arXiv subjects

Wenhui Yu

Publications and source records attributed to Wenhui Yu.

At least 19 recordsLinked to original sources

arXiv2026

Twisted-pair unilateral reconnection: A unifying driver for magnetically powered astrophysical bursts

Magnetic reconnection in twisted loops has long been invoked as an engine powering energetic transients from black hole accretion to neutron star mergers, yet never directly observed. Here we report the first direct observation of the complete reconnection of this type in a solar flare. We find a magnetic loop twisted to about 540 degrees, far exceeding the 180 degrees twist assumed in existing simulations. This extreme twist inherently enables efficient multiple X-line reconnection, akin to the role of turbulence in contemporary theory. Remarkably, the intertwined end breaks unilaterally after reconnection (unlike symmetric breaking in simulations), forming open field lines that release hot plasma -- providing a promising mechanism for coronal generation or heating. We first detect hard X-ray emission from the current sheet, directly proving it as a particle accelerator. Moreover, we discover a power-law relationship between quasi-periodic oscillation frequency and magnetic field strength across solar flares, black hole binaries, active galactic nuclei, magnetars, and gamma-ray bursts. This relation identifies twisted-pair unilateral reconnection as a common burst mechanism and provides a natural ruler for cosmic magnetic fields. These findings establish an observational foundation for future reconnection theory and simulations, offering a unified framework for magnetically powered bursts.

astro-ph.HE↗

arXiv2026

Long thermonuclear burst driven thermal-viscous instability of accretion disk: triggering an outburst-like X-ray flare

We report on NICER and MAXI observations of a long-duration thermonuclear X-ray burst and a subsequent outburst-like X-ray flare from the neutron star low-mass X-ray binary MAXI J0911--655. Prior to the burst, the source was in a persistent low/hard state with a power-law-dominated spectrum ($\Gamma \sim 1.7$) and a mass accretion rate of $\sim 1\%$ of the Eddington limit. The long burst, detected by MAXI on 2020 May 22 (MJD 58991.7101), was rapidly followed up by NICER. From time-resolved spectroscopy of the cooling tail, we estimate an exponential decay time of $\approx43$ minutes, the ignition column depth of $\approx0.1\times 10^{12}~{\rm g ~cm^{-2}}$, the burst fluence of $\approx 1.1\times 10^{-4}~{\rm erg~cm^{-2}}$, and the total energy release of $\approx1.2\times10^{42}$ erg. Approximately one day after the burst onset, the 0.5-10 keV light curve unexpectedly re-brightened, initiating an outburst-like flare. During the peak of this flare, the persistent power-law flux increased from its pre-burst level of $\sim0.27\times10^{-9}~{\rm erg~cm^{-2}~s^{-1}}$ to $1.4\times10^{-9}~{\rm erg~cm^{-2}~s^{-1}}$. This flux enhancement was accompanied by significant spectral softening, with the photon index increasing to $\Gamma \sim 2.2$. Subsequently, the flux decayed and the source returned to its baseline low/hard state. The observed timescales and energetics suggest that intense irradiation from the long burst amplified the ongoing thermal-viscous accretion process. This heating drove an inside-out heating front that temporarily enhanced the mass accretion rate, providing compelling observational evidence of a thermonuclear burst directly modulating the accretion dynamics of its surrounding disk.

astro-ph.HE↗

arXiv2026

SOLAR: SVD-Optimized Lifelong Attention for Recommendation

Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its $O(N^2 d)$ time and memory cost in sequence length $N$ makes long-context modeling expensive and often forces truncation or other heuristics. Linear attention reduces complexity to $O(N d^2)$ by reordering computation through kernel feature maps, but this reformulation drops the softmax mechanism and shifts the attention score distribution. In recommender systems, low-rank structure in matrices is not a rare case, but rather the default inductive bias in its representation learning, particularly explicit in the user behavior sequence modeling. Leveraging this structure, we introduce SVD-Attention, which is theoretically lossless on low-rank matrices and preserves softmax while reducing attention complexity from $O(N^2 d)$ to $O(Ndr)$. With SVD-Attention, we propose SOLAR, SVD-Optimized Lifelong Attention for Recommendation, a sequence modeling framework that supports behavior sequences of ten-thousand scale and candidate sets of several thousand items in cascading process without any filtering. In Kuaishou's online recommendation scenario, SOLAR delivers a 0.68\% Video Views gain together with additional business metrics improvements.

cs.IR↗

arXiv2026

X-ray and radio observations of the AMXP MAXI J1957+032 covering the 2022-2025 outbursts

