arXiv subjects

Xing Gao

Publications and source records attributed to Xing Gao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Photometric and Spectroscopic Studies of Four Low Mass-ratio Contact Binaries with Period Longer than 0.7 days

Photometric and spectroscopic studies of four long-period low mass-ratio contact binaries, V0508 And, V0844 Aur, V0699 Cep, and NSVS 6259046, are performed. V0508 And, V0844 Aur, and V0699 Cep are found to be A-type low-mass-ratio medium-contact binaries, while NSVS 6259046 is found to be an A-type deep-contact binary. O - C analysis indicates no long-term variation in V0844 Aur. However, the orbital periods of the other three targets are increasing. We conclude that V0844 Aur, V0699 Cep and NSVS 6259046 are magnetically active, as evidenced by the presence and variable nature of the O'Connell effect in these systems. By analyzing the LAMOST spectroscopic data, we find out that NSVS 6259046 and V0508 And exhibit no chromospheric activity on the dates the LAMOST spectra were taken, while the low signal-to-noise ratio in LAMOST data for V0844 Aur prevents us from obtaining reliable results. We discover that V0699 Cep is an early-type contact binary with chromospheric activity. Their initial masses and ages are calculated. All four systems are determined to be currently stable. We collect 217 contact binaries with both spectroscopic and photometric observations, and compare the differences between short-period and long-period systems in terms of mass-luminosity relation and mass-radius relation, using 0.7 days as the period boundary.

astro-ph.SR↗

arXiv2025

Itô formula for reduced rough paths

The Itô formula, also known as the change-of-variables formula, is a cornerstone of Itô stochastic calculus. Over time, this formula has been extended to apply to random processes for which classical calculus is insufficient. Since every random process exhibits some degree of regularity, rough path theory provides a natural framework for treating them uniformly. In this paper, we extend the Itô formula for reduced rough paths, broadening the range of roughness from the previously known case $\frac{1}{3} < α\leq \frac{1}{2}$ to the more singular regime $\frac{1}{4} < α\leq \frac{1}{3}$.

math.PR↗

arXiv2025

Controlled rough paths: a general Hopf-algebraic setting

We set up controlled rough paths for a class of combinatorial Hopf algebras, encompassing shuffle, Butcher-Connes-Kreimer and Munthe-Kaas--Wright Hopf algebras. The class of controls we consider encompasses both Hölder continuous paths and (not necessarily continuous) paths with bounded $p$-variation. We prove existence and uniqueness of the solution of a lifted initial value problem in this general setting by applying the fixed point method in a suitable Banach space of controlled rough paths, and we prove a universal limit theorem addressing the robustness of the solution with respect to the parameters and the initial condition.

math.PR↗

arXiv2025

Investigating Security Implications of Automatically Generated Code on the Software Supply Chain

In recent years, various software supply chain (SSC) attacks have posed significant risks to the global community. Severe consequences may arise if developers integrate insecure code snippets that are vulnerable to SSC attacks into their products. Particularly, code generation techniques, such as large language models (LLMs), have been widely utilized in the developer community. However, LLMs are known to suffer from inherent issues when generating code, including fabrication, misinformation, and reliance on outdated training data, all of which can result in serious software supply chain threats. In this paper, we investigate the security threats to the SSC that arise from these inherent issues. We examine three categories of threats, including eleven potential SSC-related threats, related to external components in source code, and continuous integration configuration files. We find some threats in LLM-generated code could enable attackers to hijack software and workflows, while some others might cause potential hidden threats that compromise the security of the software over time. To understand these security impacts and severity, we design a tool, SSCGuard, to generate 439,138 prompts based on SSC-related questions collected online, and analyze the responses of four popular LLMs from GPT and Llama. Our results show that all identified SSC-related threats persistently exist. To mitigate these risks, we propose a novel prompt-based defense mechanism, namely Chain-of-Confirmation, to reduce fabrication, and a middleware-based defense that informs users of various SSC threats.

cs.CR↗

arXiv2025

ProgD: Progressive Multi-scale Decoding with Dynamic Graphs for Joint Multi-agent Motion Forecasting

