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Di Wu

Publications and source records attributed to Di Wu.

At least 37 records · Page 2Linked to original sources
arXiv2026

SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS

Automatic video dubbing aims to generate high-fidelity speech that is temporally aligned with visual content. However, existing methods still suffer from limited speech naturalness, insufficient audio-visual synchronization, and poor scalability beyond monolingual settings. To address these challenges, we propose SyncVoice, a simple and effective dubbing framework that lightly integrates a Text-Visual Fusion Module into a pretrained text-to-speech (TTS) system. This module aligns visual features with linguistic representations, enabling temporally synchronized speech synthesis without complex architectural redesign. Experiments on the LRS3 dataset show that SyncVoice achieves state-of-the-art performance in zero-shot dubbing. Further training on a large-scale bilingual audio-visual dataset improves vocal fidelity while preserving synchronization, yielding a single unified model for both Chinese and English dubbing.

eess.AS↗
arXiv2026

Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perception but typically relies on centralized training, where multi-source SAR data are uploaded to a single server, raising privacy and security concerns. Federated learning (FL) provides an emerging computational intelligence paradigm for SAR image target recognition, enabling cross-site collaboration while preserving local data privacy. However, FL confronts critical security risks, where malicious clients can exploit SAR's multiplicative speckle noise to conceal backdoor triggers, severely challenging the robustness of the computational intelligence model. To address this challenge, we propose NADAFD, a noise-aware and dynamically adaptive federated defense framework that integrates frequency-domain, spatial-domain, and client-behavior analyses to counter SAR-specific backdoor threats. Specifically, we introduce a frequency-domain collaborative inversion mechanism to expose cross-client spectral inconsistencies indicative of hidden backdoor triggers. We further design a noise-aware adversarial training strategy that embeds $Γ$-distributed speckle characteristics into mask-guided adversarial sample generation to enhance robustness against both backdoor attacks and SAR speckle noise. In addition, we present a dynamic health assessment module that tracks client update behaviors across training rounds and adaptively adjusts aggregation weights to mitigate evolving malicious contributions. Experiments on MSTAR and OpenSARShip datasets demonstrate that NADAFD achieves higher accuracy on clean test samples and a lower backdoor attack success rate on triggered inputs than existing federated backdoor defenses for SAR target recognition.

cs.CR↗
arXiv2026

SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection

Detecting harmful memes is critical for maintaining safe online communities. However, harmful intent is often implicit, arising from visual-textual incongruity and cultural stereotypes, which challenges existing multimodal detectors. We propose SyRHM, a framework that decomposes harmful meme detection into meaning-grounded retrieval and symbolic-language-enhanced multi-stage reasoning. SyRHM retrieves semantically related memes by parsing multimodal content into textual elements and descriptions, providing grounded context beyond surface-level similarity. Building on the retrieved context, SyRHM uses a translator stage to convert multimodal inputs into symbolic intermediate representations, and then performs multi-stage reasoning via planner and solver stages, enabling expressive and interpretable analysis of harmful intent. Experiments on FHM, HarM, and MultiOff demonstrate the effectiveness of SyRHM, achieving superior performance on most evaluation settings against multimodal and reasoning-based baselines, while providing reasoning traces for harmful content. The code is available at: https://github.com/Scabbards1500/SyRHM

cs.CL↗
arXiv2026

Structure, Coloring, and Perfect Divisibility of $(P_2\cup P_4, C_3)$-Free Graphs

Goedgebeur and Schaudt [J. Graph Theory 87 (2018), 188-207] conjectured that every $4$-vertex-critical $(P_7,C_3)$-free graph belongs to a family of seven explicitly defined graphs. In this paper, we establish a structural theorem for connected $(P_2\cup P_4,C_3)$-free graphs. As a consequence, we prove that the Mycielski-Grötzsch graph is the unique $4$-vertex-critical graph in this class, thereby confirming the conjecture of Goedgebeur and Schaudt for $(P_2\cup P_4,C_3)$-free graphs. Our structural theorem also yields a characterization of the chromatic number of these graphs and an $O(n^4)$-time algorithm for deciding whether an $n$-vertex $(P_2\cup P_4,C_3)$-free graph is $3$-colorable. We further study perfect divisibility in the larger class of $(P_2\cup P_4,\text{bull})$-free graphs. We prove that a $(P_2\cup P_4,\text{bull})$-free graph is perfectly divisible if and only if it is Mycielski-Grötzsch graph-free. This result generalizes the main theorem of Deng and Chang [Graphs Combin. 41 (2025), 63].

