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Xuan Sun

Publications and source records attributed to Xuan Sun.

At least 19 recordsLinked to original sources

Collisionless Resonances Set the Size of a Weyl Exceptional Ring

Eliminating continuum degrees of freedom converts conservative dynamics into a dispersive, non-Hermitian response whose topology need not represent the causally selected kinetic dynamics. In collisionless magnetized plasma, retarded Landau and cyclotron resonances damp two Weyl modes differently and replace a cold Weyl point by an exceptional ring. Axial roots and root-normalized polarization mixing predict its off-axis radius, $R_{\rm EP}^{\rm pred}=|\Delta\Gamma|/(2|v_{\rm mix}|)$, before any double root is computed. Across five nonrelativistic scans spanning a 310-fold range of differential damping, $R_{\rm EP}^{\rm pred}$ agrees with the nonlinear double-root radius $R_{\rm EP}^{\rm full}$ within $0.12\%$. The selected retarded kinetic-response bundle is a continuous graph over the electric root line bundle and has the same first Chern class under stated conditions. The cold interface mode continues to a localized Keldysh pole with one oriented crossing in the tested line-gap window, consistent with the inherited charge.

physics.plasm-ph

SwarmX: Agentic Scheduling for Low-Latency Agentic Systems

Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their inference time and model-call structure often depend on prompt semantics, making conventional scheduling approaches ineffective for low-latency serving. This paper presents SwarmX, a system that implements agentic scheduling for low-latency agentic applications. SwarmX uses scheduling-specific neural predictors to capture prompt, device, runtime, and target-model features; exposes distributional predictions to routers and scalers for tail-aware decisions; and provides mechanisms for predictor training and online adaptation. These predictors and mechanisms are integrated into a scheduler-agent framework that provides a common substrate for integration with existing scheduling and model-serving infrastructure. We evaluate SwarmX using production deployment (nearly one thousand GPUs and one million CPU cores) and controlled experiments on a 128-GPU testbed. Across multi-agent code generation, deep research, and multimodal agentic workflows, SwarmX reduces tail latency by up to 61.5% compared to state-of-the-art schedulers and sustains up to 2x the throughput of production schedulers under the same SLO.

cs.DC

Vocal Identity Under Siege by AI Voice Cloning Technologies

The advent of sophisticated AI-driven voice cloning has brought to the fore critical legal and ethical challenges regarding the protection of vocal identity. Prompted by recent controversies - including the striking resemblance between OpenAI's ChatGPT-4o voice and that of Scarlett Johansson - this article examines how generative AI technologies undermine the unique value of the human voice and further complicate the legal questions surrounding personality right. Through a comparative analysis, the paper evaluates three principal legal frameworks: the right of publicity, personality rights, and the personal data protection right. Each framework - rooted in different legal traditions o offers distinct strengths and limitations in addressing the threats posed by AI-generated voice cloning. By analysing these doctrines' scope, remedies, and posthumous protections, the study offers a foundation for understanding how existing legal approaches may be applied to the evolving challenges of vocal identity in the era of generative AI.

cs.CY

LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss

3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. However, its standard photometric loss treats flat and structure-rich regions similarly, which may limit the recovery of sharp contours and fine details. Edge-Guided Gaussian Splatting (EGGS) improves structure awareness through edge-guided weighting, but mainly relies on first-order gradient responses and linear weighting. In this paper, we propose LEGS, a Laplacian-Enhanced Gaussian Splatting method with a nonlinearly weighted loss. LEGS replaces first-order gradient guidance with second-order Laplacian structural guidance and maps the normalized Laplacian response into pixel-wise weights through nonlinear response-to-weight functions. The proposed loss improves structure-aware Gaussian optimization while keeping the original 3DGS rendering pipeline unchanged. Experiments on the full Tanks\&Temples and Mip-NeRF360 datasets show that LEGS improves peak signal-to-noise ratio (PSNR) by up to 1.68 dB over 3DGS and up to 0.52 dB over EGGS. Incorporating the proposed second-order nonlinear weighting strategy into FastGS and FasterGS further improves PSNR by up to 1.69 dB, demonstrating its effectiveness as a general loss-level extension for Gaussian Splatting pipelines with potential applications in AR/VR, immersive visualization, and real-time 3D content generation.

