arXiv subjects

Xin Liu

Publications and source records attributed to Xin Liu.

At least 217 records · Page 12Linked to original sources

arXiv2026

Dynamics of string breaking and revival in a Rydberg atomic chain

String breaking is one of the most representative non-perturbative dynamics processes in confinement theory, typically associated with the creation of particle-antiparticle pairs. In this paper, we take a one-dimensional Rydberg atomic chain to theoretically study the dynamical of finite-length string state. Under different string tension conditions, we find that the string dynamics exhibits two clearly distinguishable evolution characteristics: one is that the string breaks and the system enters a superposition state space containing multiple meson state configurations; the other is localized string dynamics, in which the string undergoes local breaking but can then recombine and return to a state close to the initial structure, with the breaking and recombination processes recurring over a long time scale. Through the analysis of the evolution of different meson state configurations, we visually depict the redistribution of configuration weights during the string breaking process, and reveal the observable recovery characteristics of the string after breaking. Further analysis shows that the enhancement of quantum fluctuations can increase the weight of the double-meson state configurations in the system wave function without changing the dominant dynamical behavior. The above results present a rich picture of string breaking dynamics in a one-dimensional Rydberg atomic chain and provide insights for studying confinement physics and related gauge field theory phenomena on quantum simulation platforms.

cond-mat.quant-gas↗

arXiv2026

A Smooth Locally Exact Penalty Method for Optimization Problems over Generalized Stiefel Manifolds

In this paper, we consider a class of optimization problems constrained to the generalized Stiefel manifold. Such problems are fundamental to a wide range of real-world applications, including generalized canonical correlation analysis, linear discriminant analysis, and electronic structure calculations. Existing works mainly focuses on cases where the generalized orthogonality constraint is induced by a symmetric positive definite matrix M, a setting where the geometry essentially reduces to that of the standard Stiefel manifold. However, many practical scenarios involve a singular M, which introduces significant analytical and computational challenges. Therefore, we propose a Smooth Locally Exact Penalty model (SLEP) and establish its equivalence to the original problem in the aspect of stationary points under a finitly large penalty parameter. This penalty model admits the direct application of various unconstrained optimization techniques, with convergence guarantees inherited from established results. Compared to Riemannian optimization approaches, our proposed penalty mode eliminates the need for retractions and vector transports, hence significantly reducing per-iteration computational costs. Extensive numerical experiments validate our theoretical results and demonstrate the effectiveness and practical potential of the proposed penalty model SLEP.

math.OC↗

arXiv2026

Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this regime, including those that utilize raw pixel information instead of aperture photometry. However, the efficacy of image-based algorithms on high-redshift, near-infrared data remains underexplored. Here, we test the performance of Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC) on photometric redshift estimation with NIRCam images from the JWST Advanced Deep Extragalactic Survey (JADES) program. DeepDISC is designed to produce probabilistic photometric redshift estimates directly from images, after detecting and deblending sources in a scene. Using NIRCam-only images and a compiled catalog of spectroscopic redshifts, we show that DeepDISC produces reliable photo-zs and uncertainties comparable to those estimated from template fitting using HST+JWST filters; DeepDISC even outperforms template fitting (lower scatter/fewer outliers) when the input photometric filters are matched. Compared with template fitting, DeepDISC does not require measured photometry from images, and can produce a catalog of 94000 photo-zs in ~4 minutes on a single NVIDIA A40 GPU. While current spectroscopic training samples are small and incomplete in color-magnitude space, this work demonstrates the potential of DeepDISC for increasingly larger image volumes and spectroscopic samples from ongoing and future programs. We discuss the impact of the training data on applications to broader samples and produce a catalog of photo-zs for all JADES DR2 photometric sources in the GOOD-S field, with quality flags indicating caveats.

astro-ph.IM↗

arXiv2026

How is Cold Gas Loaded into Galactic Nuclear Outflows?