math.CO↗
arXiv2026

Multihair thermodynamics of Kerr-Newman-NUT-AdS$_4$ spacetimes

In this paper, we develop a multihair thermodynamic description of Lorentzian Kerr-Newman-NUT-AdS$_4$ spacetime in which rotation, electric charge, AdS pressure and polar conical tensions enter a homogeneous Christodoulou-Ruffini-type squared-mass formula. The NUT contribution is described by a rotation-like secondary hair $J_n=mn/K^2$ and a charge-like secondary hair $N=n/\sqrt K$. Both depend on the metric parameters of the physical solution family but are varied independently in the enlarged thermodynamic state space. The mass formula yields the first law and Smarr relation in a single enthalpy representation, with the area entropy and Hawking temperature. The Kerr-Newman-AdS, asymptotically flat Kerr-Newman-NUT and nonrotating RN-NUT-AdS limits check the angular normalization and the NUT and pressure contributions. We also consider alternative NUT variables, including the conformal dual mass, and derive the corresponding changes in thermodynamic volume and conical-defect lengths. This description applies to the chosen homogeneous state space and does not require the secondary hairs to be independent asymptotic or Misner-string surface charges.

hep-th↗
arXiv2026

Nonparametric Hypothesis Testing of High-dimensional Clustering With Application to Single-cell RNA Data

Single-cell RNA sequencing studies routinely use clustering to define putative cell types and cell states, yet the observed separation may arise from sampling variability rather than genuine biological heterogeneity. This paper studies formal significance testing of such clustering structure in high-dimensional data. Existing SigClust methods assess clustering significance through Monte Carlo simulation under a Gaussian single-cluster null, but this assumption can be unreliable for normalized gene expression data and other non-Gaussian settings. We propose SigClust-LCP, a nonparametric extension that models a single cluster by a log-concave distribution. To make this approach computationally feasible in moderate to high dimensions, we develop a score-matching estimator for log-concave projection inspired by recent generative modeling ideas. We establish theoretical guarantees for the estimator and for its use in clustering significance testing. Simulations show that SigClust-LCP controls Type-I error more reliably than existing methods across a range of unimodal and mixture distributions while retaining competitive power. In a single-cell RNA sequencing analysis of Hydra cells, the method avoids spurious subclusters within annotated cell populations and supports biologically meaningful separation across lineages and body-axis regions.

stat.ME↗
arXiv2026

CNA: An AI-Oriented Comprehensive Normalized Assessment for Healthy Status and Application to Optimize RRT Strategies by Reinforcement Learning

Millions worldwide require Renal Replacement Therapy (RRT) as a treatment essential for survival. However, optimizing RRT strategies via AI is challenging due to heterogeneous patient dynamics, missing data, and the absence of an AI-oriented health assessment criterion. We propose an AI-Oriented Comprehensive Normalized Assessment (CNA) for healthy status and apply it to optimize RRT strategies by using offline reinforcement learning (RL). The key idea of CNA is transforming vital-sign distributions into a standard normal space, enabling a unified, data-driven health-status score defined by deviations from referent intervals, which also provides an AI-oriented criterion to assess strategy quality and supports RL termination. We further design a structured 23-dimensional state representation that integrates 19 indicators with 4 RRT descriptors, and employ matrix decomposition to reconstruct missing vital signs, improving data completeness for learning. These components are incorporated into multiple offline RL algorithms and validated via systematic ablation studies on RRT feature subsets. Compared with physicians' observed treatments, the best learned strategy reduces mortality from 13.2% to 5.0% (reducing 62.24%) and shortens average in-hospital stay from 308.5 to 250.1 hours (reducing 18.93%), demonstrating both methodological innovation and the potential of CNA-guided RL to improve RRT outcomes in nephrology.