cs.CV

A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems

Refined trajectory inference of urban rail transit is of great significance to the operation organization. In this paper, we develop a fully data-driven approach to inferring individual travel trajectories in urban rail transit systems. It utilizes data from the Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems to infer key trajectory elements, such as selected train, access/egress time, and transfer time. The approach includes establishing train alternative sets based on spatio-temporal constraints, data-driven adaptive trajectory inference, and trave l trajectory construction. To realize data-driven adaptive trajectory inference, a data-driven parameter estimation method based on KL divergence combined with EM algorithm (KLEM) was proposed. This method eliminates the reliance on external or survey data for parameter fitting, enhancing the robustness and applicability of the model. Furthermore, to overcome the limitations of using synthetic data to validate the result, this paper employs real individual travel trajectory data for verification. The results show that the approach developed in this paper can achieve high-precision passenger trajectory inference, with an accuracy rate of over 90% in urban rail transit travel trajectory inference during peak hours.

cs.LG

ContextPilot: Fast Long-Context Inference via Context Reuse

AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples include retrieval-augmented generation, agent memory layers, and multi-agent orchestration. As input contexts get longer, prefill latency becomes the main bottleneck. Yet today's prefill acceleration techniques face a trade-off: they either preserve reasoning quality but deliver little KV-cache reuse, or improve reuse at the cost of degraded reasoning quality. We present ContextPilot, a system that accelerates prefill by introducing context reuse as a new mechanism for faster long-context inference. ContextPilot introduces a context index to identify overlapping context blocks across LLM interactions (e.g., across users and turns). It further proposes context ordering and de-duplication techniques to maximize KV-cache reuse. To preserve reasoning quality under reuse, it introduces succinct context annotations that prevent quality degradation. Finally, ContextPilot is built around a modular architecture with a clean interface that integrates with existing inference engines. Extensive evaluation shows that ContextPilot reduces LLM prefill latency by up to $3\times{}$ compared to state-of-the-art methods while preserving reasoning quality. At longer context lengths, it can even improve reasoning quality. ContextPilot is open-sourced at: https://github.com/EfficientContext/ContextPilot.

cs.LG

Focusing mKdV equation: Two-phase solutions and their stability analysis

In this work, we primarily focus on the two-phase solutions and their stability to the focusing mKdV equation. By employing the algebro-geometric approach in combination with an effective integration method, we construct explicit two-phase solutions and their corresponding wave-functions expressed in terms of the Riemann theta function. The spectral stability of two-phase solutions is examined via a modified squared-eigenfunction approach, and their stability with respect to subharmonic perturbations is further analyzed under spectrally unstable conditions. In addition, the orbital stability of the two-phase solutions is investigated. To the best of our knowledge, this study provides the first rigorous stability theory for the two-phase solutions of the focusing mKdV equation.

nlin.SI

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study

Aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning (ML) modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, AUROC, and AUPRC. To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization, and evaluated the calibration curve and decision curve. By comparing six modeling strategies, the Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUROC of 1.00 and a testing AUROC of 0.96. SHAP identified the most important radiomic features, revealing that all selected features were significantly associated with EC (P < 0.05). Radiomics feature maps also provide a feasible assessment tool for clinical applications. Decision Curve Analysis (DCA) indicated a higher net benefit for our model compared to the "All" and "None" strategies, suggesting its clinical utility in identifying high-risk cases and reducing unnecessary interventions. In conclusion, the CT radiomics-based explainable ML model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.

eess.IV

Two types of elliptic dark soliton solutions for the Hirota equation

We primarily study concave-downward and convex-upward types of elliptic dark soliton solutions for the Hirota equation, exhibiting a concave-downward shape on both upper and lower envelope surfaces and showing a convex-upward shape on the lower envelope surface, respectively. By analyzing the supremum and infimum of solutions, we provide the existence conditions for these two types of elliptic dark solitons. Additionally, we study two-elliptic dark soliton solutions combining both types with the same velocity and investigate the elastic collisions between these two types of solutions with different velocities.

nlin.SI

D2SL: Decouple Defogging and Semantic Learning for Foggy Domain-Adaptive Segmentation

