Fruit Tree Pollination Technology and Industrialization in China

This work investigates the bee pollination of fruit trees, especially apples and pears in the field. We first introduce research carried out into bee pollination of crops in China, and then our own pollination experiments with managed bees such as Apis mellifera in the field. We monitor the efficiency of bee pollination of fruit trees by regulating hive bees and tree arrangement. In addition, we develop some methods to attract bees to visit fruit trees. Our research shows that the number of beehives and the arrangement of trees greatly influence bee pollination. The results provide a comprehensive tutorial on the best practices of bee pollination of fruit trees

Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)

In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy

Dataset Condensation via Generative Model

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However, it suffers from slow optimization speed and large number of parameters to be optimized. When increasing image resolutions and classes, the number of learnable parameters grows accordingly, prohibiting condensation methods from scaling up to large datasets with diverse classes. Moreover, the relations among condensed samples have been neglected and hence the feature distribution of condensed samples is often not diverse. To solve these problems, we propose to condense the dataset into another format, a generative model. Such a novel format allows for the condensation of large datasets because the size of the generative model remains relatively stable as the number of classes or image resolution increases. Furthermore, an intra-class and an inter-class loss are proposed to model the relation of condensed samples. Intra-class loss aims to create more diverse samples for each class by pushing each sample away from the others of the same class. Meanwhile, inter-class loss increases the discriminability of samples by widening the gap between the centers of different classes. Extensive comparisons with state-of-the-art methods and our ablation studies confirm the effectiveness of our method and its individual component. To our best knowledge, we are the first to successfully conduct condensation on ImageNet-1k.Comment: old work,done in 202

Is synthetic data from generative models ready for image recognition?

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.Comment: ICLR 2023, spotligh

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy. Recently, synthetic images generated by text-to-image diffusion models, have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. To address this, we start by uncovering that diffusion models' cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent unsupervised learning techniques ( i.e., contrastive learning, masked modeling, and vision-language pretraining) and introduce customized solutions by fully exploiting the aforementioned free attention masks. Our approach is validated through extensive experiments that show consistent improvements in baseline models across various downstream tasks, including image classification, detection, segmentation, and image-text retrieval. By utilizing our method, it is possible to close the performance gap between unsupervised pretraining on synthetic data and real-world scenarios

PV3D: A 3D Generative Model for Portrait Video Generation

Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos. In this work, we propose PV3D, the first generative framework that can synthesize multi-view consistent portrait videos. Specifically, our method extends the recent static 3D-aware image GAN to the video domain by generalizing the 3D implicit neural representation to model the spatio-temporal space. To introduce motion dynamics to the generation process, we develop a motion generator by stacking multiple motion layers to generate motion features via modulated convolution. To alleviate motion ambiguities caused by camera/human motions, we propose a simple yet effective camera condition strategy for PV3D, enabling both temporal and multi-view consistent video generation. Moreover, PV3D introduces two discriminators for regularizing the spatial and temporal domains to ensure the plausibility of the generated portrait videos. These elaborated designs enable PV3D to generate 3D-aware motion-plausible portrait videos with high-quality appearance and geometry, significantly outperforming prior works. As a result, PV3D is able to support many downstream applications such as animating static portraits and view-consistent video motion editing. Code and models will be released at https://showlab.github.io/pv3d

Momentum space imaging of Cooper pairing in a half-Dirac-gas topological superconductor (a helical 2D topological superconductor)

Superconductivity in Dirac electrons has recently been proposed as a new platform between novel concepts in high-energy and condensed matter physics. It has been proposed that supersymmetry and exotic quasiparticles, both of which remain elusive in particle physics, may be realized as emergent particles in superconducting Dirac electron systems. Using artificially fabricated topological insulator-superconductor heterostructures, we present direct spectroscopic evidence for the existence of Cooper pairing in a half Dirac gas 2D topological superconductor. Our studies reveal that superconductivity in a helical Dirac gas is distinctly different from that of in an ordinary two-dimensional superconductor while considering the spin degrees of freedom of electrons. We further show that the pairing of Dirac electrons can be suppressed by time-reversal symmetry breaking impurities removing the distinction. Our demonstration and momentum-space imaging of Cooper pairing in a half Dirac gas and its magnetic behavior taken together serve as a critically important 2D topological superconductor platform for future testing of novel fundamental physics predictions such as emergent supersymmetry and quantum criticality in topological systems.Comment: Submitted June'14; Accepted to NaturePhysics, to appear AOP (2014

Licorice extract inhibits the cGAS-STING pathway and protects against non-alcoholic steatohepatitis

Background: Inflammation and fibrosis are typical symptoms of non-alcoholic steatohepatitis (NASH), which is one of the most common chronic liver diseases. The cGAS-STING signaling pathway has been implicated in the progression of NASH, and targeting this pathway may represent a new therapeutic strategy. Licorice is a widely used herb with anti-inflammatory and liver-protective properties. In this study, we assessed the effect of licorice extract on the cGAS-STING pathway.Methods: Bone marrow-derived macrophages (BMDMs) were treated with licorice extract and then stimulated with HT-DNA, 2'3'-cGAMP, or other agonists to activate the cGAS-STING pathway. Quantitative real-time PCR and western blot were conducted to analyze whether licorice extract could affect the cGAS-STING pathway. Methionine and choline-deficient diet (MCD) was used to induce NASH in mice, which were treated with licorice extract (500 mg/kg) by gavage and/or c-176 (15 mg/kg) by intraperitoneal injection every 2 days. After 6 weeks of treatment, histological analysis of liver tissue was performed, along with measurements of plasma biochemical parameters.Results: Licorice extract inhibits cGAS-STING pathway activation. Mechanistically, it might function by inhibiting the oligomerization of STING. Treatment with licorice extract reduced inflammation and fibrosis in MCD diet-induced NASH mice models. Furthermore, we found that the therapeutic effect of combination treatment with licorice extract and C-176 (STING inhibitor) on the pathology and fibrosis of MCD diet-induced NASH models was similar to that of licorice extract or C-176 administered alone.Conclusion: Licorice extract can inhibit the cGAS-STING pathway and improve hepatic inflammation and fibrosis in NASH mice models. It strongly suggests that licorice extract may be a candidate therapeutic for NASH