cs.LG↗
arXiv2026

Topological changes and response singularities in black hole thermodynamic branch structure

In this paper, we investigate how different diagnostics characterize the thermodynamic branch structure of black holes. In three charged AdS black hole examples, the thermodynamic topological transition point $τ_a$, where the total topological number changes, is separated from the point $τ_b$ where the branch response considered here, evaluated along a path with fixed charge parameters, becomes singular. Using the zero-point curves $τ(r_h)$, we identify the local mechanisms behind this separation. At $τ_a$, a zero point with nonzero winding enters or leaves the positive-radius physical branch set through the boundary of the counted domain, changing $W$ without making the corresponding positive-radius branch singular. At $τ_b$, the zero-point curve develops a finite-radius turning point, where two branches with opposite winding numbers annihilate and the branch response diverges. Thus, in the analyzed examples, a change in the topological number records a change in branch membership, whereas a response singularity records a loss of local invertibility along an admitted branch. The two diagnostics therefore probe different local events of the same zero-point branch geometry.

hep-th↗
arXiv2026

Angular power spectrum of induced gravitational waves: breaking model and parameter degeneracies in PTA observations

Large primordial curvature and tensor perturbations on small scales can generate second-order tensor-scalar induced gravitational waves (TSIGWs) through scalar-scalar, tensor-scalar, and tensor-tensor source terms. TSIGWs are recognized as one of the principal contributors to the stochastic gravitational wave background (SGWB) observations. We explore the energy density spectrum of second-order TSIGWs in the presence of non-Gaussian primordial curvature perturbations. Furthermore, we present the first calculation of the anisotropic angular power spectrum associated with second-order TSIGWs, which provides a promising tool to discriminate them from other SGWB sources.

astro-ph.CO↗
arXiv2026

Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation

Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answer attempts depend on earlier ones, and parallel, such as i.i.d. sampling with reranking. In this study, we investigate their properties in translation. First, our study shows that sequential sampling has a higher performance ceiling, providing a more diverse and effective pool of samples, particularly under smaller sampling budgets. Second, we interrogate the nature of test-time scaling through a multidimensional manual analysis. Human analysis of the Best-of-N translations demonstrates that sequential sampling substantially improves translation fluency and naturalness, but can degrade accuracy when inference budgets are large. Finally, we suggest an explanation of the mechanism through which sequential scaling improves machine translation. Our controlled analysis partially attributes the success of sequential self-improvement to the model's access to a larger target-side context. Ablation experiments on sequential sampling demonstrate its robustness across different sampling temperatures, while also revealing sensitivity to context construction, suggesting directions for future improvement.

cs.CL↗
arXiv2026

Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction

Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter benchmark for dexterous decoding. The underlying data-algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.

cs.RO↗
arXiv2026

SkillZip Pro: Execution-Aware Dynamic Compression of Progressively Loaded Skills for Self-Evolving Agents

Production agent skills are directory bundles, not isolated prompts. The root is loaded at activation; references, schemas, scripts, assets, and nested subskills are loaded only when an execution path needs them. Compressing only the root misses most deployment cost and may move branch-specific details into the always-loaded context. Flattening instead destroys progressive-loading boundaries. We introduce \method, an evaluation-free compressor for complete, progressively loaded skill bundles. It leaves the agent harness unchanged and emits an ordinary directory. The method combines two safeguards. First, it compresses \emph{across files}, removing content from a reference or subskill when the root or a declared environment contract already provides it. Second, it preserves routing, so every required file and directly callable entry remains reachable after rewriting. Users can configure \method along two independent axes. \emph{One-Shot} mode rebuilds the full bundle; \emph{Continual} mode reuses state and applies Zip-on-Write after each evolution patch. \emph{Persistent} compression rewrites the shipped bundle to reduce storage and runtime context. \emph{Transient} compression keeps that bundle byte-identical and builds a task-specific view, reducing only per-run context after build cost. Entry contracts mark private, public, and conditional resources; a multi-entry audit preserves standalone public subskills. On a production content-moderation skill evaluated by our industrial multi-round harness, \method removes \hl{38\%} of skill bundle tokens and \hl{10.4\%} of end-to-end per-run tokens with no quality loss, while an unprotected 71\% configuration loses up to 26 accuracy points to one-sided false positives. On a multi-entry bundle, \method effeciently reduces token cost while near-perfectly preserving every route and public entry.