We presented a comprehensive multi-epoch timing and multiwavelength analysis of the accreting millisecond X-ray pulsar MAXI J1957+032, covering two major outbursts in 2022 and 2025. By reanalyzing the 2022 outburst data from the Neutron Star Interior Composition Explorer (NICER), we found the spin frequency and orbital parameters from the observations in 0.3-5 keV. For the 2025 outburst, we reported the detection of pulsations with the Einstein Probe (EP). Based on the $\sim$3-year baseline between these two outbursts, we measured a significant long-term spin-down rate of $\dot\nu = (-5.73 \pm 0.28) \times 10^{-14}~{\rm Hz~s^{-1}}$. Assuming that the quiescent spin-down is driven by magnetic dipole radiation, we inferred a spin-down luminosity of $L \approx 1.1 \times 10^{36}~{\rm erg~s^{-1}}$ and a surface dipolar magnetic field of $B \approx (7.3 - 10.4) \times 10^8$ G. Furthermore, we conducted a deep radio pulsation search with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during the X-ray quiescent state in 2024, resulting in a non-detection with a 7$\sigma$ flux density upper limit of 12.3 $\mu$Jy. This corresponds to a radio efficiency upper limit of $\xi < 2.8 \times 10^{-10}$, which is significantly lower than that of typical millisecond pulsars with a similar spin-down power. This profound radio pulsation faintness can be explained by two primary scenarios: either a geometric effect, wherein the pulsar's radio beam is directed away from our line of sight, or a physical suppression of the emission mechanism, potentially caused by a persistent low-level accretion flow during the X-ray quiescent state.

astro-ph.HE↗

arXiv2025

DUET: Dual Model Co-Training for Entire Space CTR Prediction

The pre-ranking stage plays a pivotal role in large-scale recommender systems but faces an intrinsic trade-off between model expressiveness and computational efficiency. Owing to the massive candidate pool and strict latency constraints, industry systems often rely on lightweight two-tower architectures, which are computationally efficient yet limited in estimation capability. As a result, they struggle to capture the complex synergistic and suppressive relationships among candidate items, which are essential for producing contextually coherent and diverse recommendation lists. Moreover, this simplicity further amplifies the Sample Selection Bias (SSB) problem, as coarse-grained models trained on biased exposure data must generalize to a much larger candidate space with distinct distributions. To address these issues, we propose \textbf{DUET} (\textbf{DU}al Model Co-Training for \textbf{E}ntire Space C\textbf{T}R Prediction), a set-wise pre-ranking framework that achieves expressive modeling under tight computational budgets. Instead of scoring items independently, DUET performs set-level prediction over the entire candidate subset in a single forward pass, enabling information-aware interactions among candidates while amortizing the computational cost across the set. Moreover, a dual model co-training mechanism extends supervision to unexposed items via mutual pseudo-label refinement, effectively mitigating SSB. Validated through extensive offline experiments and online A/B testing, DUET consistently outperforms state-of-the-art baselines and achieves improvements across multiple core business metrics. At present, DUET has been fully deployed in Kuaishou and Kuaishou Lite Apps, serving the main traffic for hundreds of millions of users.

cs.IR↗

arXiv2025

A transition from mixed-fuel to pure-helium thermonuclear bursts in Terzan 5 X-3/Swift J174805.3-244637

We presented a detailed analysis of seven thermonuclear X-ray bursts from Terzan 5 X-3/Swift J174805.3-244637, detected by NICER during the source's 2023 outburst. Our analysis reveals a clear evolution of burst properties, identifying four non-photospheric radius expansion (non-PRE) bursts, one PRE candidate occurring in a mixed hydrogen/helium environment, and two powerful PRE bursts from pure helium ignition. The time-resolved burst spectra were well described by a model including a variable persistent emission component, quantified by a factor $f_a$, due to the Poynting-Robertson drag. The strength of this interaction scales with burst luminosity: the enhancement is absent ($f_a \approx 1$) in the faintest bursts, becomes modest ($f_a \approx 1.5-2$) for the more luminous non-PRE burst and the PRE candidate, and is very strong ($f_a \approx 6-8$) during the pure-helium PRE bursts. This observed transition from mixed-fuel to pure-helium burning as the local mass accretion rate dropped below $\sim$10% of the Eddington limit, $\dot{m}_{\rm Edd}$, aligns with theoretical predictions. We verified this scenario with two independent methods. First, at the known distance to Terzan 5, the touchdown luminosities of both the pure helium PRE bursts and the mixed-fuel PRE candidate are consistent with reaching their respective, composition-dependent Eddington limits on the same plausible, massive neutron star of $\sim 2 M_\odot$. Second, the observed recurrence times of the non-PRE bursts were consistent with predictions for mixed-fuel burning.

