Frequency Chirping of Electromagnetic Ion Cyclotron Waves in Earth's Magnetosphere

Electromagnetic ion cyclotron waves are known to exhibit frequency chirping, contributing to the rapid scattering and acceleration of energetic particles. However, the physical mechanism of chirping remains elusive. Here, we propose a new model to explain the chirping and provide direct observational evidence for validation. Our results relate the frequency chirping of the wave to both the wave amplitude and magnetic field inhomogeneity for the first time. The general applicability of the model's underlying principle opens a new path toward understanding the frequency chirping of other waves.Comment: 8 pages, 3 figure

Matrix Li-Yau-Hamilton Estimates under K\"ahler-Ricci Flow

We prove matrix Li-Yau-Hamilton estimates for positive solutions to the heat equation and the backward conjugate heat equation, both coupled with the K\"ahler-Ricci flow. As an application, we obtain a monotonicity formula.Comment: 13 pages. Comments are welcom

Derivation of the green vegetation fraction from TM data of three gorges area

AbstractFraction of green vegetation, fg is needed as one of regular parameters for vegetation cover analysis. The paper explores the potentials of deriving this variable from thermal mapper (TM) normalized difference vegetation index (NDVI) data considering the leaf area index (LAI) of agricultural field. Geometric, radiometric and atmospheric correction of the images were performed before further analysis. According to the sub-pixel structure characteristic, we choose mosaic-pixel model for calculating percent vegetation cover. A new method was put forward to achieve LAI values of non-dense vegetation where soil line equation was considered. Two schemes are produced to obtain different LAI values and type-specific accuracy is evaluated using parameter defined in this paper

Bilevel Fast Scene Adaptation for Low-Light Image Enhancement

Enhancing images in low-light scenes is a challenging but widely concerned task in the computer vision. The mainstream learning-based methods mainly acquire the enhanced model by learning the data distribution from the specific scenes, causing poor adaptability (even failure) when meeting real-world scenarios that have never been encountered before. The main obstacle lies in the modeling conundrum from distribution discrepancy across different scenes. To remedy this, we first explore relationships between diverse low-light scenes based on statistical analysis, i.e., the network parameters of the encoder trained in different data distributions are close. We introduce the bilevel paradigm to model the above latent correspondence from the perspective of hyperparameter optimization. A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes (i.e., freezing the encoder in the adaptation and testing phases). Further, we define a reinforced bilevel learning framework to provide a meta-initialization for scene-specific decoder to further ameliorate visual quality. Moreover, to improve the practicability, we establish a Retinex-induced architecture with adaptive denoising and apply our built learning framework to acquire its parameters by using two training losses including supervised and unsupervised forms. Extensive experimental evaluations on multiple datasets verify our adaptability and competitive performance against existing state-of-the-art works. The code and datasets will be available at https://github.com/vis-opt-group/BL

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization. In this paper, we propose a novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to solve the problem. We first propose a neighbor-based regularization to smooth each sample observation, which could reduce the observation noise efficiently without any extra training cost. Since the neighbor regularization highly depends on the sample density of a neighbor area, we further design a density-based acquisition function to adjust the acquisition reward and obtain more stable statistics. In addition, we design a adjustment mechanism to ensure the framework maintains a reasonable regularization strength and density reward conditioned on remaining computation resources. We conduct experiments on the bayesmark benchmark and important computer vision benchmarks such as ImageNet and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it consistently outperforms other state-of-the-art methods.Comment: Accepted by BMVC 202

Formation of Foreshock Transients and Associated Secondary Shocks

Upstream of shocks, the foreshock is filled with hot ions. When these ions are concentrated and thermalized around a discontinuity, a diamagnetic cavity bounded by compressional boundaries, referred to as a foreshock transient, forms. Sometimes, the upstream compressional boundary can further steepen into a secondary shock, which has been observed to accelerate particles and contribute to the primary shock acceleration. However, secondary shock formation conditions and processes are not fully understood. Using particle-in-cell simulations, we reveal how secondary shocks are formed. From 1D simulations, we show that electric fields play a critical role in shaping the shock's magnetic field structure, as well as in coupling the energy of hot ions to that of the shock. We demonstrate that larger thermal speed and concentration ratio of hot ions favor the formation of a secondary shock. From a more realistic 2D simulation, we examine how a discontinuity interacts with foreshock ions leading to the formation of a foreshock transient and a secondary shock. Our results imply that secondary shocks are more likely to occur at primary shocks with higher Mach number. With the secondary shock's previously proven ability to accelerate particles in cooperation with a planetary bow shock, it is even more appealing to consider them in particle acceleration of high Mach number astrophysical shocks.Peer reviewe