arXiv subjects

Yang Guo

Publications and source records attributed to Yang Guo.

At least 37 records · Page 2Linked to original sources

arXiv2025

Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope

We investigate the acceleration and transport of electrons in the highly fine-structured current sheet that develops during magnetic flux rope (MFR) eruptions. Our work combines ultra-resolved MHD simulations of MFR eruption, with test-particle studies performed using the guiding center approximation. Our grid-adaptive, fully three-dimensional, high-resolution magnetohydrodynamic simulations model MFR eruptions that form complex current sheet topologies, serving as background electromagnetic fields for particle acceleration. Within the current sheet, tearing-mode instabilities give rise to mini flux ropes. Electrons become temporarily trapped within these elongated structures, undergoing acceleration and transport processes that significantly differ from those observed in two-dimensional or two-and-a-half-dimensional simulations. Our findings reveal that these fine-scale structures act as efficient particle accelerators, surpassing the acceleration efficiency of single X-line reconnection events, and are capable of energizing electrons to energies exceeding 100 keV. High-energy electrons accelerated in different mini flux ropes follow distinct trajectories due to spatially varying magnetic field connectivity, ultimately precipitating onto opposite sides of flare ribbons. Remarkably, double electron sources at the flare ribbons originate from different small flux rope acceleration regions, rather than from the same reconnecting field line as previously suggested. Distinct small flux ropes possess opposite magnetic helicity to accelerate electrons to source regions with different magnetic polarities, establishing a novel conjugate double source configuration. Furthermore, electrons escaping from the lower regions exhibit a broken power-law energy spectrum.

astro-ph.SR↗

arXiv2025

The Kinematical Behavior of Solar Eruptive Filaments Affected by the Poloidal Magnetic Field

Kinematics of solar eruptive filaments is one of the important diagnostic parameters for predicting whether solar eruptions would induce geomagnetic storms. Particularly, some geomagnetic storms might be induced by solar filament eruptions originating from unexpected surface source regions because of non-radial ejection. The non-radial ejection of filaments has received widespread attention but remains inconclusive. We select two eruptive filaments, both of which are supported by flux ropes, as indicated by the hot channel structures seen in the 94 Å images and the hook-shaped brightenings where the filament material falls back. We measure the three-dimensional ejection trajectory of the eruptive filaments by integrating the simultaneous observations from SDO and STEREO. Furthermore, we calculate the distribution of the poloidal field along the ejection path and compare it to the ejection acceleration. It is revealed that the reinforcement of the poloidal magnetic field may lead to the suppression of the acceleration, with the acceleration resuming its increase only when the poloidal field diminishes to a certain level. Additionally, we compute the spatial distribution of the poloidal field in various directions and find that the poloidal magnetic field above the filaments is asymmetric. For both investigated events, the filaments appear to eject towards the side where the poloidal magnetic field is weaker, indicating that the eruptive filaments tend to propagate along the side with weaker strapping force. This may provide a new explanation for the inclined ejection of filaments.

astro-ph.SR↗

arXiv2025

SIP-IFVM: An observation-based magnetohydrodynamic model of coronal mass ejection

Currently, achieving a balance between computational efficiency, accuracy, and numerical stability in CME simulations, particularly in the sub-Alfv{'e}nic coronal region, remains a significant challenge. This paper aims to address the challenge by integrating observational data and developing advanced numerical algorithms, focusing on reproducing large-scale CME evolutions that are consistent with observations in the coronal region. Based on the recently developed fully implicit thermodynamic MHD coronal model (Wang et al. 2025a), we further use an observation-based RBSL flux rope to trigger a CME event during CR 2111. Additionally, we improve the temporal accuracy using a 2nd-order accurate ESDIRK2 method, with the intermediate stage solutions computed by the 2nd-order accurate BDF2 pseudo-time marching method. To enhance the numerical stability of ESDIRK2, we apply approximate linearisation in the implicitly solved intermediate stages. Furthermore, we adjust the time-evolving magnetic field B1 to zero at the end of each physical time step to further validate the extended magnetic field decomposition approach proposed by (Wang et al. 2025a). It is noticed that the model successfully reproduces the CME evolution consistent with white-light coronagraph observations, enables faster-than-real-time CME propagation simulations from solar surface to 0.1 AU using only a few dozen CPU cores on approximately 1 million grid cells, and remains numerically stable in CME simulations involving low-\b{eta} regions. The simulation results show that this novel MHD coronal model, combined with an observation-based magnetic flux rope, is sufficiently numerically stable and computationally efficient to reproduce real CME events propagating through the sub-Alfv{é}nic coronal region. Thus, the observation-based CME model is well suited for practical applications in daily space weather forecasting.

