arXiv subjects

Jiale Zhang

Publications and source records attributed to Jiale Zhang.

65 records · Page 4Linked to original sources

arXiv2024

Parametrized Black Hole Quasinormal Ringdown Formalism for Higher Overtones

We investigate the parametrized black hole quasinormal ringdown formalism, which is a robust framework used to analyze quasinormal modes in systems that closely resemble general relativity, paying particular attention to the higher overtones. We find that larger deviations from the general relativity case typically appear in the quasinormal frequencies for the higher overtones. This growing tendency for higher overtones can be understood using an analytical method, and its relations to previous works are briefly discussed. Our findings indicate that we can impose a strong constraint on gravity theories by considering the overtones of quasinormal modes.

gr-qc↗

arXiv2024

Xformer: Hybrid X-Shaped Transformer for Image Denoising

In this paper, we present a hybrid X-shaped vision Transformer, named Xformer, which performs notably on image denoising tasks. We explore strengthening the global representation of tokens from different scopes. In detail, we adopt two types of Transformer blocks. The spatial-wise Transformer block performs fine-grained local patches interactions across tokens defined by spatial dimension. The channel-wise Transformer block performs direct global context interactions across tokens defined by channel dimension. Based on the concurrent network structure, we design two branches to conduct these two interaction fashions. Within each branch, we employ an encoder-decoder architecture to capture multi-scale features. Besides, we propose the Bidirectional Connection Unit (BCU) to couple the learned representations from these two branches while providing enhanced information fusion. The joint designs make our Xformer powerful to conduct global information modeling in both spatial and channel dimensions. Extensive experiments show that Xformer, under the comparable model complexity, achieves state-of-the-art performance on the synthetic and real-world image denoising tasks. We also provide code and models at https://github.com/gladzhang/Xformer.

cs.CV↗

arXiv2023

Multiwavelength observation of an active M-dwarf star EV Lac and its stellar flare accompanied by a delayed prominence eruption

We conducted 4-night multiwavelength observations of an active M-dwarf star EV Lac on 2022 October 24$-$27 with simultaneous coverage of soft X-rays (NICER; 0.2$-$12 $\mathrm{keV}$, Swift XRT; 0.2$-$10 $\mathrm{keV}$), near-ultraviolet (Swift UVOT/UVW2; 1600$-$3500 Å), optical photometry (TESS; 6000$-$10000 Å), and optical spectroscopy (Nayuta/MALLS; 6350$-$6800 Å). During the campaign, we detected a flare starting at 12:28 UTC on October 25 with its white-light bolometric energy of $3.4 \times 10^{32}$ erg. At about 1 hour after this flare peak, our $\mathrm{Hα}$ spectrum showed a blue-shifted excess component at its corresponding velocity of $\sim 100 \: \mathrm{km \: s^{-1}}$. This may indicate that the prominence erupted with a 1-hour delay of the flare peak. Furthermore, the simultaneous 20-second cadence near-ultraviolet and white-light curves show gradual and rapid brightening behaviors during the rising phase at this flare. The ratio of flux in NUV to white light at the gradual brightening was $\sim 0.49$, which may suggest that the temperature of the blackbody is low ($< 9000 \: \mathrm{K}$) or the maximum energy flux of a nonthermal electron beam is less than $5\times10^{11} \: \mathrm{erg \: cm^{-2} \: s^{-1}}$. Our simultaneous observations of NUV and white-light flare raise the issue of a simple estimation of UV flux from optical continuum data by using a blackbody model.

astro-ph.SR↗

arXiv2023

Progressive Frequency-Aware Network for Laparoscopic Image Desmoking

Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety. Existing learning-based methods demand large datasets and high computational resources. We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN framework for laparoscopic image desmoking, combining the strengths of CNN and Transformer for progressive information extraction in the frequency domain. PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for capturing local high-frequency information and Locally-Enhanced Axial Attention Transformers (LAT) for efficiently handling global low-frequency information. PFAN efficiently desmokes laparoscopic images even with limited training data. Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000, and visual quality on the Cholec80 dataset and retains only 629K parameters. Our code and models are made publicly available at: https://github.com/jlzcode/PFAN.

eess.IV↗

arXiv2023

Fine structures of radio bursts from flare star AD Leo with FAST observations

Radio bursts from nearby active M-dwarfs have been frequently reported and extensively studied in solar or planetary paradigms. Whereas, their sub-structures or fine structures remain rarely explored despite their potential significance in diagnosing the plasma and magnetic field properties of the star. Such studies in the past have been limited by the sensitivity of radio telescopes. Here we report the inspiring results from the high time-resolution observations of a known flare star AD Leo with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We detected many radio bursts in the two days of observations with fine structures in the form of numerous millisecond-scale sub-bursts. Sub-bursts on the first day display stripe-like shapes with nearly uniform frequency drift rates, which are possibly stellar analogs to Jovian S-bursts. Sub-bursts on the second day, however, reveal a different blob-like shape with random occurrence patterns and are akin to solar radio spikes. The new observational results suggest that the intense emission from AD Leo is driven by electron cyclotron maser instability which may be related to stellar flares or interactions with a planetary companion.

astro-ph.SR↗

arXiv2023

Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression

Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regression approaches learn the beauty of composition from annotated cropping boxes. However, the bias of annotations leads to quasi-trivial recomposing results, which has an obvious tendency to the average location of training samples. The crux of this predicament is that the task is naively treated as a box regression problem, where rare samples might be dominated by normal samples, and the composition patterns of rare samples are not well exploited. Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, ie, similar composition patterns. We propose a novel Contrastive Composition Clustering (C2C) to regularize the composition features by contrasting dynamically established similar and dissimilar pairs. In this way, common composition patterns of multiple images can be better summarized, which especially benefits the rare samples and endows our model with better generalizability to render nontrivial results. Extensive experimental results show the superiority of our model compared with prior arts. We also illustrate the philosophy of our design with an interesting analytical visualization.