The origin of the multiphase gas within the Fermi/eROSITA bubbles is crucial for understanding Galactic Center (GC) feedback. We use HI4PI data to investigate the kinematics and physical properties of high-velocity clouds (HVCs) toward the GC. Our results reveal that the HVCs exhibit a distinct asymmetric distribution, closely associated with the bar-driven tilted dust lanes and the distorted overshooting streams. We propose that powerful nuclear outflows interact with these gas-rich, off-plane structures, striping and entraining cold gas from the outer Galactic regions (R_GC~0.5--1.7 kpc) rather than solely from the region of the central molecular zone (CMZ; R_GC<0.3 kpc). In this scenario, as the Galactic bar drives gas inflows along the dust lanes, nuclear outflows simultaneously break through the CMZ, sweeping up and ablating cold gas from the boundary layer of these pre-existing structures. This process naturally accounts for the observed high turbulence, complex spectral signatures, and anomalous spatial-kinematic gas patterns, as well as multiwavelength asymmetries of the bubbles. The HVCs are accelerated to about 230--340 km/s over a dynamical time of ~3--6 Myr. When the multiphase, inhomogeneous composition of the gas is included, the estimated gas outflow rate reaches ~1 Msun/yr. This value is comparable to the bar-driven inflow rate, indicating a tightly coupled gas cycle in the inner Galaxy. Our research highlights the critical role of bar-driven gas dynamics and nuclear feedback in the secular evolution of the Milky Way, offering a valuable paradigm for investigating gas cycles in external galaxies.

astro-ph.GA↗

arXiv2026

DASH: Deterministic Attention Scheduling for High-throughput Reproducible LLM Training

Determinism is indispensable for reproducibility in large language model (LLM) training, yet it often exacts a steep performance cost. In widely used attention implementations such as FlashAttention-3, the deterministic backward pass can incur up to a 37.9% throughput reduction relative to its non-deterministic counterpart, primarily because gradient accumulation operations must be serialized to guarantee numerical consistency. This performance loss stems from suboptimal scheduling of compute and gradient-reduction phases, leading to significant hardware underutilization. To address this challenge, we formulate the backward pass of deterministic attention as a scheduling problem on a Directed Acyclic Graph (DAG) and derive schedules that minimize the critical path length. Building on this formulation, we present DASH (Deterministic Attention Scheduling for High-Throughput), which encapsulates two complementary scheduling strategies: (i) Descending Q-Tile Iteration, a reversed query-block traversal that shrinks pipeline stalls in causal attention, and (ii) Shift Scheduling, a theoretically optimal schedule within our DAG model that reduces pipeline stalls for both full and causal masks. Our empirical evaluations on NVIDIA H800 GPUs demonstrate that DASH narrows the performance gap of deterministic attention. The proposed strategies improve the throughput of the attention backward pass by up to 1.28$\times$ compared to the baseline, significantly advancing the efficiency of reproducible LLM training. Our code is open-sourced at https://github.com/SJTU-Liquid/deterministic-FA3.

cs.LG↗

arXiv2026

Zero-Shot Video Restoration and Enhancement with Assistance of Video Diffusion Models

Although diffusion-based zero-shot image restoration and enhancement methods have achieved great success, applying them to video restoration or enhancement will lead to severe temporal flickering. In this paper, we propose the first framework that utilizes the rapidly-developed video diffusion model to assist the image-based method in maintaining more temporal consistency for zero-shot video restoration and enhancement. We propose homologous latents fusion, heterogenous latents fusion, and a COT-based fusion ratio strategy to utilize both homologous and heterogenous text-to-video diffusion models to complement the image method. Moreover, we propose temporal-strengthening post-processing to utilize the image-to-video diffusion model to further improve temporal consistency. Our method is training-free and can be applied to any diffusion-based image restoration and enhancement methods. Experimental results demonstrate the superiority of the proposed method.

