arXiv subjects

Hui Tian

Publications and source records attributed to Hui Tian.

At least 73 records · Page 4Linked to original sources

arXiv2024

Three types of solar coronal rain during magnetic reconnection between open and closed magnetic structures

Coronal rain (CR) is a crucial part of the mass cycle between the corona and chromosphere. It includes the flare-driven CR and two types of quiescent CR separately along the non-flaring active region closed loops and along the open structures, labeled as types I, II, and III CR, respectively. Among them, types I and III CR are generally associated with magnetic reconnection. In this study, employing data taken by the Solar Dynamics Observatory (SDO) and the Solar Upper Transition Region Imager (SUTRI) on 2022 October 11, we report three types of CR during an interchange reconnection between open and closed magnetic filed structures above the southeastern solar limb. The open and closed structures converge, with the formation of current sheet at the interface, and reconnect. The newly-formed closed and open structures then recede from the reconnection region. During the reconnection, coronal condensation occurs along the reconnecting closed loops, and falls toward the solar surface along both loop legs as the type II CR. Subsequently, condensation happens in the newly-formed closed loops, and moves down toward the solar surface along both loop legs as the type I CR. Magnetic dips of the reconnecting open structures form during the reconnection. In the dips, condensation occurs, and propagates along the open structures toward the solar surface as the type III CR. Our results suggest that the reconnection rate may be crucial for the formation of types I and III CR during the reconnection.

astro-ph.SR↗

arXiv2024

GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audio watermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%.

cs.CR↗

arXiv2024

Propagating Kink Waves in an Open Coronal Magnetic Flux Tube with Gravitational Stratification: Magnetohydrodynamic Simulation and Forward Modelling

Context. In the coronal open-field regions, such as coronal holes, there are many transverse waves propagating along magnetic flux tubes, generally interpreted as kink waves. Previous studies have highlighted their potential in coronal heating, solar wind acceleration, and seismological diagnostics of various physical parameters. Aims. This study aims to investigate propagating kink waves, considering both vertical and horizontal density inhomogeneity, using three-dimensional magnetohydrodynamic (MHD) simulations. Methods. We establish a 3D MHD model of a gravitationally stratified open flux tube, incorporating a velocity driver at the lower boundary to excite propagating kink waves. Forward modelling is conducted to synthesise observational signatures of the Fe ix 17.1 nm line. Results. It is found that resonant absorption and density stratification both affect the wave amplitude. When diagnosing the relative density profile with velocity amplitude, resonant damping needs to be properly considered to avoid possible underestimation. In addition, unlike standing modes, propagating waves are believed to be Kelvin-Helmholtz stable. In the presence of vertical stratification, however, phase mixing of transverse motions around the tube boundary can still induce small scales, partially dissipating wave energy and leading to a temperature increase, especially at higher altitudes. Moreover, forward modeling is conducted to synthesise observational signatures, revealing the promising potential of future coronal imaging spectrometers such as MUSE in resolving these wave-induced signatures. Also, the synthesised intensity signals exhibit apparent periodic variations, offering a potential method to indirectly observe propagating kink waves with current EUV imagers.

astro-ph.SR↗

arXiv2024

Simulated Coronal Mass Ejections on a young Solar-Type Star and the Associated Instantaneous Angular Momentum Loss

Coronal mass ejections (CMEs) on stars can change the stars' magnetic field configurations and mass loss rates during the eruption and propagation and therefore, may affect the stars' rotation properties on long time-scales. The dynamics of stellar CMEs and their influence on the stellar angular momentum loss rate are not yet well understood. In order to start investigating these CME-related aspects on other stars, we conducted a series of magnetohydrodynamic simulations of CMEs on a solar-type star of moderate activity levels. The propagation and evolution of the CMEs were traced in the three-dimensional outputs and the temporal evolution of their dynamic properties (such as masses, velocities, and kinetic energies) were determined. The simulated stellar CMEs are more massive and energetic than their solar analog, which is a result of the stronger magnetic field on the surface of the simulated star than that of the Sun. The simulated CMEs display masses ranging from ~10^16 g to ~10^18 g and kinetic energies from ~10^31 erg to ~10^33 erg. We also investigated the instantaneous influence of the CMEs to the star's angular momentum loss rate. Our results suggest that angular momentum can either be added to or be removed from the star during the evolution of CME events. We found a positive correlation between the amplitude of the angular momentum loss rate variation and the CME's kinetic energy as well as mass, suggesting that more energetic/massive CMEs have higher possibility to add angular momentum to the star.