astro-ph.SR↗

arXiv2025

Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection

Both transduction and rejection have emerged as important techniques for defending against adversarial perturbations. A recent work by Goldwasser et al. showed that rejection combined with transduction can give provable guarantees (for certain problems) that cannot be achieved otherwise. Nevertheless, under recent strong adversarial attacks, their work was shown to have low performance in a practical deep-learning setting. In this paper, we take a step towards realizing the promise of transduction+rejection in more realistic scenarios. Our key observation is that a novel application of a reduction technique by Tramèr, which was until now only used to demonstrate the vulnerability of certain defenses, can be used to actually construct effective defenses. Theoretically, we show that a careful application of this technique in the transductive setting can give significantly improved sample-complexity for robust generalization. Our theory guides us to design a new transductive algorithm for learning a selective model; extensive experiments using state of the art attacks show that our approach provides significantly better robust accuracy (81.6% on CIFAR-10 and 57.9% on CIFAR-100 under $l_\infty$ with budget 8/255) than existing techniques.

cs.LG↗

arXiv2025

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds

Retrieval-augmented generation (RAG) has seen many empirical successes in recent years by aiding the LLM with external knowledge. However, its theoretical aspect has remained mostly unexplored. In this paper, we propose the first finite-sample generalization bound for RAG in in-context linear regression and derive an exact bias-variance tradeoff. Our framework views the retrieved texts as query-dependent noisy in-context examples and recovers the classical in-context learning (ICL) and standard RAG as the limit cases. Our analysis suggests that an intrinsic ceiling on generalization error exists on RAG as opposed to the ICL. Furthermore, our framework is able to model retrieval both from the training data and from external corpora by introducing uniform and non-uniform RAG noise. In line with our theory, we show the sample efficiency of ICL and RAG empirically with experiments on common QA benchmarks, such as Natural Questions and TriviaQA.

cs.LG↗

arXiv2025

CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness

The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code is available at https://github.com/CRAFTinTSF/CRAFT.

cs.LG↗

arXiv2025

Quantum spin excitations in a dual-core magnetic molecule

Magnetic excitations are important quantum phenomena in magnetic systems and have been widely studied in individual magnetic atoms and molecules as well as their assembled structures over the past few decades. Using scanning tunneling microscopy/spectroscopy (STM/S) combined with density functional theory (DFT) and the state-of-the-art ab initio wavefunction calculations, we investigated the properties of a novel dual-core Cr2Br6 molecule, which consists of two Cr ions coupled via superexchange through a single near-90° Cr-Br-Cr scissors bond. Under zero magnetic field, we observed a Fano peak with multi-steps through STS. When an external magnetic field is applied, some steps exhibit additional splitting, while others change little. We find that the Cr2Br6, exhibits a spin-degenerate ground state, and the complex peak splitting arises from the coexistence of vibrational and magnetic excitations in the molecule. Our results reveal rich quantum spin behavior in a well-defined two-core magnetic trihalide complex at the atomic scale, offering not only a minimal model for superexchange-coupled multi-spin quantum excitations but also a possible foundational unit for future molecule-based quantum functionalities.

cond-mat.mtrl-sci↗

arXiv2025

Data-constrained Magnetohydrodynamic Simulation of a Filament Eruption in a Decaying Active Region 13079 on a Global Scale

Filaments are special plasma phenomena embedded in the solar atmosphere, characterized by unique thermodynamic properties and magnetic structures. Magnetohydrodynamic (MHD) simulations are useful to investigate the eruption mechanisms of filaments. We conduct a data-constrained zero-$β$ MHD simulation in spherical coordinates to investigate a C3.5 class flare triggered by an eruptive filament on 2022 August 15 in a decaying weak active region 13079. We reconstruct the three-dimensional coronal magnetic field using vector magnetograms and synoptic maps from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI). We transform vector magnetic field into Stonyhurst heliographic spherical coordinates combined with a synoptic map and constructed a potential field source surface (PFSS) model with a magnetic flux rope (MFR) embedded using the Regularized Biot--Savart Laws (RBSL). Subsequently, we conduct a spherical zero-$β$ MHD simulation using the Message Passing Interface Adaptive Mesh Refinement Versatile Advection Code (MPI-AMRVAC) and replicated the entire dynamic process of the filament eruption consistent with observations. With the calculation of time-distance profile, Qusai-Separatrix Layers (QSL), and synthetic radiation from simulated current density, we find a good agreement between our simulation and observations in terms of dynamics and magnetic topology. Technically, we provide a useful method of advanced data-constrained simulation of weak active regions in spherical coordinates. Scientifically, the model allows us to quantitatively describe and diagnose the entire process of filament eruption.