cs.CV↗

arXiv2026

HVD: Human Vision-Driven Video Representation Learning for Text-Video Retrieval

The success of CLIP has driven substantial progress in text-video retrieval. However, current methods often suffer from "blind" feature interaction, where the model struggles to discern key visual information from background noise due to the sparsity of textual queries. To bridge this gap, we draw inspiration from human cognitive behavior and propose the Human Vision-Driven (HVD) model. Our framework establishes a coarse-to-fine alignment mechanism comprising two key components: the Frame Features Selection Module (FFSM) and the Patch Features Compression Module (PFCM). FFSM mimics the human macro-perception ability by selecting key frames to eliminate temporal redundancy. Subsequently, PFCM simulates micro-perception by aggregating patch features into salient visual entities through an advanced attention mechanism, enabling precise entity-level matching. Extensive experiments on five benchmarks demonstrate that HVD not only captures human-like visual focus but also achieves state-of-the-art performance.

cs.CV↗

arXiv2026

Intention Knowledge Graph Construction for User Intention Relation Modeling

Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is crucial for modeling user behavior and predicting future actions. This paper introduces a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. Using the Amazon m2 dataset, we construct an intention graph with 351 million edges, demonstrating high plausibility and acceptance. Our model effectively predicts new session intentions and enhances product recommendations, outperforming previous state-of-the-art methods and showcasing the approach's practical utility.

cs.CL↗

arXiv2026

Model-Driven GPR Inversion Network With Surrogate Forward Solver

Data-driven deep learning is considered a promising solution for ground-penetrating radar (GPR) full-waveform inversion (FWI), while its generalization ability is limited due to the heavy reliance on abundant labeled samples. In contrast, Deep unfolding network (DUN) usually exhibits better generalization by integrating model-driven and data-driven approaches, yet its application to GPR FWI remains challenging due to the high computational cost associated with forward simulations. In this paper, we integrate a deep learning-based (DL-based) forward solver within an unfolding framework to form a fully neural-network-based architecture, UA-Net, for GPR FWI. The forward solver rapidly predicts B-scans given permittivity and conductivity models and enables automatic differentiation to compute gradients for inversion. In the inversion stage, an optimization process based on the Alternating Direction Method of Multipliers (ADMM) is unfolded into a multi-stage network with three interconnected modules: data fitting, regularization, and multiplier update. Specifically, the regularization module is trained end-to-end for adaptive learning of sparse target features. Experimental results demonstrate that UA-Net outperforms classical FWI and data-driven methods in reconstruction accuracy. Moreover, by employing transfer learning to fine-tune the network, UA-Net can be effectively applied to field data and produce reliable results.

physics.geo-ph↗

arXiv2026

Observation Timelines for the Potential Lunar Impact of Asteroid 2024 YR4

The near-Earth asteroid 2024 YR4 -- a $\sim$60 m rocky object that was once considered a potential Earth impactor -- has since been ruled out for Earth but retained a $\sim$4.3% probability of striking the Moon in 2032. Such an impact, with equivalent kinetic energy of $\sim$6.5 Mt TNT, is expected to produce a $\sim$1 km crater on the Moon, and will be the most energetic lunar impact event ever recorded in human history. Despite the associated risk, this scenario offers a rare and valuable scientific opportunity. Using a hybrid framework combining Monte Carlo orbital propagation, smoothed particle hydrodynamics (SPH) impact modeling, and N-body ejecta dynamics, we evaluate the physical outcomes and propose the observation timelines of this rare event. Our results suggest an optical flash of visual magnitude from -2.5 to -3 lasting several minutes directly after the impact, followed by hours of infrared afterglow from $\sim$2000 K molten rock cooling to a few hundred K. The associated seismic energy release would lead to a global-scale lunar reverberation (magnitude $\sim$5.0) that can be detectable by modern seismometers. Furthermore, the impact would eject $\sim$10$^8$ kg of debris that escapes the lunar gravity, with a small fraction reaching Earth to produce a lunar meteor outburst within 100 years. Finally, we integrate these results into a coordinated observation timeline, identifying the best detection windows for ground-based telescopes, lunar orbiters, and surface stations.