astro-ph.SR↗

arXiv2024

Global Coronal Plasma Diagnostics Based on Multi-slit EUV Spectroscopy

Full-disk spectroscopic observations of the solar corona are highly desired to forecast solar eruptions and their impact on planets and to uncover the origin of solar wind. In this paper, we introduce a new multi-slit design (5 slits) to obtain extreme ultraviolet (EUV) spectra simultaneously. The selected spectrometer wavelength range (184-197 Å) contains several bright EUV lines that can be used for spectral diagnostics. The multi-slit approach offers an unprecedented way to efficiently obtain the global spectral data but the ambiguity from different slits should be resolved. Using a numerical simulation of the global corona, we primarily concentrate on the optimization of the disambiguation process, with the objective of extracting decomposed spectral information of six primary lines. This subsequently facilitates a comprehensive series of plasma diagnostics, including density (Fe XII 195.12/186.89 Å), Doppler velocity (Fe XII 193.51 Å), line width (Fe XII 193.51 Å) and temperature diagnostics (Fe VIII 185.21 Å, Fe X 184.54 Å, Fe XI 188.22 Å, Fe XII 193.51 Å). We find a good agreement between the forward modeling parameters and the inverted results at the initial eruption stage of a coronal mass ejection, indicating the robustness of the decomposition method and its immense potential for global monitoring of the solar corona.

astro-ph.SR↗

arXiv2024

The Solar Origin of an Intense Geomagnetic Storm on 2023 December 1st: Successive Slipping and Eruption of Multiple Magnetic Flux Ropes

The solar eruption that occurred on 2023 November 28 (SOL2023-11-28) triggered an intense geomagnetic storm on Earth on 2023 December 1. The associated Earth's auroras manifested at the most southern latitudes in the northern hemisphere observed in the past two decades. In order to explore the profound geoeffectiveness of this event, we conducted a comprehensive analysis of its solar origin to offer potential factors contributing to its impact. Magnetic flux ropes (MFRs) are twisted magnetic structures recognized as significant contributors to coronal mass ejections (CMEs), thereby impacting space weather greatly. In this event, we identified multiple MFRs in the solar active region and observed distinct slipping processes of the three MFRs: MFR1, MFR2, and MFR3. All three MFRs exhibit slipping motions at a speed of 40--137 km s$^{-1}$, extending beyond their original locations. Notably, the slipping of MFR2 extends to $\sim$30 Mm and initiate the eruption of MFR3. Ultimately, MFR1's eruption results in an M3.4-class flare and a CME, while MFR2 and MFR3 collectively produce an M9.8-class flare and another halo CME. This study shows the slipping process in a multi-MFR system, showing how one MFR's slipping can trigger the eruption of another MFR. We propose that the CME--CME interactions caused by multiple MFR eruptions may contribute to the significant geoeffectiveness.

astro-ph.SR↗

arXiv2024

Sun-as-a-star observations of obscuration dimmings caused by filament eruptions

Filament eruptions often lead to coronal mass ejections (CMEs) on the Sun and are one of the most energetic eruptive phenomena in the atmospheres of other late-type stars. However, the detection of filament eruptions and CMEs on stars beyond the solar system is challenging. Here we present six filament eruption cases on the Sun and show that filament material obscuring part of the solar disk can cause detectable dimming signatures in sun-as-a-star flux curves of He II 304 A. Those filament eruptions have similar morphological features, originating from small filaments inside active regions and subsequently strongly expanding to obscure large areas of the solar disk or the bright flare regions. We have tracked the detailed evolution of six obscuration dimmings and estimated the dimming properties, such as dimming depths, dimming areas, and duration. The largest dimming depth among the six events under study is 6.2% accompanied by the largest dimming area of 5.6\% of the solar disk area. Other events have maximum dimming depths in a range of around 1% to 3% with maximum areas varying between about 3% to 4% of the solar disk area. The duration of the dimming spans from around 0.4 hours to 7.0 hours for the six events under study. A positive correlation was found between the dimming depth and area, which may help to set constraint on the filament sizes in stellar observations.

