arXiv subjects

Qingmei Wang

Publications and source records attributed to Qingmei Wang.

5 recordsLinked to original sources

arXiv2026

Observational Evidence for Counter-helicity Magnetic Reconnection in a Solar Eruption

Magnetic reconnection between coronal magnetic systems carrying opposite self-helicity may play a role in solar eruptions, but observational evidence remains limited. We investigate an M7.0 flare in NOAA Active Region 13615 on 2024 March 28 using multiwavelength observations and nonlinear force-free field extrapolations. The reconstructed coronal field reveals a low-lying positive-helicity core field beneath an overlying magnetic system of opposite sign. During the eruption, the footpoint connectivity of these two magnetic systems changes markedly: field lines rooted in the western footpoint region change from positive to negative helicity, and the positive-helicity domain is substantially reduced. These changes are accompanied by a remote chromospheric brightening, intermittent EUV stripe-like brightenings extending from the source region toward the remote chromospheric brightening, the subsequent formation of large-scale coronal loops, and a weak outer hard X-ray source located at a footpoint of the core field. Together, these results suggest that the eruption was closely associated with reconnection between the core field and the overlying counter-helicity system, providing observational evidence that counter-helicity reconnection can contribute to the destabilization of eruptive solar magnetic fields.

astro-ph.SR↗

arXiv2026

GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization

The placement of normalization layers, specifically Pre-Norm and Post-Norm, remains an open question in Transformer architecture design. In this work, we rethink these approaches through the lens of manifold optimization, interpreting the outputs of the Feed-Forward Network (FFN) and attention layers as update directions in optimization. Building on this perspective, we introduce GeoNorm, a novel method that replaces standard normalization with geodesic updates on the manifold. Furthermore, analogous to learning rate schedules, we propose a layer-wise update decay for the FFN and attention components. Comprehensive experiments demonstrate that GeoNorm consistently outperforms existing normalization methods in Transformer models. Crucially, GeoNorm can be seamlessly integrated into standard Transformer architectures, achieving performance improvements with negligible additional computational cost.

cs.LG↗

arXiv2025

Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization

Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we learn structure-enhanced TPPs with the help of Gromov-Wasserstein (GW) regularization, which imposes clustering structures on the sequence-level embeddings of the TPPs in the maximum likelihood estimation framework.In the training phase, the proposed method leverages a nonparametric TPP kernel to regularize the similarity matrix derived based on the sequence embeddings. In large-scale applications, we sample the kernel matrix and implement the regularization as a Gromov-Wasserstein (GW) discrepancy term, which achieves a trade-off between regularity and computational efficiency.The TPPs learned through this method result in clustered sequence embeddings and demonstrate competitive predictive and clustering performance, significantly improving the model interpretability without compromising prediction accuracy.

cs.LG↗

arXiv2025

Moving Plasma Structures and Possible Driving Mechanisms of Solar Microflares Observed with High-Resolution Coronal Imaging

Solar microflares are ubiquitous in the solar corona, yet their driving mechanisms remain a subject of ongoing debate. Using high-resolution coronal observations from the Solar Orbiter's Extreme Ultraviolet Imager (EUI), we identified about a dozen distinct moving plasma structures (hereafter, `` tiny ejections'') originating from the centers of three homologous microflares out of four successive events. These tiny ejections propagate roughly perpendicular to the flaring loops. They often originate as dot-like structures with a length scale of approximately $10^{3}$ km. While these initial dot-like shapes are observable in EUI images, they remain undetectable in the images captured by the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory. As they propagate, these dot-like structures consistently evolve into loop-like formations, possibly due to the heating of the surrounding magnetic field. Rather than being generated by a series of flux rope eruptions, the tiny ejections appear to result from small-angle magnetic reconnections within a bipolar field. Thus, the microflares associated with these ejections may be driven by magnetic reconnection within braided fields, a process similar to the proposed nanoflare mechanism and distinct from the standard large-scale flare model.

astro-ph.SR↗

arXiv2025

A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes

An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches. The code is available at \url{https://github.com/qingmeiwangdaily/BADMM_TPP}.

cs.LG↗