Accurate motion prediction of surrounding agents is crucial for the safe planning of autonomous vehicles. Recent advancements have extended prediction techniques from individual agents to joint predictions of multiple interacting agents, with various strategies to address complex interactions within future motions of agents. However, these methods overlook the evolving nature of these interactions. To address this limitation, we propose a novel progressive multi-scale decoding strategy, termed ProgD, with the help of dynamic heterogeneous graph-based scenario modeling. In particular, to explicitly and comprehensively capture the evolving social interactions in future scenarios, given their inherent uncertainty, we design a progressive modeling of scenarios with dynamic heterogeneous graphs. With the unfolding of such dynamic heterogeneous graphs, a factorized architecture is designed to process the spatio-temporal dependencies within future scenarios and progressively eliminate uncertainty in future motions of multiple agents. Furthermore, a multi-scale decoding procedure is incorporated to improve on the future scenario modeling and consistent prediction of agents' future motion. The proposed ProgD achieves state-of-the-art performance on the INTERACTION multi-agent prediction benchmark, ranking $1^{st}$, and the Argoverse 2 multi-world forecasting benchmark.

cs.AI↗

arXiv2025

GRB 240825A: Early Reverse Shock and Its Physical Implications

Early multiwavelength observations offer crucial insights into the nature of the relativistic jets responsible for gamma-ray bursts and their interaction with the surrounding medium.We present data of GRB 240825A from 17 space- and ground-based telescopes/instruments, covering wavelengths from NIR/optical to X-ray and GeV, and spanning from the prompt emission to the afterglow phase triggered by Swift and Fermi. The early afterglow observations were carried out by SVOM/C-GFT, and spectroscopic observations of the afterglow by GTC, VLT, and TNG determined the redshift of the burst ($z = 0.659$) later.A comprehensive analysis of the prompt emission spectrum observed by Swift-BAT and Fermi-GBM/LAT reveals a rare and significant high-energy cutoff at ~76 MeV. Assuming this cutoff is due to $γγ$ absorption allows us to place an upper limit on the initial Lorentz factor, $Γ_0 < 245$. The optical/NIR and GeV afterglow light curves be described by the standard external shock model, with early-time emission dominated by a reverse shock (RS) and a subsequent transition to forward shock (FS) emission. Our afterglow modelling yields a consistent estimate of the initial Lorentz factor ($Γ_{\rm 0} \sim 234$). Furthermore, the RS-to-FS magnetic field ratio ($\mathcal{R}_B \sim 302$) indicates that the reverse shock region is significantly more magnetized than the FS region. An isotropic-equivalent kinetic energy of $E_{\text{k,iso}} = 5.25 \times 10^{54}$ erg is derived, and the corresponding $γ$-ray radiation efficiency is estimated to be $η_γ$ = 3.1%. On the other hand, the standard afterglow model can not reproduce the X-ray light curve of GRB 240825A, calling for improved models to characterize all multiwavelength data.

astro-ph.HE↗

arXiv2025

Spectral Hardening Reveals Afterglow Emergence in Long-Duration Fast X-ray Transients: A Case Study of GRB 250404A/EP250404a

The prompt emission and afterglow phases of gamma-ray bursts (GRBs) have been extensively studied, yet the transition between these two phases remains inadequately characterized due to limited multiwavelength observational coverage. Among the recent growing samples of fast X-ray transients observed by Einstein Probe (EP), a subgroup of GRBs are captured with long-duration X-ray emission, potentially containing featured evolution from prompt emission to the afterglow phase. In this Letter, we present a detailed analysis of GRB 250404A/EP250404a, a bright fast X-ray transient detected simultaneously by EP and the Fermi Gamma-ray Burst Monitor in X-rays and gamma rays. Its continuous X-ray emission reveals a long-duration tail, accompanied by distinct spectral evolution manifested by the spectral index $α_{\rm X}$ with an initial softening, followed by an evident hardening, eventually reaching a plateau at the value of $\sim$ -2. Early optical and near-infrared observations enable broadband modeling with forward- and reverse-shock components, confirming that the X-ray hardening signals the emergence of the external-shock afterglow. From this spectral hardening we infer that the prompt phase in soft X-rays lasted $\sim300\;\mathrm{s}$, which is more than 3 times longer than the gamma-ray $T_{90}$. This well-tracked soft-hard-flat spectral pattern provides a clear indication of afterglow emergence from the fading prompt emission and offers a practical criterion for identifying a distinct population of GRBs among fast X-ray transients, even when the detection of the gamma-ray counterpart or obvious temporal break is absent.

