A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our \textbf{SEAttnGAN}. Code is avalilable at https://github.com/jmyissb/SEAttnGAN.Comment: 12 pages, 3 figure

Ultrafast dynamics of fractional particles in α\alpha-RuCl3_3

In a Kitaev spin liquid, electron spins can break into fractional particles known as Majorana fermions and Z$_2$ fluxes. Recent experiments have indicated the existence of such fractional particles in a two-dimensional Kitaev material candidate, $\alpha$-RuCl$_3$. These exotic particles can be used in topological quantum computations when braided within their lifetimes. However, the lifetimes of these particles, critical for applications in topological quantum computing, have not been reported. Here we study ultrafast dynamics of photoinduced excitations in single crystals of $\alpha$-RuCl$_3$ using pump-probe transient grating spectroscopy. We observe intriguing photoexcited nonequilibrium states in the Kitaev paramagnetic regime between $T_N$~7 K and $T_H$~100 K, where $T_N$ is the N\'eel temperature and $T_H$ is set by the Kitaev interaction. Two distinct lifetimes are detected: a longer lifetime of ~50 ps, independent of temperature; a shorter lifetime of 1-20 ps, with a strong temperature dependence, $T^{-1.40}$. We analyze the transient grating signals using coupled differential equations and propose that the long and short lifetimes are associated with fractional particles in the Kitaev paramagnetic regime, Z$_2$ fluxes and Majorana fermions, respectively

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

Laser array spots thermography for detection of cracks in curved structures

The laser array spot thermography (LAST) is a fully non-contact and non-destructive method for the inspection of surface cracks with high efficiency. In this study, the detection capability of this method for the inspection of surface cracks in structures with curved surfaces is experimentally studied. The influence of the inspection angle on the crack imaging results is also investigated. The experiment results show that cracks in surface of the pipes with different dimeters can be detected and imaged by LAST

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network

Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.Comment: Accepted in BMVC2022 as oral presentatio

Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks

Predicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among itself, other agents, and the local roads. Unlike most existing works, which predict a fixed number of possible future motions of an agent, we propose a map-adaptive predictor that can predict a variable number of future trajectories of an agent according to the number of lanes with candidate centerlines (CCLs). The predictor predicts not only future motions guided by single CCLs but also a scene-reasoning prediction and a motion-maintaining prediction. These three kinds of predictions are produced integrally via a single graph operation. We represent the driving scene with a heterogeneous hierarchical graph containing nodes of two types. An agent node contains its dynamics feature encoded from its historical states, and a CCL node contains the CCL's sequential feature. We propose a hierarchical graph operator (HGO) with an edge-masking technology to regulate the information flow in graph operations and obtain the encoded scene feature for the trajectory decoder. Experiments on two large-scale real-world driving datasets show that our method realizes map-adaptive prediction and outperforms strong baselines

Radio-Frequency Interference Estimation for Multiple Random Noise Sources

As more compact designs and more assembled function modules are utilized in modern electronic devices, radio-frequency interference (RFI) source reconstruction is becoming more challenging because different noise sources may contribute simultaneously. This article presents a novel methodology to reconstruct multiple random noise sources on a real-world product, including several double-data-rate (DDR) memory modules and a high-speed connector. The DDR modules located beneath a heatsink cause random noise-like signals, which renders phase measurements challenging. An approach based on the tuned-receiver mode of a vector network analyzer is developed to measure the field phase from the random DDR signals, which can be further modeled with a Huygens\u27 box using the measured field magnitude and phase. Moreover, the connector can be modeled using an equivalent magnetic dipole. Furthermore, the total RFI power from the DDR memory modules and the high-speed connector, which generate uncorrelated RFI noise, is found to equal the summation of the individual power values obtained by an root mean square detector, which can be mathematically corroborated. Using the proposed method, the reconstructed source model can predict RFI values close to measurement results with less than 5 dB deviation

Spin Coherence and Spin Relaxation in Hybrid Organic-Inorganic Lead and Mixed Lead-Tin Perovskites

Metal halide perovskites make up a promising class of materials for semiconductor spintronics. Here we report a systematic investigation of coherent spin precession, spin dephasing and spin relaxation of electrons and holes in two hybrid organic-inorganic perovskites MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3 using time-resolved Faraday rotation spectroscopy. With applied in-plane magnetic fields, we observe robust Larmor spin precession of electrons and holes that persists for hundreds of picoseconds. The spin dephasing and relaxation processes are likely to be sensitive to the defect levels. Temperature-dependent measurements give further insights into the spin relaxation channels. The extracted electron Land\'e g-factors (3.75 and 4.36) are the biggest among the reported values in inorganic or hybrid perovskites. Both the electron and hole g-factors shift dramatically with temperature, which we propose to originate from thermal lattice vibration effects on the band structure. These results lay the foundation for further design and use of lead- and tin-based perovskites for spintronic applications