astro-ph.SR↗

arXiv2024

Numerous Bidirectionally Propagating Plasma Blobs near the Reconnection Site of a Solar Eruption

Current sheet is a common structure involved in solar eruptions. However, it is observed in minority of the events and the physical properties of its fine structures during a solar eruption are rarely investigated. Here, we report an on-disk observation that displays 108 compact, circular or elliptic bright structures, presumably plasma blobs, propagating bidirectionally along a flare current sheet during a period of $\sim$24 minutes. From extreme ultraviolet images, we have investigated the temporal variation of the blob number around the flare peak time. The current sheet connects the flare loops and the erupting filament. The width, duration, projected velocity, temperature, and density of these blobs are $\sim$1.7$\pm$0.5\,Mm, $\sim$79$\pm$57\,s, $\sim$191$\pm$81\,\kms, $\sim$10$^{6.4\pm0.1}$ K, and $\sim$10$^{10.1\pm0.3}$ cm$^{-3}$, respectively. The reconnection site rises with a velocity of $\leqslant$69\,\kms. The observational results suggest that plasmoid instability plays an important role in the energy release process of solar eruptions.

astro-ph.SR↗

arXiv2024

LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification

The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection methods have been intensively studied for hyperspectral image (HSI) processing. This paper addresses this significant gap by introducing a cross-attention mechanism from the transformer architecture for the selection of HSI bands guided by LiDAR data. LiDAR provides high-resolution vertical structural information, which can be useful in distinguishing different types of land cover that may have similar spectral signatures but different structural profiles. In our approach, the LiDAR data are used as the "query" to search and identify the "key" from the HSI to choose the most pertinent bands for LiDAR. This method ensures that the selected HSI bands drastically reduce redundancy and computational requirements while working optimally with the LiDAR data. Extensive experiments have been undertaken on three paired HSI and LiDAR data sets: Houston 2013, Trento and MUUFL. The results highlight the superiority of the cross-attention mechanism, underlining the enhanced classification accuracy of the identified HSI bands when fused with the LiDAR features. The results also show that the use of fewer bands combined with LiDAR surpasses the performance of state-of-the-art fusion models.

eess.IV↗

arXiv2024

Surface Reconstruction from Point Clouds via Grid-based Intersection Prediction

Surface reconstruction from point clouds is a crucial task in the fields of computer vision and computer graphics. SDF-based methods excel at reconstructing smooth meshes with minimal error and artefacts but struggle with representing open surfaces. On the other hand, UDF-based methods can effectively represent open surfaces but often introduce noise, leading to artefacts in the mesh. In this work, we propose a novel approach that directly predicts the intersection points between line segment of point pairs and implicit surfaces. To achieve it, we propose two modules named Relative Intersection Module and Sign Module respectively with the feature of point pair as input. To preserve the continuity of the surface, we also integrate symmetry into the two modules, which means the position of predicted intersection will not change even if the input order of the point pair changes. This method not only preserves the ability to represent open surfaces but also eliminates most artefacts on the mesh. Our approach demonstrates state-of-the-art performance on three datasets: ShapeNet, MGN, and ScanNet. The code will be made available upon acceptance.