astro-ph.HE↗

arXiv2025

Weighted $K$-$k$-Schur functions and their application to the $K$-$k$-Schur alternating conjecture

We introduce the new concept of weighted $K$-$k$-Schur functions -- a novel family within the broader class of Katalan functions -- that unifies and extends both $K$-$k$-Schur functions and closed $k$-Schur Katalan functions. This new notion exhibits a fundamental alternating property under certain conditions on the indexed $k$-bounded partitions. As a central application, we resolve the $K$-$k$-Schur alternating conjecture -- posed by Blasiak, Morse, and Seelinger in 2022 -- for a wide class of $k$-bounded partitions, including all strictly decreasing $k$-bounded partitions. Our results shed new light on the combinatorial structure of $K$-theoretic symmetric functions.

math.CO↗

arXiv2025

Persistent paramagnons in high-temperature infinite-layer nickelate superconductors

The recent discovery of high-temperature superconductivity in hole-doped SmNiO$_2$, exhibiting the record-high transition temperature $T_c$ among infinite-layer (IL) nickelates, has opened a new avenue for exploring design principles of superconductivity. Experimentally determining the electronic structure and magnetic interactions in this new system is crucial to elucidating the mechanism behind the enhanced superconductivity. Here, we report a Ni $L$-edge resonant inelastic x-ray scattering (RIXS) study of superconducting Sm-based IL nickelate thin films Sm$_{1-x-y-z}$Eu$_x$Ca$_y$Sr$_z$NiO$_2$ (SECS). Dispersive paramagnonic excitations are observed in both optimally and overdoped SECS samples, supporting a spin-fluctuation-mediated pairing scenario. However, despite the two-fold enhancement of $T_c$ in the Sm-based nickelates compared to their Pr-based counterparts, the effective exchange coupling strength is reduced by approximately $20\%$. This behavior contrasts with hole-doped cuprates, where magnetic interactions correlate positively with $T_c$, highlighting essential differences in their superconducting mechanisms.

cond-mat.supr-con↗

arXiv2025

Some new operated Lie polynomial identities and Gröbner-Shirshov bases

Bremner and Elgendy developed a classification of operated polynomial identities for linear operators on associative algebras, encompassing both classical and newly discovered cases. Within the framework of Rota's Program, each of these new operated associative polynomial identities was shown to be Gröbner-Shirshov. This naturally led to a question posed by Guo and collaborators: is each corresponding operated Lie polynomial identity also Gröbner-Shirshov? In this paper, we provide an affirmative answer by proving that each such Lie analogue indeed is Gröbner-Shirshov, thereby enriching the development of Rota's Program on algebraic operators within the Lie algebraic setting.

math.RA↗

arXiv2025

Extended Non-Markovian Stochastic Schrödinger Equation with Complex Frequency Modes for General Basis Functions

We introduce an extended formulation of the non-Markovian stochastic Schrödinger equation with complex frequency modes (extended cNMSSE), designed for simulating open quantum system dynamics under arbitrary spectral densities. This extension employs non-exponential basis sets to expand the bath correlation functions, overcoming the reliance of the original cNMSSE on exponential decompositions of the spectral density. Consequently, the extended cNMSSE is applicable to environments beyond those characterized by Debye-type spectral densities. The flexibility to employ general basis functions is particularly advantageous for handling spectral densities with higher-order poles, for which exponential decompositions are often inaccurate or unavailable. The extended cNMSSE is implemented in a pseudo-Fock space using conventional ladder operators and solved efficiently via matrix product state (MPS) techniques, preserving the favorable linear-scaling and wavefunction-based nature of the original method. Benchmark simulations across four representative cases, including discrete spectral density, Ohmic spectral density with exponential and algebraic cutoffs, and critically damped Brownian spectral density, demonstrate excellent agreement with results of hierarchy of forward-backward stochastic Schrödinger equations (HFB-SSE) and extended hierarchical equation of motion (HEOM).