cs.AI↗
arXiv2026

A-Graph: A Unified Graph Representation for Cross-Stack Cost Modeling

As computer systems continue to diversify across technologies, architectures, applications, and beyond, the relevant design space has become larger and more complex. Given such trends, design space exploration (DSE) at early stages is critical to ensure agile development towards optimal performance and cost. Industry-grade EDA tools directly take in RTL code and report accurate results, but do not perform DSE. Recent works have attempted to explore the design space via simulation. However, most of these works are domain-specific and constrain the space that users are allowed to explore, offering limited flexibility between technologies, architecture, and applications. Moreover, they often demand high domain expertise to ensure high accuracy. To enable simulation that is agnostic to technology, architecture, and application at any granularity, we introduce Architecture-Graph (Agraph), a graph that unifies the system representation surrounding any arbitrary application, software, architecture, and circuit. Such a unified representation distinguishes Agraph from prior works, which focus on a single stack, allowing users to freely explore the design space across system stacks. To fully unleash the potential of Agraph, we further present Archx, a framework that implements Agraph. Archx is user-friendly in two ways. First, Archx has an easy-to-use programming interface to automatically generate and sweep design points under user constraints, boosting the programmability. Second, Archx adopts scope-based metric retrieval to analyze and understand each design point at any user-preferred hierarchy, enhancing the explainability. We conduct case studies that demonstrate Agraph's generalization across technologies, architecture, and applications with high simulation accuracy. Overall, we argue that Agraph and Archx serve as a foundation to simulate both performance and cost at will.

cs.PF↗
arXiv2026

TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation

Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into language systems for tactile commonsense reasoning, scaling such systems to realistic open-world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, providing insufficient supervision for reasoning from tactile observations to physical commonsense and hindering the learning of transferable tactile commonsense; (2) tactile signals are inherently redundant and action-specific, yet existing methods often overlook these properties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile-language framework that scales tactile commonsense reasoning to the open world from both data and representation perspectives. First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering 415 objects, 8 scenarios, and 7 sensor types, providing a solid data foundation for open-world generalization. We further introduce TouchThinker-Bench, an open-world benchmark with more realistic and diverse tasks. Then, we propose action-aware modeling mechanism to improve tactile representation efficiency and enable efficient reasoning. Experimental results demonstrate that TouchThinker achieves competitive performance against state-of-the-art models across multiple datasets. Our code and dataset will be made available at: https://github.com/lvkailin0118/TouchThinker.

cs.AI↗
arXiv2026

Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation

Data-Free Knowledge Distillation (DFKD) preserves privacy by transferring knowledge without real data access. However, existing generator-based DFKD methods suffer from over-reliance on teacher preferences and pattern collapse, exhibiting "generative shortcut learning" in the frequency domain: dependent on specific frequency components and frequency positions, resulting in inconsistent synthetic image quality and class diversity. In this paper, we propose a CSWL framework aimed at introducing insights from the frequency domain perspective to improve generator diversity and training stability to Close the phenomenon of Shortcut learning to Win in the Longer term. To address the issue of generative shortcut learning, we introduce frequency-domain augmentation at the feature level, encouraging the generator to attend to the full frequency spectrum and thereby suppress shortcut learning behavior. To tackle training instability, we propose a Cross-Stage Frequency Reconstruction (CSFR) auxiliary task, which implicitly constructs an Exponential Moving Average (EMA) mechanism to promote long-term optimization and stability. Extensive experiments, including downstream tasks and various image recognition datasets at multiple resolutions, validate the effectiveness of CSWL in improving both diversity and stability from the frequency view.