cs.CV↗

arXiv2024

Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder

Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated on analysing spectral and pixel characteristics within individual bands independently. These approaches overlook the potential benefits of integrating multiple data sources, such as Light Detection and Ranging (LiDAR), and is further challenged by the limited availability of labeled data in HSI processing, which represents a significant obstacle. To address these challenges, this paper introduces a novel unsupervised band selection framework that incorporates attention mechanisms and an Autoencoder for reconstruction-based band selection. Our methodology distinctively integrates HSI with LiDAR data through an attention score, using a convolutional Autoencoder to process the combined feature mask. This fusion effectively captures essential spatial and spectral features and reduces redundancy in hyperspectral datasets. A comprehensive comparative analysis of our innovative fused band selection approach is performed against existing unsupervised band selection and fusion models. We used data sets such as Houston 2013, Trento, and MUUFLE for our experiments. The results demonstrate that our method achieves superior classification accuracy and significantly outperforms existing models. This enhancement in HSI band selection, facilitated by the incorporation of LiDAR features, underscores the considerable advantages of integrating features from different sources.

cs.CV↗

arXiv2024

HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatial analysis. Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers. However, it avoids the associated high computational demands. HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis. This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers. HSIMamba were tested against three widely recognized datasets Houston 2013, Indian Pines, and Pavia University and demonstrated exceptional performance, surpassing existing state-of-the-art models in HSI classification. This method highlights the methodological innovation of HSIMamba and its practical implications, which are particularly valuable in contexts where computational resources are limited. HSIMamba redefines the standards of efficiency and accuracy in HSI classification, thereby enhancing the capabilities of remote sensing applications. Hyperspectral imaging has become a crucial tool for environmental surveillance, agriculture, and other critical areas that require detailed analysis of the Earth surface. Please see our code in HSIMamba for more details.

cs.CV↗

arXiv2024

Is it possible to detect coronal mass ejections on solar-type stars through extreme-ultraviolet spectral observations?

Stellar coronal mass ejections (CMEs) from host stars are an important factor that affects the habitability of exoplanets. Although their solar counterparts have been well observed for decades, it is still very difficult to find solid evidence of stellar CMEs. Using the spectral line profile asymmetry caused by the Doppler shift of erupting plasma, several stellar CME candidates have been identified from spectral lines formed at chromospheric or transition region temperatures of the stars. However, a successful detection of stellar CME signals based on the profile asymmetries of coronal lines is still lacking. It is unclear whether we can detect such signals. Here we construct an analytical model for CMEs on solar-type stars, and derive an expression of stellar extreme-ultraviolet (EUV) line profiles during CME eruptions. For different instrumental parameters, exposure times, CME propagation directions and stellar activity levels, we synthesized the corresponding line profiles of Fe IX 171.07 Å and Fe XV 284.16 Å. Further investigations provide constraints on the instrumental requirements for successful detection and characterization of stellar CMEs. Our results show that it is possible to detect stellar CME signals and infer their velocities based on spectral profile asymmetries using an EUV spectrometer with a moderate spectral resolution and signal-to-noise ratio. Our work provides important references for the design of future EUV spectrometers for stellar CME detection and the development of observation strategies.

astro-ph.SR↗

arXiv2024

Formation of Fan-spine Magnetic Topology through Flux Emergence and Subsequent Jet Production

Fan-spine magnetic structure, as a fundamental three-dimensional topology in magnetic reconnection theory, plays a crucial role in producing solar jets. However, how fan-spine configurations form in the solar atmosphere remains elusive. Using the Chinese H$α$ Solar Explorer (CHASE) and the Solar Dynamics Observatory (SDO), we present a case study on the complete buildup of fan-spine topology driven by flux emergence and the subsequent jet production. Two fan-spine structures and the two associated null points are present. Variations in null-point heights and locations were tracked over time during flux emergence. The north fan-spine structure is found to be created through magnetic reconnection between the newly emerged flux and the background field. Gentle reconnection persistently occurs after formation of the north fan-spine structure, resulting in weak plasma outflows. Subsequently, as flux emergence and magnetic helicity injection continue, the formation and eruption of mini-filaments after reconnection at the quasi-separatrix layer between the two nulls trigger three homologous jets. The CHASE observations reveal that the circular flare ribbon, inner bright patch, and remote brightening all exhibit redshifted signatures during these jet ejections. This work unveils the key role of flux emergence in the formation of fan-spine topology, and highlights the importance of mini-filaments for subsequent jet production.