astro-ph.HE↗

arXiv2025

Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems

Enhancing customer experience is essential for business success, particularly as service demands grow in scale and complexity. Generative artificial intelligence and Large Language Models (LLMs) have empowered intelligent interaction systems to deliver efficient, personalized, and 24/7 support. In practice, intelligent interaction systems encounter several challenges: (1) Constructing high-quality data for cold-start training is difficult, hindering self-evolution and raising labor costs. (2) Multi-turn dialogue performance remains suboptimal due to inadequate intent understanding, rule compliance, and solution extraction. (3) Frequent evolution of business rules affects system operability and transferability, constraining low-cost expansion and adaptability. (4) Reliance on a single LLM is insufficient in complex scenarios, where the absence of multi-agent frameworks and effective collaboration undermines process completeness and service quality. (5) The open-domain nature of multi-turn dialogues, lacking unified golden answers, hampers quantitative evaluation and continuous optimization. To address these challenges, we introduce WOWService, an intelligent interaction system tailored for industrial applications. With the integration of LLMs and multi-agent architectures, WOWService enables autonomous task management and collaborative problem-solving. Specifically, WOWService focuses on core modules including data construction, general capability enhancement, business scenario adaptation, multi-agent coordination, and automated evaluation. Currently, WOWService is deployed on the Meituan App, achieving significant gains in key metrics, e.g., User Satisfaction Metric 1 (USM 1) -27.53% and User Satisfaction Metric 2 (USM 2) +25.51%, demonstrating its effectiveness in capturing user needs and advancing personalized service.

cs.CL↗

arXiv2025

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples

We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

cs.IR↗

arXiv2025

Emission and Absorption Lines in Photospheric Radius Expansion Bursts of 4U 1820$-$30

We analyze the emission and absorption lines during photospheric radius expansion (PRE) X-ray bursts from the ultracompact binary 4U 1820--30, observed with the Neutron Star Interior Composition Explorer (NICER). Using Monte Carlo simulations to estimate the significance, we identified a 1 keV emission line from 14 bursts, a 3 keV absorption line from 12 bursts, and 1.6 keV absorption from one burst. By coadding the burst spectra at the maximum radius phase, we detected a 1.034 keV emission line with significance of $14.2\sigma$, and absorption lines at 1.64 and 3 keV with significances of $10.8\sigma$ and $11.7\sigma$, respectively. The observed energy shifts are consistent with the prediction from the burst-driven wind model, indicating that all three spectral features are produced by the PRE wind. Analysis of the ratios between the emission and absorption line energies suggests that the 1 keV feature is a superposition of several narrower Fe L-shell lines. To evaluate the scientific capabilities of the Hot Universe Baryon Surveyor (HUBS), we simulated mock observations of multiple narrow lines near 1 keV. The results demonstrate that HUBS is well suited for detailed studies of the 1 keV emission line during bursts, offering significant potential to advance our understanding of these phenomena.

astro-ph.HE↗

arXiv2024

Quenching and recovery of persistent X-ray emission during a superburst in 4U 1820$-$30

We report the superburst from 4U 1820--30 in 2021 observed by the Monitor of All-sky X-ray Image and Neutron star Interior Composition Explorer (NICER). During the tail of the superburst, we found that the NICER light curve unexpectedly increased from 1080 to 2204 ${\rm counts~s^{-1}}$ over 6.89 hr. From the time-resolved superburst spectra, we estimated the burst decay time of $\approx2.5$ hr, the ignition column depth of $\approx0.3\times 10^{12}~{\rm g ~cm^{-2}}$, the energy release per unit mass of $\approx2.4\times 10^{17}~{\rm erg~g^{-1}}$, the fluence of $\approx4.1\times 10^{-4}~{\rm erg~cm^{-2}}$, and the total energy release of $\approx3.5\times10^{42}$ erg. Notably, we found a gradual increase in the Componization flux from $8.9\times 10^{-10}~{\rm erg~s^{-1}~cm^{-2}}$ to the preburst level during the superburst. This increase can be interpreted as a consequence of superburst radiation depleting the inner accretion disk, leading to a near-complete quenching of the persistent emission. As the burst radiation decayed, the inner accretion disk gradually returned to its preburst state, as evidenced by the best-fit spectral parameters. Additionally, we observed a prominent absorption line that exhibited a gravitational redshift, shifting from 4.15 to 3.62 keV during the recovery phase of persistent emission. This absorption feature likely originates from the inner accretion disk rather than from burst emission on the neutron star (NS) surface. The observed changes in the absorption line energy suggest that the inner disk approached the NS to a distance as close as $\approx17$ km.

