trigonometric positional encodings, and
achieves comparable performance to previous training-based methods. Due to the
elimination of pre-training, TFS3D can alleviate the domain gap issue and save
a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to
train a lightweight query-support transferring attention (QUEST), which
enhances the interaction between the few-shot query and support data.
Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by
+6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the
training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has
propelled its application to diverse downstream vision tasks. To improve its
capacity on downstream tasks, few-shot learning has become a widely-adopted
technique. However, existing methods either exhibit limited performance or
suffer from excessive learnable parameters. In this paper, we propose APE, an
Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which
achieves superior accuracy with high computational efficiency. Via a prior
refinement module, we analyze the inter-class disparity in the downstream data
and decouple the domain-specific knowledge from the CLIP-extracted cache model.
On top of that, we introduce two model variants, a training-free APE and a
training-required APE-T. We explore the trilateral affinities between the test
image, prior cache model, and textual representations, and only enable a
lightweight category-residual module to be trained. For the average accuracy
over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively
outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less
learnable parameters.Comment: Code is available at https://github.com/yangyangyang127/AP
An experimental study on the rotational accuracy of variable preload spindle-bearing system
The rotational performance of the spindle-bearing system has critical influence upon the geometric shape and surface roughness of the machined parts. The effects of preload and preload method on the rotational performance of the spindle-bearing system is explored experimentally to reveal the role of preload and preload method in spindle rotational performances under different speeds. A test rig on which both the rigid preload and elastic preload can be realized, equipped with variable preload spindle-bearing system, is developed. Based on the mechanical model, the relationship of the axial preload and negative axial clearance of the spindle-bearing system is provided. Rotating sensitive radial error motion tests are conducted for evaluating synchronous and asynchronous radial errors of the variable preload spindle-bearing system under different rotating speeds and preload methods. The change regularity of synchronous and asynchronous radial errors with preloads under different rotating speeds are given. The results show that the preload plays an important role on the rotational performance of spindle-bearing system. The rigid preload is more efficient in achieving better rotational performance than elastic preload under the same rotating speed. Furthermore, this article significantly guides the preload designing and assembling of the new spindle-bearing system
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