astro-ph.SR↗

arXiv2025

Observational Study of Recurrent Jets: Evolution of Magnetic Flux, Current, and Helicity

We observed three recurrent blowout jets in an active regio with Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). Using Helioseismic Magnetic Imager (HMI) data. We found that the magnetic flux of an emerging negative pole increases steadily before declining just as the jets erupt. Certain physical quantities, like the total unsigned vertical current, align with the periodicity of the jets. The differential affine velocity of the vector magnetograms reveals strong shear around the negative pole. The Doppler velocity map, calculated from the H$α$ spectra observed by the Chinese H$α$ Solar Explorer (CHASE), shows upflows with large initial velocity before it can be observed by AIA. The magnetic field derived from the nonlinear force-free field (NLFFF) model suggests a topology akin to fan-spine structure, consistent with AIA images. We calculated the evolution of volumetric helicity ratio using the NLFFF model and found its phase aligns with the jet flux in AIA 171 Å. These results suggest that recurrent jets may be triggered by the accumulation and release of energy and helicity, driven by emergence, shearing and cancellation of photospheric magnetic field.

astro-ph.SR↗

arXiv2025

UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network

Transformer-based trackers have achieved promising success and become the dominant tracking paradigm due to their accuracy and efficiency. Despite the substantial progress, most of the existing approaches tackle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been greatly overlooked, which hampers trackers' ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack utilizes a transformer encoder to perform feature interaction between template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable or not. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, making the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods. Our code is available at https://github.com/ManOfStory/UncTrack.

cs.CV↗

arXiv2025

Astronomical image denoising by self-supervised deep learning and restoration processes

Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines.

astro-ph.IM↗

arXiv2025

LintLLM: An Open-Source Verilog Linting Framework Based on Large Language Models

Code Linting tools are vital for detecting potential defects in Verilog code. However, the limitations of traditional Linting tools are evident in frequent false positives and redundant defect reports. Recent advancements in large language models (LLM) have introduced new possibilities in this area. In this paper, we propose LintLLM, an open-source Linting framework that utilizes LLMs to detect defects in Verilog code via Prompt of Logic-Tree and Defect Tracker. Furthermore, we create an open-source benchmark using the mutation-based defect injection technique to evaluate LLM's ability in detecting Verilog defects. Experimental results show that o1-mini improves the correct rate by 18.89\% and reduces the false-positive rate by 15.56\% compared with the best-performing EDA tool. Simultaneously, LintLLM operates at less than one-tenth of the cost of commercial EDA tools. This study demonstrates the potential of LLM as an efficient and cost-effective Linting tool for hardware design. The benchmark and experimental results are open-source at URL: https://github.com/fangzhigang32/Static-Verilog-Analysis

cs.AR↗

arXiv2024

Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning

We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over the past eight years. This unique dataset supports the development and evaluation of multimodal large language models and preference-based fine-tuning algorithms for humorous caption generation. We propose novel benchmarks for judging the quality of model-generated captions, utilizing both GPT4 and human judgments to establish ranking-based evaluation strategies. Our experimental results highlight the limitations of current fine-tuning methods, such as RLHF and DPO, when applied to creative tasks. Furthermore, we demonstrate that even state-of-the-art models like GPT4 and Claude currently underperform top human contestants in generating humorous captions. As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation.

cs.LG↗

arXiv2024

Failure of a solar filament eruption caused by magnetic reconnection with overlying coronal loops

Failure of a filament eruption caused by magnetic reconnection between the erupting filament and the overlying magnetic field has been previously proposed in numerical simulations. It is, however, rarely observed. In this study, we report the reconnection between an erupting filament and its overlying coronal loops, that results in the failure of the filament eruption. On 2023 September 24, a filament was located in active region 13445. It slowly rose, quickly erupted, rapidly decelerated, and finally stopped, with an untwisting motion. As a failed eruption, the event is associated with an M4.4 flare but no coronal mass ejection. During the eruption, the filament became bright, and the overlying loops appeared first in the high-temperature channels. They have average temperatures of ~12.8 and ~9.6MK, respectively, indicating that both of them were heated. Two sets of new loops, separately connecting the filament endpoints and the overlying loop footpoints, then formed. Subsequently, the heated overlying loops were seen sequentially in the low-temperature channels, showing the cooling process, which is also supported by the light curves. Plasmoids formed, and propagated bidirectionally along the filament and the overlying loops, indicating the presence of plasmoid instability. These results suggest that reconnection occurs between the erupting filament and the overlying loops. The erupting filament eventually disappeared, with the appearance of more newly-formed loops. We propose that the reconnection between the erupting filament and the overlying loops ruins the filament completely, and hence results in the failed eruption.