quant-ph↗

arXiv2025

A Large Outburst, Coma Asymmetries, and the Color of Comet 243P/NEAT

Water ice is a fundamental building material of comets and other bodies in the outer solar system. Yet, the properties of cometary water ice are challenging to study, due to its volatility and the typical distances at which comets are observed. Cometary outbursts, impulsive mass-loss events that can liberate large amounts of material, offer opportunities to directly observe and characterize cometary water ice. We present a study of comet 243P/NEAT, instigated by a $-3$ mag outburst that occurred in December 2018. Optical images and a 251-day lightcurve were examined to characterize the outburst and the comet's quiescent activity. Variations in the quiescent lightcurve appear to be dominated by coma asymmetries, rather than changing activity levels as the comet approached and receded from the Sun. Furthermore, the lightcurve shows evidence for 1 to 2 additional small outbursts ($-0.3$ mag) occurring in September 2018. The large December 2018 outburst likely ejected water ice grains, yet no signatures of ice were found in color photometry, a color map, nor a near-infrared spectrum. We discuss possible dynamical and thermal reasons for this non-detection. In this context, we examined the comae of comets 103P/Hartley 2 and C/2013 US$_{10}$ (Catalina), and show that a one-to-one mapping between continuum color and the presence of water ice cannot be supported. We also discuss possible causes for the large outburst, and find that there is an apparent grouping in the kinetic energy per mass estimates for the outbursts of 5 comets.

astro-ph.EP↗

arXiv2025

Rough differential equations and planarly branched universal limit theorem

The universal limit theorem is a central result in rough path theory, which has been proved for: (i) rough paths with roughness $\frac{1}{3}< α\leq \frac{1}{2}$; (ii) geometric rough paths with roughness $0< α\leq 1$; (iii) branched rough paths with roughness $0< α\leq 1$. Planarly branched rough paths are natural generalizations of both rough paths and branched rough paths, in the sense that post-Lie algebras are generalizations of both Lie algebras and pre-Lie algebras. Here the primitive elements of the graded dual Hopf algebra of the Hopf algebra corresponding to the planarly branched rough paths (resp. rough paths, resp. branched rough paths) form a post-Lie (resp. Lie, resp. pre-Lie algebra). In this paper, we prove the universal limit theorem for planarly branched rough paths with roughness $\frac{1}{4}< α\leq \frac{1}{3}$, via the method of Banach fixed point theorem.

math.PR↗

arXiv2025

Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning

Large Language Models (LLMs) have shown significant challenges in detecting and repairing vulnerable code, particularly when dealing with vulnerabilities involving multiple aspects, such as variables, code flows, and code structures. In this study, we utilize GitHub Copilot as the LLM and focus on buffer overflow vulnerabilities. Our experiments reveal a notable gap in Copilot's abilities when dealing with buffer overflow vulnerabilities, with a 76% vulnerability detection rate but only a 15% vulnerability repair rate. To address this issue, we propose context-aware prompt tuning techniques designed to enhance LLM performance in repairing buffer overflow. By injecting a sequence of domain knowledge about the vulnerability, including various security and code contexts, we demonstrate that Copilot's successful repair rate increases to 63%, representing more than four times the improvement compared to repairs without domain knowledge.