astro-ph.EP↗

arXiv2026

A Scalable Pipeline for Enabling Non-Verbal Speech Generation and Understanding

Non-verbal Vocalizations (NVs), such as laughter and sighs, are vital for conveying emotion and intention in human speech, yet most existing speech systems neglect them, which severely compromises communicative richness and emotional intelligence. Existing methods for NVs acquisition are either costly and unscalable (relying on manual annotation/recording) or unnatural (relying on rule-based synthesis). To address these limitations, we propose a highly scalable automatic annotation framework to label non-verbal phenomena from natural speech, which is low-cost, easily extendable, and inherently diverse and natural. This framework leverages a unified detection model to accurately identify NVs in natural speech and integrates them with transcripts via temporal-semantic alignment method. Using this framework, we created and released \textbf{NonVerbalSpeech-38K}, a diverse, real-world dataset featuring 38,718 samples across 10 NV categories collected from in-the-wild media. Experimental results demonstrate that our dataset provides superior controllability for NVs generation and achieves comparable performance for NVs understanding.

cs.SD↗

arXiv2026

High-Ti induced planar-fault transformation toward superlattice extrinsic stacking faults and microtwins in crept CoNi-based superalloys

Controlling planar fault shearing mechanisms is key for improving the high-temperature creep performance of gamma prime-strengthened high-temperature superalloys. This work examines how the Ti concentration in L12-strengthened CoNi-based alloys affects planar fault formation during creep. Interrupted compressive creep tests were conducted at 1223 K under air with a constant load stress of 241 MPa. We found, for the first time, that high Ti additions shift the dominant gamma prime shearing mode from antiphase boundaries (APBs) in Ti-free and low-Ti alloys to superlattice extrinsic stacking faults (SESFs). Systematic ab initio calculations show that in high-Ti alloys, the elevated APB energy renders APB-shearing mode unfavorable. Nevertheless, the SESF energy decreases relative to that in low-Ti compositions, and an increased ratio of complex intrinsic stacking fault (CISF) to SESF energy promote the transformation of high-energy CISFs into lower-energy SESFs. Chemical analysis using scanning transmission electron microscopy combined with energy-dispersive X-ray spectroscopy further reveals that, SESFs in high-Ti alloys are enriched in Ti, Mo and W, yet no grid-like ordering is observed. Together with the ab initio calculations, Mo and W additions in high Ti alloys could facilitate the transformation from L12 structure to low-energy D024 structure, indicating Mo and W segregation along SESFs is energetically favourable. Furthermore, the successive SESF thickening facilitates microtwinning in the absence of D024 ordering along SESFs, as an additional big carrier for creep strain. These new findings clarify the role of Ti in controlling planar fault shearing mechanisms, providing new insights for optimizing the creep performance of next-generation CoNi-based superalloys.

cond-mat.mtrl-sci↗

arXiv2026

Plastic limit of a viscoplastic Burgers equation -- A toy model for sea-ice dynamics

We study the plastic Burgers equation in one space dimension, i.e., the Burgers equation featuring an additional term formally given by the p-Laplacian with p=1, or rather, by the multivalued subdifferential of the total variation functional. Our study highlights that the interplay of the advection term with the stresses given by the multivalued 1-Laplacian is a crucial feature of this model. Eventhough it is an interesting model in itsef, it can also be regarded as a one-dimensional version of the momentum balance of Hibler's model for sea-ice dynamics. Therein, the stress tensor is given by a term with similar properties as the 1-Laplacian in order to account for plastic effects of the ice. For our analysis we start out from a viscoplastic Burgers equation, i.e., a suitably regularized version of the plastic Burgers equation with a small regularization parameter $\varepsilon>0$. For the viscoplastic Burgers equation, we construct a global BV-solution. In the singular limit $\varepsilon\to0$ we deduce the existence of a BV-solution for the plastic Burgers equation. In addition we show that the term arising as the limit of the regularized stresses is indeed related to an element of the subdifferential of the total variation functional.

math.AP↗

arXiv2026

High-Rate Free-Running Reference-Frame-Independent Measurement-Device-Independent Quantum Key Distribution with Classified Distillation

Reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) eliminates detector side-channel attacks and avoids reference-frame calibration. While its feasibility has been widely demonstrated, existing implementations typically assume fixed or slowly drifting reference-frame misalignment, conditions rarely satisfied outside the laboratory. In realistic environments, rapid and free-running reference-frame variations can severely degrade both the key rate and transmission distance of conventional RFI-MDI-QKD. Here we propose a free-running RFI-MDI-QKD protocol that maintains high-rate key generation under rapid reference-frame variations. By introducing a classification-distillation method that reclassifies total detection events, secure keys can be extracted without modifying the experimental setup. Our protocol achieves a key rate more than nine times higher than the best previous RFI-MDI-QKD scheme and tolerates channel losses exceeding 24 dB, where earlier approaches fail. These results enable practical quantum key distribution on mobile platforms, including satellite-to-ground links and airborne nodes.

quant-ph↗

arXiv2026

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions

In this paper, we focus on the decentralized stochastic subgradient-based methods in minimizing nonsmooth nonconvex functions without Clarke regularity, especially in the decentralized training of nonsmooth neural networks. We propose a general framework that unifies various decentralized subgradient-based methods, such as decentralized stochastic subgradient descent (DSGD), DSGD with gradient-tracking technique (DSGD-T), and DSGD with momentum (DSGD-M). To establish the convergence properties of our proposed framework, we relate the discrete iterates to the trajectories of a continuous-time differential inclusion, which is assumed to have a coercive Lyapunov function with a stable set $\mathcal{A}$. We prove the asymptotic convergence of the iterates to the stable set $\mathcal{A}$ with sufficiently small and diminishing step-sizes. These results provide first convergence guarantees for some well-recognized of decentralized stochastic subgradient-based methods without Clarke regularity of the objective function. Preliminary numerical experiments demonstrate that our proposed framework yields highly efficient decentralized stochastic subgradient-based methods with convergence guarantees in the training of nonsmooth neural networks.

math.OC↗

arXiv2026

Ergodic Estimates of One-Step Numerical Approximations for Superlinear SODEs

This paper establishes the first-order convergence rate for the ergodic error of numerical approximations to a class of stochastic ODEs (SODEs) with superlinear coefficients and multiplicative noise. By leveraging the generator approach to the Stein method, we derive a general error representation formula for one-step numerical schemes. Under suitable dissipativity and smoothness conditions, we prove that the error between the accurate invariant measure $π$ and the numerical invariant measure $π_τ$ is of order $\mathscr{O}(τ)$, which is sharp. Our framework applies to several recently studied schemes, including the tamed Euler, projected Euler, and backward Euler methods.

math.NA↗

arXiv2026

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models

The development of audio foundation models has accelerated rapidly since the emergence of GPT-4o. However, the lack of comprehensive evaluation has become a critical bottleneck for further progress in the field, particularly in audio generation. Current audio evaluation faces three major challenges: (1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources, hindering fair and efficient cross-model comparison;(2) audio codecs, as a key component of audio foundation models, lack a widely accepted and holistic evaluation methodology; (3) existing speech benchmarks are heavily reliant on English, making it challenging to objectively assess models' performance on Chinese. To address the first issue, we introduce UltraEval-Audio, a unified evaluation framework for audio foundation models, specifically designed for both audio understanding and generation tasks. UltraEval-Audio features a modular architecture, supporting 10 languages and 14 core task categories, while seamlessly integrating 24 mainstream models and 36 authoritative benchmarks. To enhance research efficiency, the framework provides a one-command evaluation feature, accompanied by real-time public leaderboards. For the second challenge, UltraEval-Audio adopts a novel comprehensive evaluation scheme for audio codecs, evaluating performance across three key dimensions: semantic accuracy, timbre fidelity, and acoustic quality. To address the third issue, we propose two new Chinese benchmarks, SpeechCMMLU and SpeechHSK, designed to assess Chinese knowledge proficiency and language fluency. We wish that UltraEval-Audio will provide both academia and industry with a transparent, efficient, and fair platform for comparison of audio models. Our code, benchmarks, and leaderboards are available at https://github.com/OpenBMB/UltraEval-Audio.

cs.SD↗

arXiv2025

Multi-agent Self-triage System with Medical Flowcharts

Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization.

cs.AI↗