astro-ph.SR↗

arXiv2024

SDSPT2s: SDSPT2 with Selection

As an approximation to SDSCI [static-dynamic-static (SDS) configuration interaction (CI), a minimal MRCI; Theor. Chem. Acc. 133, 1481 (2014)], SDSPT2 [Mol. Phys. 115, 2696 (2017)] is a CI-like multireference (MR) second-order perturbation theory (PT2) that treats single and multiple roots on an equal footing. This feature permits the use of configuration selection over a large complete active space (CAS) $P$ to end up with a much reduced reference space $\tilde{P}$, which is connected only with a portion ($\tilde{Q}_1$) of the full first-order interacting space $Q$ connected to $P$. The effective interacting $\tilde{Q}$ space can further be truncated by an integral-based cutoff threshold. With marginal loss of accuracy, the selection-truncation procedure, along with an efficient evaluation and storage of internal contraction coefficients, renders SDSPT2s (SDSPT2 with selection) applicable to systems that cannot be handled by the parent CAS-based SDSPT2, as demonstrated by several challenging showcases.

physics.chem-ph↗

arXiv2024

QUEST\#4X: an extension of QUEST\#4 for benchmarking multireference wavefunction methods

Given a number of datasets for evaluating the performance of single reference methods for the low-lying excited states of closed-shell molecules, a comprehensive dataset for assessing the performance of multireference methods for the low-lying excited states of open-shell systems is still desired. For this reason, we propose an extension (QUEST\#4X) of the radial subset of QUEST\#4 [J. Chem. Theory Comput. 2020, 16, 3720] to cover 110 doublet and 39 quartet excited states. Near-exact results obtained by iCIPT2 (iterative configuration interaction with selection and second-order perturbation correction) are taken as benchmark to calibrate SDSCI (static-dynamic-static configuration interaction) and SDSPT2 (static-dynamic-static second-order perturbation theory), which are minimal MRCI and CI-like perturbation theory, respectively. It is found that SDSCI is very close in accuracy to ic-MRCISD (internally contracted multireference configuration interaction with singles and doubles), although its computational cost is just that of one iteration of the latter. Unlike most variants of MRPT2, SDSPT2 treats single and multiple states in the same way, and performs similarly as MS-NEVPT2 (multi-state n-electron valence second-order perturbation theory). These findings put SDSCI and SDSPT2 on a firm basis.

physics.chem-ph↗

arXiv2024

Duality of particle-hole and particle-particle theories for strongly correlated electronic systems

We propose a novel approach to electron correlation for multireference systems. It is based on particle-hole (ph) and particle-particle (pp) theories in the second-order, developed in the random phase approximation (RPA) framework for multireference wavefunctions. We show a formal correspondence (duality), between contributions to the correlation energy in the ph and pp pictures. It allows us to describe correlation energy by rigorously combining pp and ph terms, avoiding correlation double counting. The multireference ph, pp, and the combined correlation methods are applied to ground and excited states of systems in the intermediate and strong correlation regimes and compared with the multireference second-order perturbation method (MRPT2). It is shown that the pp approximation fails to describe dissociation of multiple bonds. The ph-pp combined method is overall superior to both ph and pp alone. It parallels good accuracy of the second-order perturbation theory for ground states and singlet excitation energies. For the singlet-triplet gaps of biradicals its accuracy is significantly better. This is impressive, taking into account that it relies only on one- and two-body density matrices, while MRPT2 methods typically require density matrices up to the four-body.

physics.chem-ph↗

arXiv2024

Modularized data-driven approximation of the Koopman operator and generator

Extended Dynamic Mode Decomposition (EDMD) is a widely-used data-driven approach to learn an approximation of the Koopman operator. Consequently, it provides a powerful tool for data-driven analysis, prediction, and control of nonlinear dynamical (control) systems. In this work, we propose a novel modularized EDMD scheme tailored to interconnected systems. To this end, we utilize the structure of the Koopman generator that allows to learn the dynamics of subsystems individually and thus alleviates the curse of dimensionality by considering observable functions on smaller state spaces. Moreover, our approach canonically enables transfer learning if a system encompasses multiple copies of a model as well as efficient adaption to topology changes without retraining. We provide finite-data bounds on the estimation error using tools from graph theory. The efficacy of the method is illustrated by means of various numerical examples.

eess.SY↗