Effect of arbuscular mycorrhizal fungus on the growth and polyphenol production of medicinal plants: Ehretia asperula and Solanum procumben

The study was conducted to evaluate the influence of arbuscular mycorrhizal fungus (Rhizophagus intradices) on growth and polyphenol production of the two important and popular medicinal plants in Vietnam: Ehretia asperula Zoll. & Mor. and Solanum procumbens Lour. The results showed a significant effect of the fungus on the growth of these two species with the growth indices such as height, weight and P content that were all higher than those of non-AM plants; although the indices of AM symbiosis in the plant roots were not as high as other plants in previous studies. The effect of AM fungus on polyphenol production was different between the two species. In E. asperula, the effect of AM fungi on polyphenol production was not significant; whereas in S. procumbens, AM symbiosis significantly increased polyphenol production in plant biomass, especially in roots. The different growth times of the two species might cause the different effects of AM fungus on polyphenol production

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

Investigation of hypersonic flow in the vki h3 wind tunnel: From facility characterization to boundary-layer interaction over low-temperature ablators

This work deals with the characterization, in terms of operating conditions, of the H3 hypersonic wind tunnel of the von Karman Institute for Fluid Dynamics (VKI), thus providing a detailed and structured benchmark for the evaluation of testing capabilities in hypersonic wind tunnels, and with the experimental study of the interaction between the boundary layer and the ablation process of low temperature ablative materials. The flow characteristics of the test section of the H3 WT have been assessed by using a pitot rake, for a wider range of operating conditions with respect to previous calibrations. A CFD analysis of the diffuser-ejector system has been carried out to assess its performance, and an experimental test campaign has been performed in order to validate the CFD analyses and completely characterize the facility operating conditions. Finally, a series of experiments with models of increasing size and different shapes has been carried out to determine the blockage effect in the tunnel test section. The H3 WT is then employed to study the boundary layer interaction with the ablative process on low temperature ablative models. These models have been built after having appropriately designed the sintering system. The Planar Laser Induced Fluorescence method has been applied to visualize the flow behavior: a laminar-turbulent transition due to the ablation process has been observed, together with the main flow structures

Music-Driven Group Choreography

Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present $\rm AIOZ-GDANCE$, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at: https://aioz-ai.github.io/AIOZ-GDANCE/Comment: accepted in CVPR 202

Zero-shot Object-Level OOD Detection with Context-Aware Inpainting

Machine learning algorithms are increasingly provided as black-box cloud services or pre-trained models, without access to their training data. This motivates the problem of zero-shot out-of-distribution (OOD) detection. Concretely, we aim to detect OOD objects that do not belong to the classifier's label set but are erroneously classified as in-distribution (ID) objects. Our approach, RONIN, uses an off-the-shelf diffusion model to replace detected objects with inpainting. RONIN conditions the inpainting process with the predicted ID label, drawing the input object closer to the in-distribution domain. As a result, the reconstructed object is very close to the original in the ID cases and far in the OOD cases, allowing RONIN to effectively distinguish ID and OOD samples. Throughout extensive experiments, we demonstrate that RONIN achieves competitive results compared to previous approaches across several datasets, both in zero-shot and non-zero-shot settings