astro-ph.HE↗

arXiv2024

Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning

In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually assume entity independence when constructing contrastive views. We argue that these methods struggle to strike a balance between semantic invariance and view hardness across the dynamic training process, both of which are critical factors in graph contrastive learning. To address the above issues, we propose a novel GCL-based recommendation framework RGCL, which effectively maintains the semantic invariance of contrastive pairs and dynamically adapts as the model capability evolves through the training process. Specifically, RGCL first introduces decision boundary-aware adversarial perturbations to constrain the exploration space of contrastive augmented views, avoiding the decrease of task-specific information. Furthermore, to incorporate global user-user and item-item collaboration relationships for guiding on the generation of hard contrastive views, we propose an adversarial-contrastive learning objective to construct a relation-aware view-generator. Besides, considering that unsupervised GCL could potentially narrower margins between data points and the decision boundary, resulting in decreased model robustness, we introduce the adversarial examples based on maximum perturbations to achieve margin maximization. We also provide theoretical analyses on the effectiveness of our designs. Through extensive experiments on five public datasets, we demonstrate the superiority of RGCL compared against twelve baseline models.

cs.IR↗

arXiv2024

IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling

The lifelong user behavior sequence provides abundant information of user preference and gains impressive improvement in the recommendation task, however increases computational consumption significantly. To meet the severe latency requirement in online service, a short sub-sequence is sampled based on similarity to the target item. Unfortunately, items not in the sub-sequence are abandoned, leading to serious information loss. In this paper, we propose a new efficient paradigm to model the full lifelong sequence, which is named as \textbf{I}nteraction \textbf{F}idelity \textbf{A}ttention (\textbf{IFA}). In IFA, we input all target items in the candidate set into the model at once, and leverage linear transformer to reduce the time complexity of the cross attention between the candidate set and the sequence without any interaction information loss. We also additionally model the relationship of all target items for optimal set generation, and design loss function for better consistency of training and inference. We demonstrate the effectiveness and efficiency of our model by off-line and online experiments in the recommender system of Kuaishou.

cs.IR↗

arXiv2024

Discovery of a long thermonuclear X-ray burst from the ultra-compact binary 4U 1850$-$087

We report the detection of a long X-ray burst triggered on MJD 60171.65 from the ultra-compact binary 4U 1850$-$087 by the Monitor of All-sky X-ray Image and Neutron Star Interior Composition Explorer (NICER). We analyse the NICER data observed in between MJD 60095.19$-$60177.43, including one observation covered part of the long X-ray burst tail, i.e., $0.15-3.8$ hr after the trigger. The persistent spectra are quite similar and well described by a combination of multi-color disk blackbody, with the inner temperature of 0.5 keV, and a thermally comptonized continuum with the asymptotic power-law photon index of $\Gamma\sim2.2$, and electron temperature of $kT_{\rm e}\sim20-30$ keV. The persistent fluxes were around $3.8\times10^{-10}~{\rm erg~cm^{-2}~s^{-1}}$, corresponding to a local accretion rate of $1\%~\dot{m}_{\rm Edd}$. Part of time-resolved burst spectra show a clear deviation from the blackbody model, which can be improved by considering the enhanced persistent emission due to the Poynting-Robertson drag, or the reflected disk emission illuminated by the burst. From the burst flux during the cooling tail, we estimate the burst duration, $\tau \approx 0.78$ hr, the burst fluence, $E_\mathrm{b} \approx 4.1 \times 10^{41}$ ergs, and the ignition column depth, $y_{\rm ign}\approx 3.5\times10^{10}~{\rm g~cm^{-2}}$. We propose that the long X-ray burst is powered by unstable burning of pure helium in deep layer. Moreover, we identify significant 1 keV emission lines in the burst spectra, which may originate from the surrounding disk.

astro-ph.HE↗

arXiv2024

RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words, which has similar underlying pattern with ChatGPT, we design a new chat framework in item index level for the recommendation task. Our novelty mainly contains three parts: model, training and inference. For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information. For the training part, we adopt the two-stage paradigm of ChatGPT, including pre-training and fine-tuning. In the pre-training stage, we train GPT model by auto-regression. In the fine-tuning stage, we train the model with prompts, which include both the newly-generated results from the model and the user's feedback. For the inference part, we predict several user interests as user representations in an autoregressive manner. For each interest vector, we recall several items with the highest similarity and merge the items recalled by all interest vectors into the final result. We conduct experiments with both offline public datasets and online A/B test to demonstrate the effectiveness of our proposed method.

