arXiv subjects
Publications and source records attributed to Ze Li.
For every integer $d\ge2$ and every subsonic speed $c\in(0,1)$, we construct a nonconstant finite-energy travelling wave for the easy-plane Landau-Lifshitz equation on $\mathbb{R}^d$. This addresses the existence problem raised in the numerical work of Papanicolaou and Spathis. The profiles satisfy the travelling-wave equation in the sense of distributions and are smooth in dimension two.
Building on the work of Song--Waldron~\cite{SongWaldron2024}, we establish stronger regularity and refined blow-up analysis results for almost-holomorphic harmonic map flow from a closed surface into a compact Kähler manifold with nonnegative holomorphic bisectional curvature. At each finite-time singularity, we improve the outer energy scale to $O((T-t)/\sqrt{|\ln(T-t)|})$. We also prove that both the stress-energy tensor and the tension field converge strongly in $L^2$ to those of the body map, by ruling out concentration of their $L^2$ energy near the singular points. Consequently, the body map belongs to $W^{2,2}$. Finally, an explicit gap theorem for the antiholomorphic energy of nonholomorphic harmonic spheres yields an explicit admissible threshold for the initial antiholomorphic energy under which these conclusions hold.
The mobility of potential eavesdroppers (Eves) poses a critical challenge to physical-layer security (PLS) in integrated sensing and communication (ISAC) systems, since rapidly varying Eve positions lead to time-varying eavesdropping channels and outdated security decisions. To address this challenge, this paper investigates sensing-driven secure transmission for reconfigurable intelligent surface (RIS)-assisted ISAC systems with a maneuvering Eve. To this end, by exploiting the dual functionality of the ISAC waveform, a multi-view angle estimation method jointly exploiting the base station (BS) and RIS is developed based on MUSIC and maximum likelihood estimation (MLE), enabling three-dimensional (3D) Eve localization, whose accuracy is characterized by the Cramer-Rao lower bound (CRLB). Subsequently, an interacting multiple model extended Kalman filter (IMM-EKF) with out-of-sequence measurement (OOSM) compensation is employed to track and predict the maneuvering Eve. Based on the predicted trajectory, the time-varying eavesdropping channel is updated, and the transmit beamforming and RIS phase shifts are jointly optimized subject to a sensing CRLB constraint. Furthermore, an alternating optimization (AO) algorithm integrating fractional programming, semidefinite relaxation (SDR), and Riemannian manifold optimization is developed to solve the resulting non-convex problem. Finally, numerical results demonstrate the effectiveness of the proposed approach in accurately tracking maneuvering Eves and enhancing secure transmission in dynamic ISAC environments.
In this paper, we study the long time dynamics of small perturbations of static domain walls in weighted Sobolev spaces for the one dimensional Schrödinger map with constant Dzyaloshinskii-Moriya interaction. The perturbation equation contains derivative nonlinearities, and its linearized operator has a threshold resonance. The results are twofold. (i) We prove the asymptotic stability of the domain wall modulo the translation and rotation symmetries. More precisely, the corresponding modulation parameters converge as time tends to infinity, while the radiation decays at the sharp rate $t^{-1/2}$. (ii) We prove that the radiation exhibits modified scattering, with an explicit logarithmic phase correction generated by the long range cubic interaction. The proof develops geometric reduction to overcome the loss of derivatives and exploits structural cancellations in the nonlinear interactions.
Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV has shown that both acoustic and contextual information are important for multilingual speaker verification. Inspired by this, we investigate whether the same holds for attacker ASV on anonymized speech. We evaluate both acoustic- and content-oriented attackers on multilingual anonymized speech and construct a multilingual voice-converted dataset to improve cross-lingual generalization. Our results show that attacker effectiveness depends on the linguistic utility of the anonymized speech. Overall, acoustic-oriented attackers achieve better performance. However, when linguistic information is well preserved, the performance gap between content- and acoustic-oriented attackers narrows compared with conditions involving stronger speech distortion. The multilingual voice-converted dataset further improves performance and partially reduces the cross-lingual gap. These findings highlight the need for more comprehensive attacker modeling and evaluation protocols that consider both privacy and utility, rather than relying on a attacker strategy\footnote{Full code and pretrained models and MultiVC Dataset link are available at: https://github.com/monkeyDarefeen/DAST
This paper investigates conditional stability for inverse source problems for the heat equation with a known temporal factor and an unknown spatial component in a bounded $C^{1,1}$ domain. We focus on observations supported on sets of low Hausdorff dimension and establish conditional stability in this setting. For boundary observations on compact sets of positive $q$-dimensional Hausdorff content, we establish logarithmic stability from full-time boundary flux observations and double-logarithmic stability from delayed-time boundary flux observations. The admissible dimensional ranges are $q>d-2$ when the observation set is contained in a flat boundary patch and $q>d-1-c_{d+1}$ on a general $C^{1,1}$ boundary, where $c_{d+1}>0$ depends only on the dimension. A key ingredient in deriving these results is a new boundary spectral inequality for the Dirichlet Laplacian, which controls a finite Dirichlet spectral sum through observations of the normal derivative of its elliptic extension on such a boundary set. Our results also cover inverse heat source problems with interior observations on sets of positive $q$-dimensional Hausdorff content for some $q>d-1$, yielding logarithmic stability from full-time observations for general sources in $H_0^1(Ω)$ and Hölder stability from terminal-time observations for sources in a suitable spectral Gevrey class.
We present a multi-wavelength study of the quasar SDSS J141945.50$+$524648.0 ($z=1.1599$), a member of the newly identified population of quasar-associated $2175\,\mathring{\mathrm{A}}$ dust absorbers. Utilizing JWST/NIRCam observations from the SAPPHIRES survey and archival data, we analyze the prominent $2175\,\mathring{\mathrm{A}}$ extinction bump detected in this object. The bump parameters are highly consistent with the Milky Way extinction curve, implying similar dust properties. Spectral analysis reveals a $\mathrm{Mg\,II}$ absorption doublet near the systemic velocity. Joint fitting of the $\mathrm{Mg\,II}$ and $\mathrm{Fe\,II}$ absorption lines favors a partial-covering model, yielding $C_{f,\rm BEL}=0.11^{+0.12}_{-0.07}$. Together with the small velocity offset ($Δv\approx96~{\rm km\,s^{-1}}$), these results favor an intrinsic absorber rather than an intervening system. Infrared SED modeling and double-peaked $\mathrm{Pa\,α}$ emission indicate consistent orientations of the dusty torus ($θ\approx60^{+7}_{-8}$ deg) and accretion disk ($i=63.7^{+10.9}_{-7.8}$ deg), suggesting a rim-penetrating line of sight through the torus. This configuration naturally explains the infrared emission, continuum extinction, and associated $2175\,\mathring{\mathrm{A}}$ bump, although a contribution from the host-galaxy ISM cannot be excluded. If associated with the torus, the carbonaceous carriers of the $2175\,\mathring{\mathrm{A}}$ feature may survive the intense AGN radiation field through localized shielding in optically thick dusty clumps. These results highlight the role of viewing geometry and dust distribution in regulating dust survival in quasar environments.
The main results of this paper are threefold. First, we prove an observability inequality for the Airy equation on the real line from Hausdorff-thick sets in a time-trace sense for every observation time $T>0$. The observation functional is a block supremum of $L^2(0,T)$ time traces over the Hausdorff-thick set. Second, we prove observability inequalities for the Airy equation on the real line with observations on some periodic sets, which in particular yields observability on a class of spatial point sequences. Third, we give a necessary and sufficient condition for finite point observability for the Airy equation on the torus with a bounded real-valued potential. Indeed, for a finite observation set $F$, a Kalman rank condition on a finite-dimensional invariant subspace is found. As a corollary, we obtain sharp point observability results for the Airy and linear KdV equations on the torus. Moreover, for potentials with finite order regularity assumptions we prove that every observation set with an accumulation point, in particular any set of positive Hausdorff dimension, gives an observability inequality for each observation time $T>0$. Since the Airy equation has neither high frequency exponential decay nor pointwise smoothing effects, which are essential in recent works on Hausdorff type observation results on heat equations, we introduce several new ideas adapted to the Airy case.
