The rheological properties of shear thickening fluid reinforced with SiC nanowires

The rheological properties of shear thickening fluid (STF) reinforced with SiC nanowires were investigated in this paper. Pure STF consists of 56 vol% silica nano-particles and polyethylene glycol 400 (PEG 400) solvent was fabricated; and a specific amount of SiC nanowires were dispersed into this pure STF, and then the volume fraction of PEG400 was adjusted to maintain the volume fraction of solid phase in the STF at a constant of 56%. The results showed there was almost 30% increase in the initial and shear thickening viscosity of the STF reinforced with SiC nanowires compared to the pure STF. Combining with the hydrodynamic cluster theory, the effect of the mechanism of SiC nanowire on the viscosity of STF was discussed, and based on the experimental results, an analytical model of viscosity was used to describe the rheological properties of STF, which agreed with the experimental results

Supramolecular Assembly of Tetramethylcucurbit[6]uril and 2-Picolylamine

The supramolecular assembly of symmetrical tetramethylcucurbit[6]uril (TMeQ[6]) and 2-picolylamine (AMPy) has been investigated via various techniques, including ultraviolet-visible (UV-vis) and nuclear magnetic resonance spectroscopy, isothermal titration calorimetry (ITC), and X-ray crystallography. The results indicated that TMeQ[6] could encapsulate the AMPy guest molecule to form a stable inclusion complex. The rotational restriction of the guest in the cavity of TMeQ[6] resulted in a large negative value of entropy. The X-ray crystal structure of the 1:1 inclusion complex between TMeQ[6] and AMPy revealed that AMPy exists in the elliptical cavity of TMeQ[6]

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition. Nevertheless, few-shot learning methods based on CLIP typically require offline fine-tuning of the parameters on few-shot samples, resulting in longer inference time and the risk of over-fitting in certain domains. To tackle these challenges, we propose the Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features guided by the few-shot samples in an online manner. With a few training samples, our method can enable effective few-shot learning capabilities and generalize to unseen data or tasks without additional fine-tuning, achieving competitive performance and high efficiency. Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3.6\% on eight image classification datasets with higher inference speed. Furthermore, our model is simple and flexible, serving as a plug-and-play module directly applicable to downstream tasks. Without further fine-tuning, Meta-Adapter obtains notable performance improvements in open-vocabulary object detection and segmentation tasks.Comment: Accepted by NeurIPS 202