cs.CV↗

arXiv2023

Accurate Image Restoration with Attention Retractable Transformer

Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of tokens are always from a dense area of the image. This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions. Obviously, this strategy could result in restricted receptive fields. To address this issue, we propose Attention Retractable Transformer (ART) for image restoration, which presents both dense and sparse attention modules in the network. The sparse attention module allows tokens from sparse areas to interact and thus provides a wider receptive field. Furthermore, the alternating application of dense and sparse attention modules greatly enhances representation ability of Transformer while providing retractable attention on the input image.We conduct extensive experiments on image super-resolution, denoising, and JPEG compression artifact reduction tasks. Experimental results validate that our proposed ART outperforms state-of-the-art methods on various benchmark datasets both quantitatively and visually. We also provide code and models at https://github.com/gladzhang/ART.

cs.CV↗

arXiv2022

Implications for additional plasma heating driving the extreme-ultraviolet late phase of a solar flare with microwave imaging spectroscopy

Extreme-ultraviolet late phase (ELP) refers to the second extreme-ultraviolet (EUV) radiation enhancement observed in certain solar flares, which usually occurs tens of minutes to several hours after the peak of soft X-ray emission. The coronal loop system that hosts the ELP emission is often different from the main flaring arcade, and the enhanced EUV emission therein may imply an additional heating process. However, the origin of the ELP remains rather unclear. Here we present the analysis of a C1.4 flare that features such an ELP, which is also observed in microwave wavelengths by the Expanded Owens Valley Solar Array (EOVSA). Similar to the case of the ELP, we find a gradual microwave enhancement that occurs about three minutes after the main impulsive phase microwave peaks. Radio sources coincide with both footpoints of the ELP loops and spectral fits on the time-varying microwave spectra demonstrate a clear deviation of the electron distribution from the Maxwellian case, which could result from injected nonthermal electrons or nonuniform heating to the footpoint plasma. We further point out that the delayed microwave enhancement suggests the presence of an additional heating process, which could be responsible for the evaporation of heated plasma that fills the ELP loops, producing the prolonged ELP emission.

astro-ph.SR↗

arXiv2022

Can we detect coronal mass ejections through asymmetries of Sun-as-a-star extreme-ultraviolet spectral line profiles?

Coronal mass ejections (CMEs) are the largest-scale eruptive phenomena in the solar system. Associated with enormous plasma ejections and energy release, CMEs have an important impact on the solar-terrestrial environment. Accurate predictions of the arrival times of CMEs at the Earth depend on the precise measurements on their three-dimensional velocities, which can be achieved using simultaneous line-of-sight (LOS) and plane-of-sky (POS) observations. Besides the POS information from routine coronagraph and extreme ultraviolet (EUV) imaging observations, spectroscopic observations could unveil the physical properties of CMEs including their LOS velocities. We propose that spectral line asymmetries measured by Sun-as-a-star spectrographs can be used for routine detections of CMEs and estimations of their LOS velocities during their early propagation phases. Such observations can also provide important clues for the detection of CMEs on other solar-like stars. However, few studies have concentrated on whether we can detect CME signals and accurately diagnose CME properties through Sun-as-a-star spectral observations. In this work, we constructed a geometric CME model and derived the analytical expressions for full-disk integrated EUV line profiles during CMEs. For different CME properties and instrumental configurations, full disk-integrated line profiles were synthesized. We further evaluated the detectability and diagnostic potential of CMEs from the synthetic line profiles. Our investigations provide important constraints on the future design of Sun-as-a-star spectrographs for CME detections through EUV line asymmetries.

astro-ph.SR↗

arXiv2022

Doppler shifts of spectral lines formed in the solar transition region and corona

Context. Emission lines formed in the transition region and corona show dominantly redshifts and blueshifts, respectively. Aims. We investigate the Doppler shifts in a 3D radiation MHD model of the quiet Sun and compare these to observed properties. We concentrate on Si IV 1394 A originating in the transition region and examine the Doppler shifts of several other spectral lines at different formation temperatures. Methods. We construct a radiation MHD model extending from the upper convection zone to the lower corona using the MURaM code. In this quiet Sun model the magnetic field is self-consistently maintained by the action of a small-scale dynamo. We synthesize the profiles of several optically thin emission lines, formed at temperatures from the transition region into the corona. We investigate the spatial structure and coverage of red- and blueshifts and how this changes with line-formation temperature. Results. The model successfully reproduces the observed change of average net Doppler shifts from red- to blueshifted from the transition region into the corona. In particular, the model shows a clear imbalance of area coverage of red- vs. blueshifts in the transition region of ca. 80% to 20%. We determine that (at least) four processes generate the systematic Doppler shifts in our model, including pressure enhancement in the transition region, transition region brightenings unrelated to coronal emission, boundaries between cold and hot plasma, and siphon-type flows. Conclusions. We show that there is not a single process that is responsible for the observed net Doppler shifts in the transition region and corona. Because current 3D MHD models do not yet fully capture the evolution of spicules, one of the key ingredients of the chromosphere, most probably these have still to be added to the list of processes responsible for the persistent Doppler shifts.

astro-ph.SR↗

arXiv2021

Structural phase transition of two-dimensional monolayer SnTe from artificial neural network

As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.

cond-mat.mtrl-sci↗