cs.IR↗

arXiv2023

NICER views moderate, strong, and extreme photospheric expansion bursts from the ultracompact X-ray binary 4U 1820$-$30

Type I X-ray bursts in the ultracompact X-ray binary 4U 1820$-$30 are powered by the unstable thermonuclear burning of hydrogen-deficient material. We report the detection of 15 type I X-ray bursts from 4U 1820$-$30 observed by NICER in between 2017 and 2023. All these bursts occurred in the low state for the persistent flux in the range of $2.5-8\times10^{-9}~{\rm erg~s^{-1}~cm^{-2}}$ in 0.1$-$250 keV. The burst spectra during the tail can be well explained by blackbody model. However, for the first $\sim$5 s after the burst onset, the time-resolved spectra showed strong deviations from the blackbody model. The significant improvement of the fit can be obtained by taking into account of the enhanced persistent emission due to the Poynting-Robterson drag, the extra emission modelled by another blackbody component or by the reflection from the surrounding accretion disk. The reflection model provides a self-consistent and physically motivated explanation. We find that the accretion disk density changed with 0.5 s delay as response to the burst radiation, which indicates the distortion of the accretion disk during X-ray bursts. From the time-resolved spectroscopy, all bursts showed the characteristic of photospheric radius expansion (PRE). We find one superexpansion burst with the extreme photospheric radius $r_{\rm ph}>10^3$ km and blackbody temperature of $\sim 0.2$ keV, thirteen strong PRE bursts for $r_{\rm ph}>10^2$ km, and one moderate PRE burst for $r_{\rm ph}\sim55$ km.

astro-ph.HE↗

arXiv2022

Type I X-ray bursts' spectra and fuel composition from the atoll and transient source 4U 1730$-$22

NICER observed two outbursts from the neutron star low-mass X-ray binary 4U~1730$-$22 in 2021 and 2022, which showed a similar spectral evolution in the hardness-intensity diagram. Seventeen type I X-ray bursts were identified in both outbursts. The X-ray burst spectra showed clear deviations from the blackbody model, firstly $\sim10$ s after onset. Adding the enhanced persistent emission due to the Poynting-Robertson drag or the reflection from the accretion disk both significantly improved the fitting results. We found that 12 out of 17 X-ray bursts showed the photospheric radius expansion (PRE) characteristic. Considering the nine PRE bursts out of ten X-ray bursts observed by Insight-HXMT, 78\% of bursts from 4U~1730--22 exhibited PRE. According to the burst rise time, the duration, the local accretion rate, and the burst fuel composition estimated from recurrence time, we propose that these PRE bursts were powered by pure helium. From the touchdown flux of PRE bursts, we estimate the source distance of $ d=7.54\pm{0.46} (X=0)$ kpc for a canonical neutron star with $M_{\rm NS}=1.4M_\odot$ and $R_{\rm NS}=10~{\rm km}$.

astro-ph.HE↗

arXiv2022

Discovery of a 584.65 Hz Burst Oscillation in the Low Mass X-Ray Binary 4U 1730--22

Type-I X-ray burst oscillations are powered by thermonuclear released on the neutron star (NS) surface in low mass X-ray binaries (LMXBs), where the burst oscillation frequencies are close to the NS spin rates. In this work, we report the detection of oscillation at 584.65 Hz during the cooling tail of a type-I X-ray bursts observed from the accreting NS LMXB 4U~1730--22 in 2022 March 20, by the \textit{ Neutron star Interior Composition Explorer} (\textit{NICER}) telescope. The oscillation signal showed a strong Leahy power, $P_{\rm m}\sim54.04$, around 584.65 Hz, which has single trial and multiple trials confidence levels of $7.05\sigma$ and $4.78\sigma$, respectively. The folded pulse profile of the oscillation in the 0.2--10 keV band showed a sinusoidal shape with the fraction amplitude rms of $(12.5\pm1.8)\%$. We found the oscillation frequency showed insignificant upward drifting, i.e., less than 0.3 Hz, during the cooling tail, similar as the behavior appearing in accreting millisecond X-ray pulsars (AMXP), and indicate the source could be an AMXP spinning at 1.71 ms.

astro-ph.HE↗

arXiv2020

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.

cs.LG↗