Short-lived repeating fast radio bursts from tidal disruption of white dwarfs by intermediate-mass black holes

The origin of repeating fast radio bursts (RFRBs) is still a mystery. We propose that short-lived RFRBs might be triggered from the tidal disruption of white dwarfs (WDs) by intermediate-mass black holes (BHs). In this model, we show that the remnant WD clusters after tidal collapse cuts the magnetic lines on the BH accretion discs, and during each fall of the clump, so that electrons are torn from the surface of the mass and instantly accelerated to the relativistic energy. The subsequent movement of these electrons along magnetic field lines will result in coherent curvature radiation. This short-lived radio transients might accompany with the accretion process. The luminosity and the timescale can be estimated to be $L_\mathrm{tot}\sim 1.96\times10^{40}~{\rm erg~s^{-1}}$ and $\Delta t\sim1.14~{\rm ms}$, respectively, which are consistent with the typical properties of RFRBs. Moreover, the total event rate of our model for generating RFRBs might be as high as $\sim 10~\rm {yr^{-1}~Gpc^{-3}}$.Comment: 5 pages, 1 figure, accepted for publication in MNRAS Letter

Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning

Plum: Prompt Learning using Metaheuristic

Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, have even revealed previously unknown reasoning capabilities within these models. However, the progress of discovering effective prompts has been slow, driving a desire for general prompt optimization methods. Unfortunately, few existing prompt learning methods satisfy the criteria of being truly "general", i.e., automatic, discrete, black-box, gradient-free, and interpretable all at once. In this paper, we introduce metaheuristics, a branch of discrete non-convex optimization methods with over 100 options, as a promising approach to prompt learning. Within our paradigm, we test six typical methods: hill climbing, simulated annealing, genetic algorithms with/without crossover, tabu search, and harmony search, demonstrating their effectiveness in black-box prompt learning and Chain-of-Thought prompt tuning. Furthermore, we show that these methods can be used to discover more human-understandable prompts that were previously unknown, opening the door to a cornucopia of possibilities in prompt optimization. We release all the codes in \url{https://github.com/research4pan/Plum}