cs.CV↗
arXiv2026

Diffractive Sail H-Reversal Trajectory: Theoretical Feasibility, Design Strategies, and Applications

With the growing threat of near-Earth asteroid, planetary defense serves as a vital shield against catastrophic disasters. Kinetic impact utilizing an angular momentum reversal (H-reversal) trajectory of a solar sail is a highly advantageous defense approach. However, traditional reflective sails (RS) are constrained during these maneuvers by attitude-thrust coupling and a degradation of solar radiation pressure utilization at the high cone angles required for transverse acceleration. To enhance impact performance and simplify control, this paper proposes an H-reversal impact scheme utilizing a Sun-facing diffractive sail (SFDS) under a one-stage diffraction angle θd strategy and a two-stage θd strategy. The feasible parameter spaces for both strategies were mapped using the hodograph method. Tailored trajectory design methods were established for both strategies based on the feasibility analysis. Apophis impact scenario was considered, and the corresponding trajectories were constructed. Simulations demonstrate that the one-stage θd SFDS outperforms RS through a 21% increase in the impact velocity and a 35% decrease in the mission duration. Furthermore, the proposed two-stage θd strategy yields an additional 9km/s gain in impact velocity. By utilizing SFDS H-reversal trajectories, this research establishes an emergency planetary defense framework characterized by rapid response and high kinetic energy.

astro-ph.EP↗
arXiv2026

Efficient production of $^{229m,g}$Th via neutron capture in VUV-transparent crystals

The low-lying isomeric state of $^{229m}$Th, owing to its unique nuclear energy structure, has been widely regarded as one of the most promising candidates for the development of a nuclear clock. However, the limited availability of suitable $^{229}$Th sources with sufficiently high activity remains a major challenge for experimental investigations of the $^{229m}$Th isomer. We propose a neutron-capture-based approach for the in-situ production of $^{229m,g}$Th by doping $^{228}$Ra into crystal hosts, where $^{229m,g}$Th is generated through neutron-capture reactions followed by a sequence of radioactive decay processes. We systematically investigate the background contributions associated with the three doped crystal hosts, namely CaF$_2$, SrF$_2$, and LiF, and evaluate their impact on the detection and identification of $^{229m}$Th. Under a neutron flux of $10^{15}\ \mathrm{n/cm^{2}/s}$ and a $^{228}$Ra doping concentration of $10^{19}\ \mathrm{cm^{-3}}$, the proposed method is capable of producing on the order of $10^{12}$ $^{229}$Th and $^{229m}$Th nuclei within only 1 s of irradiation, with a signal-to-noise ratios as high as $10^5$. In addition, the influences of detector wavelength resolution and post-irradiation measurement time on the detectability of the $^{229m}$Th signal are systematically analyzed, and the corresponding optimal measurement conditions are identified. Furthermore, the spatial distribution of neutron-produced $^{229}$Th within the crystal is investigated, providing practical guidance for optimizing crystal geometry and illumination configuration in future continuous-wave VUV absorption spectroscopy experiments. These results suggest that the proposed scheme provides a promising alternative pathway for the production and detection of $^{229\mathrm{m,g}}$Th, which may facilitate future studies toward the realization of nuclear-clock-based technologies.

nucl-ex↗
arXiv2026

When Trust Meets Truth: Trust-Truth Separability in LLM-as-Judge

LLM-as-Judge systems can produce multi-dimensional evaluations, such as trustworthiness, reliability, and factuality, and these outputs are often interpreted as independent evidence. We test this assumption for a common pair of judgments: trust scoring and binary truth classification. On correctness-controlled QA, LLM judges align trust scores with truth verdicts more tightly than human behavioral reference, suggesting weaker separations between trust and truth judgment. We then apply stress tests by changing only source cues of identical QA between Human and AI. Source attribution shifts not only trust scores but also truth verdicts and logit-derived correct-side probabilities. Results show that current LLM-as-Judge protocols should not treat trust scores as independent evidence for truth judgments.

cs.AI↗