astro-ph.SR↗

arXiv2023

SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction

Learning-based surface reconstruction based on unsigned distance functions (UDF) has many advantages such as handling open surfaces. We propose SuperUDF, a self-supervised UDF learning which exploits a learned geometry prior for efficient training and a novel regularization for robustness to sparse sampling. The core idea of SuperUDF draws inspiration from the classical surface approximation operator of locally optimal projection (LOP). The key insight is that if the UDF is estimated correctly, the 3D points should be locally projected onto the underlying surface following the gradient of the UDF. Based on that, a number of inductive biases on UDF geometry and a pre-learned geometry prior are devised to learn UDF estimation efficiently. A novel regularization loss is proposed to make SuperUDF robust to sparse sampling. Furthermore, we also contribute a learning-based mesh extraction from the estimated UDFs. Extensive evaluations demonstrate that SuperUDF outperforms the state of the arts on several public datasets in terms of both quality and efficiency. Code url is https://github.com/THHHomas/SuperUDF.

cs.CV↗

arXiv2023

Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction

Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays. Classic solutions, such as Poisson surface reconstruction, require point normals as extra input to perform reasonable results. Modern transformer-based methods can work without normals, while the results are less fine-grained due to limited encoding performance in local fusion from discrete points. We introduce a novel normalized matrix attention transformer (Tensorformer) to perform high-quality reconstruction. The proposed matrix attention allows for simultaneous point-wise and channel-wise message passing, while the previous vector attention loses neighbor point information across different channels. It brings more degree of freedom in feature learning and thus facilitates better modeling of local geometries. Our method achieves state-of-the-art on two commonly used datasets, ShapeNetCore and ABC, and attains 4% improvements on IOU on ShapeNet. Code can be accessed https://github.com/THHHomas/Tensorformer6.

cs.GR↗

arXiv2023

Performance Analysis and Optimization of Reconfigurable Multi-Functional Surface Assisted Wireless Communications

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS (MF-RIS). Different from conventional reflecting-only RIS, the proposed MF-RIS is capable of supporting multiple functions with one surface, including signal reflection, amplification, and energy harvesting. As such, our MF-RIS is able to overcome the double-fading attenuation by harvesting energy from incident signals. Through theoretical analysis, we derive the achievable capacity of an MF-RIS-aided communication network. Compared to the capacity achieved by the existing self-sustainable RIS, we derive the number of reflective elements required for MF-RIS to outperform self-sustainable RIS. To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks. Specifically, we solve a non-convex optimization problem by jointly designing the transmit beamforming and MF-RIS coefficients. As an extension, we investigate a resource allocation problem in a practical scenario with imperfect channel state information. By approximating the semi-infinite constraints with the S-procedure and the general sign-definiteness, we propose a robust beamforming scheme to combat the inevitable channel estimation errors. Finally, numerical results show that: 1) compared to the self-sustainable RIS, MF-RIS can strike a better balance between energy self-sustainability and throughput improvement; and 2) unlike reflecting-only RIS which can be deployed near the transmitter or receiver, MF-RIS should be deployed closer to the transmitter for higher spectrum efficiency.

eess.SP↗

arXiv2023

Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks

As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount of data is humongous. To process data in real-time while protecting user privacy, federated learning (FL) has been regarded as an enabling technique to push edge intelligence into IoT networks with massive devices. However, FL latency increases dramatically due to the increase of the number of parameters in deep neural network and the limited computation and communication capabilities of IoT devices. To address this issue, we propose a semi-federated learning (SemiFL) paradigm in which network pruning and over-the-air computation are efficiently applied. To be specific, each small base station collects the raw data from its served sensors and trains its local pruned model. After that, the global aggregation of local gradients is achieved through over-the-air computation. We first analyze the performance of the proposed SemiFL by deriving its convergence upper bound. To reduce latency, a convergence-constrained SemiFL latency minimization problem is formulated. By decoupling the original problem into several sub-problems, iterative algorithms are designed to solve them efficiently. Finally, numerical simulations are conducted to verify the effectiveness of our proposed scheme in reducing latency and guaranteeing the identification accuracy.

cs.IT↗