We studied the upper bounds of the holographic entanglement entropy growth rate for thermofield double (TFD) states. By comparing the cases of vacuum AdS and charged AdS black holes, we conjecture: for all static planar or spherically symmetric asymptotically Schwarzschild-AdS black holes of same mass density or entropy density, the vacuum AdS black hole gives the maximum entanglement entropy growth rate. We gave proofs by assuming dominant energy condition. We also considered the AdS black hole spacetime with real scalar fields case, where the scalar fields violate the dominant energy condition and the bulk geometry is not asymptotically Schwarzschild-AdS. Numerical results show that this case vacuum black hole still has maximal growth rate if we fixed entropy. However, in the case of fixed energy, vacuum case has maximal growth rate of entanglement entropy only under standard quantization scheme.
In holographic duality, the total energy of the dual field theory is obtained from the holographic renormalization, which depends not only on the bulk geometry but also on the choice of quantization schemes. We point out that the validity of several widely studied holographic inequalities -- including the AdS Penrose inequality, the late-time bound on entanglement entropy growth, and the growth-rate limits of CV and CA complexities -- depends on the choice of quantization schemes. Motivated by this issue, we introduce a modified total energy, which is still computed via holographic renormalization but the final value is independent of the choice of quantization schemes. We verify that this modified energy removes the apparent violations of these bounds that arise from quantization-scheme dependence in the model of massive scalar field. Our results suggest that our modified total energy provides a more robust notion of energy when we talk about above inequalities in holographic settings.
Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven decisions under partial observability. We present \textbf{ActionNex}, a production-grade agentic system that supports end-to-end outage assistance, including real-time updates, knowledge distillation, and role- and stage-conditioned next-best action recommendations. ActionNex ingests multimodal operational signals (e.g., outage content, telemetry, and human communications) and compresses them into critical events that represent meaningful state transitions. It couples this perception layer with a hierarchical memory subsystem: long-term Key-Condition-Action (KCA) knowledge distilled from playbooks and historical executions, episodic memory of prior outages, and working memory of the live context. A reasoning agent aligns current critical events to preconditions, retrieves relevant memories, and generates actionable recommendations; executed human actions serve as an implicit feedback signal to enable continual self-evolution in a human-agent hybrid system. We evaluate ActionNex on eight real Azure outages (8M tokens, 4,000 critical events) using two complementary ground-truth action sets, achieving 71.4\% precision and 52.8-54.8\% recall. The system has been piloted in production and has received positive early feedback.
Large-scale self-supervised Pre-Trained Models (PTMs) have shown significant improvements in the speaker verification (SV) task by providing rich feature representations. In this paper, we utilize w2v-BERT 2.0, a model with approximately 600 million parameters trained on 4.5 million hours of unlabeled data across 143 languages, for the SV task. The MFA structure with Layer Adapter is employed to process the multi-layer feature outputs from the PTM and extract speaker embeddings. Additionally, we incorporate LoRA for efficient fine-tuning. Our model achieves state-of-the-art results with 0.12% and 0.55% EER on the Vox1-O and Vox1-H test sets, respectively. Furthermore, we apply knowledge distillation guided structured pruning, reducing the model size by 80% while achieving only a 0.04% EER degradation. Source code and models are released at https://github.com/ZXHY-82/w2v-BERT-2.0_SV.
Multilingual speaker verification (SV) remains challenging due to limited cross-lingual data and language-dependent information in speaker embeddings. This paper presents a language-invariant multilingual SV system for the TidyVoice 2026 Challenge. We adopt the multilingual self-supervised w2v-BERT 2.0 model as the backbone, enhanced with Layer Adapters and Multi-scale Feature Aggregation to better exploit multi-layer representations. A language-adversarial training strategy with a Gradient Reversal Layer is applied to promote language-invariant speaker embeddings. Moreover, a multilingual zero-shot text-to-speech system is used to synthesize speech in multiple languages, improving language diversity. Experimental results demonstrate that fine-tuning the large-scale pretrained model yields competitive performance, while language-adversarial training further enhances robustness. In addition, synthetic speech augmentation provides additional gains under limited training data conditions. Source code is available at https://github.com/ZXHY-82/LI-MSV-TidyVoice2026.