We investigated domain adaptive semantic segmentation in foggy weather scenarios, which aims to enhance the utilization of unlabeled foggy data and improve the model's adaptability to foggy conditions. Current methods rely on clear images as references, jointly learning defogging and segmentation for foggy images. Despite making some progress, there are still two main drawbacks: (1) the coupling of segmentation and defogging feature representations, resulting in a decrease in semantic representation capability, and (2) the failure to leverage real fog priors in unlabeled foggy data, leading to insufficient model generalization ability. To address these issues, we propose a novel training framework, Decouple Defogging and Semantic learning, called D2SL, aiming to alleviate the adverse impact of defogging tasks on the final segmentation task. In this framework, we introduce a domain-consistent transfer strategy to establish a connection between defogging and segmentation tasks. Furthermore, we design a real fog transfer strategy to improve defogging effects by fully leveraging the fog priors from real foggy images. Our approach enhances the semantic representations required for segmentation during the defogging learning process and maximizes the representation capability of fog invariance by effectively utilizing real fog data. Comprehensive experiments validate the effectiveness of the proposed method.

cs.CV

Elliptic-rogue waves and modulational instability in nonlinear soliton equations

We present elliptic-rogue wave solutions for integrable nonlinear soliton equations in theta functions. Unlike solutions generated on the plane wave background, these solutions depict rogue waves emerging on elliptic function backgrounds. By refining the modified squared wave function method in tandem with the Darboux-B\"acklund transformation, we establish a quantitative correspondence between elliptic-rogue waves and the modulational instability. This connection reveals that the modulational instability of elliptic function solutions triggers rational-form solutions displaying elliptic-rogue waves, whereas the modulational stability of elliptic function solutions results in the rational-form solutions exhibiting the elliptic-solitons or elliptic-breathers. Moreover, this approach enables the derivation of higher-order elliptic-rogue waves, offering a versatile framework for constructing elliptic-rogue waves and exploring modulational stability in other integrable equations.

nlin.SI

Valid Information Guidance Network for Compressed Video Quality Enhancement

In recent years deep learning methods have shown great superiority in compressed video quality enhancement tasks. Existing methods generally take the raw video as the ground truth and extract practical information from consecutive frames containing various artifacts. However, they do not fully exploit the valid information of compressed and raw videos to guide the quality enhancement for compressed videos. In this paper, we propose a unique Valid Information Guidance scheme (VIG) to enhance the quality of compressed videos by mining valid information from both compressed videos and raw videos. Specifically, we propose an efficient framework, Compressed Redundancy Filtering (CRF) network, to balance speed and enhancement. After removing the redundancy by filtering the information, CRF can use the valid information of the compressed video to reconstruct the texture. Furthermore, we propose a progressive Truth Guidance Distillation (TGD) strategy, which does not need to design additional teacher models and distillation loss functions. By only using the ground truth as input to guide the model to aggregate the correct spatio-temporal correspondence across the raw frames, TGD can significantly improve the enhancement effect without increasing the extra training cost. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of accuracy and efficiency.

cs.CV

NFL: Robust Learned Index via Distribution Transformation

Recent works on learned index open a new direction for the indexing field. The key insight of the learned index is to approximate the mapping between keys and positions with piece-wise linear functions. Such methods require partitioning key space for a better approximation. Although lots of heuristics are proposed to improve the approximation quality, the bottleneck is that the segmentation overheads could hinder the overall performance. This paper tackles the approximation problem by applying a \textit{distribution transformation} to the keys before constructing the learned index. A two-stage Normalizing-Flow-based Learned index framework (NFL) is proposed, which first transforms the original complex key distribution into a near-uniform distribution, then builds a learned index leveraging the transformed keys. For effective distribution transformation, we propose a Numerical Normalizing Flow (Numerical NF). Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI). To validate the performance, comprehensive evaluations are conducted on both synthetic and real-world workloads, which shows that the proposed NFL produces the highest throughput and the lowest tail latency compared to the state-of-the-art learned indexes.

cs.DB

The multi elliptic-localized solutions and their asymptotic behaviors for the mKdV equation

We mainly construct and analyze the multi elliptic-localized solutions under the background of elliptic function solutions for the focusing modified Korteweg-de Vries (mKdV) equation. Based on the Darboux-B\"{a}cklund transformation, we provide a uniform expression for these solutions by the Jacobi theta functions. The asymptotic behaviors of multi elliptic-localized solutions are provided directly in two categories. By the consistent asymptotic expression of those solutions, we obtain that the collisions between the elliptic-breathers/solitons are elastic. Moreover, a sufficient condition of the strictly elastic collision between the solitons and breathers has been given by the symmetric analysis. In addition, as $k\rightarrow0^{+}$, the multi elliptic-localized solutions degenerate into solitons, breathers or soliton-breather solutions, which implies that we extend the solutions from the constant and vanishing backgrounds to the periodic solutions backgrounds. Moreover, we illustrate figures of the multi elliptic-localized solutions to visualize the above analysis.