cs.CR↗

arXiv2025

Learning-Augmented Algorithms for Boolean Satisfiability

Learning-augmented algorithms are a prominent recent development in beyond worst-case analysis. In this framework, a problem instance is provided with a prediction (``advice'') from a machine-learning oracle, which provides partial information about an optimal solution, and the goal is to design algorithms that leverage this advice to improve worst-case performance. We study the classic Boolean satisfiability (SAT) decision and optimization problems within this framework using two forms of advice. ``Subset advice" provides a random $ε$ fraction of the variables from an optimal assignment, whereas ``label advice" provides noisy predictions for all variables in an optimal assignment. For the decision problem $k$-SAT, by using the subset advice we accelerate the exponential running time of the PPSZ family of algorithms due to Paturi, Pudlak, Saks and Zane, which currently represent the state of the art in the worst case. We accelerate the running time by a multiplicative factor of $2^{-c}$ in the base of the exponent, where $c$ is a function of $ε$ and $k$. For the optimization problem, we show how to incorporate subset advice in a black-box fashion with any $α$-approximation algorithm, improving the approximation ratio to $α+ (1 - α)ε$. Specifically, we achieve approximations of $0.94 + Ω(ε)$ for MAX-$2$-SAT, $7/8 + Ω(ε)$ for MAX-$3$-SAT, and $0.79 + Ω(ε)$ for MAX-SAT. Moreover, for label advice, we obtain near-optimal approximation for instances with large average degree, thereby generalizing recent results on MAX-CUT and MAX-$2$-LIN.

cs.DS↗

arXiv2025

TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence

Recently, Large Language Models (LLMs) have made significant progress in IQ-related domains that require careful thinking, such as mathematics and coding. However, enhancing LLMs' cognitive development in social domains, particularly from a post-training perspective, remains underexplored. Recognizing that the social world follows a distinct timeline and requires a richer blend of cognitive modes (from intuitive reactions (System 1) and surface-level thinking to deliberate thinking (System 2)) than mathematics, which primarily relies on System 2 cognition (careful, step-by-step reasoning), we introduce Temporal-aware Hierarchical Cognitive Reinforcement Learning (TimeHC-RL) for enhancing LLMs' social intelligence. In our experiments, we systematically explore improving LLMs' social intelligence and validate the effectiveness of the TimeHC-RL method, through five other post-training paradigms and two test-time intervention paradigms on eight datasets with diverse data patterns. Experimental results reveal the superiority of our proposed TimeHC-RL method compared to the widely adopted System 2 RL method. It gives the 7B backbone model wings, enabling it to rival the performance of advanced models like DeepSeek-R1 and OpenAI-O3. Additionally, the systematic exploration from post-training and test-time interventions perspectives to improve LLMs' social intelligence has uncovered several valuable insights.

cs.CL↗

arXiv2025

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Large Language Models (LLMs), despite their advancements, are fundamentally limited by their static parametric knowledge, hindering performance on tasks requiring open-domain up-to-date information. While enabling LLMs to interact with external knowledge environments is a promising solution, current efforts primarily address closed-end problems. Open-ended questions, which characterized by lacking a standard answer or providing non-unique and diverse answers, remain underexplored. To bridge this gap, we present O$^2$-Searcher, a novel search agent leveraging reinforcement learning to effectively tackle both open-ended and closed-ended questions in the open domain. O$^2$-Searcher leverages an efficient, locally simulated search environment for dynamic knowledge acquisition, effectively decoupling the external world knowledge from model's sophisticated reasoning processes. It employs a unified training mechanism with meticulously designed reward functions, enabling the agent to identify problem types and adapt different answer generation strategies. Furthermore, to evaluate performance on complex open-ended tasks, we construct O$^2$-QA, a high-quality benchmark featuring 300 manually curated, multi-domain open-ended questions with associated web page caches. Extensive experiments show that O$^2$-Searcher, using only a 3B model, significantly surpasses leading LLM agents on O$^2$-QA. It also achieves SOTA results on various closed-ended QA benchmarks against similarly-sized models, while performing on par with much larger ones.

cs.CL↗

arXiv2025

Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position Predictivity

Based on the concept that ventral visual stream (VVS) mainly functions for object recognition, current unsupervised task-driven methods model VVS by contrastive learning, and have achieved good brain similarity. However, we believe functions of VVS extend beyond just object recognition. In this paper, we introduce an additional function involving VVS, named relative position (RP) prediction. We first theoretically explain contrastive learning may be unable to yield the model capability of RP prediction. Motivated by this, we subsequently integrate RP learning with contrastive learning, and propose a new unsupervised task-driven method to model VVS, which is more inline with biological reality. We conduct extensive experiments, demonstrating that: (i) our method significantly improves downstream performance of object recognition while enhancing RP predictivity; (ii) RP predictivity generally improves the model brain similarity. Our results provide strong evidence for the involvement of VVS in location perception (especially RP prediction) from a computational perspective.

cs.CE↗