Novel view synthesis from low-light, noisy, and motion-blurred imagery remains a valuable and challenging task. Current volumetric rendering methods struggle with compound degradation, and sequential 2D preprocessing introduces artifacts due to interdependencies. In this work, we introduce FLED-GS, a fast low-light enhancement and deblurring framework that reformulates 3D scene restoration as an alternating cycle of enhancement and reconstruction. Specifically, FLED-GS inserts several intermediate brightness anchors to enable progressive recovery, preventing noise blow-up from harming deblurring or geometry. Each iteration sharpens inputs with an off-the-shelf 2D deblurrer and then performs noise-aware 3DGS reconstruction that estimates and suppresses noise while producing clean priors for the next level. Experiments show FLED-GS outperforms state-of-the-art LuSh-NeRF, achieving 21$\times$ faster training and 11$\times$ faster rendering.
Speaker embedding based zero-shot Text-to-Speech (TTS) systems enable high-quality speech synthesis for unseen speakers using minimal data. However, these systems are vulnerable to adversarial attacks, where an attacker introduces imperceptible perturbations to the original speaker's audio waveform, leading to synthesized speech sounds like another person. This vulnerability poses significant security risks, including speaker identity spoofing and unauthorized voice manipulation. This paper investigates two primary defense strategies to address these threats: adversarial training and adversarial purification. Adversarial training enhances the model's robustness by integrating adversarial examples during the training process, thereby improving resistance to such attacks. Adversarial purification, on the other hand, employs diffusion probabilistic models to revert adversarially perturbed audio to its clean form. Experimental results demonstrate that these defense mechanisms can significantly reduce the impact of adversarial perturbations, enhancing the security and reliability of speaker embedding based zero-shot TTS systems in adversarial environments.
We construct new black holes using the curvature-induced scalarization mechanism in Einstein gravity coupled to a massless phantom scalar. We find that the general wormhole solutions with independent scalar charge become descalarized so that the resulting black hole scalar hair becomes secondary and a function of the mass only. The long-range force between the two identical black holes can be attractive, zero or repulsive, depending on the mass/scalar charge relation. Furthermore, the black hole mass can be negative. Our finding suggests that exotic matter responsible for wormholes can also lead to exotic black holes.
We numerically construct new charged Taub-NUT-type black hole solutions in four-dimensional Einstein-Weyl-Maxwell theory. We explore the effects of the NUT parameter and the electric charge parameter on the black hole solutions in great detail. Compared with static spherical black holes in the same theory, there is one major difference: there are three branches of NUT solutions, whereas there are only two branches of static spherical black hole solutions. Compared with neutral NUT solutions in Weyl gravity, where the solutions intersect with each other, the charged NUT solutions we find are disconnected.
Synthesizing normal-light novel views from low-light multiview images is an important yet challenging task, given the low visibility and high ISO noise present in the input images. Existing low-light enhancement methods often struggle to effectively preprocess such low-light inputs, as they fail to consider correlations among multiple views. Although other state-of-the-art methods have introduced illumination-related components offering alternative solutions to the problem, they often result in drawbacks such as color distortions and artifacts, and they provide limited denoising effectiveness. In this paper, we propose a novel Robust Low-light Scene Restoration framework (RoSe), which enables effective synthesis of novel views in normal lighting conditions from low-light multiview image inputs, by formulating the task as an illuminance transition estimation problem in 3D space, conceptualizing it as a specialized rendering task. This multiview-consistent illuminance transition field establishes a robust connection between low-light and normal-light conditions. By further exploiting the inherent low-rank property of illumination to constrain the transition representation, we achieve more effective denoising without complex 2D techniques or explicit noise modeling. To implement RoSe, we design a concise dual-branch architecture and introduce a low-rank denoising module. Experiments demonstrate that RoSe significantly outperforms state-of-the-art models in both rendering quality and multiview consistency on standard benchmarks. The codes and data are available at https://pegasus2004.github.io/RoSe.