nlin.SI

Tailoring Luminescent Properties of SrS:Ce by Modulating Defects: Sr-Deficiency and Na+ Doping

Ce3+ doped SrS phosphors with a charge-compensating Na+ for light-emitting diode (LED) applications have been successfully synthesized via a solid-state reaction method, which can be indexed to rock-salt-like crystal structures of Fm-3m space group. SrS:(Ce3+)x(x=0.005-0.05) and SrS:(Ce3+)0.01,(Na+)y(y=0.005-0.030) phosphors were excited by 430nm UV-VIS light, associated to the 5d1-4f1 transition of Ce3+. The composition-optimized SrS:(Ce3+)0.01,(Na+)0.015 phosphors showed an intense broad emission band at 430-700nm. The doping of Na+ was probed by solid-state nuclear magnetic resonance. The 430 nm pumped with white LED (w-LED) combining SrS:(Ce3+)0.01,(Na+)0.015 phosphors and Sr2Si5N8:Eu2+ phosphors shows a color-rendering index (Ra) of 89.7. The proposed strategy provides new avenues for design and realization of novel high color quality solid-state lighting emitting diodes (SS-LEDS).

cond-mat.mtrl-sci

Stability of elliptic function solutions for the focusing modified KdV equation

We study the spectral and orbital stability of elliptic function solutions for the focusing modified Korteweg-de Vries (mKdV) equation and construct the corresponding breather solutions to exhibit the stable or unstable dynamic behavior. The elliptic function solutions of the mKdV equation and related fundamental solutions of the Lax pair are exactly represented by theta functions. Based on the `modified squared wavefunction' (MSW) method, we construct all linear independent solutions of the linearized mKdV equation and then provide a necessary and sufficient condition of the spectral stability for elliptic function solutions with respect to subharmonic perturbations. In the case of spectrum stability, the orbital stability of elliptic function solutions is established in a suitable Hilbert space. Using Darboux-B\"acklund transformation, we construct breather solutions to exhibit unstable or stable dynamic behavior. Through analyzing the asymptotic behavior, we find that the breather solution under the cn-type solution background is equivalent to the elliptic function solution adding a small perturbation as $t\to\pm\infty$.

nlin.SI

Improved limits on Fierz Interference using asymmetry measurements from the UCNA experiment

The Ultracold Neutron Asymmetry (UCNA) experiment was designed to measure the $\beta$-decay asymmetry parameter, $A_0$, for free neutron decay. In the experiment, polarized ultracold neutrons are transported into a decay trap, and their $\beta$-decay electrons are detected with $\approx 4\pi$ acceptance into two detector packages which provide position and energy reconstruction. The experiment also has sensitivity to $b_{n}$, the Fierz interference term in the neutron $\beta$-decay rate. In this work, we determine $b_{n}$ from the energy dependence of $A_0$ using the data taken during the UCNA 2011-2013 run. In addition, we present the same type of analysis using the earlier 2010 $A$ dataset. Motivated by improved statistics and comparable systematic errors compared to the 2010 data-taking run, we present a new $b_{n}$ measurement using the weighted average of our asymmetry dataset fits, to obtain $b_{n} = 0.066 \pm 0.041_{\text{stat}} \pm 0.024_{\text{syst}}$ which corresponds to a limit of $-0.012 < b_{n} < 0.144$ at the 90% confidence level.

nucl-ex

Electronic effect of doped oxygen atoms in Bi2201 superconductors determined by scanning tunneling microscopy

The oxygen dopants are essential in tuning electronic properties of Bi$_2$Sr$_2$Ca$_{n-1}$Cu$_n$O$_{2n+4+\delta}$ superconductors. Here we apply the technique of scanning tunneling microscopy and spectroscopy to study the influence of oxygen dopants in an optimally doped Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+\delta}$ and an overdoped Bi$_{2-y}$Pb$_y$Sr$_2$CuO$_{6+\delta}$. In both samples, we find that interstitial oxygen atoms on the SrO layers dominate over the other two forms of oxygen dopants, oxygen vacancies on the SrO layers and interstitial oxygen atoms on the BiO layers. The hole doping is estimated from the oxygen concentration, as compared to the result extracted from the measured Fermi surface. The precise spatial location is employed to obtain a negative correlation between the oxygen dopants and the inhomogeneous pseudogap.

cond-mat.supr-con